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. 2022 Jan 7;50(D1):D439-D444.
doi: 10.1093/nar/gkab1061.

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 , Tim Green  2 , Kathryn Tunyasuvunakool  2 , Stig Petersen  2 , John 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 Kleywegt  1 , Ewan Birney  1 , Demis Hassabis  2 , Sameer Velankar  1
Affiliations

AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

Mihaly Varadi et al. Nucleic Acids Res. .

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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Figures

Figure 1.
Figure 1.
Searching AlphaFold DB. AlphaFold DB provides a search engine to find proteins of interest based on gene or protein name, UniProt accession or organism name. The search results can be filtered if required and clicking on a protein name leads to the relevant protein-specific entry page.
Figure 2.
Figure 2.
Meta-information and 3D visualization of the AlphaFold structure predictions. The protein-specific web pages display essential metadata for the protein of interest, such as known biological functions and cross-references to UniProt and PDBe-KB. Users can download the predicted models in PDB and mmCIF format, and an interactive molecular viewer visualizes the structure, coloured by the per-residue pLDDT confidence measure.
Figure 3.
Figure 3.
Visualization of Predicted Aligned Errors. Protein-specific pages contain an interactive 2D plot of the PAE values. This tool interacts with the 3D molecular viewer to facilitate the identification of domains whose relative positions and orientations AlphaFold predicts with confidence. In this example (https://alphafold.ebi.ac.uk/entry/Q93074), AlphaFold has high confidence in the relative position of domains at residues 1–500 (green) and residues 1200–1700 (blue), but not with the region between 500–1200 (orange) nor the C-terminus.
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