After AlphaFold: protein-folding contest seeks next big breakthrough (Nature)
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 (AI) tool AlphaFold. Two years later, Moult’s competition, the Critical Assessment of Structure Prediction (CASP), is still walking in AlphaFold’s long shadow. Results from this year’s edition (CASP15) — which were unveiled over the weekend at a conference in Antalya, Turkey — show that the most successful approaches to predicting protein structures from their amino-acid sequences incorporated AlphaFold, which relies on an AI approach called deep learning. “Everyone is using AlphaFold,” says Yang Zhang, a computational biologist at the University of Michigan in Ann Arbor. Yet AlphaFold’s progress has opened the floodgates for new challenges in protein-structure prediction — some included in this year’s CASP — that might require new approaches and more time to fully tackle. “The low-hanging fruit has been picked,” says Mohammed AlQuraishi, a computational biologist at Columbia University in New York City. “Some of the next problems are going to be harder.”