Harvard Medical School’s AI estimates protein structures up to 7 times faster than previous methods

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The recipe for proteins — the fundamental building blocks of tissues, muscles, enzymes, and antibodies — is encoded in DNA. It’s these genetic dictionaries that define proteins’ three-dimensional structures and determine their functions, but predicting how their amino acid components will interact is notoriously difficult. DNA only contains information about chains of amino acid residues, not those chains’ final form. In fact, scientists estimate it would take more than 13.8 billion years to figure out all the possible configurations of a typical protein’s thousands of amino acids in order to identify the right structure.

Encouragingly, scientists at Harvard Medical School have made progress toward an AI system that is capable of predicting the structure of effectively any protein and can spit out predictions upwards of a million times faster than current state-of-the-art systems without sacrificing accuracy. The work is detailed in a report published this week in the journal Cell


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