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“These algorithms are now becoming strong enough and powerful enough to be applicable to scientific problems,” DeepMind Chief Executive Officer Demis Hassabis said in a call with reporters. After four years of development “we have a system that’s accurate enough to actually have biological significance and relevance for biological researchers.”
DeepMind is now looking into ways of offering scientists access to the AlphaFold system in a “scalable way,” Hassabis said.
“Citizen Science”
CASP scientists analyzed the shape of amino acid sequences for a set of about 100 proteins. Competitors were given the sequences, and charged with predicting their shape. AlphaFold’s assessment lined up almost perfectly with the CASP analysis for two-thirds of the proteins, compared to about 10% from the other teams, and better than what DeepMind’s tool achieved two years ago
Hassabis said his inspiration for AlphaFold came from “citizen science” attempts to find unknown protein structures, like Foldit, which presented amateur volunteers with the problem in the form of a puzzle. In its first two years, the human gamers proved to be surprisingly good at solving the riddles, and ended up discovering a structure that had baffled scientists and designing a new enzyme that was later confirmed in the lab.
“Determining a single protein structure often required years of experimental effort,” said Janet Thornton, director emeritus of the European Bioinformatics Institute and one of the pioneers of using computational approaches to understanding protein structure. “A better understanding of protein structures and the ability to predict them using a computer means a better understanding of life, evolution and, of course, human health and disease.”
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2020-11-30 18:33:45Z
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