AlphaFold Protein Structure Database

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alphafold.ebi.ac.uk·alphafold.ebi.ac.uk/

AlphaFold is frequently cited in AI safety contexts as a prominent example of transformative AI capability emerging rapidly; relevant to discussions of capability jumps, beneficial AI, and the dual-use nature of advanced AI systems.

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Importance: 42/100tool pagetool

Summary

AlphaFold DB, developed by Google DeepMind and EMBL-EBI, provides open access to over 200 million AI-predicted protein 3D structures derived from amino acid sequences. It represents a landmark achievement in AI applied to scientific discovery, achieving accuracy competitive with experimental methods. The database covers nearly the entire UniProt protein sequence repository and is freely available to the global research community.

Key Points

•Contains over 200 million protein structure predictions, covering broad swaths of the known protein universe via UniProt integration.
•AlphaFold AI system achieves structure prediction accuracy competitive with experimental methods like X-ray crystallography.
•Ranked top in CASP14 (the critical assessment of protein structure prediction) by a large margin, validating its scientific impact.
•Demonstrates transformative real-world capability of deep learning, relevant to AI safety discussions about rapid capability jumps.
•Open-access database and open-source code represent a case study in responsible deployment of powerful AI systems in science.

Cited by 2 pages

Page	Type	Quality
Scientific Research Capabilities	Capability	68.0
Google DeepMind	Organization	37.0

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[**AlphaFold Protein Structure Database**](https://alphafold.ebi.ac.uk/ "Back to AlphaFold Protein Structure Database homepage")

# [AlphaFold\ \ Protein Structure Database](https://alphafold.ebi.ac.uk/ "Back to AlphaFold Protein Structure Database homepage")

## AlphaFold   Protein Structure Database

Developed by Google DeepMind and EMBL-EBI

Search

Search

Examples: [MENFQKVEKIGEGTYGV...](https://alphafold.ebi.ac.uk/search/sequence/MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREIS) [Free fatty acid receptor 2](https://alphafold.ebi.ac.uk/search/uniprotDescription/Free%20fatty%20acid%20receptor%202) [At1g58602](https://alphafold.ebi.ac.uk/search/text/At1g58602) [Q5VSL9](https://alphafold.ebi.ac.uk/search/text/Q5VSL9) [E. coli](https://alphafold.ebi.ac.uk/search/text/Escherichia%20coli)

[See search help](https://alphafold.ebi.ac.uk/faq#faq-9)[Go to online course](https://www.ebi.ac.uk/training/online/courses/alphafold/)[See our updates – March 2026](https://alphafold.ebi.ac.uk/#news)

AlphaFold DB provides open access to over 200 million protein structure predictions to accelerate scientific research.

### Background

[AlphaFold](https://deepmind.com/blog/article/putting-the-power-of-alphafold-into-the-worlds-hands) is an AI system developed by [Google DeepMind](https://deepmind.google/) 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](http://www.ebi.ac.uk/)) 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](https://www.uniprot.org/uniprotkb) (the standard repository of protein sequences and annotations). We provide individual [downloads](https://alphafold.ebi.ac.uk/download) 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](https://www.expasy.org/resources/uniprotkb-swiss-prot)).

![image1](https://alphafold.ebi.ac.uk/assets/img/Q8I3H7_1.webp)

Q8I3H7: May protect the malaria parasite against attack by the immune system. Mean pLDDT 85.57.

[View protein](https://alphafold.ebi.ac.uk/entry/Q8I3H7)

In [CASP14](https://predictioncenter.org/casp14/zscores_final.cgi), AlphaFold was the top-ranked protein structure prediction method by a large margin, producing predictions with [high accuracy](https://alphafold.ebi.ac.uk/faq#faq-3). While the system still has some [limitations](https://alphafold.ebi.ac.uk/faq#faq-6), 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 [

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