Background
AlphaFold is an AI system developed by Google DeepMind that predicts a protein’s 3D structure from its amino acid sequence. It regularly achieves accuracy competitive with experiment.
Google DeepMind and EMBL’s European Bioinformatics Institute (EMBL-EBI) have partnered to create AlphaFold DB to make these predictions freely available to the scientific community. The latest database release contains over 200 million entries, providing broad coverage of UniProt (the standard repository of protein sequences and annotations). We provide individual downloads for the human proteome and for the proteomes of 47 other key organisms important in research and global health. We also provide a download for the manually curated subset of UniProt (Swiss-Prot).
Q8I3H7: May protect the malaria parasite against attack by the immune system. Mean pLDDT 85.57.
In CASP14, AlphaFold was the top-ranked protein structure prediction method by a large margin, producing predictions with high accuracy. While the system still has some limitations, the CASP results suggest AlphaFold has immediate potential to help us understand the structure of proteins and advance biological research.
Let us know how the AlphaFold Protein Structure Database has been useful in your research, or if you have questions not answered in the FAQs, at alphafold@deepmind.com.
If your use case isn't covered by the database, you can generate your own AlphaFold predictions using this open source code, which also supports multimer prediction.
What’s new?
Protein complexes - March 2026
A new collaboration between EMBL’s European Bioinformatics Institute (EMBL-EBI), Google DeepMind, NVIDIA, and Seoul National University has made millions of AI-predicted protein complex structures openly available through the AlphaFold Database. This is the largest dataset of protein complex predictions currently available.
To maximise global health impact, the dataset prioritises proteins important for understanding human health and disease, focusing on 20 of the most studied species, including humans, as well as the World Health Organization’s priority pathogens list.
AF-0000000066503175: Homodimer of Transcription elongation factor Eaf N-terminal domain-containing protein
What’s next?
We plan to continue updating the database with structures for newly discovered protein sequences, and to improve features and functionality in response to user feedback. Please follow Google DeepMind's and EMBL-EBI’s social channels for updates.
Licence and attributions
Data is available for academic and commercial use, under a CC-BY-4.0 licence.
EMBL-EBI expects attribution (e.g., in publications, services, or products) for any of its online services, databases, or software in accordance with good scientific practice.
If you use this resource, please cite the following papers:
Note: A given structure may be associated with further publications. For authoritative information regarding the relevant publication(s), please consult the dataset collection or the structure metadata provided on the entry page.
If you use data from AlphaMissense in your work, please cite the following paper:
AlphaFold Data Provided by GDM:
AlphaFold Data Copyright (2022) DeepMind Technologies Limited.
For AlphaFold Data provided by third party data providers:
- Kinetoplastid data copyright (2025) Wheeler Lab
Wheeler RJ. A resource for improved predictions of Trypanosoma and Leishmania protein three-dimensional structure. PLoS One (2021) - AllTheBacteria data copyright (2025) AllTheBacteria Consortium
Hunt M, Lima L, Anderson D, Bouras G, Hall M, Hawkey J, Schwengers O, Shen W, Lees JA, Zamin Iqbal Z. BioRXiV (2025) - A BFVD Data Copyright (2025) The BFVD Development Team
Kim, RS et al. BFVD—a large repository of predicted viral protein structures. NAR (2024) - Viro3D dataset (2025)
Litvin, U et al. Viro3D: a comprehensive database of virus protein structure predictions. Mol Syst Biol (2025)
AlphaMissense Copyright (2023) DeepMind Technologies Limited.
EMBL-EBI training
Recorded webinar
Accessing and interpreting predicted protein structures from AlphaFold database
Online tutorial
AlphaFold
