It is using language learning models to predict how proteins fold.
First, to contextualize the importance of understanding protein shapes, here’s a brief biology lesson. Certainof nucleotides from genes are translated by a molecule in the cell called a ribosome into amino acids. Proteins are chains of amino acids that have assorted themselves into unique forms and configurations. An emerging field of science called metagenomics is using gene sequencing to discover, catalog, and annotate new proteins in the natural world.
Historically, computational biologists have used evolutionary patterns to predict the structures of proteins. Proteins, before they’re folded, are linear strands of amino acids. When the protein folds into complex structures, certain sequences that may appear far apart in the linear strand could suddenly be very close to one another.“You can think about this as two pieces in a puzzle where they have to fit together.
“What’s different about our approach is that we’re making the prediction directly from the amino acid sequence, rather than making it from this set of multiple related proteins and looking at the patterns,” Rives says. “The language model has learned these patterns in a different way. What this means is that we can greatly simplify the structure prediction architecture because we don’t need to process this set of sequences and we don’t need to search for related sequences.
How did they train this model to be able to do this task? It took two steps. First, they had to pre-train the language model across a large number of proteins that have different structures, come from different protein families, and are taken all across the evolutionary timeline. They used a version of the, where they blanked out portions of the amino acid sequence and asked the algorithm to fill in those blanks.
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