Evidence map›Paper›PMID 37933859›Full record

ArticleNucleic acids research2024

AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences.

Mihaly Varadi, Damian Bertoni, Paulyna Magana, Urmila Paramval, Ivanna Pidruchna, Malarvizhi Radhakrishnan, Maxim Tsenkov, Sreenath Nair, Milot Mirdita, Jingi Yeo and 13 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,166 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1,166citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1,166 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Accelerated mutator phenotype in a clinicalEmerging microbes & infections · 2026
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  7. AEmerging microbes & infections · 2026
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  12. Review
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1,106 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Mihaly VaradiEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.ORCID 0000-0002-3687-0839
Damian BertoniEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
Paulyna MaganaEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
Urmila ParamvalEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
Ivanna PidruchnaEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
Malarvizhi RadhakrishnanEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
Maxim TsenkovEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
Sreenath NairEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
Milot MirditaSchool of Biological Sciences, Seoul National University, Seoul, South Korea.
Jingi YeoSchool of Biological Sciences, Seoul National University, Seoul, South Korea.
Oleg KovalevskiyGoogle DeepMind, London, UK.
Kathryn TunyasuvunakoolGoogle DeepMind, London, UK.
Agata LaydonGoogle DeepMind, London, UK.
Augustin ŽídekGoogle DeepMind, London, UK.
Hamish TomlinsonGoogle DeepMind, London, UK.
Dhavanthi HariharanGoogle DeepMind, London, UK.
Josh AbrahamsonGoogle DeepMind, London, UK.
Tim GreenGoogle DeepMind, London, UK.
John JumperGoogle DeepMind, London, UK.
Ewan BirneyEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.ORCID 0000-0001-8314-8497
Martin SteineggerSchool of Biological Sciences, Seoul National University, Seoul, South Korea.
Demis HassabisGoogle DeepMind, London, UK.
Sameer VelankarEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.ORCID 0000-0002-8439-5964

Funding

Google DeepMindNational Research Foundation of Korea 2019R1A6A1A10073437National Research Foundation of Korea RS-2023-00250470Samsung DS Research FundSeoul National University
6 · The paper itself

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.

Indexed as

Artificial IntelligenceProtein Structure, SecondaryProteomeAmino Acid SequenceDatabases, ProteinProteinsSearch EngineProteinsProteome

Identifiers

PMID37933859
PMCPMC10767828

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.