書誌事項
- 公開日
- 2021-07-22
- 権利情報
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- https://creativecommons.org/licenses/by/4.0
- https://creativecommons.org/licenses/by/4.0
- DOI
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- 10.1038/s41586-021-03828-1
- 公開者
- Springer Science and Business Media LLC
この論文をさがす
説明
<jats:title>Abstract</jats:title> <jats:p> 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 <jats:sup>1</jats:sup> . Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold <jats:sup>2</jats:sup> , 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. </jats:p>
収録刊行物
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- Nature
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Nature 596 (7873), 590-596, 2021-07-22
Springer Science and Business Media LLC
