Highly accurate protein structure prediction for the human proteome

Kathryn Tunyasuvunakool (), Jonas Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, Andrew Cowie, Clemens Meyer, Agata Laydon, Sameer Velankar, Gerard J. Kleywegt, Alex Bateman, Richard Evans, Alexander Pritzel, Michael Figurnov, Olaf Ronneberger, Russ Bates, Simon A. A. Kohl, Anna Potapenko, Andrew J. Ballard, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Ellen Clancy, David Reiman, Stig Petersen, Andrew W. Senior, Koray Kavukcuoglu, Ewan Birney, Pushmeet Kohli, John Jumper () and Demis Hassabis ()
Additional contact information
Kathryn Tunyasuvunakool: DeepMind
Jonas Adler: DeepMind
Zachary Wu: DeepMind
Tim Green: DeepMind
Michal Zielinski: DeepMind
Augustin Žídek: DeepMind
Alex Bridgland: DeepMind
Andrew Cowie: DeepMind
Clemens Meyer: DeepMind
Agata Laydon: DeepMind
Sameer Velankar: European Bioinformatics Institute
Gerard J. Kleywegt: European Bioinformatics Institute
Alex Bateman: European Bioinformatics Institute
Richard Evans: DeepMind
Alexander Pritzel: DeepMind
Michael Figurnov: DeepMind
Olaf Ronneberger: DeepMind
Russ Bates: DeepMind
Simon A. A. Kohl: DeepMind
Anna Potapenko: DeepMind
Andrew J. Ballard: DeepMind
Bernardino Romera-Paredes: DeepMind
Stanislav Nikolov: DeepMind
Rishub Jain: DeepMind
Ellen Clancy: DeepMind
David Reiman: DeepMind
Stig Petersen: DeepMind
Andrew W. Senior: DeepMind
Koray Kavukcuoglu: DeepMind
Ewan Birney: European Bioinformatics Institute
Pushmeet Kohli: DeepMind
John Jumper: DeepMind
Demis Hassabis: DeepMind

Nature, 2021, vol. 596, issue 7873, 590-596

Abstract: Abstract Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure1. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold2, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective.

Date: 2021
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DOI: 10.1038/s41586-021-03828-1

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