How a supercomputer network of 700,000 PCs is helping to find a Covid-19 cure – NS Tech

The race to find a coronavirus vaccine is on, with about 35 companies and academic institutions across the world working feverishly on the case. But Sars-CoV-2, the virus that causes Covid-19, is a novel, as well as a large and complex structure. The process of discovering a vaccine is complemented and accelerated by building a solid ground layer in knowledge about the virus. One of the projects helping to plug the gaps in our understanding is Folding@home, based at Stanford University. Its a distributed computing project that links up the machines of citizen scientists across the world willing to donate excess computing resources from their devices to help run simulations of disease proteins at scale.

For the past 20 years, the project has been mapping disease proteins involved in Alzheimers and cancer, but in late February it began modelling the protein structures of Covid-19 too. This decision prompted the projects biggest ever spike in new volunteers signing up via downloadable software around 600,000 so far, putting it on track to reach one million total users. The network is now operating at an exaflop of computing power: 1,000,000,000,000,000,000 (a billion billion) operations per second.

Historically, vaccines contain enfeebled versions of the virus that trigger specific antibodies priming the human bodys immune system to react effectively to the real thing. But in the case of Covid-19, most research groups around the world are developing newer recombinant nucleic acid vaccines that contain scraps of the virus genetic code (DNA or RNA).

The ball was set rolling in mid-January when Chinese scientists published the full genome of the Covid-19 virus (all 29,903 nucleic bases). Scientists are able to use this information to single out sets of genes that correspond to specific proteins that make up the building blocks of the virus form essential information to formulating a vaccine. But this is only the beginning.

The proteins of Covid-19 are constantly shuffling and rearranging in response to their environment, and its these dynamical motions that Folding@homes molecular simulations are attempting to map. In a nutshell, this means simulating in a computer how each atom in a very large biomolecule wiggles and jiggles over time, says Vincent Voelz, associate professor in theory and computation at Temple University and a member of Folding@home. These movements indicate how the virus functions. As Voelz puts it, Covid-19 proteins are the nanoscale machines that the virus tricks an infected cell into making so it can propagate.

Of particular interest to Folding@home, and research groups investigating Covid-19 more generally, is the S-protein making up the spikes on the virus outer shell, that it uses to gain access to human cells. Folding@home has created a simulation of the spike protein, that is composed of three interlocking proteins, and a pocket that helps the virus bind to human cells and infect them.

The point of mapping proteins is to find out which parts of proteins the immune system might target, says Jim Naismith, professor of structural biology at the University of Oxford. In Covid-19, the spike protein is a particularly popular binding spot for human antibodies, meaning it could be key to developing an effective vaccine. Scientists are mapping all those epitopes [protein segments] where people are mounting good responses to them, and then theyll test those antibodies in trials, says Naismith.

Running computations to produce simulations of this type of biological puzzle is time and energy-intensive. Folding@homes distributed network of computers is able to run calculations with greater speed and efficiency than any single computer could. In effect, large calculations are broken down into smaller ones that are run concurrently on thousands of displaced machines. The power of Folding@homes distributed network is not directly comparable to one supercomputer, because the system is not operating as a single unit on a single problem. But if it was, it would be faster. The fastest supercomputers available today operate at a scale of hundreds of petaflops between a third and a half of the speed of an exaflop.

Folding@home isnt the only project directing vast quantities of computing power towards understanding Covid-19. In the US, a partnership including the US government, IBM, and others has began to grant promising Covid-19 projects access to 16 supercomputers. Summit, the worlds most powerful non-distributed computer system in the world, was tasked with identifying compounds that would be effective in binding to the spike proteins of the Covid-19 organism, thereby preventing the attachment of the virus to host cells. It came up with 77 matches.

Beyond brute computing force, artificial intelligence is also playing an increasingly important role in virus modelling. Traditionally, experiments to determine the structure have taken months or longer. But computational methods can provide a much speedier way to predict protein structures from amino acids sequences. In cases where the structure of a similar protein has already been experimentally defined, algorithms based on template modelling can provide accurate predictions of the protein structure. Googles DeepMind recently announced AlphaFold, a deep learning system that focuses on predicting protein structure accurately when no structures of similar proteins are available, called free modelling.

While Folding@homes work is not pitched directly at creating a vaccine, its useful for modern computational drug discovery, which relies on sampling the many possible conformations of the proteins, and modelling how drug molecules might bind to them. At present, there are not good experimental techniques that can probe these motions at the atomic scale that can be achieved with computational modelling, says Voelz.

Computational mapping complements structural mapping of the virus using laboratory techniques such as cryogenic electron microscopy. What you can do with computing is, if possible, use evolutionarily related proteins that we already know something about the architecture and the active site and then build a computing model using those, says Tom Blundell, biochemist and structural biologist at Cambridge University.

Folding@home has been able to go one further. Voelzs group at Temple University are partnering with researchers at the Diamond Light Source in the UK who have done groundbreaking work in solving over a thousand different crystal structures of the coronavirus main protease, and have discovered several drug fragments that bind to sites on the protein. Based on these initial fragment screening results, the computing power of Folding@home is being used to virtually screen a huge number of potential drug compounds including those from the COVID Moonshot project to prioritise which to synthesise and experimentally test.

Continued here:
How a supercomputer network of 700,000 PCs is helping to find a Covid-19 cure - NS Tech

Crusoe Energy Systems Is Donating Computing Resources to Coronavirus Vaccine Research and Discovery Efforts – Yahoo Finance

Wasted Natural Gas To Power The Fight Against COVID-19

Crusoe Energy Systems, Inc. has deployed more than twenty energy-intensive computing modules throughout Americas oil and gas fields as part of its Digital Flare Mitigation system, which captures otherwise flared or wasted natural gas to power computing processes at the wellhead. Today the company announces that it has begun allocating a portion of its computing systems to the search for a coronavirus vaccine.

Crusoe is working with the Folding@Home Consortium, a distributed computing system for life-science research launched out of Stanford University. The Consortium allows researchers to remotely utilize Crusoes computational resources for the vaccine search and discovery process, and recently launched a new protein folding simulation project specifically targeting vaccines and therapeutic antibodies for COVID-19.

Crusoe has configured eight of its most advanced graphic processing units to support the Consortiums vaccine development project, and commenced work units for COVID-19 research in Crusoes field operations center in North Dakota earlier this week. Crusoe is now one of the largest contributors of computing power to the protein folding Consortium, ranking in the top 10% of computational power providers for the vaccine research system. Crusoe ultimately plans to deploy protein folding servers to multiple flare gas-powered computing modules in the oilfield after expanding network bandwidth at selected sites.

COVID-19 is closely related to the SARS coronavirus. Both coronaviruses infect the lungs when viral proteins bind to receptor proteins in lung cells. A SARS therapeutic antibody, which is a protein that can prevent the SARS coronavirus from binding to lung receptors, has been developed previously. To develop a similar antibody for COVID-19, researchers need to better evaluate how the COVID-19 spike protein binds to receptors in the human body. The Consortiums new protein folding project simulates antibody proteins and how they might prevent COVID-19 viral infection, however, the simulation process is very computationally intensive and therefore energy intensive.

Crusoe can support this vaccine research using its distributed computing resources deployed at natural gas flaring sites in Montana, North Dakota, Wyoming and Colorado. Today, Crusoe consumes millions of cubic feet of natural gas per day that would have otherwise been wasted by burning in the air, or "flared." Instead, that waste gas powers Crusoes mobile, modular computing systems, which are deployed directly to the wellhead to mitigate flaring. Crusoes initial computational use case was blockchain processing. More recently the company has been developing high performance and general-purpose cloud computing solutions, which are used in a variety of applications including machine learning, artificial intelligence, and protein folding.

"At this time of growing global concern around the coronavirus, we are grateful to have the opportunity to support the Folding@Home Consortiums search for a vaccine," said Chase Lochmiller, CEO and co-founder of Crusoe. "Weve configured very powerful computing hardware that is typically used for machine learning and artificial intelligence research to search for helpful therapies against coronavirus. This is very much in keeping with Crusoes vision that distributed computing resources have an important role to play in solving real world problems."

Crusoe began processing work units for COVID-19 on March 15th. In addition to COVID-19, the Company has previously completed work units related to cancer research.

About Crusoe Energy Systems Inc.

Crusoe Energy Systems provides innovative solutions for the energy industry. By converting natural gas to energy-intensive computing, Crusoes Digital Flare Mitigation service delivers an environmentally sound way to create a beneficial use for otherwise wasted natural gas. Crusoe currently has flare mitigation projects operating in Wyomings Powder River Basin oilfield, Colorados Denver-Julesburg oilfield and North Dakota and Montanas Bakken oilfield. Systems are scalable up to millions of cubic feet per day and can be deployed anywhere in the United States or Canada.

Background on Flaring

Story continues

Natural gas flaring has become an acute pain point for shale oil producers, which produce natural gas as a byproduct of oil. This oil-associated natural gas production has outpaced gas pipeline infrastructure in many parts of the North American shale industry. In the absence of pipeline capacity, operators tend to burn natural gas in a process known as "flaring" or "combusting." Approximately 335 billion cubic feet of natural gas are flared annually in the United States, according to latest 2017 data from the World Banks Global Gas Flaring Reduction Partnership (GGFR), which is enough gas to power more than 7 million U.S. homes. Flaring generates pushback from the public and policymakers, who increasingly raise environmental concerns around resource waste, visual impacts and air quality.

Please reach out to info@crusoeenergy.com or visit http://www.crusoeenergy.com to learn more, and follow Crusoe on Linkedin and Twitter.

View source version on businesswire.com: https://www.businesswire.com/news/home/20200320005505/en/

Contacts

Company Contacts: Chase LochmillerCEO and Chairman

Cully CavnessPresident

info@crusoeenergy.com

See the original post:
Crusoe Energy Systems Is Donating Computing Resources to Coronavirus Vaccine Research and Discovery Efforts - Yahoo Finance

Folding@home Taking the Coronavirus Battle to ExaFLOP Levels – Screen Rant

Folding@home is a distributed computing project used to simulate protein folding. The project, which is available for free to anyone, asks users to install software on a computer so it can leverage the user's processing power to calculate data. It's something like Bitcoin mining except it can save lives. In simple terms, the data itself involves calculating all the ways a protein molecule could move over time because that movement impacts the shape of the molecule, and that shape has a strong bearing on how the molecule will ultimately function. Charting these movements requires a staggering number of calculations (literally billions), so Folding@home users are taking on some of that workload. The information gleaned from these movements can then be used by biologists to determine how things like viruses will function, and how to treat them. For more information on ways to get involved, check the following article.

Related:Here's How You (& Your Computer) Can Help Fight Coronavirus

The Folding@home project has been used in cancer research going back to the advent of broadband internet access, and it has also aided in research on Ebola: it recently led to the discovery of an unexpected way to treat Ebola with drugs, which was previously considered impossible. Now that Folding@home has been unleashed on COVID-19, its creators have pushed for the many PC owners spending time at home due to social distancing to step up and lend their computing resources to the project. Step up they have, as the Folding@home Twitter account recently revealed the project has "crossed the exaFLOP barrier". This feat of computing means they've reached enough computing power to calculate 1,000,000,000,000,000,000 (that's one quintillion) floating-point operations per second, which, according to a reply on Twitter, is more power than exists in the top 103 supercomputers on Earth combined.

This is a staggering feat considering the immense power required to do large-scale FLOP calculations for those familiar with the term "teraflop", an exaflop is one million teraflops. It's also a great sign of comradery amongst us Earthlings. Folding@home is an easy process to start, it runs relatively unobtrusively, and it can even be a little fun, but it's still a surprise to see so many people taking the effort to help with the project.

Throughout this process, tech enthusiasts and tech brands in the PC space have taken strides to promote Folding@home and get more people on board. Companies like Nvidia are sharing Folding@home leaderboards to drive friendly competition and acknowledge individual efforts. The combination of this new social media word of mouth and people's strong desire to help end the coronavirus are the primary factors for the project's recent explosion of popularity, and hopefully, this trend continues. Way to go, humans.

Next:How Apple & Facebook Were Able to Source Masks During Coronavirus

Source:Folding@home/ Twitter

Star Wars: How Lor San Tekka Knows Kylo Ren's Identity In The Force Awakens

Hubert has been a journalist in spirit since age six, and can't see any good reasons to argue with that, so here we are. He spends most of his days working to leave the world a better place than it was when he showed up and trying to be better at Street Fighter.

Go here to see the original:
Folding@home Taking the Coronavirus Battle to ExaFLOP Levels - Screen Rant

How to Fight Coronavirus With Folding@home and a Gaming PC – How-To Geek

CDC / NVIDIA

Want to help in the battle against the novel coronavirus? You can put your PCs graphics processor to work with Folding@home. Youll join an army of computers running calculations to help scientists understand the virus.

Folding@home is a distributed computing project thats been around since the year 2000. Its named after protein folding. If you install the software and join a project, it will run in the background and use spare graphics processing (GPU) power to run calculations. Your PC will be one of the hundreds of thousands of PCs running these calculations, all working together.

The software has previously been used to help find cures to cancer, Parkinsons, Huntingtons, influenza, and many other diseases. Now, Folding@home is helping scientists understand the structure of the SARS-CoV-2 virus that causes COVID-19. As Folding@home director Greg Bowman explains, a better understanding of the virus could aid in the development of life-saving drugs.

In other words, you can put your PCs GPU to work crunching numbers that will help scientists better understand and fight the novel coronavirus.You can read specifics about how Folding@home is simulating the dynamics of COVID-19 proteins to hunt for new therapeutic opportunities on the projects website.

This work is GPU-dependent and requires NVIDIA or AMD graphics hardware. It will work best on computers with powerful graphics hardware.

To put your PC to work battling coronavirus, download the Folding@home installer and run it to install the software. Its available for Windows, Mac, and Linux. Well show how it works on Windows here.

Once youve installed the Folding@home software, youll be taken to thehttps://client.foldingathome.org/ page, where you can control the software on your PC. You can choose to fold anonymously or set up an identity.

If you set up an identity, you can track your work and earn points. You can even join a team with other people and compete to see who can earn the most pointsjust a bit of friendly competition.

However, you dont need to set up an identityyou can just select Fold as Anonymous and click Start Folding to begin.

To ensure youre helping with COVID-19 research, ensure Any disease is selected under the I support research fighting box. This is the default option. With it enabled, Folding@home will prioritize work related to the novel coronavirus.

Work may not be available immediately, and your client may work on other diseases like Alzheimers, cancer, Huntingtons, or Parkinsons while waiting for COVID-19 jobs. Leave it running in the background, and it will automatically start any available work.

The Folding@home software will remain running in the backgroundeven when you have the web page closed. It will automatically use any spare resources and get out of the way when youre using your GPU for other purposes, like playing a PC game.

Look for the Folding@home icon in your computers notification area (system tray) to find options, pause it, or quit the software and prevent it from running.

If you decide you no longer want to participate, head to the Uninstall or change a program list in Windows and uninstall the FAHClient program.

Even NVIDIA has called for gamers to install Folding@home and donate any spare computing power they might have. Computers all over the world are joining the fight.

For more information, take a look at thisFAQ about the SARS-CoV-2 projects in Folding@home. Youll also find updates on Folding@homes news page.

Excerpt from:
How to Fight Coronavirus With Folding@home and a Gaming PC - How-To Geek

Creative Technology dedicates the processing power capacity of its media servers to scientific research to fight covid-19 disease. – EtNow.com

UK Creative Technology (CT) UK is home to hundreds of media servers, each with the latest in GPUs and processors. Ordinarily, these servers are key to delivering live events for clients, but following the outbreak of COVID-19 throughout Europe and beyond, and the related reduction in live events, CT has found itself in a position to get really creative with the technology. Fighting back against Coronavirus in the best way known, and this comes in the form of the Folding@Home project run by Stanford University.

CT London has set up a server farm, doing all it can to support this worthwhile project.

Researchers from all over the world can now use the CPU (Central Processing Unit) and GPU (Graphic Processing Unit) capacity of the media servers to draw, calculate and analyse complex formulas and graphics in the global fight against COVID-19.

Folding@Home is a distributed computing project for disease research that simulates protein folding, computational drug design, and other types of molecular dynamics. These scientific COVID-19 projects focus on better understanding how these Coronaviruses interact with the human ACE2 receptor required for viral entry into human host cells, and how researchers might be able to interfere with them through the design of new therapeutic antibodies or small molecules that might disrupt their interaction. There is hope to take advantage of some of the new structural biology and biochemical data that is being rapidly released by researchers around the world who are working to understand these viruses and strategies for defeating them.

Since joining the Folding at Home Project, CT announces that several other NEP Group companies have also got on-board: Screenworks, Univate, and Bexel to name just a few.

Creative Technology Group is urging all companies in the audiovisual sector to join this project by making their processing power also available for scientific purposes. Researchers are especially in need of more high-spec GPUs to help, and all the GPU projects are devoted to potential drug targets for COVID-19 right now.

Help to fight COVID-19 by joining this worldwide distributed supercomputer. Please use the Creative Technology group number 240907 to contribute your capacity to its team. You can help by downloading the Folding@Home client to your computer and following the instructions to install it.

CT cant make ventilators, but it CAN fight COVID-19!

The rest is here:
Creative Technology dedicates the processing power capacity of its media servers to scientific research to fight covid-19 disease. - EtNow.com

Nvidia’s calling on gaming PC owners to put their systems to work fighting COVID-19 – GamesRadar+ UK

If you have a gaming PC, you can lend your graphical power to fighting the COVID-19 outbreak. That's not a thing I ever thought I'd write, but it turns out 2020 is occasionally weird in good ways too.

Nvidia is putting out a call to PC gamers everywhere to download the Folding@home application and start putting their spare clock cycles toward advancing humanity's scientific knowledge of coronavirus. The program links computers into an international network that uses distributed processing power to chew through massive computing tasks - something that gaming-grade GPUs are quite good at, as it turns out. You can still turn the application off and reclaim your GPU's full power for playing games whenever you want.

Folding@home has been around for years - it was also available on PS3 back in the day - with users lending their distributed power to all kinds of research. A new wave of projects "simulating potentially druggable protein targets from SARS-CoV-2 (the virus that causes COVID-19) and the related SARS-CoV virus (for which more structural data is available)" were made available on the service earlier this week.

These projects could help researchers better understand coronavirus, and eventually even develop effective therapies against it. If you've been grappling with feelings of helplessness in the face of the worldwide outbreak, this is a small but real way you can lend your aid to the world without any medical experience. It also doesn't hurt that you don't need to leave your house to do it, since we're supposed to avoid that as much as possible anyway.

Pokemon Go is making some changes to help players keep enjoying the game while allowing for social distancing. Staying at home this weekend? Maybe you need a Disney Plus bundle to pass the time. Or if you've been thinking of upgrading your gaming PC check out our picks for the best graphics cards or best gaming laptops.

Read more:
Nvidia's calling on gaming PC owners to put their systems to work fighting COVID-19 - GamesRadar+ UK

How to Help the Fight Against Coronavirus From the Safety of Your Own Home – ExtremeTech

This site may earn affiliate commissions from the links on this page. Terms of use.

One of the difficulties with dealing with a pandemic is that successfully battling the contagion can require taking actions that are diametrically opposed to what our own instincts want to do: Namely, to do something either to protect ourselves or to protect those we care about. All of this is in direct contradiction to best medical practices, which calls for people to adopt social distancing techniques to the maximum extent possible.

There is, however, something you can do to help fight SARS-CoV-2, the coronavirus that causes the infection known as Covid-19: Contribute to Folding@Home. By downloading and installing the Folding@Home client, you can donate your spare CPU and GPU cycles to working on modeling. The Folding@Home team has made several blog posts from their efforts. The project was first explained in a Feb 23 post:

For both coronaviruses, the first step of infection occurs in the lungs, when a protein on the surface of the virus binds to a receptor protein on a lung cell. A therapeutic antibody is a type of protein that can block the viral protein from binding to its receptor, therefore preventing the virus from infecting the lung cell. A therapeutic antibody has already been developed for SARS-CoV, but to develop therapeutic antibodies or small molecules for 2019-nCoV, scientists need to better understand the structure of the viral spike protein and how it binds to the human ACE2 receptor required for viral entry into human cells.

The other coronavirus mentioned is SARS and 2019-nCoV was the old term for the virus now referred to as SARS-CoV-2. They continue:

Proteins are not stagnantthey wiggle and fold and unfold to take on numerous shapes. We need to study not only one shape of the viral spike protein, but all the ways the protein wiggles and folds into alternative shapes in order to best understand how it interacts with the ACE2 receptor, so that an antibody can be designed. Low-resolution structures of the SARS-CoV spike protein exist and we know the mutations that differ between SARS-CoV and 2019-nCoV. Given this information, we are uniquely positioned to help model the structure of the 2019-nCoV spike protein and identify sites that can be targeted by a therapeutic antibody. We can build computational models that accomplish this goal, but it takes a lot of computing power.

And the truth is? The combined computing power of human nerddom is capable of delivering performance that would blow Summit out of the water, though how much of it Folding@Home could practically leverage would be something of a question. Its true that the speed of any single commodity system is going to be slow in comparison to the challenge. The speed of all of our commodity hardware, on the other hand, might actually make a difference.

In an updated blog post from March 10, John Chodera writes:

Folding@home team has released an initial wave of projects simulating potentially druggable protein targets from SARS-CoV-2 (the virus that causes COVID-19) and the related SARS-CoV virus (for which more structural data is available) into full production on Folding@home This initial wave of projects focuses on better understanding how these coronaviruses interact with the human ACE2 receptor required for viral entry into human host cells, and how researchers might be able to interfere with them through the design of new therapeutic antibodies or small molecules that might disrupt their interaction.

The goal of the Folding@Home project is to target the most promising drug targets to find alternate conformations or hidden pockets the sort that you can only find in simulation. Basically, Folding@Home wants to take the most likely candidates and then examine them with a fine-toothed comb to make sure no stone goes unturned. Download and install FAH, and youll be asked if you want to mine anonymously or for a team. If you dont care about the entire team thing, you can just hit Anonymous. The application devotes to a web interface, but you can activate a desktop Advanced Control panel just by searching for Folding.

The basic control panel. Ive only just started my own system mining again, after 15 years or so away.

Any is the default setting here, and its the right one. There isnt a specific allocation for Coronavirus yet. From the tone of the blog posts, it sounds as if work on SARS-CoV-2 is still ramping up. No, contributing to a distributed computing project isnt going to magically make a solution appear out of thin air but we might make a difference to how long it takes to find a cure the scientific way. Ill be bringing some testbeds online towards this purpose. Hope youll join me.

Oh. And please wash your hands.

Top image by NIAID-RML/CC BY SA 2.0

Now Read:

Read the rest here:
How to Help the Fight Against Coronavirus From the Safety of Your Own Home - ExtremeTech

The keto diet: Its highs and lows plus 5 recipes – The Gazette

By Daniel Neman, St. Louis Post-Dispatch

My friends Mike and Robin have been on the keto diet for a couple of years. They lost a ton of weight, and they look fit and trim better than Ive ever seen them, and Ive known them more than 30 years.

Their results are not unusual. The keto diet often leads to rapid weight loss.

The trick lies in keeping it off. Mike and Robin have done it well, but a lot of people cant. And therein lies a problem, according to Jennifer McDaniel, a registered dietitian and the owner of McDaniel Nutrition Therapy in St. Louis.

While you might gain benefits in the short term just like any other restrictive diet, most people like, 90% of the people have trouble staying on it. When they lose the weight and they cant maintain the weight that they attained, thats just another failed diet, she said.

The keto diet is a high-fat, low-carbohydrates diet its like the Atkins diet on steroids, McDaniel said. People on the diet strive to consume 70% to 80% of their calories through fats, as little as 5% through carbohydrates and the rest through protein.

This helps us lose weight because it forces our bodies to burn fat for energy instead of its preferred fuel, carbohydrates.

McDaniel recommends that her clients not go on the keto diet. The diet changes the microbiome in their bodies (the bacteria, fungi and more that live inside us). It is difficult for people on the diet to consume enough fiber, which can lead to constipation and other gastrointestinal nastiness. And because carbohydrates hold onto water, people on the diet are often dehydrated, she said.

And yet, as Mike and Robin and thousands of others can attest, it works. So I decided to try a few recipes to see how they tasted.

ARTICLE CONTINUES BELOW ADVERTISEMENT

The rules of the keto diet are highly restrictive, which can make cooking difficult. You need to avoid fruit, sugars, grains, beans and legumes, rice, potatoes, candy and juice.

Ingredients to be encouraged include meat, fatty fish, eggs, butter and cream, cheese, nuts and seeds, certain oils (olive, avocado, coconut) and low-carb vegetables most green vegetables, tomatoes, onions, peppers and the like.

Its a lot to take in, so I began with a simple and entirely wonderful dish of Citrus-Marinated Olives. These are a marvelous treat, combining the heady earthiness of olives with bright notes of orange and lemon. Though the flavors are disparate, they work surprisingly well together.

Best of all, you make them in mere minutes.

Another winner was Keto Egg Cups, a dish that concisely presents everything that is good about keto cooking: Little cups made from prosciutto hold eggs mixed with cream, spinach, roasted red peppers and mozzarella and Parmesan cheeses.

Its a delightful conglomeration of high-fat goodies that come together into a hand-held snack. And its just as fun at room temperature as it is warm.

Two entrees came next. First, I took a recipe for Instant-Pot Keto Mediterranean Chicken and made it a recipe for Keto Mediterranean Chicken Without an Instant Pot. It only took about five minutes longer than the Instant-Pot version, and it was deeply satisfying.

Ill admit, though, that I could not commit to full keto cooking with this one. As written, the recipe calls for searing six chicken thighs and then cooking the dish in the resulting fat.

My six thighs rendered out a half cup of fat. I just couldnt do it. I couldnt cook with and I certainly couldnt eat that much fat. I know the keto diet requires what seems like a shocking amount of fat to work, but I just couldnt see it. I poured out half of the fat, and the dish still felt greasy to me.

ARTICLE CONTINUES BELOW ADVERTISEMENT

Greasy, but delicious. Meaty chicken thighs are paired with olives, capers, oregano and a cutting splash of vinegar. Its presented with a fresh-tasting yogurt sauce, making an impressive presentation. Id happily eat it anytime, especially if I poured out all but one tablespoon of that fat.

The other entree, Keto Breaded Chicken Cutlets, isnt bad but Id only make it again if I were on the keto diet. The chicken is dredged through almond flour before frying, which gives it a duller flavor than wheat flour.

With wheat flour, the same recipe would be excellent, if familiar. If youre on the keto diet, almond flour is definitely the way to go. Just be sure to use a lot of salt.

The last dish I made was a dessert called Black and White Keto Fat Bombs. Seriously, thats the name, and seriously, thats what they are. They are chocolate-and-vanilla candies that are made with coconut oil and almonds, plus low-carb, powdered sweetener, sugar-free vanilla extract and unsweetened cocoa powder.

How did they taste? Not bad, actually, or at least not too bad. But the texture was so oily and off-putting that most taste testers threw away their samples. One said it was like eating butter.

If youre on the keto diet and youre looking for an extra infusion of fat, then Id say to go ahead and make it. Otherwise, this is one to avoid.

My friend Robin swears by the keto diet and says she is passionate about it. Her health indicators are all great, and she says she has higher energy and alertness. And though the diet is restrictive, she likes what she can eat: cheese, olive oil, butter, nuts and dark chocolate.

The biggest thing she misses is fruit, but she does not miss the 40 pounds she lost.

Then again, I have another friend, Roger, who lost 65 pounds. He just eats more healthfully and mindfully, and walks every day. That sounds easier.

BLACK AND WHITE KETO FAT BOMBS

Yield: 15 servings

2 cups slivered almonds

1 cup coconut oil

1 to 2 tablespoons of your favorite low-carb powdered sweetener

1 teaspoon orange zest

2 teaspoons vanilla extract (sugar-free if on keto diet)

Small pinch salt

2 tablespoons unsweetened cocoa powder

Line a mini-muffin tin with mini liners.

ARTICLE CONTINUES BELOW ADVERTISEMENT

Pulse and then process the almonds, oil, sweetener, vanilla, zest and salt until smooth except for small chunks of almond (like chunky peanut butter). Remove half to a small bowl and stir in the cocoa powder.

Fill half of one liner with the vanilla mixture and then quickly fill the other half with the chocolate mixture (it should remind you of a black-and-white cookie). Repeat with the remaining vanilla and chocolate mixtures. Tap the tin on the counter a few times.

Freeze until solid, about 30 minutes. You can remove the liners if youd like. Refrigerate in an airtight container for up to 5 days.

Per serving: 243 calories; 24 g fat; 13 g saturated fat; no cholesterol; 4 g protein; 5 g carbohydrate; 1 g sugar; 3 g fiber; 155 mg sodium; 53 mg calcium

Source: Food Network

KETO MEDITERRANEAN CHICKEN

Yield: 4 servings

1 tablespoon olive oil

8 bone-in, skin-on chicken thighs

Salt and pepper

3 garlic cloves, thinly sliced

1 cup pitted kalamata olives

2 tablespoons capers

2 tablespoons white wine vinegar

1 1/2 teaspoons dried oregano

1 cup whole-milk Greek yogurt

1/4 cup fresh flat-leaf parsley, roughly chopped

2 tablespoons fresh mint leaves, roughly chopped

1 teaspoon lemon zest

1 tablespoon lemon juice

Heat oil in a large pot or Dutch oven over medium heat. Liberally sprinkle chicken with salt and pepper and cook half of the pieces, skin-side down, until the skin is deeply browned, 6 to 8 minutes. Flip and cook until the other side is deeply browned, 4 to 6 minutes. Transfer to a plate and repeat with remaining pieces of chicken.

Pour out all but 1 tablespoon of the fat (if on the keto diet, keep all the fat in the pot). Return pot to heat and add garlic; cook until golden brown, about 1 minute. Add the olives, capers, vinegar, oregano and 1/2 cup water; stir to combine and to scrape up any browned bits at the bottom of the pot. Return chicken pieces to pot and stir to mix.

Cover and cook at a simmer until chicken is done, about 30 minutes. Remove the chicken and boil the sauce to reduce it by half.

Meanwhile, mix the yogurt, parsley, mint, lemon zest and lemon juice, plus a large pinch of salt and pepper. Stir to combine. Taste and season with more salt and pepper, if needed.

Serve the chicken with its sauce, and the yogurt sauce on the side.

ARTICLE CONTINUES BELOW ADVERTISEMENT

Per serving (nutrition calculated using all the fat from step 2): 627 calories; 48 g fat; 12 g saturated fat; 285 mg cholesterol; 42 g protein; 5 g carbohydrate; 1 g sugar; 2 g fiber; 1,146 mg sodium; 91 mg calcium

Source: Adapted from a recipe by the Food Network

KETO EGG CUPS

Yield: 3 servings

1 tablespoon butter, see note

6 large, thin slices of prosciutto

1/3 cup shredded mozzarella cheese

1/4 cup grated Parmesan

1/4 cup packed baby spinach leaves, chopped

1/4 cup roasted red peppers, chopped

6 large eggs

1/4 cup heavy cream

Salt and pepper

Note: To make this recipe even more keto-friendly, instead of greasing the muffin tin with 1 tablespoon of butter, melt 2 tablespoons and brush the tin with it. It will pool in the bottom of each cup, but that is OK.

Position an oven rack in the center of the oven and preheat to 400 degrees.

Grease the cups of a muffin tin with the 1 tablespoon of butter (or brush with 2 tablespoons of melted butter). Line each cup with a slice of prosciutto, folding and overlapping so that the entire surface of the cup is covered and no metal is peeking through. Divide the mozzarella evenly among the cups. Repeat with the Parmesan, spinach and roasted red peppers.

Whisk the eggs and cream in a large measuring cup or small pitcher; add some salt and a few grinds of pepper. Pour the egg mixture in each cup, making sure not to overfill.

Bake until the eggs are set and wobble only slightly, 10 to 12 minutes (the eggs will continue to cook after they come out). Let cool 5 minutes, then use a thin knife or offset spatula, if necessary, to loosen the prosciutto from the edges of each cup. Transfer to a plate for serving.

Per serving: 321 calories; 22 g fat; 10 g saturated fat; 426 mg cholesterol; 28 g protein; 4g carbohydrate; 1 g sugar; 1 g fiber; 1,779 mg sodium; 272 mg calcium

Source: Food Network

CITRUS-MARINATED OLIVES

Yield: 4 to 6 servings

1/4 cup olive oil

1/4 teaspoon crushed red pepper flakes

2 sprigs fresh thyme

1 garlic clove, thinly sliced

1 strip lemon zest, removed with a vegetable peeler

1 strip orange zest, removed with a vegetable peeler

Salt and pepper

1 cup Castelvetrano olives

1 tablespoon lemon juice

1 tablespoon orange juice

Heat the olive oil in a small saucepan over medium heat. Add the red pepper flakes, thyme, garlic, lemon zest, orange zest, salt and pepper to taste and cook, stirring occasionally, until garlic is pale golden, about 2 minutes. Stir in olives and cook until just warm, 2 minutes. Remove from heat and stir in lemon juice and orange juice. Serve warm or at room temperature.

Per serving (based on 4): 167 calories; 18 g fat; 3 g saturated fat; no cholesterol; 1 g protein; 4 g carbohydrate; 1 g sugar; 2 g fiber; 829 mg sodium; 39 mg calcium

Read the original post:
The keto diet: Its highs and lows plus 5 recipes - The Gazette

The Evolution of the Eye, Demystified – Discovery Institute

How did the eye evolve? Michael Behe in 2006 and JonathanWellsin 2017 wrote about the irreducible complexity of the light-sensing cascade that makes vision possible. Yet Darwinists persist in asserting that this wondrous organ emerged, without guidance or direction, from a presumed ancestral eyespot.

This is an update on that important subject. I wish to emphasize the irreducible complexity of the visual cycle, on top of the sheer anatomical complexity of the human eye with its over two million working parts, second only to the human brain in complexity.

Eyespots only perform a function when embedded in an interdependent system such as the one devoted to locomotion in the green algae Chlamydomonas. Phototaxis is a movement that occurs when a whole organism moves either closer to, or away from a light source, such as the sun. It is essential, for example, for green algae, which can move towards light to perform photosynthesis, capturing light and transforming it into chemical energy. Yet green algae also move away from the light to protect themselves against an intense source of illumination. Eyespots are the simplest eyes found in nature. They are composed of rhodopsins, which are light-sensitive proteins, and orange-red colored pigment granules, which have their color by selectively absorbing or reflecting light. The color spectrum, which is reflected, is the one that becomes visible to our eyes.

The pigment spot reduces the illumination from one direction or changes the wavelength of the incident light falling on the photoreceptor. It thus allows the organism to move in the direction of the light or away from it.

As an interdependent system, this visual system requires certain essential components, including rhodopsin proteins, a pigment spot, and ion flux. If one part is missing, the organism cannot move by phototaxis. Natural selection will not select any intermediate evolutionary step, since the system, with any of the required elements missing, would confer no function, and thus no survival advantage.

While proponents of unguided evolution characterize the light-sensitive spot of some ancestral creatures as simple, it is anything but that. As a 2015 article in Frontiers in Plant Science notes, eyespots have a high ultrastructural complexity. Of course, this may be said, all the more so, of more advanced eyes. Consider some of the details. In forms ranging from the simplest, most rudimentary eye, such as eyespots in unicellular organisms, e.g. Chlamydomonas, to complex vertebrate eyes, such as our own camera eyes, rhodopsin proteins capture the light and are the first and central players in a complex chain of biochemical events. There is no vision without rhodopsin proteins. Unless rhodopsin transforms light into a signal, and that signal is used by a signal transduction pathway to promote phototaxis, neither rhodopsins nor eyespots would have a function on their own.

Rhodopsins themselves are complex. They are composed of two parts: opsin proteins, which are made of seven -helices forming a circle, and retinal, which is a light-absorbing chromophore. Retinal is covalently linked to the opsins and horizontally positioned in the pocket inside the opsin tunnel. When a single photon hits retinal, a small conformational change is triggered in the opsin, and that triggers a cascade of several chemical reactions and biochemical transformations, ultimatively leading to sight. A 2016 article in Nature Communications observed that rhodopsin functions as a molecular offon switch; it isdesigned to be fully inactive in the dark and to rapidly convert to a fully active structure in the light.

As a general note, functional molecules, such as those within the catalytic sites of enzymes (in our case, retinal cofactors), require high specificity in their form and are thus well conserved (unchanged, or non-evolved ) across organisms. That is because mutations within these sites usually do not confer any advantage.

In seeking to explain how biological novelties arise, evolutionists often point to the recruiting and co-option of extant building blocks. In such a scenario, the building blocks are incorporated into new systems by natural selection of new functions. Rhodopsin would have to undergo evolution by recruiting retinal cofactors, which it would have to find fully formed and functional, finely tuned and just the right size to fit the binding pocket of opsin, a molecule obtained by a complex multistep biosynthesis pathway starting with carotenoid organic pigments from fruits, flowers, trees, or vegetables. It would require elaborate import mechanisms from the outside into the eyespot and the information on how to insert it in the opsin binding pocket to form rhodopsin and attach it at the right place.

In their book The Retina and Its Disorders, Joseph Besharse andDean Bokstate (p. 641) that the chromophore-binding pocket is well defined, suggesting that the binding pocket has high specificity for the Schiff base and the ionone ring. The precise and correct binding of retinal to the opsin is essential to trigger the change of the shape of retinal, and thus necessary for visual sight. It must be specific and functional from the beginning.

So the following is required:

Unless all of these specific points are right from the beginning, rhodopsin will not be functional. A coordinated and finely tuned interplay and precise orchestration between opsin and retinal right from the start is thus indispensible.

Hundreds of rhodopsins are embedded in the lipid bilayer of the membrane of Chlamydomonas, each using seven protein transmembrane domains, forming a pocket where retinal chromophores are inserted.

The precision with which opsins must fold into their seven-transmembrane configuration is staggering, as JILA (formerly the Joint Institute for Laboratory Astrophysics) reported:

Biophysicists at JILA have measured protein folding in more detail than ever before, revealing behavior that is surprisingly more complex than previously known.

[T]he JILA team identified 14 intermediate states seven times as many as previously observed in just one part of bacteriorhodopsin, a protein in microbes that converts light to chemical energy and is widely studied in research.

The increased complexity was stunning, said project leader Tom Perkins, a National Institute of Standards and Technology (NIST) biophysicist Better instruments revealed all sorts of hidden dynamics that were obscured over the last 17 years when using conventional technology.

If you miss most of the intermediate states, then you dont really understand the system, he said.

Knowledge of protein folding is important because proteins must assume the correct 3-D structure to function properly. Misfolding may inactivate a protein or make it toxic. Several neurodegenerative and other diseases are attributed to incorrect folding of certain proteins. [Emphasis added.]

An article in the journal Eye (Light and the evolution of vision) confirms:

[E]ven as far back as the prokaryotes the complex seven transmembrane domain arrangement of opsin molecules seems to prevail without simpler photoreceptors existing concurrently. Darwins original puzzle over ocular evolution seems still to be with us but now at a molecular level.

As for retinal, the second essential component of rhodopsin, a paper in the journal Vision Research reports:

11-cis-Retinal is a unique molecule with a chemical design that allows optimal interaction with the opsin apoprotein in its binding pocket, and this is essential for the formation of the light-activated conformation of the receptor.

Remarkably, all structural details in the retinal chromophore are functionally important. As another paper, this one in the journal Trends in Biochemical Sciences, finds:

Although there is an intriguing evolutionary conservation of the key components involved in the production and recycling of chromophores, these genes have also adapted to the specific requirements of insect and vertebrate vision.

We have, so far, only scratched the surface. But we can safely say that the origin of both vision and its key player, rhodopsins, cannot be explained by the evolutionary mechanisms of random mutations and natural selection. Instead they must have existed from inception as a unified and codified system. Such an observation, I believe, is best explained by intelligent design.

Image credit: Steve LongviaUnsplash.

See the original post here:
The Evolution of the Eye, Demystified - Discovery Institute

U of T’s Peter Wittek, who will be remembered at Feb. 3 event, on why the future is quantum – News@UofT

In September of 2019, Peter Wittek, an assistant professor at the University of Toronto, went missing during a mountaineering expedition in the Himalayas after reportedly being caught in an avalanche. A search and rescue mission was launched but the conditions were very difficult and Wittek was not found.

Peters loss is keenly felt, said Professor Ken Corts, acting dean of the Rotman School of Management. He was the Founding Academic Director of the CDL Quantum Stream, a valued instructor in the MMA program, data scientist in residence with the TD Management Data and Analytics Lab, an exceptional contributor to Rotman and U of T and a wonderful colleague.

A ceremony to remember Wittek will take place on Feb. 3 from 3 to 4:30 pm in Desautels Hall at the Rotman School of Management.

Quantum computing and quantum machine learning an emerging field that counted Wittek as one of its few experts was the topic of his final interview inRotman Management Magazine. It is reprinted below:

You oversee the Creative Destruction Labs Quantum stream, which seeks entrepreneurs pursuing commercial opportunities at the intersection of quantum computing and machine learning. What do those opportunities look like?

Weve been running this stream for three years now, and we were definitely the first to do this in an organized way. However, the focus has shifted slightly. We are now interested in looking at any application of quantum computing.

These are still very early days for quantum computing. To give you a sense of where we are at, some people say its like the state of digital computing in the 1950s, but Id say its more like the 1930s. We dont even agree yet on what the architecture should look likeand, as a result, we are very limited with respect to the kind of applications we can build.

As a result, focusing on quantum is still quite risky. Nevertheless, so far we have had 45 companies complete our program. Not all of them survived, but a good dozen of them have raised funding. If you look at the general survival rate for AI start-ups, our record is roughly the same and given how new this technology is, that is pretty amazing.

What are the successful start-ups doing? Can you give an example of the type of problems theyre looking to solve?

At the moment I would say the main application areas are logistics and supply chain. Another promising area is life sciences, where all sorts of things can be optimized with this technology. For instance, one of our companies,Protein-Qure, is folding proteins with quantum computers.

Finance is another attractive area for these applications. In the last cohort we had a company that figured out a small niche problem where they had both the data and the expertise to provide something new and innovative; they are in the process of raising money right now. The other area where quantum makes a lot of sense is in material discovery. The reason we ever even thought of building these computers was to understand quantum materials, back in the 1980s. Today, one of our companies is figuring out how to discover new materials using quantum processing units instead of traditional supercomputers.

We have a company calledAgnostic, which is doing encryption and obfuscation for quantum computers. Right nowIBM,Rigetti ComputingandD-Wave Systemsare building quantum computers for individual users. They have access to everything that you do on the computer and can see all the data that youre sending. But if youre building a commercial application, obviously you will want tohide that. Agnostic addresses this problem by obfuscating the code you are running. One application weve seen in the life sciences is a company calledEigenMed, which addresses primary care. They provide novel machine learning algorithms for primary care by using quantum-enhanced sampling algorithms.

We also seed companies that dont end up using quantum computing. They might try out a bunch of things and discover that it doesnt work for the application they have in mind, and they end up being 100 per cent classical.StratumAIis an example of this. It uses machine learning to map out the distribution of ore bodies under the ground. The mining industry is completely underserved by technology, and this company figured out thatto beat the state-of-the-art by a significant margin, it didnt even need quantum. They just used classical machine learning and they already have million dollar contracts.

Which industries will be most affected by this technology?

Life sciences will be huge because, as indicated, it often has complex networks and probability distributions, and these are very difficult to analyze with classical computers. The way quantum computers work, this seems to be a very good fit, so that is where I expect the first killer app to come from. One company,Entropica Labs, is looking at various interactions of several genomes to identify how the combined effects cause certain types of disease. This is exactly the sort of problem that is a great fit for a quantum computer.

You touched on quantum applications in primary care. If I walked into a doctors office, how would that affect me?

Its trickybecause, like mining, primary care is vastly underserved by technology. So, if you were to use any machine learning, you would only do better. But EigenMed was actually founded by an MD. He realized that there are certain machine learning methods that we dont use simply because their computational requirements are too high but that they happen to be a very good fit for primary care, because the questions you can ask the computer are similar to what a GP would ask.

For instance, if a patient walks in with a bunch of symptoms, you can ask, What is the most likely disease? and What are the most likely other symptoms that I should verify to make sure it is what I suspect? These are the kinds of probabilistic questions that are hard to ask on current neural network architectures, but they are exactly the kind of questions that probabilistic graphical models handle well.

Are physicians and other health-care providers open to embracing this technology, or do they feel threatened by it?

First of all, health care is a heavily regulated market, so you need approval for everything. Thats not always easy to getand, as a result, it can be very difficult to obtain data. This is the same problem that any machine learning company faces. Fine, they have this excellent piece of technology and theyve mastered it,but if you dont have any good data, you dont have a company. I see that as the biggest obstacle to machine learning-based progress in health care and life sciences.

You have said that QML has the potential to bring about the next wave of technology shock. Any predictions as to what that might look like?

I think its going to be similar to what happened with deep learning. The academic breakthrough happened about nine years ago, but it took a long time to get into the public discussion. This is currently happening with AI which, at its core, is actually just very simple pattern recognition. Its almost embarrassing how simplistic AI is and yet it is already changing entire industries.

Quantum is next not just quantum machine learning but quantum computing in general. Breakthroughs are happening every day, both on the hardware side and in the kind of algorithms you can build with quantum computers. But its going to take another 10 years until it gets into public discussions and starts to disrupt industries. The companies we are seeding today are going to be the ones that eventually disrupt industries.

Alibaba is one of the companies at the forefront of embracing quantum, having already committed $15 billion to it. What is Alibaba after?

First of all, I want to say a huge thank you toAlibaba becausethe moment it made that commitment, everyone woke up and said, Hey, look: the Chinese are getting into quantum computing! Almost immediately, the U.S. government allocated $1.3 billion to invest in and develop quantum computers, and a new initiative is also coming together in Canada.

The worlds oldest commercial quantum computing company is actually from Canada:D-Wave Systemsstarted in 1999 in British Columbia. Over its 20-year history, it managed to raise over $200 million. Then Alibaba came along and announced it was committing $15 billion to quantumand this completely changed the mindset. People suddenly recognized that theres a lot of potential in this area.

What does Alibaba want from quantum? You could ask the same question ofGoogle, which is also building a quantum computer. For them, its because they want to make their search and advertisement placement even better than it already is. Eventually, this will be integrated into their core business. I think Alibaba is looking to do something similar. As indicated, one of the main application areas for quantum is logistics and supply chain. Alibaba has a lot more traffic thanAmazon. Its orders are smaller, but the volume of goods going through its warehouses is actually much larger. Any kind of improved optimization it can achieve will translate into millions of dollars in savings. My bet is that Alibabas use of quantum will be applied to something that is critical to its core operation.

The mission of CDLs Quantum stream is that, by 2022, it will have produced more revenue-generating quantum software companies than the rest of the world combined. What is the biggest challenge you face in making that a reality?

People are really waking up to all of this. There is already a venture capital firm that focuses exclusively on quantum technologies. So, the competition is steep, but we are definitely leading in terms of the number of companies created. In Canada, the investment community is a bit slow to put money into these ventures. But every year we are recruiting better and better people and the cohorts are more and more focused and, as a result, I think we are going to see more and more success stories.

It seems like everyone is interested in quantum andthey are thinking about investing in it, but they are all waiting for somebody else to make the first move. Im waiting for that barrier to break and, in the meantime, we are making progress.Xanadujust raised $32 million in Series A financing, which indicates that it has shown progress in building its business model and demonstrated the potential to grow and generate revenue. ProteinQure raised a seed of around $4 million dollars. And another company,BlackBrane, raised $2 million. So, already, there are some very decent financing rounds happening around quantum. It will take lots of hard work, but I believe we will reach our goal.

Peter Wittekwas an Assistant Professor at the Rotman School of Management and Founding Academic Director of the Creative Destruction Labs Quantum stream. The author ofQuantum Machine Learning: What Quantum Computing Means to Data Mining(Academic Press, 2016),he was also a Faculty Affiliate at the Vector Institute for Artificial Intelligence and the Perimeter Institute for Theoretical Physics.

This article appeared in theWinter 2020 issueof Rotman ManagementMagazine.Published by the University of Torontos Rotman School of Management,Rotman Managementexplores themes of interest to leaders, innovators and entrepreneurs.

See the rest here:
U of T's Peter Wittek, who will be remembered at Feb. 3 event, on why the future is quantum - News@UofT

The DeepMind algorithm to solve two complex problems of biology – The Times Hub

The algorithm was developed by experts of the company DeepMind, solve two complex tasks in the field of biology. The network will investigate the processes of protein folding and the operation of the human brain.

Scientists believe that some of the program, based on machine learning can, like the human brain, to work on the reward system. Usually it is based on the production of dopamine. Experiments on mice showed that the probable scheme of award to build certain neurons.

The neural network also needs to predict a proteins fold. The work is to understand the structures of the compounds with amino acid composition. The problem is particularly acute in medicine and biology so as to identify all configurations of the protein, scientists will need at least 13.8 billion years.

Demis of Hassabis created DeepMind to develop algorithms to beat people in chess. Now the company has delivered more challenging goal is the use of artificial intelligence to solve difficult problems with science.

Natasha Kumar is a general assignment reporter at the Times Hub. She has covered sports, entertainment and many other beats in her journalism career, and has lived in Manhattan for more than 8 years. She studies in University of Calcutta. Natasha has appeared periodically on national television shows and has been published in (among others) Hindustan Times, Times of India

Excerpt from:
The DeepMind algorithm to solve two complex problems of biology - The Times Hub

The Importance of Understanding TargetProtein Interactions in Drug Discovery – Technology Networks

Youre unwell, you see a doctor, they prescribe you a medicine and you take it. But how exactly is that drug having an effect? What is its mechanism of action? Drugs exhibit their effects through specific protein-target interactions.

But in some cases, there may not be a treatment available. In approximately 30% of cases, drugs fail during clinical development, and toxicity which can be caused by off-target binding is often to blame.

Andrew Lynn, Chief Executive Officer at Fluidic Analytics discusses why understanding proteintarget interactions is so important, the common challenges researchers face when attempting to determine these interactions, and touches on the relationship between the drug "attrition rate" crisis and the off-target effects of drugs.

Laura Lansdowne (LL): Could you discuss the importance of understanding proteintarget interactions in drug discovery, and the implications of not knowing your target?Andrew Lynn (AL): Understanding proteintarget interactions is crucial we are talking about the difference between finding a lifesaving drug/therapy and wasting hundreds of millions of dollars developing a drug with the wrong mechanism of action.A recent paper from Jason Sheltzers group showed that ten anticancer drugs undergoing clinical trials had a completely different mechanism of action from the one originally attributed to them. Briefly, when the protein targeted by each of the drugs was removed from cancer cells, the group expected the drugs to stop working. But what they found was that the drugs continued to work as normal and thus had to be working through off-target binding.This is crucial because it means potentially there are many more drugs out there that are working through off-target binding; it also means that many other drug candidates that have previously been disregarded may have unrecognized promise. This problem is about to become even more acute as research expands into conditions with difficult targets like Alzheimer's disease.The way in which we discover the exact mechanism of action between proteins and potential drug candidates needs better technologies for characterizing on-target and off-target interactions We cannot discover new information relying solely on technologies that have fallen short for decades.LL: What challenges do drug discovery researchers face when trying to identify targetprotein interactions?AL: Drug discovery and development is a lengthy, complex and costly process with a high degree of uncertainty whether a drug will succeed. The two biggest challenges are: First, not understanding the pathophysiology of many disorders, such as neurodegenerative disorders, which makes target identification challenging. Second, the lack of validated diagnostic and therapeutic biomarkers to objectively detect and measure biological states.At the heart of both challenges is the ability to characterize protein-drug target interactions. Unfortunately, the methods currently employed by researchers to do this research are outdated.

An example of this can be seen when scientists try to characterize interactions involving intrinsically disordered proteins (IDPs) such as the ones associated with Parkinsons disease. Current characterization methods modify proteins by fixing them to a surface or putting them in artificial environments. So, its no surprise that many drugs are great at targeting proteins with these modifications but poor at targeting these same proteins as they exist in vivo in solution and not tethered to an artificial surface.

This is why were building new tools and methods for researchers to more accurately characterize binding events in solution: to better understand how drugs interact with their protein targets in their native environment.

LL: What is microfluidic diffusional sizing and how can this be used to measure the binding affinity of proteinprotein interactions?AL: Microfluidic diffusional sizing (MDS) characterizes proteins and their interactions in solution based on the size (or more specifically hydrodynamic radius) of proteins and protein complexes as they diffuse within a microfluidic laminar flow. Characterizing in solution avoids artefacts from surfaces or matrices; gathering information about size to give crucial insights into stoichiometry, on- and off-target binding, oligomerization and folding.

MDS can be used to measure binding affinity by tracking changes in the size of a protein as it binds at different concentrations. The size of the complex can also give a strong indication of whether the protein is forming a protein-target complex at the expected size (on-target binding) or something with a completely different or unexpected size (off-target binding). A major additional advantage of MDS is that, because of the absence of surfaces or matrices, it can be used to characterize binding involving difficult targets such as intrinsically disordered proteins and membrane proteins.

LL: Could you discuss the relationship between the drug "attrition rate" crisis and the off-target effects of drugs?AL: Compound failure rates due to toxicity before human testing is very high. A recent review from a top-20 pharma company cited toxicity as the reason why, between 2005-2010, 82% of drugs were rejected at the preclinical stage and 35% in phase 2a. Overall, concerns surrounding toxicity account for as much as 30% of drug attrition occurring during the clinical stage of development.For many potential drugs, toxicity is due to off-target binding. By employing new methods to characterize drug candidates binding to protein targets in native conditions, we can identify off-target binding more effectively. This could help save billions of dollars in development costs and reduce the attrition rate we are currently facing.

LL: There has currently been very limited success in the development of effective therapies for Alzheimers disease (AD). Could you touch on some of the successes and highlight the molecules of interest in AD as well as the challenges related to their study.AL: One recent success is the anti-amyloid drug, aducanumab. After Biogen re-examined the data from the clinical trials, they found that exposure to high doses of Aducanumab reduced clinical decline in patients exhibiting early stages of Alzheimers disease.If approved, aducanumab would become the first therapy to slow the cognitive decline that accompanies Alzheimer's disease. This a massive step forward and a much-needed source of hope for patients and their families.But aducanumab doesnt cure Alzheimers disease. A major challenge impeding the development of further AD drugs is the ability to understand the mechanism of action via which candidate drugs interact with targets. Amyloid- is known to be a particularly difficult-to-characterize peptide, and even aducanumab doesnt have a well-understood mechanism of action. Any breakthroughs in being able to characterize how it or other Alzheimers disease drugs interact with difficult targets would be a major breakthrough in drug development.However, the majority of Alzheimers patients do not carry the dominantly inherited genetic mutation for the disease, and we dont know why amyloid proteins aggregate within their brains.

It follows that there wont be a single cause but rather many causes. Thus, the common consensus is that there wont be a single miracle drug that cures Alzheimers disease for everyone.

Andrew Lynn was speaking with Laura Elizabeth Lansdowne, Senior Science Writer, Technology Networks.

See more here:
The Importance of Understanding TargetProtein Interactions in Drug Discovery - Technology Networks

How DeepMind is unlocking the secrets of dopamine and protein folding with AI – VentureBeat

Demis Hassabis founded DeepMind with the goal of unlocking answers to some of the worlds toughest questions by recreating intelligence itself. His ambition remains just that an ambition but Hassabis and colleagues inched closer to realizing it this week with the publication of papers in Natureaddressing two formidable challenges in biomedicine.

The first paper originated from DeepMinds neuroscience team, and it advances the notion that an AI research development might serve as a framework for understanding how the brain learns. The other paper focuses on DeepMinds work with respect to protein folding work which it detailed in December 2018. Both follow on the heels of DeepMinds work in applying AI to the prediction of acute kidney injury, or AKI, and to challenging game environments such as Go, shogi, chess, dozens of Atari games, and Activision Blizzards StarCraft II.

Its exciting to see how our research in [machine learning] can point to a new understanding of the learning mechanisms at play in the brain, said Hassabis. [Separately, understanding] how proteins fold is a long-standing fundamental scientific question that could one day be key to unlocking new treatments for a whole range of diseases from Alzheimers and Parkinsons to cystic fibrosis and Huntingtons where misfolded proteins are believed to play a role.

In the paper on dopamine, teams hailing from DeepMind and Harvard investigated whether the brain represents possible future rewards not as a single average but as a probability distribution a mathematical function that provides the probabilities of occurrence of different outcomes. They found evidence of distributional reinforcement learning in recordings taken from the ventral tegmental area the midbrain structure that governs the release of dopamine to the limbic and cortical areas in mice. The evidence indicates that reward predictions are represented by multiple future outcomes simultaneously and in parallel.

The idea that AI systems mimic human biology isnt new. A study conducted by researchers at Radboud University in the Netherlands found that recurrent neural networks (RNNs) can predict how the human brain processes sensory information, particularly visual stimuli. But, for the most part, those discoveries have informed machine learning rather than neuroscientific research.

In 2017, DeepMind built an anatomical model of the human brain with an AI algorithm that mimicked the behavior of the prefrontal cortex and a memory network that played the role of the hippocampus, resulting in a system that significantly outperformed most machine learning model architectures. More recently, DeepMind turned its attention to rational machinery, producing synthetic neural networks capable of applying humanlike reasoning skills and logic to problem-solving. And in 2018, DeepMind researchers conducted an experiment suggesting that the prefrontal cortex doesnt rely on synaptic weight changes to learn rule structures, as once thought, but instead uses abstract model-based information directly encoded in dopamine.

Reinforcement learning involves algorithms that learn behaviors using only rewards and punishments as teaching signals. The rewards serve to reinforce whatever behaviors led to their acquisition, more or less.

As the researchers point out, solving a problem requires understanding how current actions result in future rewards. Thats where temporal difference learning (TD) algorithms come in they attempt to predict the immediate reward and their own reward prediction at the next moment in time. When this comes in bearing more information, the algorithms compare the new prediction against what it was expected to be. If the two are different, this temporal difference is used to adjust the old prediction toward the new prediction so that the chain becomes more accurate.

Above: When the future is uncertain, future reward can be represented as a probabilitydistribution. Some possible futures are good (teal), others are bad (red).

Image Credit: DeepMind

Reinforcement learning techniques have been refined over time to bolster the efficiency of training, and one of the recently developed techniques is called distributional reinforcement learning.

The amount of future reward that will result from a particular action is often not a known quantity, but instead involves some randomness. In such situations, a standard TD algorithm learns to predict the future reward that will be received on average, while a distributional reinforcement algorithm predicts the full spectrum of rewards.

Its not unlike how dopamine neurons function in the brains of animals. Some neurons represent reward prediction errors, meaning they fire i.e., send electrical signals upon receiving more or less reward than expected. Its called the reward prediction error theory a reward prediction error is calculated, broadcast to the brain via dopamine signal, and used to drive learning.

Above: Each row of dots corresponds to adopamine cell, and each color corresponds to a different reward size.

Image Credit: DeepMind

Distributional reinforcement learning expands upon the canonical reward prediction error theory of dopamine. It was previously thought that reward predictions were represented only as a single quantity, supporting learning about the mean or average of stochastic (i.e., randomly determined) outcomes, but the work suggests that the brain in fact considers a multiplicity of predictions. In the brain, reinforcement learning is driven by dopamine, said DeepMind research scientist Zeb Kurth-Nelson. What we found in our paper is that each dopamine cell is specially tuned in a way that makes the population of cells exquisitely effective at rewiring those neural networks in a way that hadnt been considered before.

One of the simplest distributional reinforcement algorithms distributional TD assumes that reward-based learning is driven by a reward prediction error that signals the difference between received and anticipated rewards. As opposed to traditional reinforcement learning, however, where the prediction is represented as a single quantity the average over all potential outcomes weighted by their probabilities distributional reinforcement uses several predictions that vary in their degree of optimism about upcoming rewards.

A distributional TD algorithm learns this set of predictions by computing a prediction error describing the difference between consecutive predictions. A collection of predictors within apply different transformations to their respective reward prediction errors, such that some predictors selectively amplify or overweight their reward errors. When the reward prediction error is positive, some predictors learn a more optimistic reward corresponding to a higher part of the distribution, and when the reward prediction is negative, they learn more pessimistic predictions. This results in a diversity of pessimistic or optimistic value estimates that capture the full distribution of rewards.

Above: As a population, dopamine cells encode the shape of the learned reward distribution:We can decode the distribution of rewards from their firing rates. The gray shaded area is the true distribution of rewards encountered in the task.

Image Credit: DeepMind

For the last three decades, our best models of reinforcement learning in AI have focused almost entirely on learning to predict the average future reward. But this doesnt reflect real life, said DeepMind research scientist Will Dabney. [It is in fact possible] to predict the entire distribution of rewarding outcomes moment to moment.

Distributional reinforcement learning is simple in its execution, but its highly effective when used with machine learning systems its able to increase performance by a factor of two or more. Thats perhaps because learning about the distribution of rewards gives the system a more powerful signal for shaping its representation, making it more robust to changes in the environment or a given policy.

The study, then, sought to determine whether the brain uses a form of distributional TD. The team analyzed recordings of dopamine cells in 11 mice that were made while the mice performed a task for which they received stimuli. Five mice were trained on a variable-probability task, while six were trained on a variable-magnitude task. The first group was exposed to one of four randomized odors followed by a squirt of water, an air puff, or nothing. (The first odor signaled a 90% chance of reward, while the second, third, and fourth odors signaled a 50% chance of reward, 10% chance of reward, and 90% chance of reward, respectively.)

Dopamine cells change their firing rate to indicate a prediction error, meaning there should be zero prediction error when a reward is received thats the exact size a cell predicted. With that in mind, the researchers determined the reversal point for each cell the reward size for which a dopamine cell didnt change its firing rate and compared them to see if there were any differences.

They found that some cells predicted large amounts of reward, while others predicted little reward, far beyond the differences that might be expected from variability. They again saw diversity after measuring the degree to which the different cells exhibited amplifications of positive versus negative expectations. And they observed that the same cells that amplified their positive prediction errors had higher reversal point, indicating they were tuned to expect higher reward volumes.

Above: Complex 3D shapes emerge from a string of amino acids.

Image Credit: DeepMind

In a final experiment, the researchers attempted to decode the reward distribution from the firing rates of the dopamine cells. They report success: By performing inference, they managed to reconstruct a distribution that was a match to the actual distribution of rewards in the task in which the mice were engaged.

As the work examines ideas that originated within AI, its tempting to focus on the flow of ideas from AI to neuroscience. However, we think the results are equally important for AI, said DeepMind director of neuroscience research Matt Botvinick. When were able to demonstrate that the brain employs algorithms like those we are using in our AI work, it bolsters our confidence that those algorithms will be useful in the long run that they will scale well to complex real-world problems and interface well with other computational processes. Theres a kind of validation involved: If the brain is doing it, its probably a good idea.

The second of the two papers details DeepMinds work in the area of protein folding, which began over two years ago. As the researchers note, the ability to predict a proteins shape is fundamental to understanding how it performs its function in the body. This has implications beyond health and could help with a number of social challenges, like managing pollutants and breaking down waste.

The recipe for proteins large molecules consisting of amino acids that are the fundamental building block of tissues, muscles, hair, enzymes, antibodies, and other essential parts of living organisms are encoded in DNA. Its these genetic definitions that circumscribe their three-dimensional structure, which in turn determines their capabilities. Antibody proteins are shaped like a Y, for example, enabling them to latch onto viruses and bacteria, while collagen proteins are shaped like cords, which transmit tension between cartilage, bones, skin, and ligaments.

But protein folding, which occurs in milliseconds, is notoriously difficult to determine from a corresponding genetic sequence alone. DNA contains only information about chains of amino acid residues and not those chains final form. In fact, scientists estimate that because of the incalculable number of interactions between the amino acids, it would take longer than 13.8 billion years to figure out all the possible configurations of a typical protein before identifying the right structure (an observation known as Levinthals paradox).

Thats why instead of relying on conventional methods to predict protein structure, such as X-ray crystallography, nuclear magnetic resonance, and cryogenic electron microscopy, the DeepMind team pioneered a machine learning system dubbed AlphaFold. It predicts the distance between every pair of amino acids and the twisting angles between the connecting chemical bonds, which it combines into a score. A separate optimization step refines the score through gradient descent (a mathematical method of improving the structure to better match the predictions), using all distances in aggregate to estimate how close the proposed structure is to the right answer.

The most successful protein folding prediction approaches thus far have leveraged whats known as fragment assembly, where a structure is created through a sampling process that minimizes a statistical potential derived from structures in the Protein Data Bank. (As its name implies, the Protein Data Bank is an open source repository of information about the 3D structures of proteins, nucleic acids, and other complex assemblies.) In fragment assembly, a structure hypothesis is modified repeatedly, typically by changing the shape of a short section while retaining changes that lower the potential, ultimately leading to low potential structures.

With AlphaFold, DeepMinds research team focused on the problem of modeling target shapes from scratch without drawing on solved proteins as templates. Using the aforementioned scoring functions, they searched the protein landscape to find structures that matched their predictions and replaced pieces of the protein structure with new protein fragments. They also trained a generative system to invent new fragments, which they used along with gradient descent optimization to improve the score of the structure.

The models trained on structures extracted from the Protein Data Bank across 31,247 domains, which were split into train and test sets comprising 29,427 and 1,820 proteins, respectively. (The results in the paper reflect a test subset containing 377 domains.) Training was split across eight graphics cards, and it took about five days to complete 600,000 steps.

The fully trained networks predicted the distance of every pair of amino acids from the genetic sequences it took as its input. A sequence with 900 amino acids translated to about 400,000 predictions.

Above: The top figure features the distance matrices for three proteins, where the brightness of each pixel represents the distance between the amino acids in the sequence comprising the protein. The bottom row shows the average of AlphaFolds predicted distancedistributions.

Image Credit: DeepMind

AlphaFold participated in the December 2018 Critical Assessment of protein Structure Prediction competition (CASP13), a competition that has been held every every two years since 1994 and offers groups an opportunity to test and validate their protein folding methods. Predictions are assessed on protein structures that have been solved experimentally but whose structures have not been published, demonstrating whether methods generalize to new proteins.

AlphaFold won the 2018 CASP13 by predicting the most accurate structure for 24 out of 43 proteins. DeepMind contributed five submissions chosen from eight structures produced by three different variations of the system, all of which used potentials based on the AI model distance predictions, and some of which tapped structures generated by the gradient descent system. DeepMind reports that AlphaFold performed particularly well in the free modeling category, creating models where no similar template exists. In point of fact, it achieved a summed z-score a measure of how well systems perform against the average of 52.8 in this category, ahead of 36.6 for the next-best model.

The 3D structure of a protein is probably the single most useful piece of information scientists can obtain to help understand what the protein does and how it works in cells, wrote head of the UCL bioinformatics group David Jones, who advised the DeepMind team on parts of the project. Experimental techniques to determine protein structures are time-consuming and expensive, so theres a huge demand for better computer algorithms to calculate the structures of proteins directly from the gene sequences which encode them, and DeepMinds work on applying AI to this long-standing problem in molecular biology is a definite advance. One eventual goal will be to determine accurate structures for every human protein, which could ultimately lead to new discoveries in molecular medicine.

Read the original post:
How DeepMind is unlocking the secrets of dopamine and protein folding with AI - VentureBeat

Wow your New Year’s Eve guests with a puff pastry appetizer – KARE11.com

GOLDEN VALLEY, Minn. Chef Lindsay Guentzel stopped by KARE 11 Saturday to share a simple and delicious appetizer idea for New Year's Eve celebrations. Her recipe for Holiday Brie En Croute uses puff pastry with an egg wash that helps the pastry bake perfectly, as the protein and fats in the egg give you that perfectly golden brown finish.

Holiday Brie En Croute

1 sheet frozen puff pastry, thawed out8 oz. brie cheese, sliced cup dried cranberries cup walnuts, choppedHoney, drizzledEgg wash

Preheat oven to 400.

Line baking sheet with parchment paper. Lay out rectangular puff pastry.

Using palms, gently spread out dough.

Starting at one end of pastry shell, place brie in a line down the center running the long way (think long like a hot dog bun, not short like a hamburger bun). The slices will overlap.

Spread cranberries and walnuts over brie evenly and drizzle with honey.

Starting at one end, slowly fold the sides of the pastry shell up over the brie by pinching the corners of the dough between your fingers, lifting up and twisting over (the twists add texture and dimension to the top of the pastry). Move a few inches down the pastry shell and repeat folding movements, gently shaping the dough as you go along.

Using a pastry brush, gently brush egg wash over the pastry shell.

Bake for 20 minutes until golden brown.

Serve on platter warm with knife and serving spatula.

Follow this link:

Wow your New Year's Eve guests with a puff pastry appetizer - KARE11.com

Structure of Drosophila melanogaster ARC1 reveals a repurposed molecule with characteristics of retroviral Gag – Science Advances

INTRODUCTION

Activity-regulated cytoskeleton-associated protein (ARC) is an immediate early gene product induced in response to high levels of synaptic activity and is directed to neuronal synapses through signaling sequences in its 3 untranslated region (1). Mammalian ARC (mam-ARC) is essential for neuronal plasticity and is involved in memory (2) acting as a regulator of AMPA receptors (AMPARs) (3, 4). ARC has also been implicated in neurological disorders, including Alzheimers disease (5), fragile X syndrome (6), and schizophrenia (7, 8). In Drosophila melanogaster, two homologs of mam-ARC are expressed: dARC1 and dARC2 (9). dARC1 is present at neuromuscular junctions and, along with its mRNA, has been implicated in regulating the behavioral starvation response but is not involved in synaptic plasticity (10). Therefore, comparing the structural and functional properties of mam-ARC and dARC1 might lead to a better understanding of cognition and memory consolidation.

The ARC gene is thought to be derived from the gag gene of a Ty3/Gypsy retrotransposon (11) that, subsequent to genomic insertion, has been repurposed to perform an advantageous function to the host (12). This connection between ARC and retrotransposons was made when sequence alignments revealed that the ARC proteins shared sequence similarity with the Gag protein of retroviruses or retrotransposons (11). These data also suggested that ARC is evolutionarily related to the Ty3/Gypsy family of retrotransposons. Further evidence came from crystal structures of two -helical domains from Rattus norvegicus ARC (rARC) (13), which revealed that rARC N- and C-terminal capsid (CA) domains were structurally homologous to the N- and C-terminal CA domains of both Orthoretrovirinae (13) and Spumaretrovirinae (14). Further phylogenetic analysis revealed that, despite mam-ARC and dARC1 seemingly providing related functions in the host, dARC1 and the tetrapod ARCs most likely arose from separate lineages of Ty3/Gypsy, because dARC1 clustered with insect Ty3/Gypsy retrotransposons and tetrapod ARCs clustered with fish Ty3/Gypsy retrotransposons (12).

The relevance of ARCs retrotransposon origin to its function in synaptic plasticity was not immediately obvious until the recent observation that mam-ARC and dARC1 can self-assemble into particles and package RNA for potential transfer between cells (9, 12), similarly to retrotransposons and retroviruses (15, 16). In D. melanogaster, it is proposed that dARC1 expressed at neuromuscular junction presynaptic boutons assembles into particles that encapsidate dARC1 mRNA. Loaded particles might then be packaged and released as extracellular vesicles for intercellular transfer to the postsynapse, where mRNA release and translation can take place (9, 12). Similarly, mam-ARC can also encapsidate ARC mRNA into particles, allowing transfer from donor to recipient neurons, where ARC mRNA can be translated (12).

Because both dARC1 and mam-ARC are able to form CA-like particles (9, 12), it seems likely that they share a degree of structural similarity. To date, crystal structures of the individual domains from rARC have been determined (13), along with the solution nuclear magnetic resonance (NMR) structure of the rARC CA (17). Here, we report two crystal structures of the entire CA region of dARC1 at 1.7 and 2.3 and consider these structures in comparison to those of rARC and retroviral CA. dARC1 comprises two -helical domains with a fold related to that observed in the CA-NTD and CA-CTD of orthoretroviral and spumaretroviral CA. However, we observe significant divergence in the NTD of dARC1, where an extended hydrophobic strand that packs against 1 and 3 of the core fold replaces the N-terminal hairpin and helix 1 found in orthoretroviral CAs. In the rARC structure, this hydrophobic strand is replaced by peptides from the binding partners Ca2+/calmodulin-dependent protein kinase 2A (CamK2A) and transmembrane AMPAR regulatory protein 2 (TARP2) and may represent a functional adaptation for the recruitment of partner proteins. We also show that dARC1 uses the same CTD-CTD interface required for the assembly of retroviral CA into mature particles and propose that this obligate dimer represents a building block for dARC1 particle assembly. Further examination of the relationship between dARC1, mam-ARC, and Gag from Ty retrotransposon families reveals that, although dARC1 and mam-ARC are functional orthologs, the structural divergence in dARC1 and mam-ARC CA domains is consistent with the notion of Ty3/Gypsy Gag exaptation on two separate occasions. We suggest that they may have undergone different adaptations after appropriation into the tetrapod and insect genomes.

We determined the crystal structure of the CA domain region of dARC1, residues S39 to N205 (dARC1 CA), using single-wavelength anomalous diffraction (SAD) and crystals of Se-Met substituted protein. The structure was determined in both an orthorhombic and a hexagonal crystal form. The orthorhombic crystals diffracted to higher resolution, allowing the structure to be refined to a final resolution of 1.7 with an R factor of 18.1% and a free R factor of 21.3%. Details of data collection, phasing, and refinement are presented in table S1. The asymmetric unit (ASU) contains two chains, each containing an -helical N-terminal (CA-NTD) and C-terminal domain (CA-CTD) (Fig. 1A). The chains are arranged in a dimer with a distinct U-shape reminiscent of a glacial trough (Fig. 1A, right). The CA-CTDs form the base of the trough and pack together to form a homodimer interface, and the CA-NTDs form the sides of the trough and are separated by ~45 . Inspection of each domain reveals that the CA-NTD is made up from an extended N-terminal strand and a four-helix core (1 to 4), and the CA-CTD comprises a further five -helix bundle (5 to 9) (Fig. 1B, i and ii). The tertiary folds of each domain are particularly similar and can be superimposed with a root mean square deviation (RMSD) of 2.2 over 49 C atoms (Fig. 1C). Moreover, it can be seen that the dARC1 N-terminal strand is topologically equivalent to 5 in the CTD, while NTD 1 to 4 are equivalent to CTD 6 to 9. This strong similarity of dARC1 CA domains provides further evidence for the notion that tandem domains of CA arose as the result of a gene duplication event (14). The hexagonal crystal form was independently solved and refined to a resolution of 2.3 and reveals an almost identical dimeric ASU that aligns with an RMSD of only 0.247 over 133 C pairs (fig. S1, A to C). Both structures appear especially stable around the CTD-mediated dimeric interface and, when aligned through their CTDs, show only small differences in the positioning of NTDs with respect to the CTDs (fig. S1D).

(A) Cartoon representation of the dARC1 CA dimer. The N-terminal extended strand and helices are numbered sequentially from the N terminus to the C terminus. Monomer A is colored cyan, and monomer B is colored wheat. The right-hand panel is a view at 90 relative to the left-hand panel. (B) Close-up cartoon representations of dARC1 CA-NTD (left) and dARC CA-CTD (right) showing the helical topology of each domain. (C) Three-dimensional (3D) C structural alignment of dARC1 CA-NTD (blue cartoon) with dARC1 CA-CTD (red cartoon), with secondary structure elements labeled.

The dARC1 CA-CTD monomer consists of a five-helix core comprising 5 (residues A125 to Q134), 6 (residues I143 to Q156), 7 (residues E164 to L171), 8 (residues I177 to H182), and 9 (residues F191 to N204). The dimer interface is located between CA-CTDs, where the outer surfaces of 5 and 7 pack against 5 and 7 of the opposing monomer (Fig. 2A). The homodimer interface encompasses 768 2 of the buried surface and is defined by numerous intermolecular interactions. The interface is largely hydrophobic with contributions from side-chain packing of the Y126, Y129, M130, F133, L170, F172, and L174 hydrophobic and aromatic residues that are exposed on 5 and 7 and form a continuous apolar network with Y129 and F133 at its center (Fig. 2A, left). This is apparent in the analysis of the dARC1 CA surface hydrophobicity profile, which reveals a distinct apolar patch that locates to the center of the CA-CTD homodimer interface (fig. S2A). In addition, at the periphery of the interface, there is also a salt bridge between R161 on the 6-7 connecting loop with D169 at the C terminus of 7, providing further stabilization (Fig. 2A, right). The number and hydrophobic nature of interactions within the homodimer interface suggest that the dimer constitutes a relatively stable or obligate structure.

(A) Cartoon representation of dARC1 CA-CTD dimer. Helices are numbered sequentially from the N terminus to the C terminus. Monomer A is colored cyan, and monomer B is colored wheat. The right-hand panel is a view at 180 relative to the left-hand panel. Insets: Close-up views of molecular details of interactions at the dARC1 dimer interface. Residues that make interactions are shown in stick representation colored by atom type. Salt-bridge interactions between R161 and D169 are shown as dashed lines. (B) SEC-MALLS analysis of dARC1 CA. The sample loading concentrations were 400 M (8 mg/ml) (red), 200 M (4 mg/ml) (orange), 100 M (2 mg/ml) (yellow), 50 M (1 mg/ml) (green), and 25 M (0.5 mg/ml) (blue). The differential refractive index is plotted against column retention time, and the molar mass, determined at 1-s intervals throughout the elution of each peak, is plotted as points. The dARC1 CA monomer and dimer molecular mass are indicated with the gray dashed lines. (C) C(S) distributions derived from sedimentation velocity data recorded from dARC1 CA at 25 M (blue), 50 M (green), and 100 M (red). The curves represent the distribution of the sedimentation coefficients that best fit the sedimentation data (/0 = 1.41). (D) Multispeed sedimentation equilibrium profile determined from interference data collected on dARC1 CA at 70 M. Data were recorded at the three speeds indicated. The solid lines represent the global best fit to the data using a single-species model (Mw = 38.9 1 kDa). The lower panel shows the residuals to the fit.

Given the unexpected nature of the dimer observed in the crystal structure, the solution molecular mass, conformation, and self-association properties of dARC1 CA were examined using a variety of solution hydrodynamic methods. Initial assessment by size exclusion chromatographycoupled multi-angle laser light scattering (SEC-MALLS) was performed with protein concentrations ranging from 25 to 400 M that yielded an invariant solution molecular weight of 40.0 kDa for dARC1 CA (Fig. 2B). By comparison, the dARC1 CA sequence-derived molecular weight is 19.6 kDa. Given this value, together with the lack of a concentration dependency of the molecular weight, it is apparent that dARC1 CA also forms strong dimers in solution. To confirm and better analyze dARC1 CA oligomerization, we measured the hydrodynamic properties using sedimentation velocity (SV-AUC) and sedimentation equilibrium (SE-AUC) analytical ultracentrifugation. A summary of the experimental parameters, molecular weights derived from these data, and statistics relating to the quality of fits are shown in table S2. Analysis of the sedimentation velocity data for dARC1 CA using both discrete component and the C(S) continuous sedimentation coefficient distribution function (Fig. 2C) revealed a predominant single species with S20,w of 2.92 0.03 S and no significant concentration dependency of the sedimentation coefficient over the range measured (25 to 90 M). These data show that dARC1 CA comprises a single stable 2.92 S species with a molecular weight derived from either the C(S) function or discrete component analysis (S20,w/D20,w) of 38 kDa (table S2), consistent with a dARC1 CA dimer. The frictional ratio (f/fo) obtained from the analysis of the sedimentation coefficients is 1.41 (table S2), suggesting that the solution dimer has an elongated conformation and is consistent with the U-shaped conformation observed in the crystal structures. Moreover, analysis of the crystal structure using HYDROpro (18) gives calculated S20,w and D20,w values in close agreement with that observed in solution (table S2), supporting the idea that the dimer observed in the crystal structures is wholly representative of the solution conformation. To further ascertain the affinity of dARC1 CA self-association, multispeed SE-AUC studies at varying protein concentration were carried out and typical equilibrium distributions for dARC1 CA are presented in Fig. 2D. Analysis of individual gradient profiles showed no concentration dependency of the molecular weight, and so, all the data were fitted globally with a single ideal molecular species model, producing a weight-averaged molecular weight of 38.9 kDa (table S2). The lack of any concentration dependency precludes any analysis of homodimer affinity but confirms that dARC1 CA forms a stable dimeric structure that has the expected properties of the dimer we observe in the crystal structure.

Attempts to mildly disrupt the central apolar network by introduction of an F133A mutation had no effect on dimerization when assessed by SEC-MALLS (fig. S2B). More aggressive mutations F133A + Y129A and F133A + R161A resulted in complete loss of protein solubility and an inability to purify the constructs, further suggesting that, in dARC1 CA, homodimerization is a requirement for protein folding/structural integrity and likely forms a key building block of dARC1 particle assembly. Analysis of the electrostatic surface potential of the dimeric structure reveals a differential distribution of charge, where the surface of the glacial trough has a net negative charge that spreads across both domains of each dARC1, and the underside where the C-terminus projects has a more positively charged character (fig. S2C), suggesting that, upon assembly, dARC1 particles would have a negatively charged exterior and a more positively charged interior where nucleic acid is contained.

Given that mam-ARC and dARC1 share functional similarities, we assessed the relationship between rARC and dARC1 by comparing the dARC1 structure with the individual domains from rARC. Overall, the alignments are excellent, reflecting the evolutionary relationship, but there are significant differences between dARC1 and rARC in both their NTDs and CTDs.

There are two crystal structures of the rARC NTD in complex with peptide ligands [Protein Data Bank (PDB): 4X3H and 4X3I] (13) and a recent solution NMR structure [6GSE; (17)] of the entire rARC CA domain that resolves the NTD in an apo form. Superficially, the dARC1 CA-NTD aligns well with all available structures of the rARC CA-NTD, with DALI Z scores of 8 to 10 and RMSDs between 1.5 and 1.9 (Fig. 3A).

(A) Left: 3D structural alignment of dARC1 CA-NTD (teal cartoon) and apo-rARC CA-NTD (PDB: 6GSE; lilac cartoon). Secondary structure elements are labeled. Circled are the ordered N-terminal strand of dARC1 and the disordered N-terminal strand of apo-rARC. Right: 3D structural alignment of dARC1 CA-NTD and the peptide-complex structures of rARC CA-NTDs (PDB: 4X3H and 4X3I). The protein backbones are shown in cartoon representation, colored according to the legend. Secondary structure elements are labeled. The arrow indicates the different positioning of the extended N-terminal strand between the dARC1 and rARC structures. (B, i to iv) Individual views of the structures presented in (A): (i) apo-dARC1, (ii) apo-rARC, (iii) rARC-TARP2, and (iv) rARC-CaMK2B. Residues that constitute the hydrophobic NTD cleft are shown in stick format, colored by atom type. In each structure, the side chains of the aromatic residues buried in the interface (F45 and F52, dARC1 CA-NTD; Y229*, rARC CA-NTDTARP2; F313*, rARC CA-NTDCaMK2B) are colored purple, yellow, and orange, respectively. The conserved main-chain hydrogen bonding interactions between the backbone amide and carbonyl of F52 with the carbonyl of L89 and the amide of Y91 (dARC1), of Y229 with the carbonyl of H245 and the amide of N247 (rARC CA-NTDTARP2), and of F313 (rARC CA-NTDCaMK2B) with the carbonyl of H245 and the amide of N247 are shown as dashed lines.

Examination of the dARC1 CA-NTD reveals an N-terminal extended strand (NT-strand), residues G43 to R56, with a short configuration that packs against the core of the NTD. The NT-strand makes many interactions with the apolar and aromatic side chains that extend from 1, 2, and 4, burying 803 2 of surface in the interface [Fig. 3, A and B (i), and fig. S3A], and the same configuration is observed in all four instances of the NTDs that we see in our two crystal structures (fig. S3B). The NT-strand residues are highly conserved in dARC genes across Drosophilidae but not with the mam-ARCs (fig. S3C). In particular, two highly conserved aromatic residues, F45 and F52, are entirely buried, surrounded by the conserved side chains of F64, L89, I115, and F119, and act to anchor the NT-strand into the hydrophobic 1-to-4 cleft of the CA-NTD. In addition, there is a main-chain interaction between the backbone amide and carbonyl of F52 with the carbonyl of L89 and the amide of Y91 that further stabilizes the conformation of the NT-strand [Fig. 3B (i) and fig. S3A].

In apo-rARC CA-NTD (6GSE), the helical core aligns very well with the corresponding region of dARC1 (RMSD = 1.45 ). However, here, the rARC NT-strand residues D210 to E216 have a disordered conformation (Fig. 3, A and B, ii), and the 1-to-4 hydrophobic cleft, which in dARC1 contains the native NT-strand, is unoccupied in rARC, suggesting that there is a functional divergence for the NT-strand between the dARC1 and mam-ARC families. This notion is supported by the inspection of the rARC CA-NTDTARP2 and CA NTDCaMK2B complexes (4X3H and 4X3I), where the 1-to-4 cleft of rARC is now occupied by the bound TARP2- or CaMK2B-derived peptides (Fig. 3B, iii and iv), and the bound peptides adopt the same extended configuration as the native NT-strand in the dARC1 structure (fig. S3D) and bury a comparable amount of surface, 772 and 641 , respectively. Moreover, both bound peptides contain an aromatic residue equivalent to dARC1 F52, Y229 in TARP2, and F313 in CaMK2B that packs into the core of rARC CA-NTD and makes an identical main-chain interaction with the backbone carbonyl of H245 and the amide of N247 as that observed between the backbone amide and carbonyl of F52 with the carbonyl of L89 and the amide of Y91 in dARC1 (fig. S3D). In these peptide-complex structures, the rARC NT-strand, D210 to E216, that is disordered in the apo structure now adopts a parallel configuration to pack against the bound peptides (Fig. 3B, iii and iv), and it is possible that the propensity to form this stabilizing configuration has been selected for. This notion is supported by the inspection of the dARC and mam-Arc multiple sequence alignment (fig. S3C) that reveals a conserved TQIF motif in Amniota that retains -branched residues, favored in structure, at the T and I position. This motif is not present in amphibians or in Latimeria chalumnae Gypsy2, the closest known relative to the transposon from which tetrapod ARC was exapted, suggesting that this feature, and possibly peptide binding ability, arose within Amniota.

The structures of dARC1 CA-CTD and rARC CA-CTD (PDB: 4X3X) also superimpose well (RMSD = 2.7 ). However, the CTD of the apo-rARC CA NMR structure more closely matched the structure of dARC1 CA-CTD (RMSD = 2.2 ), with all five helices overlaying (Fig. 4A). However, in contrast to our solution studies of dARC1 (Fig. 2, A to C, and fig. S2A), the rARC CA domain was monomeric in solution, even at the high concentrations under which NMR was performed (17).

(A) 3D structural alignment of dARC1 CA-CTD and rARC CA-CTD from apo-rARC (PDB: 6GSE). The structures are shown in cartoon, with equivalent helices labeled and shown as cylinders. dARC1 is colored cyan, and rARC is colored light blue. (B and C) Details of the CA-CTD homodimer interfaces. Cartoon representations of the protein backbone of dARC1 CA-CTD (B) and rARC CA-CTD (C) are shown, colored as in (A). The view is of one monomer looking into the dimer interface. Residues that make interactions in dARC1 CA and their equivalents in rARC are shown in stick representation, color-coded by residue type (purple, hydrophobic/aromatic; green, polar; red, acidic; blue, basic). (D and E) Hydrophobic surface representations of (B) and (C), respectively. Circled in (D) is a distinct hydrophobic patch on the surface of dARC1 CA-CTD, which is absent in rARC. (F) Multiple sequence alignment of ARC, dARC1, and dARC2 CA-CTDs and parent retrotransposon sequences. Group 1 contains tetrapod ARC (tARC) sequences and the closely related Latimeria chalumnae (L. ch) Gypsy2 transposon. Top: Secondary structure of rARC; numbers according to the rARC (R. norvegicus) sequence. Group 2 contains dARC1, dARC2, and closely related Linepithema humile (L. h) Gypsy11 retrotransposon. Bottom: Secondary structure of dARC1; numbers according to the dARC1 (D. melanogaster) sequence. Red box and white text represent invariant residues shared between groups. Red text represents residues conserved within a group. Asterisks mark the residues at the dARC1 CTD dimer interface and their equivalents in tARCs, as shown in (B) and (C).

In dARC1, a large proportion of the CTD dimer interface results from the packing of hydrophobic side chains projecting from helices 5 and 7 (Fig. 2A). However, upon comparison of the external 5/7 surfaces of dARC1 and rARC (Fig. 4, B and C), it is apparent that the exposed Y126, Y129, M130, F133, L170, F172, and L174 side chains that are responsible for the hydrophobic character of the dARC1 dimer interface are not conserved in rARC and are replaced by E282, Q285, R286, D289, Y324, V326, and T328 in rARC. Therefore, the hydrophobic patch present on the surface of dARC1 is not evident in the same surface on rARC (Fig. 4, D and E). In addition, R161 and D169, which make a salt bridge interaction in the dARC1 interface, are also not conserved, being replaced by D315 and Q323 in rARC (Fig. 4, B and C). These sequence differences are also apparent throughout the entire dARC and mam-ARC families. Hence, there is strong sequence conservation of residues that constitute the core fold of the CA-CTD across both dARC and mam-ARCs, but the hydrophobic CA-CTD dimer interface residues are only present in the dARC lineage (Fig. 4F). Together, these data reveal that, while tertiary structure topology of dARC1 and rARC CA-CTDs is conserved, there are substantial differences in the character of the surface that is presented around 5 to 7; in dARC1, the hydrophobic nature of this surface drives the formation of a strong CTD dimer, whereas in rARC, the more polar nature of this surface may explain why the protein is monomeric in solution. Given these differences, although there is strong evidence for the assembly of both dARC1 and mam-ARC into CA-like particles (9, 12), it seems likely that if dARC1 and mam-ARC use the 5/7 interface in a particle assembly pathway, the interface may be substantially weaker for mam-ARC.

(A) Pairwise DALI 3D C structural alignment of dARC1 CA-NTD with HIV CA-CTD (left), RSV CA-CTD (middle), and HIV-NTD (right). In each panel, the cartoon of the dARC1 CA-NTD backbone is shown in blue, and the backbone of the aligned structures is shown in gray. (B) Pairwise 3D C structural alignment of dARC1 CA-CTD with HIV CA-CTD (left) and RSV CA-CTD (right). In each panel, the cartoon of the dARC1 CA-CTD backbone is shown in red, and the backbone of the aligned structures is shown in gray. (C) Pairwise 3D C structural alignment of dARC1 CA-NTD with prototypic foamy virus (PFV) CA-NTD (left) and dARC1 CA-CTD with PFV CA-CTD (right). (D) DALI Z scores, RMSD, number of aligned residues, and sequence identities for 3D C alignments.

The topology of the -helical two-domain fold of dARC1 is highly reminiscent of retroviral CA structures. Interrogation of the PDB database with dARC1 CA using the DALI alignment/search engine (19) produced an overwhelming number of matches to Gag proteins (87%, Z score 5.0) and identified rARC, together with many orthoretroviral and spumaretroviral CA-NTD and CA-CTD structures. Alignments with CA-NTDs and CA-CTDs from HIV CA, Rous sarcoma virus (RSV) CA, and prototypic foamy virus CA (PFV) are presented in Fig. 5. The best structural alignments to dARC1-NTD were with retroviral Gag CA-CTD structures rather than with Gag CA-NTD structures (Fig. 5, A and D), indicating that the dARC1 CA-NTD is more closely related to the orthoretroviral CA-CTD than to the orthoretroviral CA-NTD. Alignments with dARC1-CTD also had the best structural alignment with orthoretroviral Gag CA-CTD structures (Fig. 5, B and D), perhaps not unexpected given the observation of close resemblance of dARC1 CA-NTD to dARC1 CA-CTD (Fig. 1B, iii). Alignments with PFV CA-NTD and CA-CTD were also found (Fig. 5, C and D); although not as significant as with the orthoretroviral CA, these data support previous observations of a relationship of spumaretroviral Gag with mam-ARC (14).

These data provide evidence for a structural conservation between orthoretroviral CA and ARC proteins, and the weaker alignments observed with orthoretroviral CA-NTDs suggest that orthoretroviral CA-NTDs have undergone much more structural divergence than has occurred in the Ty3 family or ARC proteins. Moreover, these data further support the previously proposed idea that a duplication of a CA-CTD progenitor first gave rise to double domain ancestors and that subsequent divergence of domains resulted in spumaretroviral, orthoretroviral, and Metaviridae-derived proteins, such as ARC, that are found presently (14, 20).

Given the existence of the dARC1 CA dimer and the distant relationship with orthoretroviral CA, we next looked to see whether the dimer interface was conserved between dARC1 and the CTD dimers of HIV-1 CA and RSV CA that are known to be essential for CA assembly in orthoretroviruses. For these comparisons, the interhexamer CA CTD-CTD dimers observed in HIV-1 and RSV CA-hexamer crystal structures (21, 22) were used, as these most closely relate to those observed in cryo-electron microscopy (cEM) studies of whole CA assemblies (22, 23). Cartoon representations of the dARC1, HIV-1, and RSV CA-CTD dimers are shown in Fig. 6 (A to C). In each, the domain arrangement that presents the dimer interface is the same, and this is also seen in the CA-CTD dimer of native Ty3 particles visualized by cEM (24), but with some repositioning of the CA-NTDs (fig. S4). The structures have been aligned to find the best C alignment over the entire dimer (HIV, RMSD = 2.8 over 117 C; RSV, RMSD = 3.1 over 101 C) (Fig. 6, D and E), and it is apparent that each interface is made up from interactions between residues on CTD helices 5 and 7 of dARC1, which correspond to 7 and 8 in the orthoretroviral CA-CTD structures. Notably, in the orthoretroviruses, 7 is reduced to a single turn, and the monomers are rotated with respect to each other. Therefore, in dARC1, residues on 5 and 7 contribute equally to the interface, while in the orthoretroviruses, 8 contributes more to the interface than does 7. This combination of the larger contribution of 5 in dARC1, together with the rotation and displacement of CA-CTDs seen in the orthoretroviruses, has the effect of reducing the surface area that is buried at the interface from 768 2 in dARC1 to 452 2 in HIV-1. Notably, the homodimer affinity for orthoretroviral CA-CTD dimers is much weaker than the dARC1 dimer. Equilibrium dissociation constants ranging between 10 and 20 M have been reported for HIV-1 (25, 26), and CA-CTD dimerization is undetectable for other genera (2729). Nevertheless, given the domain organization and the similarity in character of the orthoretroviral and dARC1 CA-CTD dimers, we suggest that this interface is a key building block of CA assembly, retained in dARC1 and conserved from Ty3/Gypsy transposable elements to orthoretroviridae.

(A to C) Cartoon representations of CA-CTD dimers. (A) dARC1 is colored cyan and wheat. (B) HIV-1 is colored magenta and pale green (PDB: 2XFX). (C) RSV is colored gray and red (PDB: 3G21). The orthoretroviral structures are aligned with respect to the dARC1 dimer. CTD helices 5 to 9 are labeled in the dARC1 structure, and the equivalent 7 to 10 are labeled in the orthoretroviral structures. The buried surface area (2) and free energy of interaction (iG) of each interface, calculated in PDBePISA, are displayed below each structure. (D and E) Structural alignment of dARC1 CA with HIV-1 CA and RSV CA dimers, respectively. Protein backbones are colored as in (A) to (C).

Our crystal structures demonstrate that the central region of dARC1 contains two largely -helical domains that, despite the lack of sequence conservation, have the same predominantly -helical folds observed in the structures of CA domains from the ortho- and spumaretroviruses. A more detailed inspection of dARC1 CA-NTD and CA-CTD reveals that they comprise four- and five-helix bundles, respectively, with a topology that aligns well with the arrangement of secondary structure elements observed in orthoretroviral CA NTDs and CTDs (Fig. 5). However, it is apparent that both the ARC CA-NTD and CA-CTD are much more closely related to the orthoretroviral CA-CTDs than they are to orthoretroviral CA-NTDs (Fig. 5), consistent with our previous notion that an ancient domain duplication was a key event during retrotransposon evolution (14). Notably, orthoretroviral CA-NTDs contain an extra N-terminal hairpin and an additional two helices compared to the ARCs and the CA domains of Ty3/Gypsy transposons (fig. S4) (24). This suggests that unique aspects of the retroviral life cycle might be driving specific changes in the structure of the retroviral CA-NTD. One such pressure might be associated with the process of maturation that follows retrovirus budding from the cell. Maturation involves proteolytic cleavage of immature viral cores, followed by CA reassembly to yield mature virions and although it is proposed that dARC1 and mam-ARC transport mRNA between cells, it is thought likely that particles are packaged into extracellular vesicles for cell-to-cell transfer (9, 12). Similarly, maturation events do not occur in Ty3 elements, which also do not bud from the cell and have Gag that assembles directly into mature forms (24). The absence of maturation also characterizes spumaviruses, and it was observed previously that the CA NTDequivalent region of PFV Gag showed greater similarity to rARC than to orthoretroviral CA (14).

Our three-dimensional (3D) superimpositions have demonstrated that there is a large degree of structural conservation between the dARC1 and mam-ARC CA structures. However, despite this strong similarity, two regions of distinct differences between the dARC1 and rARC structure are apparent. The first region concerns the ARC CA-NTD and the interaction with potential binding partners; the second region concerns the putative dimerization domain of the CTD.

Functionally important interactions between mam-ARC and a variety of neuronal proteins, including the TARP2 and CaMK2B proteins, as well as the NMDA (N-methyl-d-aspartate) receptor, have been defined (13, 17). However, no such interactions have been reported for dARC1. In the rARC structures with bound TARP2 or CaMK2B peptides, the disordered N-terminal region of rARC seen in the apo structure now forms a short parallel sheet, with the bound peptide stabilizing the peptide binding within a hydrophobic cleft on rARC. It is apparent that the conformation of these rARC-bound peptides strongly resembles that of the NT-strand of dARC1 NTD (Fig. 3). Therefore, given the sequence differences in the NT-strand region between the dARC and mam-ARCs (fig. S3C), one notion is that mam-ARC has evolved an N-terminal strand that no longer binds into the CA-NTD hydrophobic cleft but has gained the ability to promote the binding of synaptic protein ligands, perhaps acting as a sensor of synaptic stimuli. This sensing property might then contribute control to a functional role for ARC based on assembly and mRNA trafficking.

There are also significant differences between dARC1 and rARC CA-CTD, illustrated in Fig. 4. Overall, our crystal structure of dARC1 and the NMR structure of full-length rARC (17) are very similar, with good overlay in all five helices. However, inspection of the dARC1 surface reveals a substantial hydrophobic patch that is absent in rARC (Fig. 4, D and E). This hydrophobic patch is shared with the orthoretroviruses (25, 30) and seems to be associated with the formation of stable dARC1 dimers, whereas rARC is monomeric. Whether this translates to differences in the stability of assembled particles in vivo remains to be determined; however, it is possible that differences in the physiological roles of dARC1 and mam-ARC may mean that mam-ARC has evolved to require a weaker interface that facilitates disassembly. Alternatively, it is possible that mam-ARC may require a conformational change to facilitate dimerization or uses a completely different assembly mechanism that uses other surfaces of the molecule.

The observation that residues at the dARC1 CA-CTD interface are not conserved between the insect and mam-ARC lineages suggests the possibility that, although mam-ARC particles have been observed in vitro and in cells, their mode of assembly may not use an obligate CA-CTD dimer as a building block. This type of observation has been made with orthoretroviruses that assemble through a combination of NTD-NTD, NTD-CTD, and CTD-CTD interactions to form the viral CA shell, where the relative contribution that different types of CA interaction make to the overall formation of the viral core varies depending on the retroviral genera. For instance, in lentiviruses, it is apparent that CA assembly requires a strong intrinsic CTD-CTD dimeric interaction (25, 30). However, more generally, CA shell formation requires three types of interaction: intrahexamer NTD-NTD self-association (3033), intrahexamer NTD-CTD interactions between adjacent CA monomers (30, 34, 35), and interhexamer CTD-CTD interactions (25, 30). Therefore, it is entirely possible that, in dARC1 and mam-ARC particles, the relative contributions of each type of interface may also differ.

Mam-ARC and dARC1 appear to have different biological properties. However, it remains to be determined whether these differences result from the capture of two different Ty3/Gypsy elements or they reflect evolutionary adaptations. Perhaps the best studied example of the appropriation of retroelement encoded genes by mammalian hosts is the case of syncytin, a fusagenic protein essential for proper placenta formation (36). It is evident that syncytin capture appears to have occurred on multiple independent occasions, involving envelope proteins from different retroviruses (37, 38), resulting in placentae with subtly different morphologies (39). Determining whether this is also the case with the ARC genes, as well as their close relatives in the mammalian genome (11), will require further characterization of existing retrotransposon elements using structural methods not reliant on the comparative similarities in related nucleic acid sequences that have disappeared with the passage of time.

dARC1 residues S39 to N205 were determined to represent the CA domain according to multiple sequence alignment and secondary structural analysis performed in ClustalX (40) and Psipred (41). An Escherichia coli codon-optimized complementary DNA (cDNA) for D. melanogaster dARC1 (UniProt, Q7K1U0) was synthesized (GeneArt), and the relevant sequence was polymerase chain reactionamplified and subcloned into a pET22b plasmid (Novagen). The resulting construct comprised residues 39 to 205 of dARC1, with an N-terminal Met and a C-terminal PLEHHHHHH His-tag extension. Proteins were expressed in E. coli strain BL21 (DE3) grown in LB broth by induction of log-phase cultures with 1 mM isopropyl--d-thiogalactopyranoside (IPTG) and incubated overnight at 20C. Cells were pelleted and resuspended in 50 mM tris-HCl, 150 mM NaCl, 10 mM imidazole, 5 mM MgCl2, and 1 mM dithiothreitol (pH 8.0), supplemented with lysozyme (1 mg/ml; Sigma-Aldrich), deoxyribonuclease (DNase) I (10 g/ml; Sigma-Aldrich), and one Protease Inhibitor cocktail tablet (EDTA-free, Pierce) per 40 ml of buffer. Cells were lysed using an EmulsiFlex-C5 homogenizer (Avestin), and dARC1 CA was captured from clarified lysate using immobilized metal ion affinity on a 5-ml Ni2+-NTA superflow column (Qiagen). Bound dARC1 CA was eluted in nonreducing buffer (50 mM tris-HCl, 150 mM NaCl, and 300 mM imidazole), and carboxypeptidase A (CPA; Sigma-Aldrich, C9268) was added at a ratio of ~100 mg of dARC1 per mg of CPA. The resulting mixture was incubated overnight at 4C to allow digestion of the C-terminal His-tag. The CPA was inactivated by the addition of TCEP-HCl [tris (2-carboxyethyl) phosphine hydrochloride] to 2 mM. dARC1 CA was further purified by size exclusion chromatography using a Superdex 75 (26/60) (GE Healthcare) column, equilibrated in 20 mM tris-HCl, 150 mM NaCl, and 1 mM TCEP (pH 8.0). Purified protein eluted in a single peak. Selenomethionine derivative protein was produced using an identical procedure, but with Methionine auxotroph E. coli B834 (DE3) cells, grown in selenomethionine medium (Molecular Dimensions, Newmarket, United Kingdom), used to express the protein. Electrospray-ionization mass spectrometry was used to confirm the identity of dARC1 and, where applicable, selenomethionine incorporation. It also confirmed that the N-terminal Met had been processed and that the His-tag had been completely digested, leaving the motif PLE at the C terminus. Protein was concentrated by centrifugal ultrafiltration (Vivaspin; molecular weight cutoff, 10 kDa), then snap-frozen, and stored at 80C. Protein concentrations were determined by ultraviolet-visible absorbance spectroscopy using an extinction coefficient at 280 nm derived from the tyrosine and tryptophan content.

dARC1 CA was crystallized using sitting drop vapor diffusion at 18C using Swissci MRC two-drop trays (Molecular Dimensions), with drops set using a Mosquito LCP robot with a humidity chamber (TTP Labtech). Native protein was initially concentrated to 20 mg/ml. Typically, drops were 200 to 300 nl, made by mixing protein:mother liquor in a 3:1 or 1:1 ratio, with a 75-l reservoir. Initial crystal hits were obtained using the Structure Screen 1&2 (Molecular Dimensions) under a condition containing 4.3 M NaCl and 0.1 M Hepes (pH 7.5). Two crystal forms could be observed in these conditions: thin rods, which had a primitive orthorhombic (oP) lattice, and hexagonal disks or trapezoidal prisms, which had a primitive hexagonal (hP) lattice. Datasets were collected for these native crystals, but they could not be solved by molecular replacement methods. SeMet dARC1 CA was crystallized under conditions that optimized protein concentration, NaCl concentration, and pH. The best crystals grew in 300- to 400-nl drops set with protein at 12.5 to 16 mg/ml, with mother liquor NaCl ranging between 2.8 and 3.3 M. Rods were ~400 m 30 m 30 m, and hexagons/trapezoids were ~130 m across and up to 30 m thick. Crystals were harvested using MiTeGen lithographic loops. The best cryoprotection was achieved using sodium malonate mixed into mother liquor to a concentration of 1.6 M. This was added directly to the drop, or crystals were bathed in this solution before flash freezing in liquid nitrogen.

Data were collected at the tunable SLS beamline PXIII. For the orthorhombic crystal form, a peak dataset was collected to 2.06 (see table S1). Data were processed by the SLS GoPy pipeline in P212121 using XDS (42) and showed significant anomalous signal to 2.82 . The resultant dataset was solved using SAD methods with Phenix (43), and despite a relatively low Figure of Merit (FOM), the experimental map was readily interpretable and it was possible to almost completely autobuild an initial structure with BUCCANEER (44). A higher-resolution (1.55 ) dataset was collected at a non-anomalous, low-energy remote wavelength (table S1). This dataset was processed using the Xia2 (45) pipeline, DIALS (46) for indexing and integration, and AIMLESS (47) for scaling and merging. This dataset was initially used for refinement to 1.7 and manual model building in COOT (48). It was evident that the data were anisotropic and that they might benefit from anisotropic correction. Diffraction images were reprocessed using the autoPROC pipeline (49), XDS, POINTLESS (50), AIMLESS, and STARANISO (http://staraniso.globalphasing.org/cgi-bin/staraniso.cgi). This dataset was used for further refinement of the model, and there was an improvement in map quality, and in agreement between model and data. For the hexagonal crystal form, a highly redundant peak dataset was collected to 2.14 . This was processed using the Xia2 pipeline, DIALS for indexing and integration, and AIMLESS for scaling and merging, showing significant anomalous signal to 2.59 , in P6122. This dataset was solved using SAD methods in Phenix. Again, the experimental map was readily interpretable, and it was possible to almost completely autobuild an initial structure with BUCCANEER. Refinement and model building were carried out in Phenix and COOT, respectively. Anomalous signal was very strong in this dataset, and so, Friedel pairs were treated separately during refinement. MolProbity (51) and PDB_REDO (52) were used to monitor and assess model geometry. Details of data collection, phasing, and structure refinement statistics are presented in table S1.

SEC-MALLS was used to determine the molar mass of dARC CA. Samples ranging from 25 to 400 M were applied in a volume of 100 l to a Superdex INCREASE 200 10/300 GL column equilibrated in 20 mM tris-HCl, 150 mM NaCl, 0.5 mM TCEP, and 3 mM NaN3 (pH 8.0) at a flow rate of 1.0 ml/min. The scattered light intensity and the protein concentration of the column eluate were recorded using a DAWN HELEOS laser photometer and an OPTILAB-rEX differential refractometer, respectively. The weight-averaged molecular mass of material contained in chromatographic peaks was determined from the combined data from both detectors using the ASTRA software version 6.0.3 (Wyatt Technology Corp., Santa Barbara, CA).

Sedimentation velocity experiments were performed in a Beckman Optima Xl-I analytical ultracentrifuge using conventional aluminum double-sector centerpieces and sapphire windows. Solvent density and the protein partial specific volumes were determined as described (53). Before centrifugation, dARC1 CA samples were prepared by exhaustive dialysis against the buffer blank solution, 20 mM tris-HCl (pH 8), 150 mM NaCl, and 0.5 mM TCEP (tris buffer). Samples (420 l) and buffer blanks (426 l) were loaded into the cells, and centrifugation was performed at 50,000 rpm and 293 K in an An50-Ti rotor. Interference data were acquired at time intervals of 180 s at varying sample concentrations (25, 50, and 100 M). Data recorded from moving boundaries were analyzed in terms of the size distribution functions C(S) using the program Sedfit (54).

Sedimentation equilibrium experiments were performed in a Beckman Optima XL-I analytical ultracentrifuge using aluminum double-sector centerpieces in an An-50 Ti rotor. Before centrifugation, samples were dialyzed exhaustively against the buffer blank (tris buffer). Samples (150 l) and buffer blanks (160 l) were loaded into the cells, and after centrifugation for 30 hours, interference data were collected at 2 hourly intervals until no further change in the profiles was observed. The rotor speed was then increased, and the procedure was repeated. Data were collected on samples of different concentrations of dARC1 CA (25, 50, and 70 M) at three speeds, and the program SEDPHAT (55) was used to determine weight-averaged molecular masses by nonlinear fitting of individual multispeed equilibrium profiles to a single-species ideal solution model. Inspection of these data revealed that the molecular mass of dARC1 CA showed no significant concentration dependency, and so, global fitting incorporating the data from multiple speeds and multiple sample concentrations was applied to extract a final weight-averaged molecular mass.

Amino acid alignments were produced with MAFFT v7.271 (57), within tcoffee v11.00.8cbe486 (58), weighting alignments using three-state secondary-structure predictions produced with RaptorX Property v1.02 (59). Alignment images were produced with ESPript (60).

Acknowledgments: We thank the Swiss Light Source for beamtime and the staff of beamline PXIII. Funding: This work was supported by the Francis Crick Institute, which receives its core funding from the Cancer Research UK (FC001162 and FC001178), the UK Medical Research Council (FC001162 and FC001178), and the Wellcome Trust (FC001162 and FC001178), and by the Wellcome Trust (108014/Z/15/Z and 108012/Z/15/Z). Author contributions: M.A.C., S.C.L., and I.A.T. performed experiments. M.A.C., S.C.L., G.R.Y., J.P.S., and I.A.T. contributed to experimental design, data analysis, and manuscript writing. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors. The coordinates and structure factors for dARC1 CA (S39 to N205) have been deposited in the PDB under accession numbers 6S7X and 6S7Y.

See the rest here:

Structure of Drosophila melanogaster ARC1 reveals a repurposed molecule with characteristics of retroviral Gag - Science Advances

The structure of the RCAN1:CN complex explains the inhibition of and substrate recruitment by calcineurin – Science Advances

INTRODUCTION

Calcineurin (CN, PP2B, and PP3) is a Ca2+-dependent Ser/Thr phosphatase with critical functions in many physiological processes, including development, cardiac function, and the immune response (1, 2). Calcium-activated CN dephosphorylates many substrates, including the nuclear factor of activated T cell (NFAT) transcription factors (3). Once dephosphorylated, the NFATs translocate into the nucleus and initiate NFAT-regulated gene transcription. In 2006, Crabtree and co-workers (4) demonstrated that Nfatc2/ and Nfatc4/ knockout mice have facial characteristics similar to those observed in patients with Down syndrome (DS; also known as trisomy 21), suggesting that the disruption of NFAT signaling, potentially via CN, plays a central role in this disease. Consistent with this observation, mice with forebrain-specific deletions of CNB, a subunit of CN responsible for making CN activation sensitive to Ca2+, also exhibit defects in learning and memory, both established hallmarks of DS (5). Studies during the last two decades have revealed that deletions of CNB render CN incapable of forming the LxVP-binding pocket, which is essential for NFAT dephosphorylation and, in turn, prevents NFAT translocation to the nucleus.

Individuals with DS have three copies of chromosome 21, resulting in a 1.5 dosage of these genes. Studies of patients with incomplete trisomy 21 led to the identification of the Down syndrome critical region (DSCR), the chromosomal fragment of chromosome 21 that is hypothesized to contain the genes responsible for the major DS phenotypes. The protein product of one of these genes, DSCR1 (Adapt78, MCIP1, RCN1, and calcipressin; now known as RCAN1; Fig. 1A), was found to be an inhibitor of CN (68). Consistent with this, overexpression of both full-length and a C-terminal fragment (amino acids 115 to 197) of RCAN1 in calcium-stimulated human cells prevented NFAT translocation to the nucleus (6), suggesting that RCAN1 overexpression may contribute to DS by blocking NFAT signaling. Supporting this hypothesis, Northern blots showed that the levels of RCAN1 mRNA in DS brain tissue were significantly higher compared to tissue from non-DS patients (6). Together, these data suggest that RCAN1, via its ability to inhibit CN and in turn disrupt NFAT signaling, contributes to DS phenotypes.

(A) RCAN1 domain structure showing the N-terminal RRM domain (yellow, amino acids 6 to 82; PDB ID 1WEY), the LxVP motif (green, amino acids 96 to 99), the SPPASPP motif (cyan, amino acids 108 to 114), the PxIxIT motif (orange, amino acids 154 to 159), and the TxxP motif (purple, amino acids 186 to 189). Partially populated helices 1 and 2 [gray cylinders; see (D)] and the regions that remain flexible when bound to CN [dotted circles; flex1 and flex2; see (C)] are also indicated. Constructs used in this study are shown below, with mutations shown in red letters. Sequence alignment of multiple RCAN1 species (amino acids 89 to 197), with the residues corresponding to motifs highlighted; * indicates residues phosphorylated by active p38. RCAN1 orthologs used in sequence alignment are described in Materials and Methods. RCAN1 residue numbers are labeled according to human RCAN1 isoform 2. (B) Cartoon diagram of calcineurin (CN), with CNA shown as a white surface, CNB shown as a gray surface, the PxIxIT motifbinding pocket shown in orange, the LxVP motifbinding pocket shown in green, and the location of the active site indicated. (C) Overlay of the 2D [1H,15N] HSQC spectra of 15N-labeled RCAN1 in the absence (black) and presence (red) of CN. Peaks that do not interact directly with CN are labeled. (D) Secondary-structure propensity data plotted against RCAN1 residue numbers. These data indicate regions with transient secondary structure (SSP > 0, helix; SSP < 0, strand). (E) Overlay of the 2D [1H,15N] HSQC spectra of 15N-labeled RCAN1 (black) and 15N-labeled RCAN1core (blue).

RCAN1 is also overexpressed in patients with Alzheimers disease (AD), where RCAN1 levels are ~3-fold higher than in nondiseased individuals (9, 10). DS and AD are linked diseases, as it is well established that many patients with DS who reach middle age (40s) suffer from early-onset AD (1113). One of the hallmarks of AD is neurofibrillary tangles composed of hyperphosphorylated tau protein. Many Ser/Thr and Tyr phosphatases are critical for maintaining tau in a proper (healthy) phosphorylation state (14, 15). Because CN is highly enriched in the neuronal tissues, making up 1% of the total protein in brain, CN plays an especially critical role in the control of tau dephosphorylation (16). Consistent with this, overexpression of RCAN1 in mice increased tau phosphorylation (17). Thus, excess inhibition of CN due to increased levels of RCAN1 has profound consequences on the brain. However, despite extensive efforts (1821), very little is known about how RCAN1 binds and regulates CN at a molecular level.

Recently, it was shown that CN (composed of subunits A and B, CNA and CNB, respectively; Fig. 1B) recruits regulators, inhibitors, and substrates using two short linear motifs (SLiMs), the PxIxIT and the LxVP motif (22, 23). These SLiMs are typically found in intrinsically disordered proteins/regions (IDPs/IDRs) and bind to the corresponding PxIxIT- and LxVP-binding pockets in CN. The PxIxIT motif binds the catalytic domain of CNA (22), whereas the LxVP motif binds to a hydrophobic cleft at the interface of the CNA and B subunits (23). While it has been known for more than a decade that RCAN1 contains a PxIxIT motif, a number of studies have suggested that additional motifs (21, 24, 25), both N- and C-terminal to the noncanonical PxIxIT motif, play a key role in RCAN function and CN activity (inhibition), including a TxxP motif, which we have recently shown is critical for CN active site substrate recruitment (26). In Nhe1, the TxxP motif binds CN in an identical manner to the previous determined autoinhibitory domain (AID), which blocks the CN active site (27). Upon an increase in [Ca2+], the AID dissociates from the catalytic site, allowing for phosphatase activity.

Here, using a combination of structural and biochemical methods, we determined how, at a molecular level, RCAN1 binds and inhibits CN. We found that RCAN1 interacts extensively with CN, at both the canonical PxIxIT- and LxVP-binding pockets but also beyond these regions, including the catalytic site. Further, we found that CN binding leads to a folding-upon-binding event, which creates a novel extended PxIxIT-type interaction that defines the central core of the RCAN1:CN interaction. We also found that in addition to physically blocking substrate binding by binding the PxIxIT and LxVP binding pockets, RCAN1 also inhibits CN activity directly by binding and blocking its active site via two mechanisms. Last, both the activity and the binding of RCAN1 to CN are regulated by phosphorylation, providing an example of how SLiM interactions are actively regulated by phosphorylation. Together, these data reveal how RCAN1 controls CN activity and, by extension, how RCAN1-mediated inhibition disrupts CN signaling, ultimately leading to hyperphosphorylation of CN substrates.

RCAN1 is a two-domain protein with a structured N-terminal RNA-recognition motif (RRM) domain that binds mRNA (amino acids 1 to 88; Fig. 1A) and a C-terminal domain that is responsible for CN binding (amino acids 89 to 197, the RCAN1 CN-binding domain; hereafter referred to as RCAN1; Fig. 1A). The two-dimensional (2D) [1H,15N] heteronuclear single-quantum coherence (HSQC) spectrum of RCAN1 exhibits very little chemical-shift dispersion in the 1HN dimension, indicating that the CN-binding domain is an IDR (Fig. 1C). To gain structural insights, we used chemical shift analysis (CSI/SSP) to test for preferred secondary structure in RCAN1. Two regions with preferred -helical secondary structure (helix 1, amino acids 129 to 137, ~40% populated; helix 2, amino acids 168 to 178, ~55% populated; Fig 1D) were identified. Consistently, these regions also have reduced fast time scale dynamics as determined using a 15N-{1H} nuclear Overhauser effect (15N-{1H}NOE) experiment (fig. S1A). Together, these data show that the CN-binding domain of RCAN1 is intrinsically disordered with two partially populated -helices.

RCAN1 binds CN tightly, with a KD of ~10 nM [isothermal titration calorimetry (ITC); the thermogram is atypical, with two independent binding events that have differing enthalpy changes; fig. S1B]. Given the atypical ITC results, we used surface plasmon resonance (SPR) spectroscopy as a complementary approach, which reported a similar KD of ~1 nM (Table 1, table S1, and fig. S1B). As ITC does not report time-dependent events, the easiest interpretation for the atypical biphasic thermogram is two sequential processes that lead to CN binding, i.e., the first binding event is an RCAN1 rearrangement (i.e., possibly due to charge:charge interactions within RCAN1) and, upon successful rearrangement, the second binding event reports the interaction with CN, which we used to extract KD values. To identify the RCAN1 residues that bind directly to CN, we used nuclear magnetic resonance (NMR) spectroscopy. We formed the RCAN1:CN complex, in which only RCAN1 was 15N-labeled. Because free RCAN1 lacks any significant long-range intramolecular interactions, the CN-bound and CN-unbound residues of RCAN1 will have significantly different NMR relaxation properties. As a consequence, RCAN1 residues that bind directly to CN will be invisible in a 2D [1H,15N] HSQC spectrum, while unbound RCAN1 residues will retain their original positions in the 2D [1H,15N] HSQC spectrum, allowing all nonbinding residues to be identified (Fig. 1C). Using this well-established procedure, we found that most RCAN1 residues interact directly with CN (amino acids 89 to 110, 128 to 164, and 180 to 197), many more than expected on the basis of previously described SLiM-based interactions with CN. However, there are also RCAN1 regions that remain flexible and unbound, including amino acids 111 to 124 (flex1) and 168 to 178 (flex2) (Fig. 1, A and C); the latter flexible region, flex2, overlaps with helix 2, demonstrating that this helix does not bind CN.

The RCAN1 regions that bind directly to CN include the putative LxVP (96LAPP99), PxIxIT (154PSVVVH159), and TxxP (186TRRP189) motifs (Fig. 1A). To determine which motifs contribute to CN binding, we generated three RCAN1 variants in which the canonical residues of the putative motifs were mutated to alanines (RCAN1LxVPdead, HLAPPAAAAA; RCAN1PxlxlTdead, PSVVVHASAVAA; RCAN1TxxPdead TRRPAAAA) and measured the affinity of the variants for CN using either ITC or SPR. The data showed that the LxVP motif only minimally contributes to CN binding, as the KD of the RCAN1LxVPdead variant for CN is essentially unchanged. In contrast, both the TxxP and especially the PxIxIT motifs contribute significantly to binding, as the KD values of the corresponding dead variants increase (RCAN1TxxPdead variant, ~3-fold increase in KD; RCAN1PxlxlTdead, ~145-fold increase in KD; fig. S1B, Table 1, and table S1).

Together, the NMR and ITC data suggest that residues 128 to 164 constitute the core CN-binding domain of RCAN1, as this region is between the two flexible domains that do not bind to CN and also includes the tightly binding RCAN1 PxIxIT motif. ITC shows that RCAN128164 binds strongly to CN, albeit more weakly than RCAN189197 (Table 1 and fig. S1B). To facilitate our structural NMR studies (note that all other studies are performed with wt-RCAN1), we converted the RCAN1 PxIxIT motif to a strong PxIxIT sequence [~4-fold change; PSVVVH PSVVIT (22); hereafter referred to as RCAN1core; Table 1 and fig. S1B]. To confirm that RCAN1core behaves identically to the same domain within the context of the full RCAN1 CN-binding domain (RCAN1), we used NMR spectroscopy. First, an overlay of the 2D [1H,15N] HSQC spectra of free RCAN1 with free RCAN1core revealed that no chemical shift perturbations (CSPs) are observed for corresponding peaks, beyond those expected at the RCAN1core N and C termini (Fig. 1E and fig. S1C). This demonstrates that the free state of RCAN1core is identical to that present within the context of the full CN-binding domain. Second, a CSI/SSP analysis performed after completing the sequence-specific backbone assignment of RCAN1core showed that helix 1 is ~45% populated (Fig. 2A). This is identical to what was observed for the same helix in RCAN1 (Fig. 1D). These data demonstrate that the residues that define the RCAN1core behave identically in both constructs (RCAN1core and RCAN1).

(A) Secondary-structure propensity data plotted against RCAN1core residue numbers (SSP > 0, helix; SSP < 0, strand). RCAN1core, blue; CNA-bound RCAN1core, pink. (B) Overlay of the 2D [1H,15N] TROSY spectrum of free 15N-labeled RCAN1core (blue) with the 2D [1H,15N] TROSY spectrum of (2H,15N)-labeled RCAN1core bound to (2H)-labeled CNA (pink). Arrows indicate the peak shifts in RCAN1core upon binding CN. (C) 2mFo DFc electron density map (blue) contoured at 1 corresponding to the RCAN1core PxIxIT motif (orange sticks; PxIxIT motif residues underlined) bound to CN (gray). CN residues that form the PxIxIT motif hydrophobic docking pocket shown as sticks and labeled. (D) Structure of RCAN1core:CNA complex obtained using NMR and x-ray data corefinement. RCAN1core, orange (PxIxIT motif shown as sticks; strand 1, arrow, helix 1, cylinder); CNA, white surface with the CNA residues that experience CSPs upon RCAN1core binding shown in blue (>2 SD) or light blue (>1 SD); dark gray surface corresponds to unassigned residues. Residues that experience CSPs are also labeled. (E) Overlay of the five lowest-energy corefined structures. (F) Overlay of the 2D [1H,15N] TROSY spectra of (2H,15N)-labeled CNA in the absence (black) and presence (red) of the RCAN1core. Peaks that shift in the presence of the RCAN1core are shown with arrows and labels. Label colors correspond to chemical shift perturbation deviation magnitudes [see (D)].

The interaction of the RCAN1core domain with the CNA was then examined using NMR spectroscopy. In this experiment, both RCAN1core and CNA are isotopically labeled (2H,15N-labeled RCAN1core; 2H-labeled CNA), allowing all peaks to be detected. The data showed that RCAN1core folds upon CNA binding, as the dispersion of the peaks in the 1HN dimension widened significantly, owing to the formation of new hydrogen bonds in novel secondary-structure elements (Fig. 2B). Repeating this experiment using RCAN1 and CN (CNA/B) showed an identical folding pattern (fig. S2A), confirming that the RCAN1core binds identically to both CN and CNA and that the RCAN1core domain folds-upon-binding in a manner identical to that of RCAN1 (fig. S2A). To understand this conformational change in molecular detail, we completed the CNA-bound RCAN1core sequence-specific backbone assignment (2H,13C,15N-labeled RCAN1core, 2H-labeled CNA; 47-kDa complex; Fig. 2B). The CSI/SSP analysis showed that when RCAN1core is bound to CNA, most of the partially populated helix 1 becomes 100% populated, with the final turn being slightly lower populated. Further, two new strands are formed, 1 (amino acids 142 to 145) and 2 (amino acids 155 to 159; Fig. 2A).

To determine the RCAN1core:CNA 3D structure, we first used x-ray crystallography. The structure of the RCAN1core:CNA complex was determined by molecular replacement and refined to 1.85 resolution (table S2). Strong difference electron density was observed for the PxIxIT motif and adjacent residues, allowing RCAN1 residues 153TPSVVITVC161 to be readily modeled (Fig. 2C). The RCAN1 PxlxlT motif binds CN in an extended strand conformation, burying ~495 2 of solvent accessible surface area. As observed in other CN:PxIxIT complexes (fig. S2B), the RCAN1 PxIxIT motif strand hydrogen bonds with CNA strand 14 in a parallel arrangement, extending one of CNAs two central sheets (Fig. 2A and fig. S2C). In addition to the electron density corresponding to the PxIxIT sequence, weak difference density corresponding to a short strand was observed just above the PxIxIT motif strand, further extending the CNA central sheet by one more strand (fig. S2D). The presence of a second strand is consistent with the CSI/SSP NMR data that showed two strands form when RCAN1core binds CNA (Fig. 2A). Despite extensive efforts, no electron density was identified for RCAN1 residues 128 to 140, likely due to the fact that the PxIxIT binding pocket is located at a crystal contact and, as a consequence, displaced these residues in the crystal (fig. S2E).

To determine the structure of the RCAN1core:CNA complex, we used a hybrid structural biology approach, integrating the NMR and crystallographic data. First, we determined the solution structure of RCAN1core bound to CNA using chemical shiftbased dihedral angle restraints and NOE distance restraints, the latter of which were derived from 13C-ILV-methyl-methyl resolved [1H,1H] NOESY (NOE spectroscopy), 13C-methyl-ILV-15N resolved [1H,1H] NOESY, and 15N-resolved [1H,1H] NOESY spectra. A total of 84 NOE restraints were detected and used for structure determination (table S4). Next, we developed a corefinement protocol that used both the NMR-derived (NOE) and crystallographic-derived (H-bond) restraints with established protein stereochemical restraints to refine the structure of the CNA:RCAN1core complex (Fig. 2, D and E). Atoms allowed to change during corefinement included the atoms from all RCAN1core residues and CNA residues belonging to CNA strand 14. The ensemble of RCAN1core domains adopted a compact conformation defined by the secondary structures of a single helix (1, 129YDLLYAISKL138), two strands (1, 142EKYE145; 2, PxIxIT motif, 154PSVVIT159), and two turns. The strands bind one another in an antiparallel manner, with the helical axis of the helix aligning parallel and adjacent to both strands (because of the limited number of NOE restraints, the helix adopted a small range of orientations relative to the two-stranded sheet). The compact tertiary structure is stabilized by a central hydrophobic core defined by RCAN1 residues Leu131, Ile135, Leu138, Val156, and Ile158. The RCAN1core is anchored to CNA via its PxIxIT motif. This ordering of RCAN1core PxIxIT residues (including Val156 and Ile158), together with the CNA PxIxIT-binding pocket residues (especially Met290 and Ile331), provides a hydrophobic platform that enhances the stability of the folded conformation of the RCAN1core. The observation that the entire RCAN1core folds-upon-binding CNA has never been observed for any other CN-regulator.

To confirm the experimental accuracy of the NMR and crystallographically corefined structure, we performed an additional NMR experiment. Namely, we formed the RCAN1core:CNA complex using 2H,13C,15N-labeled CNA and unlabeled RCAN1core and completed the sequence-specific backbone assignment of CNA in its RCAN1core-bound conformation of most detectable NH cross peaks (Fig. 2F). Because of incomplete H/D back exchange (as commonly observed for proteins expressed in D2O), the backbone assignment of CNA is ~30% [this percentage is about the same as the published NMR backbone assignment of free CNA (28)]. Overlaying the 2D [1H,15N] TROSY (Transverse relaxation optimized spectroscopy) spectra of 2H,15N-labeled CNA in the presence and absence of unlabeled RCAN1core showed, as expected, that many CSPs belong to the residues corresponding to the CN PxIxIT-binding pocket (residues Val104, Asn192, Glu325, Asn327, Asn330-Arg332, and Phe334). However, CSPs were also observed beyond the expected PxIxIT CNbinding pocket including residues Ser294, Thr296, Gly298, and Ser301, in a pocket in which the N terminus of the RCAN1core engages CNA, confirming the structure of the RCAN1core:CN complex (Fig. 2, D to F).

Previous studies have shown that CN activity can be inhibited by distinct mechanisms. First, CN can be competitively inhibited by binding and blocking the active site (27); this is how the CN AID inhibits CN before Ca2+ activation. Second, CN can be inhibited by blocking substrate binding; this is how the viral protein inhibitor A238L inhibits CN, as it binds CN and blocks both the PxIxIT- and LxVP-binding pockets (23). To understand how RCAN1 regulates CN, we measured CN activity using para-nitrophenylphosphate (pNPP; a small substrate mimic that reports active site inhibition) in the presence and absence of RCAN1 and a series of RCAN1 motif variants. The data show that RCAN1 potently inhibits CN activity against pNPP (Fig. 3, A and B, and table S3). Mutating the primary SLiM motifs (LxVP; PxIxIT; Figs. 1A and 3B) in RCAN1 alters CN inhibition in a manner consistent with their role in CN binding. Namely, the loss of the LxVP motif (RCAN1LxVPdead), which contributes very little to CN binding, has no effect on the RCAN1-mediated inhibition of CN phosphatase activity against pNPP. In contrast, the loss of the PxIxIT motif (RCAN1PxIxITdead), which is essential for CN binding, significantly reduced the RCAN1-mediated inhibition of CN phosphatase activity (Fig. 3, A and B). Together, these data show that although RCAN1 binds to both CN-specific SLiM-binding pockets (LxVP and PxIxIT), it must also bind and block the CN active site, and this interaction requires binding via the PxIxIT motif.

(A) Enzymatic activity of CN in the absence (black) and presence of RCAN1 (red) and its SLiM-binding motif variants (PxIxITdead, orange; LxVPdead, green); pNPP assays; SE, n = 3. (B) RCAN1 domain diagram illustrating the RCAN1 mutants tested with the corresponding CN activity (relative to free CN). (C) Same as (A) but with RCAN1 TxxP variants. (D) Cartoon illustrating how the TxxP motif engages the CN active site. Inset: Model of the RCAN1 TxxP (TRRP; purple) motif bound to CN based on the structure of the CNA AID domain (27); CN is shown as an electrostatic potential energy surface. (E) Same as (A) but with RCAN1 SSPASSP and TxxP variants. (F) Same as (D), illustrating CN inhibition in the absence of a functional TxxP motif. Inset: Model of the RCAN1 108SPP (cyan) motif bound to CN based on the structure of the CNA AID domain.

Two additional motifs have previously been suggested to facilitate RCAN1-mediated inhibition of CN: the 108SPPASPP114 motif, which resembles NFAT substrate sequences (25), and the 186TxxP189 motif, which we recently showed is critical for active site substrate recruitment (26). To determine whether the RCAN1 TxxP motif contributes to the RCAN1-mediated inhibition of CN, we measured CN activity when bound to the RCAN1TxxPdead variant (Fig. 3C). Although mutating this motif has only a minor effect on CN binding (threefold), it strongly reduced the RCAN1-mediated inhibition of pNPP dephosphorylation (29), resulting in a 50% increase in CN activity (Fig. 3, B and C); this suggests that the TxxP motif interacts directly with the catalytic site. Using the crystal structure of CN bound to the AID as a model for TxxP binding (AID residues 481ERMP484; Glu481 carboxyl binds the CN active site metals), it became evident that the RCAN1 i+2 arginine (TRRP) is perfectly poised to bind a negatively charged pocket formed by the side chains of CN residues Cys153 and Glu220 and the peptide carboxyls from residues Asn150 and Pro221 (Fig. 3D, inset). To test whether Arg188 is important for RCAN1-mediated inhibition of CN, we generated a TxxP motif variant in which only this residue is mutated to alanine, RCAN1TRAP (Fig. 3, B and C). This single amino acid change results in a 50% reduction in RCAN1-mediated inhibition of CN. Inclusion of additional mutations in this motif (TAAP; TAAA) had only minor effects (Fig. 3, B and C). Together, the data show that the TxxP motif is critical for the RCAN1-mediated inhibition of CN at the active site, with TxxP residue Arg188 playing a key role in TxxP motif binding (Fig. 3D).

Although the TxxP motif is critical for the RCAN1-mediated inhibition of CN, the RCAN1TxxPdead variant does not allow CN activity to return to RCAN1-free levels, demonstrating that additional elements of RCAN1 also contribute to CN inhibition. Previous studies suggested that the RCAN1 108SPPASPP114 motif may function as an NFAT-like pseudosubstrate inhibitor of CN activity (25). To test this, we again used mutagenesis coupled with pNPP activity assays (Fig. 3, B to E). First, mutation of either the 108SPP or 112SPP motif to AAA did not alter RCAN1-mediated inhibition of CN. We hypothesized that this may be due to the presence of the 186TRRP189 motif, which may play a dominant role in CN inhibition. Thus, we generated the same mutants in the RCAN1TxxPdead variant, i.e., 108SPPdead/TxxPdead and 112SPPdead/TxxPdead. The data show that in the absence of a functional TxxP motif, mutating the 112SPP motif led to a further ~20% reduction in RCAN1-mediated CN inhibition. Mutating the 108SPP motif completely abolished the RCAN1-mediated inhibition of CN, rendering CN fully active. These data demonstrate that both the RCAN1 TxxP motif and the RCAN1 108SPP pseudosubstrate sequence are responsible for the RCAN1-mediated inhibition at the CN active site (Fig. 3, D to F). To confirm these results, we formed the 2H,15N-RCAN1TXXPdead:2H-CN complex and recorded a 2D [1H,15N] TROSY spectrum (fig. S3). The NMR data showed that upon deletion of the TxxP motif in RCAN1, residues that are part of or surrounding the 108SPPASPP114 motif are missing or shifted (e.g., Ser112, Gly116, Lys118, and Thr124), directly showing that these residues are now in a different chemical environment, i.e., binding the CN active site (as indicated by the activity assays).

RCAN1 has eight serine and six threonine residues (Fig. 1A). Of these, Ser93, Ser94, Ser108, Ser112, Thr124, Ser136, Thr153, Ser163, Thr186, and Thr192 have been experimentally identified to be phosphorylated in vivo (16, 30, 31), suggesting that they may be important for regulating RCAN1 binding to and/or inhibition of CN. Consistent with this, a subset of these residues are part of and/or adjacent to known RCAN1 motifs (Ser94, LxVP motif; Ser108/Ser112, SPPASPP pseudosubstrate motif; Thr153/Ser163, PxIxIT motif; Thr186/Thr192, TxxP motif; Fig. 1A). To determine how phosphorylation of RCAN1 alters its ability to bind and inhibit CN, we incubated 15N-labeled RCAN1 with MKK6-activated p38 (pp38) (32), a proline-directed kinase that has been shown to phosphorylate RCAN1 (31, 33). An overlay of the nonphosphorylated and phosphorylated 2D [1H,15N] HSQC spectra reveals large CSPs for several peaks, indicative of phosphorylation (fig. S4A). After completing the sequence-specific backbone assignment of p38-phosphorylated RCAN1 (p-RCAN1), we determined that Ser108, Ser112, Thr124, Thr153, and Thr192 are phosphorylated (pS108, pS112, pT124, pT153, and pT192; pT152 also becomes partially phosphorylated, although much more slowly, suggesting that it is nonspecific). CSI/SSP analysis shows that p-RCAN1 maintains the same secondary-structure preferences as nonphosphorylated RCAN1 (RCAN1), and thus, p-RCAN1 is identical to RCAN1 in solution (fig. S4B). Despite this, p-RCAN1 binds CN ~30-fold more weakly than RCAN1 (ITC; fig. S1B and Table 1), demonstrating that RCAN1 phosphorylation negatively affects CN binding.

We reasoned that the reduction in affinity was due to the phosphorylation of Thr153, which is in a loop connecting RCAN1core strands 1 and 2 and immediately N-terminal to the RCAN1 PxIxIT motif, which is essential for CN binding (Figs. 1A and 2D). To test this, we generated the RCAN1T153A variant and phosphorylated it using pp38. The same residues, with the exception of Thr153 (now T153A), were phosphorylated; further, no nonspecific phosphorylation of Thr152 was observed (Fig. 4A). However, in contrast to p-RCAN1, the p-RCAN1T153A variant binds CN with the same affinity as nonphosphorylated RCAN1T153A (fig. S1B and Table 1), both of which are nearly identical to RCAN1. Together, these data show that phosphorylation of Thr153 in RCAN1 is a key mechanism regulating RCAN1:CN complex formation and, in turn, the ability of RCAN1 to inhibit CN.

(A) Overlay of the 2D [1H,15N] HSQC spectra of free 15N-labeled RCAN1T153A with 15N-labeled p-RCAN1T153A. Peaks corresponding to phosphorylated residues in p-RCAN1T153A are labeled. (B) Progress curves monitoring the dephosphorylation of pNPP for the indicated p-RCAN1T153A:CN complexes. (C) Cartoon diagrams of the RCAN1 variants tested in (B), with the indicated phosphorylated residues and resulting CN activity at the 30-min time point relative to CN alone.

Next, we investigated how RCAN1 phosphorylation affects the ability of RCAN1 to inhibit CN. To exclude any effects from a change in RCAN1:CN binding affinity, RCAN1T153A was used throughout these studies. pNPP was used as a model substrate and its dephosphorylation was monitored over time. As expected, RCAN1T153A completely inhibited CN activity (Fig. 4, B and C). In contrast, p-RCAN1T153A showed a ~30% reduction in inhibition, demonstrating that phosphorylation of either Ser108, Ser112, Thr124, or Thr192 negatively affects the ability of RCAN1 to inhibit CN (Fig. 4, B and C). Since our results showed that the 108SPPASPP114 motif, especially 108SPP, plays a key role in the RCAN1-mediated inhibition of CN, we reasoned that the loss of inhibition was due to the phosphorylation of Ser108. To test this, we repeated the activity assay using a variant of RCAN1 in which both residues were mutated to alanine and subsequently phosphorylated by pp38 (p-RCAN1T153A/S108A). Preventing Ser108 phosphorylation restored the ability of RCAN1 to inhibit CN, demonstrating that phosphorylated pS108 relieves 108SPP-mediated inhibition (Fig. 4, B and C).

Last, we tested the importance of the TxxP motif in p-RCAN1-mediated inhibition. Thus, we measured the activity of CN bound to p-RCAN1T153A in the TxxPdead background, i.e., p-RCAN1T153A/TxxPdead. The data show that this variant completely lost its ability to inhibit CN. Moreover, the CN activity increased threefold in the presence of p-RCAN1T153A/TxxPdead (Fig. 4, B and C). This result is consistent with previous studies that have shown that the activity of CN increases in the presence PxIxIT- and LxVP-containing CN-specific regulators, due to stabilization of the enzyme (23). Together, these data not only confirmed the key role of the TxxP motif in the RCAN1-mediated inhibition of CN independent of the RCAN1 phosphorylation state but also revealed that phosphorylation of either Thr153 or Ser108 reduces RCAN1-mediated inhibition either by weakening the affinity of RCAN1 for CN (pT153) or by preventing the SPPASPP pseudosubstrate motif from engaging and blocking the catalytic site (pS108).

Next, we used NMR spectroscopy to determine whether p-RCAN1 is also a CN substrate. Because the affinity of CN for p-RCAN1, but not p-RCAN1T153A, is considerably weaker than the corresponding nonphosphorylated variant, we again used the RCAN1T153A variant for these experiments. The data show that all p-RCAN1 residues phosphorylated by pp38 are dephosphorylated by CN, with pS108 and pT192 being the residues most rapidly dephosphorylated (Fig. 5A; pS112 is also dephosphorylated, but the dephosphorylation does not go to completion).

(A) Dephosphorylation of p-RCAN1T153A residues pS108, pS112, pT124, and pT192. (B) Dephosphorylation of p-RCAN1T153A/LxVPdead residues pS108, pS112, pT124, and pT192. (C) Dephosphorylation of p-RCAN1T153A/S108A residues pS112, pT124, and pT192. (D) Dephosphorylation of p-RCAN1T153A/TxxPdead residues pS108, pS112, pT124, and pT192.

The 108SPPASPP114 motif is C-terminal to the RCAN1 96LxVP99 motif. Our NMR, ITC, and SPR data show that while the RCAN1 LxVP motif binds CN, it does not contribute significantly to its affinity. This weaker affinity of the LxVP versus PxIxIT motif in RCAN1 has also been observed in other CN substrates, which has led to the hypothesis that LxVP motifs facilitate CN-mediated dephosphorylation of specific substrate residues. To test this hypothesis for RCAN1, we repeated the NMR-based dephosphorylation experiments using an RCAN1 variant in which the LxVP motif was nonfunctional (RCAN1LxVPdead) (Fig. 5B). The data show that the dephosphorylation rates for the phosphorylated residues either slowed (pT124 and pT192) or went to zero (pS108 and pS112). Thus, the residue that is most rapidly dephosphorylated in wild-type (WT) p-RCAN1, pS108, is unable to be dephosphorylated in the absence of the LxVP motif (p-RCAN1LxVPdead). These data strongly support the hypothesis that, in at least a subset of CN substrates, the LxVP functions to optimally position phosphosites for rapid dephosphorylation by CN. Inhibiting Ser108 phosphorylation using p-RCAN1T153A/108SPPdead also completely prevented the partial dephosphorylation of pS112, demonstrating the inability of this residue to effectively engage the active site in the absence of the 108SPP110 motif (Fig. 5C). Last, we also measured RCAN1 dephosphorylation using a variant with an inactive TxxP motif: pRCAN1T153A/TxxPdead. As expected, the dephosphorylation rates increased markedly, demonstrating that the TxxP motif functions limit access to the active site, fully consistent with our molecular and inhibition data (Fig. 5D).

Over 20 years ago, RCAN1 was found to be a potent, endogenous inhibitor of CN; however, how RCAN1 interacted with and regulated the activity of CN has remained elusive. This lack of mechanistic knowledge has limited our ability to effectively combat RCAN1-mediated inhibition to enhance NFAT dephosphorylation in syndromes and diseases associated with RCAN1 up-regulation, including DS and AD. Here, we show that the RCAN1 CN-interaction domain, which is intrinsically disordered, forms a tight complex with CN via its multiple SLiMs, including the LxVP, PxIxIT, and TxxP motifs (Fig. 6A). Unexpectedly, we found that the RCAN1 PxlxlT interaction is distinct from canonical PxlxlT:CN interactions, as PxIxIT motif binding to CN causes its 30 N-terminal residues to undergo a folding-upon-binding event. This results in the formation of a stable, tertiary domain stabilized by an extensive network of hydrophobic residues from both CN and RCAN1. This observation is important for multiple reasons. First, this unique example highlights the emerging diversity of SLiM-based interactions, demonstrating that even established interactions like that of the PxIxIT with CN can be augmented and their interaction strengths can be modulated by additional protein stabilization interactions. It will be interesting to see whether similar extended interactions exist for other SLiMs. Second, detecting this folding-upon-binding event and determining the folded structure required a hybrid approach that integrated NMR and crystallographic data, highlighting the importance of using both methods, but especially NMR spectroscopy, for the study of IDP:protein interactions.

(A) RCAN1 is a potent CN inhibitor. RCAN1 (magenta, with key sequence and structural features indicated) binds and inhibits CN (gray/beige; active site in yellow) via two mechanisms. First, RCAN1 blocks CN-specific substrate LxVP and PxIxIT interaction grooves by binding these pockets using its LxVP and especially its PxIxIT motifs. This prevents canonical CN substrates, like the NFATs, from binding CN. Second, RCAN1 directly blocks the CN active site via its TxxP motif and, to a lesser extent, its SPPASSP motif. This further reduces CN activity against its endogenous substrates. The RCAN1core folds upon binding CN. (B) Phosphorylation of RCAN1 T153 (pT153) inhibits CN binding. Active p38 phosphorylates RCAN1 on pS108, pS112, pT124, pT153, and pT192. Phosphorylation of RCAN1 T153 (pT153), which is immediately N-terminal to the PxIxIT motif, lowers the affinity of RCAN1 for CN, leading to dissociation of the complex and increased CN activity. (C) Phosphorylated RCAN1 (pRCAN1) is weakly dephosphorylated by CN. In the absence of phosphorylation at T153 (pRCAN1T153A; T153A represented as an orange start), pRCAN1 is able to bind CN with strong affinity. This results in the slow dephosphorylation of pS108, pT124, and pT192, with pS112 becoming partially dephosphorylated (top). However, with the exception of pT192, these dephosphorylation events require the presence of the nearby LxVP motif. That is because, in the LxVPdead mutant (T153A and LxVPdead represented as orange stars), pS108, pS112, and pT124 remain phosphorylated, even after 20 hours (middle). Last, if the inhibitory TxxP sequence is inactivated (TxxPdead; T153A and TxxPdead represented as orange stars), the ability of CN to dephosphorylate pRCAN1 is substantially enhanced, with dephosphorylation rates increasing ~10-fold (bottom).

We also found that this novel RCAN1 interaction domain is the target of posttranslational modifications, namely, phosphorylation. Thr153 is a proline-directed kinase phosphorylation site that is readily phosphorylated by activated p38. As we show, phosphorylation of Thr153 reduces the binding affinity of RCAN1 for CN by more than 30-fold, which greatly attenuates its ability to bind, and, in turn, inhibit CN. Others have reported that phosphorylation of a nearby RCAN1 residue, Ser136, also transforms RCAN1 from an inhibitor to an activator, which enhances CN-NFAT signaling via NFAT translocation to the nucleus (34). Our new structural data show that Ser136 is located at the center of the newly formed PxIxIT domain, strongly suggesting that phosphorylation of Ser136 also destabilizes the RCAN1:CN interaction in a manner similar to that observed for Thr153. Together, these data show how phosphorylation functions as a molecular switch to convert RCAN1 from a potent to a weak inhibitor (sometimes called activator) of CN.

Our data reveal why RCAN1 is such a potent inhibitor of CN. Namely, it uses a combination of distinct yet complementary inhibitory mechanisms. First, RCAN1 binds to and blocks the PxIxIT and LxVP substrate-binding sites. This is the same mechanism used by the potent swine flu viral inhibitor A238L (23) and the blockbuster immunosuppressant drugs FK-506/cyclosporin-A (27, 35); that is, RCAN1 sterically occludes other substrates from binding the PxIxIT and LxVP substrate-binding pockets, which, in turn, prevents them from being dephosphorylated. The efficacy of FK-506/cyclosporin-A in preventing CN-mediated NFAT dephosphorylation demonstrates the effectiveness of this inhibitory strategy (36). However, RCAN1 also uses a second mechanism to inhibit CN activity. Namely, it binds and blocks the active site directly. Recently, we showed that S/TxxP motif substrate phosphosites are directly recruited to the CN active site (26). It is exactly such a motif, TRRP in RCAN1, which binds and blocks the CN active site, inhibiting its ability to dephosphorylate both small substrate mimetics (pNPP) and protein substrates (pRCAN1). In particular, we showed that the RP residues in TRRP are key for the local interaction at the active site and thus the potent inhibition of CN. Our data also showed that RCAN1 engages the active site using a second motif, one that was originally proposed to mimic known dephosphorylation sites in NFAT, SPPASPP (108SPPA112SPP). Thus, in addition to binding and sterically blocking known CN substrate interaction motifs, RCAN1 also exploits two distinct pseudosubstrate motifs to bind and inhibit the CN active site directly, demonstrating why RCAN1 potently inhibits CN activity.

What can we learn from these mechanisms of CN binding and inhibition by RCAN1 for CN inhibition and substrate selection? First, diffusion-controlled small-molecule dephosphorylation (e.g., for pNPP) and protein substrate dephosphorylation are different because the latter has the advantage of tethering via the CN-specific substrate interaction motifs (PxIxIT and LxVP). This tethering markedly increases the local substrate (phosphosite) concentration, which, in turn, enhances its dephosphorylation. This is why pNPP dephosphorylation by CN is completely inhibited by RCAN1, while substrate-like engagements, e.g., by p-RCAN1, leads to dephosphorylation, albeit exceedingly slowly (as we show, a functional TxxP motif greatly slows CN-mediated dephosphorylation of p-RCAN1). Furthermore, we show that for effective substrate dephosphorylation, the LxVP motif is critical, as, without it, the phosphosites immediately C-terminal to the LxVP motif are no longer dephosphorylated. Recently, we showed that the LxVP binding affinity for CN strictly depends on its on-rate (kon), i.e., how accessible the LxVP motif is in the ensemble of structures formed by RCAN1 in solution (37). Despite the fact that the RCAN1 LxVP site (LAPP) engages CN only very weakly, these data show that the LxVP is still essential for the specific and efficient dephosphorylation of 108SPPA112SPP, with a preference for 108SPP (the preference suggests that a PPA sequence N-terminal to a dephosphorylation site is unfavorable for dephosphorylation by CN). Together, these results reveal how RCAN1 inhibits CN and how this inhibition is regulated by phosphorylation. The mode of active site engagement is likely mirrored by CN substrates and thus this work also provides molecular insights into substrate engagement with the CN active site.

Last, these data also provide the essential molecular details needed to develop therapeutics that disrupt RCAN1-mediated inhibition of CN in syndromes and disorders associated with RCAN1 overexpression. Namely, RCAN1 dissociation can be achieved by specifically limiting the formation of the hydrophobic network necessary to form the extended PxIxIT interaction, e.g., by small molecules. Furthermore, up-regulation of kinases that allow for RCAN1 phosphorylation on residues Ser136 and Thr153 are equally good routes to limit the effectiveness of this interaction and thus ultimately CN inhibition. Last, identification and inhibition of the necessary phosphatases for Ser136 and Thr153 will be another possibility for inhibition release. While none of these are short-term projects, the molecular insights presented here lay the foundation for these important efforts.

DNA coding the human RCAN1 CN-binding domain (residues 89 to 197) was subcloned into pTHMT (His6MBP-TEV-) as previously described (38). RCAN1 variants [RCAN1core (residues 128 to 164), 108SPPdead (S108A/P109A/P110A), 112SPPdead (S112A/P113A/P114A), TxxPdead (T186A/R187A/R188A/P189A), 108SPPdead/TxxPdead, 112SPPdead/TxxPdead, LxVPdead (H95A/L96A/P98A/P99A), PxlxlTdead (P154A/V156A/V158A/H159A), T153A, T153A/S108A, T153A/LxVPdead, and T153A/TxxPdead] were generated using site-directed mutagenesis following recommended protocols (QuikChange; Agilent). CNA (residues 27 to 348DD; for NMR; CN isoform was used throughout the study) and CNA (residues 27 to 339; for crystallography) were subcloned into pRP1B, containing an N-terminal His6-tag. CN391 (CNA1391/CNB1170) and CN370 (CNA1370/CNB1170) were cloned into the p11 bicistronic bacterial expression vector as a single cassette, which contains an N-terminal His6-tag followed by a TEV (Tobacco etch virus) protease cleavage site, as previously described (23). These CN constructs do not contain the AID. For protein expression, plasmid DNAs were transformed into Escherichia coli BL21 (DE3) RIL cells (Agilent). Cells were grown in Luria broth in the presence of selective antibiotics at 37C to an OD600 (optical density at 600 nm) of ~1.0, and expression was induced by the addition of 1 mM isopropyl--d-thiogalactopyranoside. Induction proceeded for ~4 hours at 37C or overnight at 18C before harvesting by centrifugation at 8000g. Cell pellets were stored at 80C until purification.

For NMR measurements, expression of uniformly (1H,15N)-, (2H,15N)-, (1H,15N,13C)-, or (2H,15N,13C)-labeled proteins was achieved by growing cells in H2O- or D2O-based M9 minimal media containing 15NH4Cl (1 g/liter) and/or [1H,13C]- or [2H,13C]-d-glucose [4 g/liter; CIL (Cambridge Isotope Laboratories) or Isotec] as the sole nitrogen and carbon sources, respectively, using established protocols (39). Selectively /-[13CH3, 12CD3]-Val/Leu, -[13CH3]-Ile, [U]-2H,15N-labeled RCAN1 were prepared in D2O-based M9 minimal media containing 15NH4Cl (1 g/liter) and/or [2H,12C]-d-glucose (4 g/liter) through addition, 1 hour before induction, of -ketoisovaleric acid (120 mg/liter; CDLM-7317; Cambridge Isotope Laboratories) and -ketobutyric acid (60 mg/liter; CDLM-7318; Cambridge Isotope Laboratories).

Cell pellets were lysed in lysis buffer [25 mM tris (pH 8.0), 500 mM NaCl, 5 mM imidazole, and 0.1% Triton X-100] containing EDTA-free protease inhibitor cocktail (Roche) using high-pressure homogenization (Avestin C3). The lysate was clarified by centrifugation at 42,000g and filtered through a 0.22-m PES (polyethersulfone) filter before loading onto a His-trap HP column (GE Healthcare). Bound proteins were washed with buffer A [50 mM tris (pH 8.0), 500 mM NaCl, and 5 mM imidazole] and eluted with increasing amounts of buffer B [50 mM tris (pH 8.0), 500 mM NaCl, and 500 mM imidazole] using a 5 to 500 mM imidazole gradient. Peak fractions were pooled and dialyzed overnight at 4C in high-salt dialysis buffer [50 mM tris (pH 8.0), 500 mM NaCl, and 0.5 mM TCEP] with 5:1 volume ratio of TEV protease overnight for RCAN1 or low-salt dialysis buffer [50 mM tris (pH 8.0), 50 mM NaCl, 0.5 mM TCEP, and 1 mM CaCl2] for CN. The next day, a subtraction His6-tag purification was performed to remove TEV and the cleaved His6-tag. RCAN1 was concentrated to ~6 ml; 5 mM (final concentration) DTT was added and further heat-purified (80C; 10 min). RCAN1 was further purified using size exclusion chromatography (Superdex 75 26/60) in either assay buffer [150 mM Hepes (pH 7.5), 150 mM NaCl, 0.5 mM TCEP, 1 mM CaCl2, and 0.5 mM MgCl2] or NMR buffer [20 mM Hepes (pH 6.8), 50 mM NaCl, 0.5 mM TCEP, and 1 mM CaCl2]. Cleaved CN was further purified using a HiTrap Q HP anion exchange column (GE Healthcare). Purified protein was either used immediately or flash-frozen in liquid nitrogen for storage at 80C.

The activities of freshly prepared CN in complex with RCAN1 and variants were measured in assay buffer containing varying concentrations of pNPP (0 to 120 mM). CN (0.1 M) and 0.5 M RCAN1s were incubated with the substrate at 30C for 30 min. The reaction was stopped using 1 M NaOH, and the absorbance was measured at 405 nm using a plate reader (BioTek). The rate of pNPP dephosphorylation was determined using the molar extinction coefficient for pNPP of 18,000 M1 cm1 and an optical path length of 0.3 cm (96-well plates). Km and Vmax were determined by fitting to the Michaelis-Menten equation, y = Vmax*x/(Km + x); kcat was extracted using y = Et*kcat*x/(Km + x). The catalytic efficiency was obtained as kcat/Km. SigmaPlot 12.5 was used for data analysis including the statistical analysis. The activities of freshly prepared CN in complex with p-RCAN1 and p-RCAN1 variants were measured in assay buffer containing 100 mM pNPP. CN (0.1 M) with 0.5 M p-RCAN1s were incubated with the substrate at 30C; the absorbance at 405 nm was measured every 30 s for 30 min. All experiments were carried out in triplicate.

NMR data were collected on either Bruker NEO 600- and 800-MHz spectrometers or a Bruker Avance III HD 850-MHz spectrometer equipped with TCI HCN z-gradient cryoprobes at 298 K. NMR measurements of RCAN1 were recorded using (1H,15N)-, (2H,15N)-, (1H,15N,13C)-, or selectively /-[13CH3, 12CD3]-Val/Leu, -[13CH3]-Ile, [U]-2H,15N-labeled protein at a final concentration of 0.6 mM in NMR buffer and 90% H2O/10% D2O. The sequence-specific backbone assignments of RCAN1 and variants, as well as CN-bound RCAN1 were achieved using 3D triple-resonance experiments including 2D [1H,15N] HSQC/TROSY, 3D HNCA, 3D HN(CO)CA, 3D HN(CO)CACB, and 3D HNCACB. All NMR data were processed using TopSpin 4.05 (Bruker BioSpin) and analyzed using CARA (http://cara.nmr.ch) and/or CcpNMR (40). 2D [1H,13C]-HMQC, 3D 13C-ILV-methyl-methyl resolved [1H,1H] NOESY, 13C-methyl-ILV-15N resolved [1H,1H] NOESY, and 15N-resolved [1H,1H] NOESY spectra were recorded with a mixing time (TM) of 120 ms using the selectively /-[13CH3, 12CD3]-Val/Leu, -[13CH3]-Ile, [U]-2H,15N-labeled RCAN1 and [U]-2H-labeled CNA complex. The NOE data were also used to assign the chemical shifts of /-CH3 of Val/Leu, -[CH3]-Ile.

Dephosphorylation was initiated by the addition of unlabeled active CN (CN activity was always tested using pNPP as a substrate before its use in NMR experiments) with 50 M 15N-labeled RCAN1 (and variants) with a molar excess of CN:RCAN1 of 1:10 or 1:20. A reference 2D [1H,15N] HSQC spectrum was recorded before the addition of CN. Dephosphorylation was monitored by extracting the intensities from a series of 2D [1H,15N] HSQC spectra. Apparent rates of dephosphorylation were extracted from global nonlinear least square fits of disappearing phosphorylated peaks and/or reporting neighbor peaks to single exponentials in SigmaPlot.

Purified CNA27339 and RCAN1core were incubated together on ice in a 1:1.2 molar ratio for 6 hours before complex purification using size exclusion chromatography in complex buffer [10 mM tris (pH 7.4), 50 mM NaCl, and 1 mM dithiothreitol (DTT)]. The peak corresponding to the complex was pooled and concentrated to 10.3 mg/ml. The CNA:RCAN1core complex crystallized in 0.1 M sodium cacodylate (pH 5.5), 12% polyethylene glycol 8000, and 0.1 M calcium acetate (sitting drop vapor diffusion). Crystals were cryoprotected using a 15-s soak in mother liquor supplemented with 30% (v/v) glycerol and immediately flash-frozen in liquid nitrogen. Data were collected at the APS GM/CAT 23ID-D and processed using HKL3000 (41). The structure was phased using the CNA subunit of Protein Data Bank (PDB) ID 4F0Z as a search model [PHASER as implemented in Phenix (42)]. A solution was obtained in space group P212121; clear electron density for the RCAN1 PxIxIT motif was observed in the initial maps. The initial models of the complex were built without RCAN1core using AutoBuild, followed by iterative rounds of refinement in Phenix and manual building using Coot (43). The RCAN1core PxIxIT sequence was then modeled, followed by additional rounds of refinement and model building. The final model includes RCAN1 residues 153 to 161. Additional electron density, corresponding to ~4 amino acids, was immediately adjacent to the RCAN1 residues at a crystal contact; however, no residues were built due to a lack of features that allowed the sequence or chain direction to be confidently determined. Data collection and refinement details are provided in table S2. Molecular figures were generated using PyMOL (44).

RCAN1128164 structures in complex with CNA were calculated using a corefinement protocol closely following that described in (45) and implemented using version 2.50 of Xplor-NIH (46). Interproton distances derived from NOEs were represented by restraints allowing a distance range of 1.8 to 6 . The dihedral angles and were calculated using TALOS (chemical shifts of HN, HA, HB, CA, and CB) (47). Xplor-NIHs PosDiffPot term was used to restrain the C coordinates to values determined in the crystal structure of CNA bound to the RCAN1128164 PxlxlT strand (amino acids 153 to 161) with 0.5 root mean square deviation allowed with zero energy penalty. In addition to the PosDiffPot term, the HBPot energy (48) term was included to form and improve any hydrogen bonding geometry. RCAN1 atoms, all CNA side-chain atoms, and all atoms of CNAs interfacial residues (amino acids 327 to 336, 318, 286, 288, 290, 293, 299, and 300) were allowed torsion angle degrees of freedom during simulated annealing and all degrees of freedom during a final Cartesian minimization. Backbone atoms of the noninterfacial CNA atoms were held rigid throughout. RCAN1128164 was folded from randomized extended coordinates in the initial step of refinement. An initial temperature of 3500 K and a final temperature of 25 K were used for simulated annealing. A total of 100 complex structures were calculated during the first cofold step and 10 structures with the lowest energies were used as inputs for a refinement step. A total of five refinement iterations were performed, and 10 conformers with the lowest energies were used to represent the structure of RCAN1128164 when bound to CN. For the final ensemble of RCAN1128164 structures, no distance violations more than 0.5 and no torsion angle violation more than 3 were identified.

Expression and purification of human p38 and MKK6 were carried out as previously described (32). Phosphorylated p38 was produced by p38 incubation with constitutively active MKK6S207E/S211E (1:40 molar ratio). The reaction components were added to a 50-ml conical tube in the following order to achieve the reported final concentrations in a 30-ml volume: 20 mM Hepes (pH 7.5), 0.5 mM EDTA, 2 mM DTT, 20 mM MgCl2, 0.05 M MKK6S207E/S211E, 2 M p38, and 4 mM adenosine 5-triphosphate (ATP). The mixture was mixed by pipetting up/down several times and was incubated at 27C for 5 min before adding ATP (Roche). After the addition of ATP, the reaction was incubated in a water bath at 27C for 5 hours. Following incubation, the mixture was exchanged into buffer A [20 mM tris (pH 7.6), 75 mM NaCl, and 0.5 mM TCEP (tris(2-carboxyethyl)phosphine)] to remove ATP using an Amicon Ultra-15 filter (Millipore). Upon ATP removal, the solution was filtered and loaded onto a Mono Q 5/50 GL column (GE Healthcare) pre-equilibrated in buffer A and eluted with a linear gradient of 0 to 100% buffer B [20 mM tris (pH 7.6), 0.4 M NaCl, and 0.5 mM TCEP]. RCAN1 variants were phosphorylated by pp38 in buffer [20 mM Hepes (pH 7.5), 20 mM MgCl2, 1 mM DTT, and 4.8 mM ATP]. pp38 (0.05 M) was added to 2 M RCAN1 and variants. The phosphorylation reaction was incubated at 27C for 24 hours and stopped by heating at 65C for 10 min.

Protein concentration of CN and RCAN1 variants was measured in triplicate using either the Pierce 660 (Thermo Fisher Scientific) or the AccuOrange Protein Quantitation assays (Biotium). CN and RCAN1 variants were equilibrated in 20 mM tris (pH 7.5), 150 mM NaCl, 1 mM CaCl2, and 0.5 mM TCEP. RCAN1 variants (80 to 100 M, syringe) were titrated into CN (5 to 10 M, cell) using a VP-ITC microcalorimeter (Malvern) or an Affinity-ITC microcalorimeter (TA Instruments) with a 250-s interval at 25C. Twenty-five injections were delivered during each experiment over a period of 20 s (VP-ITC microcalorimeter) or 10 s (Affinity-ITC microcalorimeter), and the solution in the sample cell was stirred at 307 rpm (VP-ITC microcalorimeter) or 125 to 200 rpm (Affinity-ITC microcalorimeter) to ensure rapid mixing. All ITC data were analyzed using NITPIC (49) and fitted to a single-site binding model using SEDPHAT (50); figures were generated using GUSSI (51). To distinguish between the different transitions, we defined a H 0.35 kcal/mol as baseline, which allows for a completely unbiased data analysis.

SPR measurements were performed at 25C and a sampling rate of 5 Hz using a four-channel Reichert 4SPR instrument fitted with an autosampler and a degassing pump (Reichert Technologies). SPR buffers containing 20 mM tris (pH 7.5), 500 mM NaCl, 1 mM CaCl2, 0.5 mM TCEP, and 0.05% Tween were sterile-filtered and degassed in autoclaved glassware. Running buffers were used to prime and run both the sample and syringe pump reservoirs before each experiment. Gold sensor chips modified with Ni-NTAfunctionalized dextran (NiD50L) were installed and equilibrated under flow conditions (100 l/min) for 60 min. Surface contaminants were cleared by a pair of 120-l injections of 10 mM NaOH during equilibration. Experiments were initiated by injecting 120 l of His6-CN370 (200 nM) diluted in 20 mM tris (pH 7.5), 500 mM NaCl, 1 mM CaCl2, 0.5 mM TCEP, and 0.05% Tween onto channels 1, 2, and 3 for 120 s at 50 l/min, which resulted in between 450 and 500 RIU of surface loading (channel 4 was used as reference surfaces). The sensor chip was allowed to equilibrate for 20 min at 50 l/min before beginning the experiments. Purified RCAN1 variants were diluted into running buffer to final concentrations of 1.25, 2.5, 5, 10, and 20 nM. A single 60-l RCAN1 injection was applied for 60 s at 50 l/min followed by a dissociation step of 180 s. For each concentration of RCAN1 injection, chip surface was prepared with stripping with 350 mM EDTA (pH 8), reconditioning the surface with 10 mM NaOH to remove nonspecifically bound CN aggregates, charging the surface with 40 mM NiSO4, and reloading fresh CN onto the surface. For all experiments, two buffer blank injections were included to achieve double-referencing. Technical replicates were obtained by using three channels per chip. Kinetic parameters were determined by curve-fitting using TraceDrawer software (Ridgeview Instruments AB) fit with a one-to-one model using local Bmax.

Multiple sequence alignment was performed using RCAN1 C-terminal CN-binding domains from human (Hs), Ovis aries (Oa), Bos taurus (Bt), Canis lupus dingo (Cl), Mus musculus (Mm), Rattus norvegicus (Rn), Xenopus tropicalis (Xt), Danio rerio (Dr), Drosophila novamexicana (Dn), Apis mellifera (Am), and Caenorhabditis elegans (Ce).

Acknowledgments: We are grateful to A. Oot for help at the beginning stages of this project, and we thank T. Moon for help with SPR data collection and evaluation. We thank M. Cyert for CN discussions. Funding: This work was supported by grant R01NS091336 from the National Institute of Neurological Disorders and Stroke to W.P. and grant R01GM098482 from the National Institute of General Medicine to R.P. Crystallographic data were collected on beamline 23-ID-D at APS, Argonne National Laboratory. This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under contract no. DE-AC02-06CH11357. CDS was supported by the Intramural Research Program of the Center for Information Technology at the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author contributions: W.P., R.P., and Y.L. developed the concept. Y.L. designed, optimized, and performed in vitro phosphorylation and dephosphorylation experiments using NMR spectroscopy. S.G. performed RCAN189-197 backbone assignment, SSP, and hetNOE analysis. Y.L. performed CN and all other RCAN1 variant NMR backbone assignments and NMR studies. R.P. and S.R.S. designed, optimized, and performed crystallization and structure determination experiments. Y.L. and S.G. performed and analyzed ITC experiments. Y.L. performed SPR measurements and analysis and CN activity experiments and analysis. Y.L. and C.D.S. performed structure corefinement. W.P., R.P., and Y.L. wrote the manuscript with comments and inputs from all coauthors. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors. All NMR chemical shifts have been deposited in the BioMagResBank (BMRB: 27801, 27994, 27995, 27996, and 27997). Atomic coordinates and structure factors have been deposited in the Protein Data Bank (PDB: 6UUQ).

Read more from the original source:
The structure of the RCAN1:CN complex explains the inhibition of and substrate recruitment by calcineurin - Science Advances

Phyllo, cheese, heaven: Balkan women have been making these treats for centuries – Waterbury Republican American

For many Balkan women, making the dish is like a reflex. Its technique gets passed down from generation to generation.

For my baba, gibanica is a cheese and phyllo-dough creation she lovingly feeds (overfeeds?) her family. For Loryn Nalic of Balkan Treat Box, the same dish is called sirnica, one of the first Balkan recipes she learned. For me, its the cheese-filled wonder Id always hoped to learn how to make, partly as a way of carrying on my familys heritage, partly because its just so delicious.

Even though my name might seem foreign to some (its O-BRAD-o-vic, and its Serbian), the food that derives from the culture of it might not. St. Louis has quickly become a haven for Balkan cuisine. With the influx of immigrants over the past 20 years, Balkan foods and restaurants such as the acclaimed Balkan Treat Box have become a part of St. Louis food culture.

My dads family came to the U.S. when he was 8. He later married my Italian mother. Unfortunately, not many of my dads Serbian traditions were handed down to my two brothers and me. We dont speak Serbian (except for curse words thanks, Uncle Dennis), we dont go to Serbian church, and we definitely dont roast a whole pig on a spit in our front yard.

But what we do have is gibanica.

Gibanica is to Serbs what pizza is for Americans. Its a simple dish consisting mostly of eggs, cheese and oil sandwiched between layers of phyllo dough. People eat it for breakfast, lunch, dinner, a snack, to fix hangovers.

Every time my family goes to visit my Serbian grandmother, or Baba, we joke how the whole neighborhood smells like Serbian food. No matter how much my father stresses that were just stopping by, Baba will make enough food to feed a small village. Theres never enough gibanica, though.

Its the first food my family eats at gatherings, and its the first food to disappear.

Everything about it evokes nostalgia in me: the gooey, cheesy texture; the crunch of the outside layers. Besides the calories, you cant go wrong with gibanica.

Theres no one way to make gibanica, the same way there isnt a single way to make a hamburger. Almost every Balkan or Slavic country has a version of the dish or something similar to it. Many countries make Burek, a phyllo dough-based pie stuffed with beef and sometimes cheese. Greeks have their spinach pie, spanakopita.

The word gibanica itself is a combination of two separate languages: the Croatian verb gbati and Serbian verb ??????, meaning to fold, sway, rock. Altogether, it means folded pie.

When I asked Balkan Treat Boxs owner and chef Loryn Nalic about the dish, she knew it as sirnica.

Its one of my kids favorite dishes, Nalic says. It was the first thing I learned to make when Edo and I were together because he loves it so much.

Loryn and her husband, Edo, turned their food truck into a brick-and-mortar restaurant last year to national acclaim. They invited me into their restaurant on a Monday afternoon, when the day was dim but the wood fire in their oven burned bright.

Everyone makes gibanica and its variants differently. Nalic makes hers with fresh dough and cheese she makes herself. I use store-bought phyllo dough and cottage cheese. Loryn lines dollops of cheese and rolls the filling with the dough into one big coil. I sprinkle oil and cheese on layer after layer of dough.

Its a pretty universally loved dish, Nalic says.

Despite that, its not on the menu at Balkan Treat Box yet. Nalic says the restaurant recently got a few new ovens and may start serving it. As a special treat, the restaurant will serve it Wednesday and Thursday.

Its a very simple dish once you get the technique down. Theres a certain way to handle the dough, whether youre making it yourself or buying it from a store.

Nalic says the first time she watched someone make phyllo dough from scratch, it brought tears to her eyes. It is an art form, she says.

When I saw Nalic and her mother-in-law, Zeta, make and stretch the phyllo dough, I was near tears, too. The way she expertly expanded the dough on a table brought to my mind the countless generations of Balkan women teaching their daughters how to make it and how that knowledge spread to my Baba through a great-grandmother I never met, and now me.

Each time I ask Baba for a written recipe (there have been many times), shell recite the ingredients and say, Just make it. I asked her to teach me how to make it again for this article, with pen and paper in hand.

Now, Im the one making the neighborhood smell like Serbian food.

GIBANICA

Yield: 10 servings

2 pounds phyllo dough, preferably the thickest, country style type

7 eggs

1 1/2 pounds cottage cheese

1 teaspoon salt

1 teaspoon baking powder

1/2 pound farmers cheese or feta, crumbled

3 tablespoons soda water

2/3 cup corn oil, divided

Notes: Use the deepest metal baking pan you have, preferably at least 21/2 inches.

1. Thaw phyllo dough according to package instructions. Grease bottom of an extra-large baking pan, preferably 11-by-16-inches (available at European markets). Preheat oven to 425 degrees.

2. Whisk eggs in a large bowl, then stir in the cottage cheese, salt, baking powder and farmers cheese. Stir in the soda water.

3. Cover the bottom of the pan with a single layer of phyllo dough, making sure some of the pieces hang over the sides. Evenly sprinkle 1 tablespoon oil over the dough.

4. Take a piece of phyllo dough and wrinkle it into the dish with as many bumps as possible so it doesnt lie flat. Depending on the size of your pan, use 2 to 3 pieces of dough for each layer. Evenly sprinkle 1 tablespoon oil on the dough, including the sides and corners. Do not allow the oil to pool.

5. Sprinkle 1/2 to 3/4 cup egg-and-cheese mixture on the dough, including the sides, enough to make sure the edges and crevices are covered. Do not allow the mixture to pool.

6. Repeat laying down 2 to 3 pieces of wrinkled dough and sprinkling them with oil and the egg-and-cheese mixture until you have 1 layer of dough left. Cover the top of the dish with that last remaining layer, folding in any excess on the sides. Cover the top with a final layer of the egg-and-cheese mixture and oil, but do not dump any mixture leftovers on top.

7. Bake 40 minutes or until the top turns golden brown and the sides separate from the pan.

Per serving: 568 calories; 30 g fat; 9 g saturated fat; 43 mg cholesterol; 21 g protein; 53 g carbohydrate; 4 g sugar; 2 g fiber; 1,139 mg sodium; 260 mg calcium

Recipe by Monica Obradovic

Related

Continued here:
Phyllo, cheese, heaven: Balkan women have been making these treats for centuries - Waterbury Republican American

Covid-19: Crowdsourced virtual supercomputer revs up virus research – The Star Online

WASHINGTON: Gamers, bitcoin miners and companies large and small have teamed up for an unprecedented data-crunching effort that aims to harness idle computing power to accelerate research for a coronavirus treatment.

The project led by computational biologists has effectively created the world's most powerful supercomputer that can handle trillions of calculations needed to understand the structure of the virus.

More than 400,000 users downloaded the application in the past two weeks from Folding@Home, according to director Greg Bowman, a professor of biochemistry and molecular biophysics at Washington University in St. Louis, where the project is based.

The distributed computing effort ties together thousands of devices to create a virtual supercomputer.

The project originally launched at Stanford University 20 years ago was designed to use crowdsourced computing power for simulations to better understand diseases, especially protein folding anomalies that can make pathogens deadly.

The simulations allow us to watch how every atom moves throughout time, Bowman told AFP.

The massive analysis looks for pockets or holes in the virus where a drug can be squeezed in.

Our primary objective is to hunt for binding sites for therapeutics, Bowman said.

Druggable targets

The powerful computing effort can test potential drug therapies, a technique known as computational drug design.

Bowman said he is optimistic about this effort because the team previously found a druggable target in the Ebola virus and because Covid-19 is structurally similar to the SARS virus which has been the subject of many studies.

The best opportunity for the near-term future is if we can find an existing drug that can bind to one of these sites, he said.

If that happens it could be used right away.

This is likely to include drugs like the antimalarials chloroquine and hydroxychloroquine which may be repurposed for Covid-19.

Bowman said the project has been able to boost its power to some 400 petaflops with each petaflop having a capacity to carry out one quadrillion calculations per second or three times more powerful than the world's top supercomputers.

Other supercomputers are also working in parallel. The Oak Ridge National Laboratory said earlier this month that by using IBM's most powerful supercomputer it had identified 77 potential compounds that could bind to the main spike protein of the coronavirus to disarm the pathogen.

No end to compute power

The Folding@Home project is fueled by crowdsourced computing power from people's desktops, laptops and even PlayStation consoles, as well as more powerful business computers and servers.

There is no end to the compute power than we can use in principle, Bowman said. Large tech firms including Microsoft-owned GitHub are also participating, and the project is in discussions with others.

Anyone with a relatively recent computer can contribute by installing a program which downloads a small amount of data for analysis.

People can choose which disease they wish to work on.

It's like bitcoin mining, but in the service of humanity, said Quentin Rhoads-Herrera of the security firm Critical Start, which has provided its powerful password hash cracker computer designed to decrypt passwords to the project.

Rhoads-Herrera said his team of security researchers, sometimes described as white hat hackers, were encouraging more people to get involved.

Fighting helplessness

Computer chipmaker Nvidia, which makes powerful graphics processors for gaming devices, called on gamers to join the effort as well.

The response has been record-breaking, with tens of thousands of new users joining, said Nvidia spokesman Hector Marinez.

One of the largest contributions comes from a Reddit group of PC enthusiasts and gamers which has some 24,000 members participating.

It is a fantastic weapon against the feeling of helplessness, said Pedro Valadas, a lawyer in Portugal who heads the Reddit community and is a part of the project's advisory board.

The fact that anyone, at home, with a computer, can play a role and help fight against (disease) for the common good is a powerful statement, Valadas told AFP. AFP

Read the rest here:
Covid-19: Crowdsourced virtual supercomputer revs up virus research - The Star Online

Scots tech firm joins battle to find Covid-19 vaccine – The National

A SCOTS tech firm has joined the quest to help find a vaccine for coronavirus by giving US researchers access to thousands of pounds worth of its own computer equipment.

Glasgow-based Ground Level Up specialises in the creation of visual content, data gathering, and 3D mapping using drones and as such uses high-end hardware for its work.

However, its staff are working from home during the virus lockdown, and the firm has donated the use of its two 96 terabyte servers, which are currently sitting idle, to researchers at Stanford Medical School in California.

Company boss Carrick McLelland, who is amongst those working from home, said he and his business partner, Alistair Snowie, started researching how they could put the powerful servers to good use during what could be a potentially lengthy period of self-isolation.

READ MORE:Scots scientist says a million coronavirus vaccines will be ready by end of 2020

He discovered Folding@home, a project that allows researchers to remotely use systems that have downloaded their software to carry out studies requiring huge amounts of computer power including protein folding and other molecular dynamics. By simulating what are called protein dynamics, it is hoped more rapid progress will be made in combatting the coronavirus.

McLelland said he believed not many firms know about projects such as Folding@home, and has urged other digital and professional editing companies throughout Glasgow and the rest of Scotland to follow suit.

Two 96 terabyte servers provide the same memory as around 400 laptops

He said many would be able to donate the use of their storage servers, especially if they are lying unused while their staff work on their own computers at home.

McLelland said: These servers have huge processing power multiple people are able to use them to download, edit, and render digital material at the same time.

I think its not only vital to keep massively powerful pieces of equipment like this in use, but also to use them in the fight against coronavirus. At the moment, I cant think of a more important purpose for them than that.

READ MORE:Expect a directive from the EU requiring coherent planning

Ground Level Up has four permanent staff involved in work that sees them create visual content for businesses on social media as well as using drones for filming, data gathering and 3D mapping in some of the worlds most inaccessible areas.

Their work has been hit by the pandemic as countries close their borders and businesses cut costs. But McLelland said his firm has the resources to keep everyone employed and he added that he and his staff are ready to help any organisations, be it the NHS, the Scottish Government, or universities and colleges, make information videos should they be urgently needed.

He added: Naturally, we are still a business, and need to make ends meet, but we can offer highly-accessible opportunities to anyone who needs information videos produced with same-day turnaround.

See original here:
Scots tech firm joins battle to find Covid-19 vaccine - The National

Join the Folding at Home Neowin team to fight the novel Coronavirus – Neowin

The Folding at Home project has been around for two decades and is still going strong. For the uninitiated, the project conducts disease research by carving out units of work that can be shipped to an individual's computer so that those machines can conduct protein folding simulations. When your computer is done crunching the numbers, it sends the results back to the Folding at Home servers and requests another unit of work.

The group has recently started assisting scientists in finding a cure for the novel Coronavirus, COVID-19. What this means is that your spare CPU cycles can be donated to the project to help find a cure to the pandemic that's impacting everyone's lives around the world. The project is aiming to recruit a million volunteers.

Helping out is easy: Simply download the program from their website, type in what name you want to use and optionally what team you want to join, and let it go. You can configure how much machine power you want to donate, and you can even click on the Configure button to setup how many CPU cores you want to provide. As a warning, if you let it consume your entire machine, it will definitely peg the CPU at 100% and generate quite a bit of heat. My workstation is powered by the Ryzen 3900x, and after initially giving the tool access to all 24 cores, I noticed the CPU temperature was extremely hot, so I limited it to only 12 of the cores, which is still plenty. The tool can also use your PC's GPU for even more processing, and that's currently the method used for the COVID-19 tests. You can search any of the projects to find out who is using the research and what it's for on the Folding at Home website.

Neowin has had our own team since 2007, so when doing the install, it'd be great if you used our team number: 55186. The front-end servers are getting hammered recently with thousands of people rushing to sign up and help fight the disease, so you'll often receive a "Bad Gateway" error when checking, but when things are working, you can check the status directly on the Folding at Home page by typing your name or team number into the search box.

Read the original here:
Join the Folding at Home Neowin team to fight the novel Coronavirus - Neowin