Two years after DeepMind’s revolutionary AI swept a competition for predicting protein structures, researchers are building on AlphaFold’s success.
“In some sense the problem is solved,” computational biologist John Moult declared in late 2020. The London-based company DeepMind had just swept a biennial contest co-founded by Moult that tests teams’ abilities to predict protein structures — one of biology’s grandest challenges — with its revolutionary artificial-intelligence tool AlphaFold.
Entries are assessed according to how well predictions for entire proteins, or independently folding subunits called domains, match the experimental structures. Some ofwere more or less indistinguishable from the experimental models — the first time such accuracy had been achieved. At CASP15, the most successful teams were those that had adapted and built on AlphaFold in various ways, leading to modest gains in predicting the shape of individual proteins and domains. “The accuracy is already so high that it’s hard to get much better,” says Moult.To make the competition more relevant in a post-AlphaFold world, Moult and his team added new challenges and tweaked some existing ones.
Such efforts seem to have paid off, because CASP15 saw a marked increase in the number of accurate complexes, compared with previous contests, mainly due to methods that adapted AlphaFold. “It’s a new game for us to be close to experimental accuracy with complexes.” says Moult. “We’ve got some failures too.”
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