Open Access
Nucleic Acids Research, volume 50, issue D1, pages D439-D444
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
Mihaly Varadi
1
,
Stephen Anyango
1
,
Mandar Deshpande
1
,
Sreenath Nair
1
,
Cindy Natassia
1
,
Galabina Yordanova
1
,
David Yuan
1
,
Oana Stroe
1
,
Gemma Wood
1
,
Agata Laydon
2
,
Augustin Žídek
2
,
Timothy F G Green
2
,
Kathryn Tunyasuvunakool
2
,
Stig Petersen
2
,
John M. Jumper
2
,
Ellen Clancy
2
,
Richard Green
2
,
Ankur Vora
2
,
Mira Lutfi
2
,
Michael Figurnov
2
,
Andrew Cowie
2
,
Nicole Hobbs
2
,
Pushmeet Kohli
2
,
Gerard J. Kleywegt
1
,
Ewan Birney
1
,
D. Hassabis
2
,
Publication type: Journal Article
Publication date: 2021-11-17
Journal:
Nucleic Acids Research
scimago Q1
SJR: 7.048
CiteScore: 27.1
Impact factor: 16.6
ISSN: 03051048, 13624962
PubMed ID:
34791371
Genetics
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.
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