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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
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.
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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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