DeepMind software that can predict the 3D shape of proteins is already changing biology.
, as well as the entire ‘proteomes’ of 20 other widely studied organisms, such as mice and the bacterium, in Hinxton, UK. That database has since swelled to almost one million structures.This year, DeepMind plans to release a total of more than 100 million structure predictions. That is nearly half of all known proteins — and hundreds of times more than the number of experimentally determined proteins in the Protein Data Bank structure repository.
Where AlphaFold is less confident, “it’s very good at telling you when it doesn’t work”, Elofsson says. In such cases, predicted structures can resemble floating spaghetti strands . This often corresponds to regions of proteins that lack a defined shape, at least in isolation. Such intrinsically disordered regions — which make up around one-third of the human proteome — might become well defined only when another molecule, such as a signalling partner, is present.
The same is true for labs specializing in cryo-EM, which captures pictures of flash-frozen proteins. In some instances, AlphaFold’s models have accurately predicted unique features of proteins called G-protein-coupled receptors — which are important drug targets — that other computational tools got wrong, says Bryan Roth, a structural biologist and pharmacologist at the University of North Carolina at Chapel Hill.
“It’s hard to say ‘this is a panacea’; that because you can do it very well for one structure — surprisingly and excitingly well — that it is eminently applicable to all structures. It clearly isn’t,” Akinsanya says. And she and her colleagues have found that AlphaFold’s accuracy predictions don’t show whether a structure will be useful for later drug screening. AlphaFold structures will never fully replace experimental ones in drug discovery, she says.
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