Researchers have used a machine-learning algorithm to study protein appearance and discover common features that influence function, which could be used to design artificial cells.<\/p>\n
Researchers at EPFL have developed a new way to predict a protein's interactions with other proteins and biomolecules and its biochemical activity, merely by observing its surface (credit: Laura Persat \/ 2019 EPFL).<\/p>\n
A new machine learning-driven technique has been able to predict the interactions between proteins and describe biochemical activity based on surface appearance.<\/p>\n
The study was conducted at the Laboratory of Protein Design & Immunoengineering (LPDI), Switzerland, in collaboration with other researchers. <\/p>\n
According to the team, the method, known as MaSIF, could support the development of protein-based components for artificial cells in novel therapeutics.<\/p>\n
Scientists have developed a new way to predict a proteins interactions with other proteins and biomolecules and its biochemical activity, merely by observing its surface (credit: Laura Persat \/ 2019 EPFL).<\/p>\n
The researchers took a vast set of protein surface data and fed the chemical and geometric properties into a machine-learning algorithm, training it to match these properties with particular behaviour patterns and activity. They used the remaining data to test the algorithm.<\/p>\n
By scanning the surface of a protein, our method can define a fingerprint, which can then be compared across proteins, says Pablo Gainza, the first author of the study.<\/p>\n
The team found that proteins performing similar interactions share common features.<\/p>\n
The algorithm can analyse billions of protein surfaces per second, says LPDI director Bruno Correia. Our research has significant implications for artificial protein design, allowing us to program a protein to behave a certain way merely by altering its surface chemical and geometric properties.<\/p>\n
The method could also be used to analyse the surface structure of other types of molecules, say the researchers.<\/p>\n
The findings were published in Nature Methods.<\/p>\n
<\/p>\n
Read the rest here: <\/p>\n