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AlphaFold Spreads through Protein Science


AlphaFold-predicted structure of estrogen receptor protein, seen binding to DNA

Credit: Veronica Falconieri Hays

Two years ago, as the COVID-19 pandemic swept across the world, researchers at DeepMind, the artificial intelligence (AI) and research laboratory subsidiary of Alphabet Inc., demonstrated how it could use machine learning to achieve a breakthrough in the ability to predict how proteins, the work-horses of the living cell, fold into the intricate shapes they take on. The work gave hope to biologists that they could use this kind of tool to tackle diseases such as the SARS-CoV-2 coronavirus much more quickly in the future.

Researchers were able to assess the abilities of DeepMind's AlphaFold2 thanks to its inclusion in the 14th Critical Assessment of Structure Prediction (CASP14), a benchmarking competition that ran through 2020 and which added a parallel program to uncover the structures of key proteins from the SARS-CoV2 virus to try to accelerate vaccine and drug development. The organizers of CASP14 declared the tool represented "an almost complete solution to the problem of computing three-dimensional structure from amino-acid sequences," though some caveats lie behind that statement.


 

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