AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences
By Mihaly Varadi, Damian Bertoni, Paulyna Magana, Urmila Paramval, Ivanna Pidruchna, Mala L. Radhakrishnan, M. Tsenkov, S. Nair, Milot Mirdita, Jingi Yeo and 13 more. Nucleic Acids Research, 2023
1,666 citations recorded by the source corpus.
Abstract The AlphaFold Database Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled by the groundbreaking AlphaFold2 artificial intelligence (AI) system, the predictions archived in AlphaFold DB have been integrated into primary data resources such as PDB, UniProt, Ensembl, InterPro and MobiDB. Our manuscript details subsequent enhancements in data archiving, covering successive releases encompassing model organisms, global health proteomes, Swiss-Prot integration, and a host of curated protein datasets. We detail the data access mechanisms of AlphaFold DB, from direct file access via FTP to advanced queries using Google Cloud Public Datasets and the programmatic access endpoints of the database. We also discuss the improvements and services added since its initial release, including enhancements to the Predicted Aligned Error viewer, customisation options for the 3D viewer, and improvements in the search engine of AlphaFold DB.
Subfields: Materials Chemistry, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/a938303f20b7fcde2bfdb7ca95ff5b69739430d3
How researchers see this paper
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In Materials Chemistry
- Damiano Piovesan
- Silvio C. E. Tosatto
- Michael Heinzinger
- Burkhard Rost
- Lucy J. Colwell
- Martin Steinegger
- Peer Bork
- Konstantin Weissenow
In Molecular Biology
- Burkhard Rost
- Ivica Letunic
- Peer Bork
- Michael Heinzinger
- Aron Marchler-Bauer
- Damiano Piovesan
- Silvio C. E. Tosatto
- Martin Steinegger
Citations in the corpus
- Bilingual language model for protein sequence and structure (2024, background): “This revolution opens up exciting possibilities to explore the dual nature of proteins - from their one-dimensional (1D) amino acid sequences to their unique three-dimensional (3D) structures.”
- InterPro: the protein sequence classification resource in 2025 (2024, positive): “…and visualisation of AlphaFold predicted structures in InterPro Over 85% of UniProtKB entries now feature predicted protein structures generated by AlphaFold, an artificial intelligence method developed by Google DeepMind that infers protein structures from their amino acid sequences ( 39 ,40 ).”