AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
Problem
Structural coverage of known protein-sequence space was limited; the paper delivers an openly accessible database of high-accuracy predicted protein structures.
Why it matters
Open access to high-accuracy structure predictions expands structural coverage of protein-sequence space at unprecedented scale.
What the AI did
AlphaFold v2.0 predicts atomic coordinates with per-residue and pairwise confidence estimates, served via an open database with programmatic access and interactive visualization.
Results
Initial release contains over 360,000 predicted structures across 21 model-organism proteomes, planned to expand to over 100 million UniRef90 sequences.
Impact
Enabled an unprecedented expansion of structural coverage of known protein-sequence space, openly accessible.
Record
| Venue | Nucleic Acids Research |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Mihály Váradi, Stephen Anyango, Mandar Deshpande, Sreenath Sasidharan Nair, Cindy Natassia, Galabina Yordanova, David Yu Yuan, Oana Stroe et al. |
| Citations | 7,782 (OpenAlex, at import time) |
| References | 24 |
| Model or method | AlphaFold v2.0 |
| Organization | DeepMind |
| Primary source | https://doi.org/10.1093/nar/gkab1061 |
| Open access | gold |
| Keywords | AlphaFold, protein structure, database, DeepMind, proteome, prediction |
Importance rating
major — millions of lives, an industry or a field
Abstract states unprecedented expansion of structural coverage of known protein-sequence space, openly accessible; foundational for structural biology with 360,000+ structures and 100M+ sequences planned.
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