Proteins with multiple structures open up AlphaFold’s black box

Biochemists in the US have found limits to what AlphaFold, the usually highly accurate protein structure prediction artificial intelligence system developed by Google DeepMind, can do. Lauren Porter’s team at the US National Library of Medicine at the National Institutes of Health looked at how AlphaFold performed with proteins that adopted more than one stable structure. The methods tested correctly identified one of seven proteins that could switch between structures that it hadn’t previously encountered.

‘Current implementations of AlphaFold – the best protein structure predictor – are limited in their ability to predict fold-switching proteins,’ Porter tells Chemistry World. The study opens the ‘black box’ of how AlphaFold predicts a protein’s 3D structure from its amino acid sequence. The findings indicate that it’s memorising structures rather than considering how amino acids coevolve in related proteins, as scientists suggest happens in some methods. ‘If we want to use [AlphaFold] effectively, we need to understand what is driving these predictions,’ says team member Devlina Chakravarty.