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Tunyasuvunakool K, Adler J, Wu Z, Green T, Zielinski M, Žídek A, Bridgland A, Cowie A, Meyer C, Laydon A, Velankar S, Kleywegt GJ, Bateman A, Evans R, Pritzel A, Figurnov M, Ronneberger O, Bates R, Kohl SAA, Potapenko A, Ballard AJ, Romera-Paredes B, Nikolov S, Jain R, Clancy E, Reiman D, Petersen S, Senior AW, Kavukcuoglu K, Birney E, Kohli P, Jumper J, Hassabis D. Highly accurate protein structure prediction for the human proteome. Nature 2021; 596:590-596. [PMID: 34293799 PMCID: PMC8387240 DOI: 10.1038/s41586-021-03828-1] [Citation(s) in RCA: 1441] [Impact Index Per Article: 480.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 07/16/2021] [Indexed: 02/07/2023]
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.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Gerard J Kleywegt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
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Fleischman NM, Das D, Kumar A, Xu Q, Chiu HJ, Jaroszewski L, Knuth MW, Klock HE, Miller MD, Elsliger MA, Godzik A, Lesley SA, Deacon AM, Wilson IA, Toney MD. Molecular characterization of novel pyridoxal-5'-phosphate-dependent enzymes from the human microbiome. Protein Sci 2014; 23:1060-76. [PMID: 24888348 DOI: 10.1002/pro.2493] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2014] [Revised: 05/27/2014] [Accepted: 05/27/2014] [Indexed: 11/10/2022]
Abstract
Pyridoxal-5'-phosphate or PLP, the active form of vitamin B6, is a highly versatile cofactor that participates in a large number of mechanistically diverse enzymatic reactions in basic metabolism. PLP-dependent enzymes account for ∼1.5% of most prokaryotic genomes and are estimated to be involved in ∼4% of all catalytic reactions, making this an important class of enzymes. Here, we structurally and functionally characterize three novel PLP-dependent enzymes from bacteria in the human microbiome: two are from Eubacterium rectale, a dominant, nonpathogenic, fecal, Gram-positive bacteria, and the third is from Porphyromonas gingivalis, which plays a major role in human periodontal disease. All adopt the Type I PLP-dependent enzyme fold and structure-guided biochemical analysis enabled functional assignments as tryptophan, aromatic, and probable phosphoserine aminotransferases.
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Koonin EV. New variants of known folds: do they bring new biology? Acta Crystallogr Sect F Struct Biol Cryst Commun 2010; 66:1226-9. [PMID: 20944215 PMCID: PMC2954209 DOI: 10.1107/s1744309110013242] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2010] [Accepted: 04/09/2010] [Indexed: 05/30/2023]
Abstract
New distinct versions of known protein folds provide a powerful means of protein-function prediction that complements sequence and genomic context analysis. These structures do not supplant direct biochemical experiments, but are indispensable for the complete characterization of proteins.
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Affiliation(s)
- Eugene V Koonin
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, Maryland 20894, USA.
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