Changing the game in protein structure prediction
Have AlphaFold and other machine learning techniques essentially solved the formerly fiendish problem, or is there still more to be done? Clare Sansom reports
How must it feel to find that an apparently intractable scientific problem that you have devoted years of your research career to working on has, suddenly, been largely solved? Something like that happened to some biochemists in the autumn and early winter of 2020. The problem concerned was that of predicting the structure of a protein – essentially, the overall shape of the molecule, and the position of each of its atoms in space – given only the sequence of its constituent amino acids. This breakthrough is all the more important because the ‘gold standard’ of experimental structure determination is still expensive and relatively slow.
The solution had not come from a specialist research lab or a well-resourced pharma company, but from a generalist software company called DeepMind, founded in 2010 to develop and apply problem-solving artificial intelligence techniques. Its first successes were in gaming, with its AlphaGo program becoming the first computer program to beat a professional Go player.