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AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known pr...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728224/ https://www.ncbi.nlm.nih.gov/pubmed/34791371 http://dx.doi.org/10.1093/nar/gkab1061 |
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author | Varadi, Mihaly Anyango, Stephen Deshpande, Mandar Nair, Sreenath Natassia, Cindy Yordanova, Galabina Yuan, David Stroe, Oana Wood, Gemma Laydon, Agata Žídek, Augustin Green, Tim Tunyasuvunakool, Kathryn Petersen, Stig Jumper, John Clancy, Ellen Green, Richard Vora, Ankur Lutfi, Mira Figurnov, Michael Cowie, Andrew Hobbs, Nicole Kohli, Pushmeet Kleywegt, Gerard Birney, Ewan Hassabis, Demis Velankar, Sameer |
author_facet | Varadi, Mihaly Anyango, Stephen Deshpande, Mandar Nair, Sreenath Natassia, Cindy Yordanova, Galabina Yuan, David Stroe, Oana Wood, Gemma Laydon, Agata Žídek, Augustin Green, Tim Tunyasuvunakool, Kathryn Petersen, Stig Jumper, John Clancy, Ellen Green, Richard Vora, Ankur Lutfi, Mira Figurnov, Michael Cowie, Andrew Hobbs, Nicole Kohli, Pushmeet Kleywegt, Gerard Birney, Ewan Hassabis, Demis Velankar, Sameer |
author_sort | Varadi, Mihaly |
collection | PubMed |
description | The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set. |
format | Online Article Text |
id | pubmed-8728224 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-87282242022-01-05 AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models Varadi, Mihaly Anyango, Stephen Deshpande, Mandar Nair, Sreenath Natassia, Cindy Yordanova, Galabina Yuan, David Stroe, Oana Wood, Gemma Laydon, Agata Žídek, Augustin Green, Tim Tunyasuvunakool, Kathryn Petersen, Stig Jumper, John Clancy, Ellen Green, Richard Vora, Ankur Lutfi, Mira Figurnov, Michael Cowie, Andrew Hobbs, Nicole Kohli, Pushmeet Kleywegt, Gerard Birney, Ewan Hassabis, Demis Velankar, Sameer Nucleic Acids Res NAR Breakthrough Article The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set. Oxford University Press 2021-11-17 /pmc/articles/PMC8728224/ /pubmed/34791371 http://dx.doi.org/10.1093/nar/gkab1061 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | NAR Breakthrough Article Varadi, Mihaly Anyango, Stephen Deshpande, Mandar Nair, Sreenath Natassia, Cindy Yordanova, Galabina Yuan, David Stroe, Oana Wood, Gemma Laydon, Agata Žídek, Augustin Green, Tim Tunyasuvunakool, Kathryn Petersen, Stig Jumper, John Clancy, Ellen Green, Richard Vora, Ankur Lutfi, Mira Figurnov, Michael Cowie, Andrew Hobbs, Nicole Kohli, Pushmeet Kleywegt, Gerard Birney, Ewan Hassabis, Demis Velankar, Sameer AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models |
title | AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models |
title_full | AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models |
title_fullStr | AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models |
title_full_unstemmed | AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models |
title_short | AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models |
title_sort | alphafold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models |
topic | NAR Breakthrough Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728224/ https://www.ncbi.nlm.nih.gov/pubmed/34791371 http://dx.doi.org/10.1093/nar/gkab1061 |
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