{"id":"doi:10.1038/s41586-021-03828-1","kind":"article","title":"Highly accurate protein structure prediction for the human proteome","authors":["Kathryn Tunyasuvunakool","Jonas Adler","Zachary Wu","Tim Green","Michal Zielinski","Augustin Žídek","Alex Bridgland","Andrew Cowie","Clemens Meyer","Agata Laydon","Sameer S Velankar","Gerard J. Kleywegt","Alex G. Bateman","Richard Evans","Alexander Pritzel","Michael Figurnov","Olaf Ronneberger","Russ Bates","Simon Köhl","Anna Potapenko","Andrew J. Ballard","Bernardino Romera‐Paredes","Stanislav Nikolov","Rishub Jain","Ellen Clancy"],"author_count":33,"year":2021,"published":"2021-07-22","venue":"Nature","cited_by":3023,"doi":"10.1038/s41586-021-03828-1","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-021-03828-1","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-021-03828-1","updated":null,"pmid":"34293799","pmcid":"PMC8387240","openalex_id":"W3183475563","pdf_url":"https://www.nature.com/articles/s41586-021-03828-1.pdf","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 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.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Protein Structure and Dynamics"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"Highly accurate protein structure prediction for the human proteome\" by Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu et al., https://doi.org/10.1038/s41586-021-03828-1, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"protein-design","name":"AI Protein Design & Structure Prediction","domain_slug":"biotech"}],"cited_in_dossiers":[]}