Neural networks have been taught to quickly read the surfaces of proteins — molecules critical to many biological processes. The advance is already being used to create defenses for the virus responsible for COVID-19.
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The computational biologist Bruno Correia used to have a rule in his lab: No allowed. He didn’t consider it real science. Now Correia has used it to detect potential interactions between proteins — the complex folded molecules responsible for many biological processes — 40,000 times faster than conventional methods. The journal Nature Methods featured his system on its cover in February 2020 . Correia said of his early reluctance to embrace
What changed his mind? Geometric
Proteins interact by fitting their bumpy, irregular shapes together like three-dimensional puzzle pieces. Researchers have spent decades trying to figure out how they do so. The well-known protein folding problem , which has challenged scientists since the mid-20th century, attempts to understand protein interaction by decoding the link between a protein’s constituent amino acids and its final 3D shape. In 1999, IBM began developing its line of Blue Gene supercomputers to tackle the folding problem; 20 years later , DeepMind applied state-of-the-art
Correia’s system, called MaSIF (short for molecular surface interaction fingerprinting), avoids the inherent complexity of a protein’s 3D shape by ignoring the molecules’ internal structure. Instead, the system scans the protein’s 2D surface for what the researchers call interaction fingerprints: features learned by a neural network that indicate that another protein could bind there. “The idea [is that when] any two molecules come together, what they’re essentially presenting to one another is that surface. So that’s all you need,” said Mohammed AlQuraishi, a protein researcher at Harvard Medical School who also uses
MaSIF’s surface-focused framework for predicting protein interactions could help accelerate so-called de novo protein design, which tries to synthesize useful proteins from scratch rather than relying on the naturally occurring variety. But it could also be used for basic biology, said Michael Bronstein, a geometric
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