Protein structure prediction with a language model improves accuracy for orphan and designed proteins NBTNV
, that were nearly as accurate as experimental methods for protein structure determination. But both algorithms consume large amounts of computing resources, and because they depend on multiple sequence alignments as input, they are less successful in predicting the structure of so-called ‘orphan’ proteins — proteins with few or no homologs. Writing in.
report substantial progress on both of these challenges. Their recurrent geometric network 2 method, which relies on a protein language algorithm, uses orders of magnitude less computing time than AlphaFold2 and RoseTTAFold while outperforming them on average in predicting the structures of orphan proteins. These results highlight the breakneck pace of the field and suggest that further leaps in computational speed lie ahead.
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