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Nature

Resumen/Descripción – provisto por la editorial en inglés

Nature is a weekly international journal publishing the finest peer-reviewed research in all fields of science and technology on the basis of its originality, importance, interdisciplinary interest, timeliness, accessibility, elegance and surprising conclusions. Nature also provides rapid, authoritative, insightful and arresting news and interpretation of topical and coming trends affecting science, scientists and the wider public.

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Información

Tipo de recurso:

revistas

ISSN impreso

0028-0836

ISSN electrónico

1476-4687

Editor responsable

Springer Nature

País de edición

Reino Unido

Fecha de publicación

1869-

Cobertura temática

Ciencias naturales

Ingeniería y tecnología

Ciencias médicas y de la salud

Ciencias agrícolas y veterinarias

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doi: 10.1038/s41586-021-03828-1

Highly accurate protein structure prediction for the human proteome

Kathryn Tunyasuvunakool; Jonas Adler; Zachary Wu; Tim Green; Michal Zielinski; Augustin Žídek; Alex Bridgland; Andrew Cowie; Clemens Meyer; Agata Laydon; Sameer Velankar; Gerard J. Kleywegt; Alex Bateman; Richard Evans; Alexander Pritzel; Michael Figurnov; Olaf Ronneberger; Russ Bates; Simon A. A. Kohl; Anna Potapenko; Andrew J. Ballard; Bernardino Romera-Paredes; Stanislav Nikolov; Rishub Jain; Ellen Clancy; David Reiman; Stig Petersen; Andrew W. Senior; Koray Kavukcuoglu; Ewan Birney; Pushmeet Kohli; John Jumper; Demis Hassabis

<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 toolÂ that will allow new questions to be addressed from a structural perspective.</jats:p>

Palabras clave: Multidisciplinary.

Pp. 590-596