Demis Hassabis

O. Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, A. Dudzik, Junyoung Chung, David Choi, Richard Powell, T. Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, M. Kroiss, Ivo Danihelka, Aja Huang, L. Sifre, Trevor Cai, J. Agapiou, Max Jaderberg, A. Vezhnevets, R. Leblond, Tobias Pohlen, Valentin Dalibard, D. Budden, Yury Sulsky, James Molloy, T. Paine, Caglar Gulcehre, Ziyun Wang, T. Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, T. Schaul, T. Lillicrap, K. Kavukcuoglu, D. Hassabis, C. Apps, David Silver

A. Senior, Richard Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, Augustin Žídek, A. Nelson, Alex Bridgland, Hugo Penedones, S. Petersen, K. Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, K. Kavukcuoglu, D. Hassabis

S. McKinney, Marcin Sieniek, Varun Godbole, Jonathan Godwin, Natasha Antropova, H. Ashrafian, Trevor Back, Mary Chesus, Greg C. Corrado, A. Darzi, M. Etemadi, Florencia Garcia-Vicente, F. Gilbert, M. Halling-Brown, D. Hassabis, Sunny Jansen, A. Karthikesalingam, Christopher J. Kelly, Dominic King, J. Ledsam, David S. Melnick, Hormuz Mostofi, L. Peng, J. Reicher, B. Romera-Paredes, Richard Sidebottom, M. Suleyman, Daniel Tse, K. Young, J. Fauw, S. Shetty

Kathryn Tunyasuvunakool, J. Adler, Zachary Wu, T. Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, A. Cowie, Clemens Meyer, Agata Laydon, S. Velankar, G. Kleywegt, A. Bateman, R. Evans, A. Pritzel, Michael Figurnov, O. Ronneberger, Russ Bates, Simon A A Kohl, Anna Potapenko, A. Ballard, B. Romera-Paredes, Stanislav Nikolov, Rishub Jain, Ellen Clancy, D. Reiman, S. Petersen, A. Senior, K. Kavukcuoglu, E. Birney, Pushmeet Kohli, J. Jumper, D. Hassabis

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. 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.

M. Váradi, Damian Bertoni, Paulyna Magana, Urmila Paramval, Ivanna Pidruchna, Mala L. Radhakrishnan, M. Tsenkov, S. Nair, M. Mirdita, Jingi Yeo, Oleg Kovalevskiy, Kathryn Tunyasuvunakool, Agata Laydon, Augustin Žídek, Hamish Tomlinson, D. Hariharan, J. Abrahamson, T. Green, John Jumper, E. Birney, Martin Steinegger, D. Hassabis, S. Velankar

Abstract The AlphaFold Database Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled by the groundbreaking AlphaFold2 artificial intelligence (AI) system, the predictions archived in AlphaFold DB have been integrated into primary data resources such as PDB, UniProt, Ensembl, InterPro and MobiDB. Our manuscript details subsequent enhancements in data archiving, covering successive releases encompassing model organisms, global health proteomes, Swiss-Prot integration, and a host of curated protein datasets. We detail the data access mechanisms of AlphaFold DB, from direct file access via FTP to advanced queries using Google Cloud Public Datasets and the programmatic access endpoints of the database. We also discuss the improvements and services added since its initial release, including enhancements to the Predicted Aligned Error viewer, customisation options for the 3D viewer, and improvements in the search engine of AlphaFold DB.