Behind the screens of AlphaFold

Not so long ago, a list of ‘holy grails of chemistry’ like that recently compiled by Chemistry World might very probably have included ‘solving the protein folding problem’. It was widely believed that the ability to predict the structure of a protein from just its amino-acid sequence would be of immense value to the life sciences.

At the start of December, many media headlines announced what appeared to be the realisation of that goal. The artificial-intelligence company DeepMind has shown that their AlphaFold deep-learning algorithm can predict many protein structures from their sequence with an atomic-scale precision often comparable to that obtained from the best crystallographic analyses. It has been hailed as a major breakthrough. ‘It will change everything’, evolutionary biologist Andrei Lupas told Nature, while structural biologist Janet Thornton said the advance will ‘really help us to understand how human beings operate and function’. Some reports would have us believe cures for diseases such as Alzheimer’s (which stems from protein misfolding) are now just around the corner.

But such assertions have been contested. Some biochemists pointed out that the accuracy of prediction was not always so impressive and is in general unlikely to be accepted without experimental corroboration from, say, crystallography, NMR studies or cryo-electron microscopy. While the majority of predicted structures were within experimental resolution, one can’t tell a priori which are and which aren’t – so you need experiments to check. Also, it’s still not yet clear that the accuracy meets what’s needed for, say, finding drug candidates that might bind to the protein’s active site to block its function.