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AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
Abstract 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...
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Published in: | Nucleic acids research 2022-01, Vol.50 (D1), p.D439-D444 |
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container_title | Nucleic acids research |
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creator | 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 |
description | Abstract
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.
Lay Summary
The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an extensive, public database of highly accurate protein structure models. The models are the products of AlphaFold2, an Artificial Intelligence algorithm developed by DeepMind. AlphaFold enabled scientists to investigate an unprecedented number of protein structures. The database we describe here provides access to these predicted models and information on their accuracy. The first version of AlphaFold DB contains over 360,000 models of 21 biologically essential species. |
doi_str_mv | 10.1093/nar/gkab1061 |
format | article |
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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.
Lay Summary
The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an extensive, public database of highly accurate protein structure models. The models are the products of AlphaFold2, an Artificial Intelligence algorithm developed by DeepMind. AlphaFold enabled scientists to investigate an unprecedented number of protein structures. The database we describe here provides access to these predicted models and information on their accuracy. The first version of AlphaFold DB contains over 360,000 models of 21 biologically essential species.</description><identifier>ISSN: 0305-1048</identifier><identifier>ISSN: 1362-4962</identifier><identifier>EISSN: 1362-4962</identifier><identifier>DOI: 10.1093/nar/gkab1061</identifier><identifier>PMID: 34791371</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Amino Acid Sequence ; Animals ; Bacteria - genetics ; Bacteria - metabolism ; Databases, Protein ; Datasets as Topic ; Dictyostelium - genetics ; Dictyostelium - metabolism ; Fungi - genetics ; Fungi - metabolism ; Humans ; Internet ; Models, Molecular ; NAR Breakthrough ; Plants - genetics ; Plants - metabolism ; Protein Conformation, alpha-Helical ; Protein Conformation, beta-Strand ; Protein Folding ; Proteins - chemistry ; Proteins - genetics ; Proteins - metabolism ; Software ; Trypanosoma cruzi - genetics ; Trypanosoma cruzi - metabolism</subject><ispartof>Nucleic acids research, 2022-01, Vol.50 (D1), p.D439-D444</ispartof><rights>The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. 2022</rights><rights>The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c459t-77ea1792ab545aa687323d90033b5f88500eb0b6667b7c51f514543a63be01873</citedby><cites>FETCH-LOGICAL-c459t-77ea1792ab545aa687323d90033b5f88500eb0b6667b7c51f514543a63be01873</cites><orcidid>0000-0001-8314-8497 ; 0000-0002-3687-0839 ; 0000-0002-8439-5964 ; 0000-0002-9043-7665</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728224/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728224/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,1604,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34791371$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Varadi, Mihaly</creatorcontrib><creatorcontrib>Anyango, Stephen</creatorcontrib><creatorcontrib>Deshpande, Mandar</creatorcontrib><creatorcontrib>Nair, Sreenath</creatorcontrib><creatorcontrib>Natassia, Cindy</creatorcontrib><creatorcontrib>Yordanova, Galabina</creatorcontrib><creatorcontrib>Yuan, David</creatorcontrib><creatorcontrib>Stroe, Oana</creatorcontrib><creatorcontrib>Wood, Gemma</creatorcontrib><creatorcontrib>Laydon, Agata</creatorcontrib><creatorcontrib>Žídek, Augustin</creatorcontrib><creatorcontrib>Green, Tim</creatorcontrib><creatorcontrib>Tunyasuvunakool, Kathryn</creatorcontrib><creatorcontrib>Petersen, Stig</creatorcontrib><creatorcontrib>Jumper, John</creatorcontrib><creatorcontrib>Clancy, Ellen</creatorcontrib><creatorcontrib>Green, Richard</creatorcontrib><creatorcontrib>Vora, Ankur</creatorcontrib><creatorcontrib>Lutfi, Mira</creatorcontrib><creatorcontrib>Figurnov, Michael</creatorcontrib><creatorcontrib>Cowie, Andrew</creatorcontrib><creatorcontrib>Hobbs, Nicole</creatorcontrib><creatorcontrib>Kohli, Pushmeet</creatorcontrib><creatorcontrib>Kleywegt, Gerard</creatorcontrib><creatorcontrib>Birney, Ewan</creatorcontrib><creatorcontrib>Hassabis, Demis</creatorcontrib><creatorcontrib>Velankar, Sameer</creatorcontrib><title>AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models</title><title>Nucleic acids research</title><addtitle>Nucleic Acids Res</addtitle><description>Abstract
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.
Lay Summary
The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an extensive, public database of highly accurate protein structure models. The models are the products of AlphaFold2, an Artificial Intelligence algorithm developed by DeepMind. AlphaFold enabled scientists to investigate an unprecedented number of protein structures. The database we describe here provides access to these predicted models and information on their accuracy. The first version of AlphaFold DB contains over 360,000 models of 21 biologically essential species.</description><subject>Amino Acid Sequence</subject><subject>Animals</subject><subject>Bacteria - genetics</subject><subject>Bacteria - metabolism</subject><subject>Databases, Protein</subject><subject>Datasets as Topic</subject><subject>Dictyostelium - genetics</subject><subject>Dictyostelium - metabolism</subject><subject>Fungi - genetics</subject><subject>Fungi - metabolism</subject><subject>Humans</subject><subject>Internet</subject><subject>Models, Molecular</subject><subject>NAR Breakthrough</subject><subject>Plants - genetics</subject><subject>Plants - metabolism</subject><subject>Protein Conformation, alpha-Helical</subject><subject>Protein Conformation, beta-Strand</subject><subject>Protein Folding</subject><subject>Proteins - chemistry</subject><subject>Proteins - genetics</subject><subject>Proteins - metabolism</subject><subject>Software</subject><subject>Trypanosoma cruzi - genetics</subject><subject>Trypanosoma cruzi - metabolism</subject><issn>0305-1048</issn><issn>1362-4962</issn><issn>1362-4962</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><recordid>eNp9kc1u1DAUhS0EokNhxxp5BwtC_RsnLJCq0kKlSiABa-vGufmBJA52MjAP0Peu0UwruunmenE_fz7WIeQlZ-84K-XJBOGk_QUVZzl_RDZc5iJTZS4ekw2TTGecqeKIPIvxJ2Ncca2ekiOpTMml4RtyfTrMHVz4oaZfg1-wn-i3JaxuWQPSj7BABRHf0xFi7Lc47Cj-nWGq-6mlS4c0HlgYqPNbDNAi9Q2d96os4u8VJ5e4GdL80y8d7fq2y8C5dMnt6OhrHOJz8qSBIeKLw3lMflycfz_7nF19-XR5dnqVOaXLJTMGgZtSQKWVBsgLI4WsS8akrHRTFJoxrFiV57mpjNO80VxpJSGXFTKe6GPyYe-d12rE2uG0pOh2Dv0IYWc99Pb-Zuo72_qtLYwohFBJ8OYgCD59LS527KPDYYAJ_Rqt0GXJjMhFmdC3e9QFH2PA5u4Zzuy_5mxqzt42l_BX_0e7g2-rSsDrPeDX-WHVDQX2pgs</recordid><startdate>20220107</startdate><enddate>20220107</enddate><creator>Varadi, Mihaly</creator><creator>Anyango, Stephen</creator><creator>Deshpande, Mandar</creator><creator>Nair, Sreenath</creator><creator>Natassia, Cindy</creator><creator>Yordanova, Galabina</creator><creator>Yuan, David</creator><creator>Stroe, Oana</creator><creator>Wood, Gemma</creator><creator>Laydon, Agata</creator><creator>Žídek, Augustin</creator><creator>Green, Tim</creator><creator>Tunyasuvunakool, Kathryn</creator><creator>Petersen, Stig</creator><creator>Jumper, John</creator><creator>Clancy, Ellen</creator><creator>Green, Richard</creator><creator>Vora, Ankur</creator><creator>Lutfi, Mira</creator><creator>Figurnov, Michael</creator><creator>Cowie, Andrew</creator><creator>Hobbs, Nicole</creator><creator>Kohli, Pushmeet</creator><creator>Kleywegt, Gerard</creator><creator>Birney, Ewan</creator><creator>Hassabis, Demis</creator><creator>Velankar, Sameer</creator><general>Oxford University Press</general><scope>TOX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-8314-8497</orcidid><orcidid>https://orcid.org/0000-0002-3687-0839</orcidid><orcidid>https://orcid.org/0000-0002-8439-5964</orcidid><orcidid>https://orcid.org/0000-0002-9043-7665</orcidid></search><sort><creationdate>20220107</creationdate><title>AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models</title><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></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c459t-77ea1792ab545aa687323d90033b5f88500eb0b6667b7c51f514543a63be01873</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Amino Acid Sequence</topic><topic>Animals</topic><topic>Bacteria - genetics</topic><topic>Bacteria - metabolism</topic><topic>Databases, Protein</topic><topic>Datasets as Topic</topic><topic>Dictyostelium - genetics</topic><topic>Dictyostelium - metabolism</topic><topic>Fungi - genetics</topic><topic>Fungi - metabolism</topic><topic>Humans</topic><topic>Internet</topic><topic>Models, Molecular</topic><topic>NAR Breakthrough</topic><topic>Plants - genetics</topic><topic>Plants - metabolism</topic><topic>Protein Conformation, alpha-Helical</topic><topic>Protein Conformation, beta-Strand</topic><topic>Protein Folding</topic><topic>Proteins - chemistry</topic><topic>Proteins - genetics</topic><topic>Proteins - metabolism</topic><topic>Software</topic><topic>Trypanosoma cruzi - genetics</topic><topic>Trypanosoma cruzi - metabolism</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Varadi, Mihaly</creatorcontrib><creatorcontrib>Anyango, Stephen</creatorcontrib><creatorcontrib>Deshpande, Mandar</creatorcontrib><creatorcontrib>Nair, Sreenath</creatorcontrib><creatorcontrib>Natassia, Cindy</creatorcontrib><creatorcontrib>Yordanova, Galabina</creatorcontrib><creatorcontrib>Yuan, David</creatorcontrib><creatorcontrib>Stroe, Oana</creatorcontrib><creatorcontrib>Wood, Gemma</creatorcontrib><creatorcontrib>Laydon, Agata</creatorcontrib><creatorcontrib>Žídek, Augustin</creatorcontrib><creatorcontrib>Green, Tim</creatorcontrib><creatorcontrib>Tunyasuvunakool, Kathryn</creatorcontrib><creatorcontrib>Petersen, Stig</creatorcontrib><creatorcontrib>Jumper, John</creatorcontrib><creatorcontrib>Clancy, Ellen</creatorcontrib><creatorcontrib>Green, Richard</creatorcontrib><creatorcontrib>Vora, Ankur</creatorcontrib><creatorcontrib>Lutfi, Mira</creatorcontrib><creatorcontrib>Figurnov, Michael</creatorcontrib><creatorcontrib>Cowie, Andrew</creatorcontrib><creatorcontrib>Hobbs, Nicole</creatorcontrib><creatorcontrib>Kohli, Pushmeet</creatorcontrib><creatorcontrib>Kleywegt, Gerard</creatorcontrib><creatorcontrib>Birney, Ewan</creatorcontrib><creatorcontrib>Hassabis, Demis</creatorcontrib><creatorcontrib>Velankar, Sameer</creatorcontrib><collection>Oxford Open Access Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Nucleic acids research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Varadi, Mihaly</au><au>Anyango, Stephen</au><au>Deshpande, Mandar</au><au>Nair, Sreenath</au><au>Natassia, Cindy</au><au>Yordanova, Galabina</au><au>Yuan, David</au><au>Stroe, Oana</au><au>Wood, Gemma</au><au>Laydon, Agata</au><au>Žídek, Augustin</au><au>Green, Tim</au><au>Tunyasuvunakool, Kathryn</au><au>Petersen, Stig</au><au>Jumper, John</au><au>Clancy, Ellen</au><au>Green, Richard</au><au>Vora, Ankur</au><au>Lutfi, Mira</au><au>Figurnov, Michael</au><au>Cowie, Andrew</au><au>Hobbs, Nicole</au><au>Kohli, Pushmeet</au><au>Kleywegt, Gerard</au><au>Birney, Ewan</au><au>Hassabis, Demis</au><au>Velankar, Sameer</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models</atitle><jtitle>Nucleic acids research</jtitle><addtitle>Nucleic Acids Res</addtitle><date>2022-01-07</date><risdate>2022</risdate><volume>50</volume><issue>D1</issue><spage>D439</spage><epage>D444</epage><pages>D439-D444</pages><issn>0305-1048</issn><issn>1362-4962</issn><eissn>1362-4962</eissn><abstract>Abstract
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.
Lay Summary
The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an extensive, public database of highly accurate protein structure models. The models are the products of AlphaFold2, an Artificial Intelligence algorithm developed by DeepMind. AlphaFold enabled scientists to investigate an unprecedented number of protein structures. The database we describe here provides access to these predicted models and information on their accuracy. The first version of AlphaFold DB contains over 360,000 models of 21 biologically essential species.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>34791371</pmid><doi>10.1093/nar/gkab1061</doi><orcidid>https://orcid.org/0000-0001-8314-8497</orcidid><orcidid>https://orcid.org/0000-0002-3687-0839</orcidid><orcidid>https://orcid.org/0000-0002-8439-5964</orcidid><orcidid>https://orcid.org/0000-0002-9043-7665</orcidid><oa>free_for_read</oa></addata></record> |
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language | eng |
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source | PubMed Central; Oxford Open Access Journals |
subjects | Amino Acid Sequence Animals Bacteria - genetics Bacteria - metabolism Databases, Protein Datasets as Topic Dictyostelium - genetics Dictyostelium - metabolism Fungi - genetics Fungi - metabolism Humans Internet Models, Molecular NAR Breakthrough Plants - genetics Plants - metabolism Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Protein Folding Proteins - chemistry Proteins - genetics Proteins - metabolism Software Trypanosoma cruzi - genetics Trypanosoma cruzi - metabolism |
title | AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models |
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