DeepMinds AlphaFold Will Solve Most Protein Structures by End of 2021

A transformative artificial intelligence (AI) tool called AlphaFold, which has been developed by Google’s sister company DeepMind in London, has predicted the structure of nearly the entire human proteome (the full complement of proteins expressed by an organism). In addition, the tool has predicted almost complete proteomes for various other organisms, ranging from mice and maize (corn) to the malaria parasite.

The more than 350,000 protein structures, which are available through a public database, vary in their accuracy. But researchers say the resource — which is set to grow to 130 million structures by the end of the year — has the potential to revolutionize the life sciences.

Biggest Science Contribution Made by AI

“It’s totally transformative from my perspective. Having the shapes of all these proteins really gives you insight into their mechanisms,” says Christine Orengo, a computational biologist at University College London (UCL).

“This is the biggest contribution an AI system has made so far to advancing scientific knowledge. I don’t think it’s a stretch to say that,” says Demis Hassabis, co-founder and chief executive of DeepMind.

But researchers emphasize that the data dump is a beginning, not an end. They will want to validate the predictions and, more importantly, apply them to experiments that were hitherto impossible. “It’s an amazing first step, that we have all this data on that scale,” says David Jones, a UCL computational biologist who advised DeepMind on an earlier iteration of AlphaFold.

Deluge of data
The approximately 365,000 structure predictions deposited this week should swell to 130 million — nearly half of all known proteins — by the year’s end, says Sameer Velankar, a structural bioinformatician at EMBL-EBI. The database will be updated as new proteins are identified and predictions improved. “This is not a resource you expect to have access to,” says Tunyasuvunakool, and she is eager to see what scientists come up with.

Researchers are already using AlphaFold and related tools to help make sense out of experimental data generated using X-ray crystallography and cryo-electron microscopy. Marcelo Sousa, a biochemist at the University of Colorado Boulder, used AlphaFold to make models from X-ray data of proteins that bacteria use to evade an antibiotic called colistin. The parts of the experimental model that differed from the AlphaFold prediction were typically regions that the software had assigned with low confidence, Sousa notes, a sign that AlphaFold is accurately predicting its limits.

SOURCES- Nature
Written by Brian Wang, Nextbigfuture.com

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