Highly accurate protein structure prediction for the human proteome

• Published in: Nature (London) Vol. 596; no. 7873; pp. 590 - 596
• Main Authors: Tunyasuvunakool, Kathryn, Adler, Jonas, Wu, Zachary, Green, Tim, Zielinski, Michal, Žídek, Augustin, Bridgland, Alex, Cowie, Andrew, Meyer, Clemens, Laydon, Agata, Velankar, Sameer, Kleywegt, Gerard J., Bateman, Alex, Evans, Richard, Pritzel, Alexander, Figurnov, Michael, Ronneberger, Olaf, Bates, Russ, Kohl, Simon A. A., Potapenko, Anna, Ballard, Andrew J., Romera-Paredes, Bernardino, Nikolov, Stanislav, Jain, Rishub, Clancy, Ellen, Reiman, David, Petersen, Stig, Senior, Andrew W., Kavukcuoglu, Koray, Birney, Ewan, Kohli, Pushmeet, Jumper, John, Hassabis, Demis
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 26.08.2021; Nature Publishing Group
• Subjects: 631/114/1305; 631/114/2411; 631/1647/2067; 631/535; Accuracy; Bioinformatics; Biological activity; Computational Biology - standards; Confidence; Datasets; Datasets as Topic - standards; Deep Learning - standards; Diacylglycerol O-Acyltransferase - chemistry; Drug development; Glucose-6-Phosphatase - chemistry; Humanities and Social Sciences; Humans; Hypotheses; Learning algorithms; Machine learning; Membrane Proteins - chemistry; Methods; Models, Molecular; multidisciplinary; Predictions; Protein Conformation; Protein Folding; Protein structure; Protein structure prediction; Proteins; Proteome - chemistry; Proteomes; Reproducibility of Results; Residues; Science; Science (multidisciplinary); Site-directed mutagenesis; United Kingdom
• ISSN: 0028-0836; 1476-4687; 1476-4687
• DOI: 10.1038/s41586-021-03828-1

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 structure 1 . Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold 2 , 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. AlphaFold is used to predict the structures of almost all of the proteins in the human proteome—the availability of high-confidence predicted structures could enable new avenues of investigation from a structural perspective.