Researchers Use Machine Learning to Model Proteins Linked to Cancer – Livermore Independent

Lawrence Livermore National Laboratory (LLNL) researchers and a multi-institutional team of scientists have developed a machine learning-backed model showing the importance of lipids to the signaling dynamics of RAS, a family of proteins whose mutations are linked to numerous cancers.

Lipids are fatty acid organic compounds that are insoluble in water, but soluble in organic solvents.

In a paper published in the Proceedings of the National Academy of Sciences, researchers detail the methodology behind the Multiscale Machine-Learned Modeling Infrastructure (MuMMI), which simulates the behavior of RAS proteins on a cell membrane, their interactions with lipids which help make up cell membranes and the activation of RAS signaling on a macro and molecular level.

According to the researchers, the data indicates that lipids rather than protein interfaces govern both RAS orientation and the accumulation of RAS proteins.

We always knew lipids were important, said LLNL computer scientist and lead author Helgi Ingolfsson. You need some of them, otherwise you dont have this behavior. But after that, scientists didnt know what was important about them.

Normally, RAS proteins receive and follow signals to switch between active and inactive states, but as the proteins move along the cell membrane they combine with other proteins and can activate signaling behavior.

Mutated RAS proteins can become stuck in an uncontrollable, always on growth state, which is seen in the formation of about 30% of all cancers, particularly pancreatic, lung and colorectal cancers.

The research is showing us that lipids are a key player, Ingolfsson said. By modulating the lipids and different lipid environments, RAS changes its orientation, and you can actually change the signaling (between grow and not grow) by changing the lipids underneath.

Researchers said the MuMMI framework represents a fundamentally new technology in computational biology and could be used to improve their basic understanding of RAS protein binding.

The research is part of a pilot project of the Joint Design of Advanced Computing Solutions for Cancer, a collaboration between the Department of Energy, National Cancer Institute, and other organizations.

Traditional researchers can simulate only a small, fixed number of proteins and one lipid composition at a time, Ingolfsson explained, and they need to know which lipids are important to model beforehand. With the MuMMI framework, researchers can simulate thousands of different cell compositions derived from the macro model, allowing them to answer questions about RAS-lipid interactions that previously would be possible only with a multiscale simulation.

Were demonstrating that the old way of doing things is starting to be outdated, Ingolfsson said. At Livermore, we have enormous computing power, we have a lot of people working on this and we can show what can be possible.

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Researchers Use Machine Learning to Model Proteins Linked to Cancer - Livermore Independent