DeepMind and the organizers of the long-running Critical Assessment of protein Structure Prediction (CASP) competition announced an AI has huge impact on protein research. The latest version of DeepMind’s AlphaFold, a deep-learning system that can accurately predict the structure of proteins to within the width of an atom, has cracked one of biology’s grand challenges. “It's the first use of AI to solve a serious problem,” says John Moult at the University of Maryland, who leads the team that runs CASP.
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In this year’s CASP, AlphaFold predicted the structure of dozens of proteins with a margin of error of just 1.6 angstroms—that’s 0.16 nanometers, or atom-sized. This far outstrips all other computational methods and for the first time matches the accuracy of experimental techniques to map out the structure of proteins in the lab, such as cryo-electron microscopy, nuclear magnetic resonance and x-ray crystallography. These techniques are expensive and slow: it can take hundreds of thousands of dollars and years of trial and error for each protein. AlphaFold can find a protein’s shape in a few days.
According to Andrei Lupas, an evolutionary biologist at the Max Planck Institute for Developmental Biology, “It’s a game changer”. AlphaFold has already helped him find the structure of a protein that has vexed his lab for a decade, and he expects it will alter how he works and the questions he tackles. “This will change medicine. It will change research. It will change bioengineering. It will change everything,” Lupas adds.
Read More: ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures
AlphaFold builds on the work of hundreds of researchers around the world. DeepMind also drew on a wide range of expertise, putting together a team of biologists, physicists and computer scientists. DeepMind trained AlphaFold on around 170,000 proteins taken from the protein data bank, a public repository of sequences and structures. It compared multiple sequences in the data bank and looked for pairs of amino acids that often end up close together in folded structures. It then uses this data to guess the distance between pairs of amino acids in structures that are not yet known.
And although computational predictions aren’t yet accurate enough to be widely used in drug design, the increasing accuracy allows for other applications, such as understanding how a mutated protein contributes to disease or knowing which part of a protein to turn into a vaccine for immunotherapy