Google’s DeepMind aces protein folding

Turns out mastering chess and Go was just for starters. On 2 December, the Google-owned artificial intelligence firm DeepMind took top honors in the 13th Critical Assessment of Structure Prediction (CASP), a biannual competition aimed at predicting the 3D structure of proteins.

The contest worked like this: Competing teams were given the linear sequence of amino acids for 90 proteins for which the 3D shape is known but not yet published. Teams then computed how those sequences would fold. Though London-based DeepMind had not previously joined this competition, the predictions of its AlphaFold software were, on average, more accurate than those of its 97 competitors.

How close was the race? By one metric, not very. For protein sequences for which no other information was known – 43 of the 90 – AlphaFold made the most accurate prediction 25 times. That far outpaced the second place finisher, which won three of the 43 tests.

AlphaFold won a lot of rounds, with an average margin of 15% accuracy improvement over other groups on the toughest 43 tests, says John Moult, CASP’s lead organizer and a computational biologist at the University of Maryland in Rockville.