AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
Open accessAbstract
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.
AI snapshot
Written from the paper’s full text.
TL;DR
AlphaFold DB is an openly accessible database of high-accuracy protein-structure predictions generated by AlphaFold v2.0, initially containing over 360,000 predicted structures across 21 model-organism proteomes. It provides atomic coordinates, per-residue pLDDT confidence, and pairwise predicted aligned errors through FTP, API, and interactive web pages, with planned expansion to >100 million UniRef90 representative sequences.
Key findings
- The initial release contains over 360,000 predicted structures across 21 model-organism proteomes, including 23,391 human, 55,799 soybean, 43,649 Asian rice, 39,299 maize, 27,434 Arabidopsis, 24,664 zebrafish, 21,615 mouse, and 21,272 rat structures.
- Predictions include atomic coordinates, per-residue confidence pLDDT on a 0–100 scale, and pairwise Predicted Aligned Error (PAE) in Ångströms capped at 31.75 Å; pLDDT bands are ≥90 very high, 90 > pLDDT ≥ 70 confident, 70 > pLDDT ≥ 50 low, and <50 very low.
- Data are available via bulk FTP downloads, a public API keyed on UniProt accessions, and protein-specific web pages with interactive visualization using Mol*; metadata are indexed with Apache Solr.
- Planned expansions aim to exceed 1 million structures, including SwissProt and neglected-disease proteomes, followed by >100 million structures for most UniRef90 representative sequences.
Methods
- Database/resource paper describing AlphaFold DB, created by DeepMind and EMBL-EBI, using AlphaFold v2.0 predictions.
- Initial version predicted most sequences in UniProt reference proteomes for 21 organisms in the 16–2700 amino-acid length range, plus 1400-residue fragments for longer human proteins; sequences with non-standard amino acids were excluded, and multiple isoforms were not provided.
- Data archival includes PDB and mmCIF coordinate files, PAE JSON files, and metadata JSON files; access is through FTP, API, and web pages, with confidence metrics stored in B-factor fields and used for coloring in the 3D viewer.
Limitations
- Stated: the initial release excludes isoforms and sequences with unknown or non-standard amino acids, and predictions are limited to 16–2700 amino acids, with 1400-residue fragments for longer human proteins.
- Stated: PAE is asymmetric and capped at 31.75 Å; high PAE values indicate unreliable relative domain positions/orientations, and pLDDT reflects local confidence rather than global accuracy.
- Evident: this is a database/resource description, not an experimental validation study; initial coverage is limited to 21 proteomes, and the paper does not report new accuracy benchmarks beyond citing AlphaFold/CASP14 and prior human-proteome analyses.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
0 of 24 cited sources checked · 0 supported · 0 flagged
- Findingown data
The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes.
“The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes”
- Also stated in: § IMPLEMENTATION · plain
“The initial version of AlphaFold DB contains over 360 000 predicted structures, corresponding meta-information and confidence metrics.”
- Also stated in: § CONCLUSION AND OUTLOOK · plain
“The first release of AlphaFold DB contains over 360 000 predicted structures from 21 model-organism proteomes.”
Quote in the paperSubstantial claim - Also stated in: § IMPLEMENTATION · plain
- Findingown data
The first release of AlphaFold DB covers the human proteome and those of 20 other model organisms.
“The first release described here covers the human proteome and those of 20 other model organisms (Table 1).”
Quote in the paperNot a substantial claim - Findingnone given
Model confidence can vary significantly along a chain.
“Model confidence can vary significantly along a chain, making it essential to analyse the confidence measures before interpreting structural features.”
Quote in the paperNot a substantial claim - Premisecitation
UniProt archives almost 220 million unique protein sequences.
“However, while the Universal Protein Resource (UniProt) archives almost 220 million unique protein sequences, the Protein Data Bank (PDB) holds only just over 180 000 3D structures for over 55 000 distinct proteins, thus severely limiting the coverage of the sequence space to support biomolecular research globally (5–7).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [5] Bateman, 2021, UniProt: the universal protein knowledgebase in 2021, Nucleic Acids Res. source
Not checked in the free run.
- cites [6] Armstrong, 2019, PDBe: improved findability of macromolecular structure data in the PDB, Nucleic Acids Res.
Not checked in the free run.
- cites [7] Varadi, 2019, PDBe-KB: a community-driven resource for structural and functional annotations, Nucleic Acids Res.
Not checked in the free run.
- Interpretationown data
AlphaFold DB has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space.
“Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space.”
Quote in the paperSubstantial claim
- Interpretationreasoning
The unprecedented accuracy and speed of AlphaFold allowed the creation of an extensive database of structure predictions at a large scale.
“The unprecedented accuracy and speed of AlphaFold allowed the creation of an extensive database of structure predictions at a large scale.”
Quote in the paperSubstantial claim - Premisecitation
In 2020, the organizers of the CASP14 benchmark recognized AlphaFold as a solution to the protein-structure-prediction problem.
“In 2020, the organizers of the CASP14 benchmark recognized AlphaFold as a solution to the protein–structure–prediction problem (12).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [12] Pereira, 2021, High-accuracy protein structure prediction in CASP14, Proteins Struct. Funct. Bioinf. source
Not checked in the free run.
- Premisecitation
Recent advances in electron cryo-microscopy and hybrid and integrative methods have accelerated the pace of structure determination.
“Although recent advances in the field of electron cryo-microscopy and hybrid and integrative methods (I/HM) for structure determination have accelerated the pace of structure determination, the gap between known protein sequences and experimental protein structures continues to expand (6,8).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [6] Armstrong, 2019, PDBe: improved findability of macromolecular structure data in the PDB, Nucleic Acids Res.
Not checked in the free run.
- cites [8] de Oliveira, 2021, Cryo-EM: the resolution revolution and drug discovery, SLAS Discov. source
Not checked in the free run.
- Interpretationreasoning
The coverage of the sequence space by experimental structures is severely limited.
“thus severely limiting the coverage of the sequence space to support biomolecular research globally (5–7).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [5] Bateman, 2021, UniProt: the universal protein knowledgebase in 2021, Nucleic Acids Res. source
Not checked in the free run.
- cites [6] Armstrong, 2019, PDBe: improved findability of macromolecular structure data in the PDB, Nucleic Acids Res.
Not checked in the free run.
- cites [7] Varadi, 2019, PDBe-KB: a community-driven resource for structural and functional annotations, Nucleic Acids Res.
Not checked in the free run.
- Premisecitation
The gap between known protein sequences and experimental protein structures continues to expand.
“Although recent advances in the field of electron cryo-microscopy and hybrid and integrative methods (I/HM) for structure determination have accelerated the pace of structure determination, the gap between known protein sequences and experimental protein structures continues to expand (6,8).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [6] Armstrong, 2019, PDBe: improved findability of macromolecular structure data in the PDB, Nucleic Acids Res.
Not checked in the free run.
- cites [8] de Oliveira, 2021, Cryo-EM: the resolution revolution and drug discovery, SLAS Discov. source
Not checked in the free run.
- Premisereasoning
Achieving higher coverage of the sequence space with experimentally determined high-resolution structures is very labour-intensive.
“Achieving a higher coverage of the sequence space with experimentally determined high-resolution structures is very labour-intensive.”
Quote in the paperSubstantial claim - Interpretationreasoning
We only know the 3D structure of a tiny fraction of all proteins currently known to science.
“Thus, we only know the 3D structure of a tiny fraction of all proteins currently known to science.”
Quote in the paperSubstantial claim
- May combine two claims. 10
- Classified as probably not a substantial claim. 19
May combine two claims.
Findingnone givenAlphaFold DB is an openly accessible, extensive database of high-accuracy protein-structure predictions.
“The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenPredicted structures in AlphaFold DB contain atomic coordinates and per-residue confidence estimates on a scale from 0 to 100, with higher scores corresponding to higher confidence.
“The predicted structures contain atomic coordinates and per-residue confidence estimates on a scale from 0 to 100, with higher scores corresponding to higher confidence.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Findingnone givenAlphaFold DB is powered by AlphaFold v2.0 of DeepMind.
“Powered by AlphaFold v2.0 of DeepMind”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenAlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors.
“AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenAll data in AlphaFold DB are publicly accessible through a cloud-based infrastructure.
“All the data are publicly accessible through a cloud-based infrastructure.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenAlphaFold DB provides access to all entries through a public API endpoint, keyed on a UniProt accession.
“We provide access to all entries through a public API endpoint, keyed on a UniProt accession.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenAlphaFold DB archives and provides access to atomic coordinates in PDB and mmCIF formats, PAEs in JSON format and corresponding metadata in JSON format.
“AlphaFold DB archives and provides access to the atomic coordinates in PDB and mmCIF formats, PAEs in JSON format and corresponding metadata in JSON format.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
FindingcitationEach protein has a dedicated structure page that shows basic information and three separate outputs of the AlphaFold model.
“Each protein has a dedicated structure page that shows basic information (drawn from UniProt (5) and PDBe (6)) and three separate outputs of the AlphaFold model.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [5] Bateman, 2021, UniProt: the universal protein knowledgebase in 2021, Nucleic Acids Res. source
Not checked in the free run.
- cites [6] Armstrong, 2019, PDBe: improved findability of macromolecular structure data in the PDB, Nucleic Acids Res.
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenData files in the AlphaFold DB archive are versioned, and previous snapshots of the data will be available via FTP, but the web pages will always display the latest version.
“The data files in the archive are versioned, and previous snapshots of the data will be available via FTP, but the web pages will always display the latest version.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenAlphaFold DB provides graphical access to and interactive visualization of all the predictions and meta-information for the broader scientific community through web pages.
“AlphaFold DB provides graphical access to and interactive visualization of all the predictions and meta-information for the broader scientific community through web pages.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenThe third output of the AlphaFold model is a pairwise confidence prediction, which helps to assess the reliability of relative domain positions and orientations as well as the global topology of the protein.
“The third output is a pairwise confidence prediction, which helps to assess the reliability of relative domain positions and orientations as well as the global topology of the protein (Figure 3).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenAlphaFold DB's initial dataset contains proteins with sequence lengths of 16-2700 and excludes isoforms and sequences with unknown or non-standard amino acids.
“The data set contains proteins with sequence lengths of 16–2700 and excludes isoforms and sequences with unknown or non-standard amino acids.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
FindingcitationUniProt, Pfam, InterPro and PDBe-KB use the AlphaFold DB API to display AlphaFold models on their web pages.
“UniProt (5), Pfam (18), InterPro (19) and PDBe-KB (7) use this API to display AlphaFold models on their web pages.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [5] Bateman, 2021, UniProt: the universal protein knowledgebase in 2021, Nucleic Acids Res. source
Not checked in the free run.
- cites [18] Mistry, 2021, Pfam: The protein families database in 2021, Nucleic Acids Res. source
Not checked in the free run.
- cites [19] Blum, 2021, The InterPro protein families and domains database: 20 years on, Nucleic Acids Res. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenAlphaFold DB provides predictions through several data-access mechanisms: bulk downloads via FTP; programmatic access via an application programming interface (API); download and interactive visualization of individual predictions on protein-specific web pages keyed on UniProt accessions.
“AlphaFold DB provides predictions through several data-access mechanisms: (i) bulk downloads via FTP; (ii) programmatic access via an application programming interface (API); (iii) download and interactive visualization of individual predictions on protein-specific web pages keyed on UniProt accessions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningIf PAE values (x, y) are low, AlphaFold predicts the domains to have well-defined relative positions and orientations.
“For residues x and y in two different domains, if the PAE values (x, y) are low, AlphaFold predicts the domains to have well-defined relative positions and orientations.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationAlphaFold is an AI system developed by DeepMind that makes state-of-the-art predictions of protein structures from their amino-acid sequences.
“AlphaFold is an AI system developed by DeepMind that makes state-of-the-art predictions of protein structures from their amino-acid sequences (9).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [9] Jumper, 2021, Highly accurate protein structure prediction with AlphaFold, Nature source
Not checked in the free run.
May combine two claims.
PremisecitationThe PDB holds just over 180,000 3D structures for over 55,000 distinct proteins.
“However, while the Universal Protein Resource (UniProt) archives almost 220 million unique protein sequences, the Protein Data Bank (PDB) holds only just over 180 000 3D structures for over 55 000 distinct proteins, thus severely limiting the coverage of the sequence space to support biomolecular research globally (5–7).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [5] Bateman, 2021, UniProt: the universal protein knowledgebase in 2021, Nucleic Acids Res. source
Not checked in the free run.
- cites [6] Armstrong, 2019, PDBe: improved findability of macromolecular structure data in the PDB, Nucleic Acids Res.
Not checked in the free run.
- cites [7] Varadi, 2019, PDBe-KB: a community-driven resource for structural and functional annotations, Nucleic Acids Res.
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningpLDDT reflects local confidence in the structure.
“As a confidence metric based on lDDT, pLDDT also reflects local confidence in the structure, and should be used, for example, to assess confidence within a single domain.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [14] Mariani, 2013, lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests, Bioinformatics source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationVery low confidence pLDDT scores correlate with high propensities for intrinsic disorder.
“It was recently described that very low confidence pLDDT scores correlate with high propensities for intrinsic disorder (17).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [17] Akdel, 2021, A structural biology community assessment of AlphaFold 2 applications Biophysics
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningIf PAE values are high, the relative position and orientation of the two domains are unreliable.
“If the PAE values are high, then the relative position and orientation of the two domains are unreliable, and users should not attach biological or structural relevance to these.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone givenFiguring out the exact structure of a protein remains an expensive and often time-consuming process.
“However, figuring out the exact structure of a protein remains an expensive and often time-consuming process.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe lower confidence bands appear to correlate well with backbone flexibility and intrinsic disorder.
“The lower confidence bands appear to correlate well with backbone flexibility and intrinsic disorder (13) (Figure 2).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [13] Tunyasuvunakool, 2021, Highly accurate protein structure prediction for the human proteome, Nature source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSince the mid-1950s, the scientific community has determined over 180,000 structures of proteins, nucleic acids, and complexes in atomic detail and archived them in the PDB.
“Since the mid-1950s, the scientific community has been using ever-more advanced experimental methods to determine over 180 000 structures of proteins, nucleic acids, and complexes in atomic detail, and archive them in the PDB, the single worldwide archive of experimental macromolecular structure data managed by the wwPDB consortium (21).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [21] wwPDB consortium, 2018, Protein Data Bank: the single global archive for 3D macromolecular structure data, Nucleic Acids Res. source
Not checked in the free run.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The new CC+ database includes both experimentally derived CCs from the RCSB PDB and predicted AlphaFold2 models for 48 proteomes.
“The new CC+ database includes both experimentally derived CCs from the RCSB PDB (Bittrich et al., 2022) and predicted AlphaFold2 models for 48 proteomes (https://alphafold.ebi.ac.uk/; Varadi et al., 2022).”
From <scp> CC <sup>+</sup> </scp> : A searchable database of validated coiled coils in <scp>PDB</scp> structures and <scp>AlphaFold2</scp> models (2023) · cites this paper as Varadi et al., 2022
The Abstract and Data Availability section establish that AlphaFold DB provides predicted protein structures that can be downloaded by proteome. The paper does not describe CC+ or say that CC+ includes models from 48 proteomes; it reports 21 proteomes in AlphaFold DB’s initial release. That initial-release count does not, by itself, contradict a later selection of 48 proteomes for CC+. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe AlphaFold Protein Structure Database contains more than 200 million precomputed AF2 predictions.
“houses over 200 million pre-run AF2 predictions”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [6]
AF2 also produces a PAE matrix that assesses the relative orientation and position of different model parts or domains.
“AF2 also generates a Predicted Aligned Error (PAE) matrix, which evaluates the relative orientation and position of different parts (i.e., domains) of the model6”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [6]
AlphaFold2 provides a per-residue confidence measure that allows researchers to judge the quality of the predicted structure.
“Likewise, AlphaFold2 provides a per-residue confidence measure that allows researchers to judge the quality of the predicted structure.281”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [281]
Over 214 million predicted protein structures are available in the AlphaFold database.
“over 214 million predicted structures are available in the AlphaFold database2”
From Clustering predicted structures at the scale of the known protein universe (2023) · cites this paper as [2]
The European Bioinformatics Institute holds over 214 million structures predicted by AlphaFold2.
“The European Bioinformatics Institute already holds over 214 million structures predicted by AlphaFold2 (ref. 3)”
From Fast and accurate protein structure search with Foldseek (2024) · cites this paper as [3]
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Highly accurate protein structure prediction with AlphaFold
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UniProt: the Universal Protein Knowledgebase in 2023
The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1052 · Cited by 7,210 · see citing works
“Over 85% of UniProt entries now contain a predicted protein structure, provided by AlphaFold, an artificial intelligence system developed by DeepMind that makes predictions of protein structures from their amino-acid sequences (1).”
BackgroundEvolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
UCSF ChimeraX : Tools for structure building and analysis
Meng, E. C., Goddard, T. D., Pettersen, E. F., Couch, G. S., Pearson, Z., Morris, J. H., & Ferrin, T. E. (2023) · Protein Science · doi:10.1002/pro.4792 · Cited by 4,575 · see citing works
“ChimeraX has tools to run new AlphaFold predictions, find existing predictions in the EBI AlphaFold Database (Varadi et al., 2022), and plot error metrics for evaluating model quality.”
MethodPaysan‐Lafosse, T., Blum, M., Chuguransky, S., Grego, T., Pinto, B. L., Salazar, G. A., Bileschi, M. L., Peer Bork, B., Bridge, A. J., Colwell, L. J., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac993 · Cited by 2,371 · see citing works
“DeepMind and EMBL’s European Bioinformatics Institute (EMBL-EBI) have launched the AlphaFold Protein Structure Database (AlphaFold DB) (27), a joint project to openly and freely share millions of AlphaFold protein structure predictions with the scientific community.”
Fast and accurate protein structure search with Foldseek
van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2022) · doi:10.1101/2022.02.07.479398 · Cited by 2,598 · see citing works
Fast and accurate protein structure search with Foldseek
van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01773-0 · Cited by 2,596 · see citing works
Search and sequence analysis tools services from EMBL-EBI in 2022
Madeira, F., Pearce, M., Tivey, A. R., Basutkar, P., Lee, J., Edbali, O., Madhusoodanan, N., Kolesnikov, A., & López, R. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac240 · Cited by 2,272 · see citing works
“IPD-NHKIR (23) coding and genomic sequences, as well as sequences from the AlphaFold DB (24), EMDB (25) and PDBe-KB, have also been made available for sequence similarity search in the JD tools framework and retrieval via Dbfetch.”
Accurate proteome-wide missense variant effect prediction with AlphaMissense
Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum, T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., et al. (2023) · Science · doi:10.1126/science.adg7492 · Cited by 2,163 · see citing works
Self-citationMartin, F. J., Amode, M. R., Aneja, A., Austine-Orimoloye, O., Azov, A. G., Barnes, I., Becker, A., Bennett, R., Berry, A. E., Bhai, J. N. N. T., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac958 · Cited by 980 · see citing works
“Structures from AlphaFold DB (25) for 19 013 human protein-coding genes are now viewable through our new AlphaFold browser widget, accessible via the ‘AlphaFold predicted model’ link in the left-hand menu of any transcript page.”
MethodDali server: structural unification of protein families
Holm, L. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac387 · Cited by 913 · see citing works
“The publically available AlphaFold Database version 1 (AF-DB) covers the Uniprot reference proteomes of human and 20 other key organisms (9).”
Generalized biomolecular modeling and design with RoseTTAFold All-Atom
Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., et al. (2024) · Science · doi:10.1126/science.adl2528 · Cited by 978 · see citing works
The CCP 4 suite: integrative software for macromolecular crystallography
Agirre, J., Atanasova, M., Bagdonas, H., Ballard, C. C., Baslé, A., Beilsten‐Edmands, J., Borges, R. J., Brown, D. G., Burgos-Mármol, J. J., Berrisford, J. M., et al. (2023) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798323003595 · Cited by 930 · see citing works
Self-citationBurley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chao, H., Chen, L., Craig, P., Crichlow, G. V., Dalenberg, K., Duarte, J. M., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1077 · Cited by 903 · see citing works
“At the time of writing, CSMs for nearly every protein sequence represented in UniProt (43) are publicly accessible from AlphaFold DB (41,44,45).”
BackgroundMethodSelf-citationDALI shines a light on remote homologs: One hundred discoveries
Holm, L., Laiho, A., Törönen, P., & Salgado, M. G. (2022) · Protein Science · doi:10.1002/pro.4519 · Cited by 685 · see citing works
“A total of 59% of the Pfam families are represented in AlphaFold Database version 1 (AFDB1), which covers the human proteome as well as the proteomes of other important model organisms (Varadi et al., 2022).”
BackgroundMethodInfluentialA structural biology community assessment of AlphaFold2 applications
Akdel, M., Pires, D. E. V., Pardo, E. P., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00849-w · Cited by 675 · see citing works
Self-citationAutomated model building and protein identification in cryo-EM maps
Jamali, K., Käll, L., Zhang, R., Brown, A., Kimanius, D., & Scheres, S. H. W. (2024) · Nature · doi:10.1038/s41586-024-07215-4 · Cited by 686 · see citing works
Automated model building and protein identification in cryo-EM maps
Jamali, K., Käll, L., Zhang, R., Brown, A., Kimanius, D., & Scheres, S. H. W. (2023) · doi:10.1101/2023.05.16.541002 · Cited by 686 · see citing works
Boltz-2: Towards Accurate and Efficient Binding Affinity PredictionPreprint
Passaro, S., Corso, G., Wohlwend, J., Reveiz, M., Thaler, S., Somnath, V. R., Getz, N., Portnoi, T., Roy, J., Stark, H., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.14.659707 · Cited by 848 · see citing works
The protein corona from nanomedicine to environmental science
Mahmoudi, M., Landry, M. P., Moore, A. V., & Coreas, R. (2023) · Nature Reviews Materials · doi:10.1038/s41578-023-00552-2 · Cited by 659 · see citing works
The HADDOCK2.4 web server for integrative modeling of biomolecular complexes
Honorato, R. V., Trellet, M. E., Jiménez‐García, B., Schaarschmidt, J., Giulini, M., Reys, V., Koukos, P. I., Rodrigues, J., Karaca, E., van Zundert, G. C. P., et al. (2024) · Nature Protocols · doi:10.1038/s41596-024-01011-0 · Cited by 653 · see citing works
AlphaFold2 and its applications in the fields of biology and medicine
Yang, Z., Zeng, X., Zhao, Y. C., & Chen, R. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01381-z · Cited by 606 · see citing works
Bugnon, M., Röhrig, U. F., Goullieux, M., Perez, M. A. S., Daina, A., Michielin, O. A., & Zoete, V. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae300 · Cited by 584 · see citing works
“SwissDock 2024 also provides an advanced target search option in three different structural databases: PDBe ( 26 ), SWISS-MODEL ( 29 ), and the AlphaFold protein structure database ( 28 ,37 ).”
BackgroundMethodPredicting multiple conformations via sequence clustering and AlphaFold2
Wayment-Steele, H. K., Ojoawo, A., Otten, R., Apitz, J. M., Pitsawong, W., Hömberger, M., Ovchinnikov, S., Colwell, L., & Kern, D. (2024) · Nature · doi:10.1038/s41586-023-06832-9 · Cited by 545 · see citing works
Genenames.org: the HGNC resources in 2023
Seal, R. L., Braschi, B., Gray, K. A., Jones, T. E. M., Tweedie, S., Haim-Vilmovsky, L., & Bruford, E. A. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac888 · Cited by 536 · see citing works
Zhou, N., Yuan, X., Du, Q., Zhang, Z., Shi, X., Bao, J., Ning, Y., & Peng, L. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac935 · Cited by 505 · see citing works
The next-generation Open Targets Platform: reimagined, redesigned, rebuilt
Ochoa, D., Hercules, A., Carmona, M., Suveges, D., Baker, J., Malangone, C., Lopez, I., Miranda, A., Cruz-Castillo, C., Fumis, L., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1046 · Cited by 432 · see citing works
“The recent inclusion of the Al-phaFold protein structure predictions in the target profile page allows the identification of potential druggable pock-ets in previously unresolved 3D models (34).”
BackgroundLearning inverse folding from millions of predicted structuresPreprint
Hsu, C., Verkuil, R., Liu, J. J., Lin, Z., Hie, B., Sercu, T., Lerer, A. K., & Rives, A. W. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.10.487779 · Cited by 434 · see citing works
FlyBase: updates to the Drosophila genes and genomes database
Öztürk‐Çolak, A., Marygold, S. J., Antonazzo, G., Attrill, H. L., Goutte-Gattat, D., Jenkins, V. K., Matthews, B. B., Millburn, G., dos Santos, G., Tabone, C. J., et al. (2024) · Genetics · doi:10.1093/genetics/iyad211 · Cited by 417 · see citing works
“To this end, we have added images of 3D protein structures predicted by AlphaFold (Varadi et al .”
MODOMICS: a database of RNA modifications and related information. 2023 update
Cappannini, A., Ray, A., Purta, E., Mukherjee, S., Boccaletto, P., Moafinejad, S. N., Lechner, A., Barchet, C., Klaholz, B. P., Stefaniak, F., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1083 · Cited by 412 · see citing works
“Whenever available, we linked the MODOMICS entries to 3D structural predictions from the Al-phaFold database ( 25 ).”
MethodAhdritz, G., Bouatta, N., Floristean, C., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T. J., Berenberg, D., Fisk, I., Zanichelli, N., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02272-z · Cited by 439 · see citing works
Chai-1: Decoding the molecular interactions of life
Discovery, C., Boitreaud, J., Dent, J., McPartlon, M., Meier, J., Reis, V., Rogozhnikov, A., & Wu, K. (2024) · doi:10.1101/2024.10.10.615955 · Cited by 517 · see citing works
A phosphoinositide signalling pathway mediates rapid lysosomal repair
Tan, J. X., & Finkel, T. (2022) · Nature · doi:10.1038/s41586-022-05164-4 · Cited by 368 · see citing works
Høie, M. H., Kiehl, E. N., Petersen, B., Nielsen, M., Winther, O., Nielsen, H., Hallgren, J., & Marcatili, P. (2022) · Nucleic Acids Res. · doi:10.1093/nar/gkac439 · Cited by 325 · see citing works
Clustering predicted structures at the scale of the known protein universe
Barrio-Hernandez, I., Yeo, J., Jänes, J., Mirdita, M., Gilchrist, C. L. M., Wein, T., Varadi, M., Velankar, S., Beltrao, P., & Steinegger, M. (2023) · Nature · doi:10.1038/s41586-023-06510-w · Cited by 362 · see citing works
“(4) Structural alignment is performed in two stages: first an ungapped alignment based on shared diagonal information is performed, hits are pre-clustered and second the remaining sequences are aligned using Foldseek's structural smithwaterman .”
BackgroundSelf-citationBenchmarking <scp>AlphaFold</scp> for protein complex modeling reveals accuracy determinants
Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G. (2022) · Protein Science · doi:10.1002/pro.4379 · Cited by 335 · see citing works
The Pfam protein families database: embracing AI/ML
Paysan-Lafosse, T., Andreeva, A., Blum, M., Chuguransky, S., Grego, T., Pinto, B. L., Salazar, G. A., Bileschi, M. L., Llinares-López, F., Meng-Papaxanthos, L., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae997 · Cited by 382 · see citing works
“High-accuracy structure predictions, particularly those generated by tools such as AlphaFold ( 5 ), have significantly enhanced our ability to identify and characterize protein families.”
Ren, F., Ding, X., Zheng, M., Korzinkin, M., Cai, X., Zhu, W., Mantsyzov, A., Aliper, A., Aladinskiy, V., Cao, Z., et al. (2023) · Chemical Science · doi:10.1039/d2sc05709c · Cited by 365 · see citing works
Conformational ensembles of the human intrinsically disordered proteome
Tesei, G., Trolle, A. I., Jonsson, N., Betz, J., Knudsen, F. E., Pesce, F., Johansson, K. E., & Lindorff‐Larsen, K. (2024) · Nature · doi:10.1038/s41586-023-07004-5 · Cited by 318 · see citing works
Annotation of biologically relevant ligands in UniProtKB using ChEBI
Coudert, E., Gehant, S., de Castro, E., Pozzato, M., Baratin, D., Neto, T., Sigrist, C. J. A., Redaschi, N., Bridge, A., The UniProt Consortium, et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac793 · Cited by 350 · see citing works
Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants
Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G. (2021) · doi:10.1101/2021.10.23.465575 · Cited by 335 · see citing works
AI-powered therapeutic target discovery
Pun, F. W., Ozerov, I. V., & Zhavoronkov, A. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2023.06.010 · Cited by 341 · see citing works
Before and after AlphaFold2: An overview of protein structure prediction
Bertoline, L. M. F., Lima, A. N., Krieger, J. E., & Teixeira, S. K. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1120370 · Cited by 309 · see citing works
Empowering biomedical discovery with AI agents
Gao, S., Fang, A., Huang, Y., Giunchiglia, V., Noori, A., Schwarz, J. R., Ektefaie, Y., Kondic, J., & Zitnik, M. (2024) · Cell · doi:10.1016/j.cell.2024.09.022 · Cited by 315 · see citing works
Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery
Wong, F., Krishnan, A., Zheng, E. J., Stärk, H., Manson, A. L., Earl, A. M., Jaakkola, T., & Collins, J. J. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211081 · Cited by 284 · see citing works
“As shown in the workflow here, in order to leverage AlphaFold 2 for docking, we first downloaded all 296 AlphaFold 2 -predicted E. coli essential protein structures from the AlphaFold Protein Structure Database (Jumper et al , 2021 ; Varadi et al , 2022 ).”
BackgroundMethodInfluentialVEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center in 2023
Álvarez-Jarreta, J., Amos, B., Aurrecoechea, C., Bah, S. Y., Barba, M., Barreto, A., Basenko, E. Y., Belnap, R., Blevins, A. S., Böhme, U., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1003 · Cited by 257 · see citing works
AlphaFold Protein Structure Database and 3D-Beacons: New Data and Capabilities
Fleming, J., Magaña, P., Nair, S. S., Tsenkov, M. I., Bertoni, D., Pidruchna, I., Afonso, M. Q. L., Midlik, A., Paramval, U., Žídek, A., et al. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.168967 · Cited by 311 · see citing works
Self-citationIntrinsically Disordered Proteins: An Overview
Trivedi, R., & Nagarajaram, H. A. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232214050 · Cited by 253 · see citing works
AlphaFold2 models indicate that protein sequence determines both structure and dynamics
Guo, H.-B., Perminov, A., Bekele, S., Kedziora, G., Farajollahi, S., Varaljay, V., Hinkle, K., Molinero, V., Meister, K., Hung, C., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-14382-9 · Cited by 248 · see citing works
SaProt: Protein Language Modeling with Structure-aware VocabularyPreprint
Su, J., Han, C., Zhou, Y., Shan, J., Zhou, X., & Yuan, F. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.01.560349 · Cited by 251 · see citing works
“Following ESM-2 and BERT, during training, 15% of the SA tokens in each batch are masked.”
BackgroundMethodA Guide to In Silico Drug Design
Chang, Y., Hawkins, B. A., Du, J. J., Groundwater, P. W., Hibbs, D. E., & Lai, F. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics15010049 · Cited by 280 · see citing works
De novo design of protein interactions with learned surface fingerprints
Gaínza, P., Wehrle, S., Hall‐Beauvais, A. V., Marchand, A., Scheck, A., Harteveld, Z., Buckley, S., Ni, D., Tan, S., Sverrisson, F., et al. (2023) · Nature · doi:10.1038/s41586-023-05993-x · Cited by 241 · see citing works
Discovery of deaminase functions by structure-based protein clustering
(2023) · doi:10.1016/j.cell.2023.05.041 · Cited by 240 · see citing works
“5,6 9 These protein structure prediction methods should thus enable large-scale mining and classifications of proteins with specific functions.”
BackgroundSystematic identification of conditionally folded intrinsically disordered regions by AlphaFold2
Alderson, T. R., Pritišanac, I., Kolarić, Đ., Moses, A. M., & Forman-Kay, J. D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304302120 · Cited by 240 · see citing works
“For each structure, the pLDDT scores in the regions of secondary structure range from “high” to “very high” (Figure 2B), suggestive of atomic-level accuracy and an overall high level of confidence in the structural models (Jumper et al. 2021a; Varadi et al. 2021).”
Structural and mechanistic insights into fungal β-1,3-glucan synthase FKS1
Hu, X., Yang, P., Chai, C., Liu, J., Sun, H., Wu, Y., Zhang, M., Zhang, M., Liu, X., & Yu, H. (2023) · Nature · doi:10.1038/s41586-023-05856-5 · Cited by 213 · see citing works
Fanzor is a eukaryotic programmable RNA-guided endonuclease
Saito, M., Xu, P., Faure, G., Maguire, S., Kannan, S., Altae-Tran, H., Vo, C.-D.-T., Desimone, A., Macrae, R. K., & Zhang, F. (2023) · Nature · doi:10.1038/s41586-023-06356-2 · Cited by 215 · see citing works
New insights into protein–protein interaction modulators in drug discovery and therapeutic advance
Nada, H., Choi, Y., Kim, S.-D., Jeong, K. S., Meanwell, N. A., & Lee, K. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-02036-3 · Cited by 233 · see citing works
Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery
Buniello, A., Süveges, D., Cruz-Castillo, C., Llinares, M. B., Cornu, H., López, I., Tsukanov, K., Roldán‐Romero, J. M., Mehta, C., Fumis, L., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1128 · Cited by 239 · see citing works
“This new resource, funded through an Open Targets project ( 35 ) (Figure 2 A), includes predicted effects of missense variation based on AlphaMissense ( 36 ), interaction interfaces derived from AlphaFold ( 37 ) and protein abundance and stability based on protein structures using FoldX ( 38 ).”
GPCRdb in 2023: state-specific structure models using AlphaFold2 and new ligand resources
Pándy‐Szekeres, G., Caroli, J., Mamyrbekov, A., Kermani, A. A., Keserű, G. M., Kooistra, A. J., & Gloriam, D. E. I. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1013 · Cited by 205 · see citing works
How good are AlphaFold models for docking-based virtual screening?
Scardino, V., Di Filippo, J. I., & Cavasotto, C. N. (2023) · iScience · doi:10.1016/j.isci.2022.105920 · Cited by 207 · see citing works
“AF-modeled structures were obtained from the AlphaFold Protein Structure Database.(30) Four docking programs were used, AutoDock 4, ICM, rDock, and PLANTS, which have different search algorithms and scoring functions.”
MethodInfluentialMachine Learning-Guided Protein Engineering
Kouba, P., Kohout, P., Haddadi, F., Bushuiev, A., Samusevich, R., Sedlář, J., Damborský, J., Pluskal, T., Šivic, J., & Mazurenko, S. (2023) · ACS Catalysis · doi:10.1021/acscatal.3c02743 · Cited by 214 · see citing works
Elfmann, C., & Stülke, J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad350 · Cited by 191 · see citing works
Jakubec, D., Škoda, P., Krivák, R., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac389 · Cited by 203 · see citing works
Visualization of translation and protein biogenesis at the ER membrane
Gemmer, M., Chaillet, M. L., van Loenhout, J., Arenas, R. C., Vismpas, D., Gröllers‐Mulderij, M., Koh, F., Albanese, P., Scheltema, R. A., Howes, S. C., et al. (2023) · Nature · doi:10.1038/s41586-022-05638-5 · Cited by 176 · see citing works
ELM—the Eukaryotic Linear Motif resource—2024 update
Kumar, M., Michael, S., Alvarado-Valverde, J., Zeke, A., Lázár, T., Glavina, J., Nagy‐Kanta, E., Donagh, J. M., Kálmán, Z. E., Pascarelli, S., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1058 · Cited by 185 · see citing works
“ELM annotators now routinely use available structure predictions from the AlphaFold database ( 108 ,109 ) as they aid assessing SLiM instance plausibility, taking advantage of properties such as estimated disorderliness (a low pLDDT score) ( 109 ).”
MethodTransformer-based deep learning for predicting protein properties in the life sciences
Chandra, A. A., Tünnermann, L., Löfstedt, T., & Gratz, R. (2023) · eLife · doi:10.7554/elife.82819 · Cited by 192 · see citing works
Modeling conformational states of proteins with AlphaFold
Sala, D., Engelberger, F., Mchaourab, H. S., & Meiler, J. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102645 · Cited by 182 · see citing works
DHODH inhibitors sensitize to ferroptosis by FSP1 inhibition
Mishima, E., Nakamura, T., Zheng, J., Zhang, W., Mourão, A., Sennhenn, P., & Conrad, M. (2023) · Nature · doi:10.1038/s41586-023-06269-0 · Cited by 205 · see citing works
Protein–Ligand Docking in the Machine-Learning Era
Yang, C., Chen, E. A., & Zhang, Y. (2022) · Molecules · doi:10.3390/molecules27144568 · Cited by 206 · see citing works
Expanding PROTACtable genome universe of E3 ligases
Liu, Y., Yang, J., Wang, T., Luo, M., Chen, Y., Chen, C., Ronai, Z. A., Zhou, Y., Ruppin, E., & Han, L. (2023) · Nature Communications · doi:10.1038/s41467-023-42233-2 · Cited by 193 · see citing works
Emerging Roles of β-Glucanases in Plant Development and Adaptative Responses
Perrot, T., Pauly, M., & Ramírez, V. (2022) · Plants · doi:10.3390/plants11091119 · Cited by 178 · see citing works
BacPROTACs mediate targeted protein degradation in bacteria
Morreale, F. E., Kleine, S., Leodolter, J., Junker, S., Hoi, D. M., Ovchinnikov, S., Okun, A., Kley, J., Kurzbauer, R., Junk, L., et al. (2022) · Cell · doi:10.1016/j.cell.2022.05.009 · Cited by 196 · see citing works
Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials
Mortazavi, B. (2024) · Advanced Energy Materials · doi:10.1002/aenm.202403876 · Cited by 191 · see citing works
AlphaFold, Artificial Intelligence (AI), and Allostery
Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04346 · Cited by 190 · see citing works
The landscape of tolerated genetic variation in humans and primates
(2023) · doi:10.1126/science.abn8197 · Cited by 182 · see citing works
“PrimateAI-3D can use protein structures from either experimental sources or computational prediction (72–76); we used AlphaFold DB (72, 73) and HHpred (74) predicted structures for the broadest coverage across human genes.”
Language models generalize beyond natural proteins
Verkuil, R., Kabeli, O., Du, Y., Wicky, B. I. M., Milles, L. F., Dauparas, J., Baker, D., Ovchinnikov, S., Sercu, T., & Rives, A. (2022) · doi:10.1101/2022.12.21.521521 · Cited by 181 · see citing works
“[37] Mihaly Varadi, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan, Oana Stroe, Gemma Wood, Agata Laydon, Augustin Žı́dek, Tim Green, Kathryn Tunyasuvunakool, Stig Petersen, John Jumper, Ellen Clancy, Richard Green, Ankur Vora, Mira Lutfi, Michael Figurnov, Andrew Cowi…”
BackgroundRapid protein stability prediction using deep learning representations
Blaabjerg, L. M., Kassem, M. M., Good, L. L., Jonsson, N., Cagiada, M., Johansson, K. E., Boomsma, W., Stein, A., & Lindorff-Larsen, K. (2023) · eLife · doi:10.7554/elife.82593 · Cited by 177 · see citing works
PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces
Krapp, L. F., Abriata, L. A., Rodríguez, F. C., & Peraro, M. D. (2023) · Nature Communications · doi:10.1038/s41467-023-37701-8 · Cited by 159 · see citing works
Uncovering new families and folds in the natural protein universe
Durairaj, J., Waterhouse, A. M., Mets, T., Brodiazhenko, T., Abdullah, M., Studer, G., Tauriello, G., Akdel, M., Andreeva, A. E., Bateman, A. G., et al. (2023) · Nature · doi:10.1038/s41586-023-06622-3 · Cited by 151 · see citing works
Cheng, J., Yan, J., Liu, Y., Shi, J., Wang, H., Zhou, H., Zhou, Y., Zhang, T., Zhao, L., Meng, X., et al. (2023) · Cell Metabolism · doi:10.1016/j.cmet.2023.04.017 · Cited by 182 · see citing works
MobiDB: 10 years of intrinsically disordered proteins
Piovesan, D., Conte, A. D., Clementel, D., Monzón, A. M., Bevilacqua, M., Aspromonte, M. C., Iserte, J., Orti, F., Marino‐Buslje, C., & Tosatto, S. C. E. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1065 · Cited by 158 · see citing works
Hatos, A., Tosatto, S. C. E., Vendruscolo, M., & Fuxreiter, M. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac386 · Cited by 158 · see citing works
Application of Computational Biology and Artificial Intelligence in Drug Design
Zhang, Y., Luo, M., Wu, P., Wu, S., Lee, T.-Y., & Bai, C. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232113568 · Cited by 160 · see citing works
Wu, H., Liu, J., Jiang, T., Zou, Q., Qi, S., Cui, Z., Tiwari, P., & Ding, Y. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.11.018 · Cited by 183 · see citing works
Intrinsic protein disorder and conditional folding in <scp>AlphaFoldDB</scp>
Piovesan, D., Monzon, A. M., & Tosatto, S. C. E. (2022) · Protein Science · doi:10.1002/pro.4466 · Cited by 160 · see citing works
“6 AlphaFold-pLDDT uses 1 - pLDDT as output.”
BackgroundA fluid biomarker reveals loss of TDP-43 splicing repression in presymptomatic ALS–FTD
Irwin, K. E., Jasin, P., Braunstein, K. E., Sinha, I. R., Garret, M. A., Bowden, K. D., Chang, K., Troncoso, J. C., Moghekar, A., Oh, E. S., et al. (2024) · Nature Medicine · doi:10.1038/s41591-023-02788-5 · Cited by 165 · see citing works
AlphaFold2 structures guide prospective ligand discovery
Lyu, J., Kapolka, N. J., Gumpper, R. H., Alon, A., Wang, L., Jain, M. K., Barros-Álvarez, X., Sakamoto, K., Kim, Y., DiBerto, J. F., et al. (2024) · Science · doi:10.1126/science.adn6354 · Cited by 182 · see citing works
Beyond PI3Ks: targeting phosphoinositide kinases in disease
Burke, J. E., Triscott, J., Emerling, B. M., & Hammond, G. R. V. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-022-00582-5 · Cited by 156 · see citing works
EMDB—the Electron Microscopy Data Bank
Turner, J., Abbott, S., Fonseca, N., Pye, R., de Oliveira, L. C., Duraisamy, A. K., Salih, O., Wang, Z., Kleywegt, G. J., Morris, K. L., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1019 · Cited by 154 · see citing works
“In addition to the aforementioned structural archives, entries in EMDB can also be associated with entries in the AlphaFold Protein Structure Database ( 28 ), Small Angle Scattering Biological Data Bank (SASBDB) ( 29 ), PDB-Dev ( 30 ) and EMPIAR ( 18 ).”
BackgroundSelf-citationProtein remote homology detection and structural alignment using deep learning
Hamamsy, T., Morton, J. T., Blackwell, R., Berenberg, D., Carriero, N. J., Gligorijević, V., Strauss, C. E. M., Leman, J. K., Cho, K., & Bonneau, R. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01917-2 · Cited by 145 · see citing works
An atlas of protein homo-oligomerization across domains of life
Schweke, H., Pacesa, M., Levin, T., Goverde, C. A., Kumar, P., Duhoo, Y., Dornfeld, L. J., Dubreuil, B., Georgeon, S., Ovchinnikov, S., et al. (2024) · Cell · doi:10.1016/j.cell.2024.01.022 · Cited by 144 · see citing works
DisProt in 2024: improving function annotation of intrinsically disordered proteins
Aspromonte, M. C., Nugnes, M. V., Quaglia, F., Bouharoua, A., Sagris, V., Promponas, V. J., Chasapi, A., Fichó, E., Balatti, G. E., Parisi, G. D., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad928 · Cited by 142 · see citing works
MIB2: metal ion-binding site prediction and modeling server
Lu, C., Chen, C.-C., Yu, C., et al. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac534 · Cited by 157 · see citing works
Artificial Intelligence in Regenerative Medicine: Applications and Implications
Nosrati, H., & Nosrati, M. (2023) · Biomimetics · doi:10.3390/biomimetics8050442 · Cited by 158 · see citing works
Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities
Flynn, C. D., & Chang, D. (2024) · Diagnostics · doi:10.3390/diagnostics14111100 · Cited by 173 · see citing works
BioLiP2: an updated structure database for biologically relevant ligand–protein interactions
Zhang, C., Zhang, X., Freddolino, L., & Zhang, Y. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad630 · Cited by 142 · see citing works
“…only contains search through protein and ligand IDs which limit the search capacity, BioLiP2 extends the search to both protein / ligand sequence and structure search by the integration of the quick structure alignment algorithms ( 36 ,37 ) and a new AI-based protein structure library ( 38 ).”
Protein complexes in cells by AI‐assisted structural proteomics
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“Bacillus subtilis is a model Gram-positive bacterium, with extensive genetic data (Michalik et al , 2021) and its protein structures modeled to a high degree of accuracy (Varadi et al , 2022).”
The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins
Agarwal, V., & McShan, A. C. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01638-w · Cited by 155 · see citing works
“Several efforts have alleviated these limitations, including the Alpha-Fold Protein Structure Database 6,7 , which houses over 200 million pre-run AF2 predictions, as well as ColabFold 8 and OpenFold 9 , which allow users to run a modified AF2 protocol on open-access servers in minutes.”
BackgroundLarge-scale mapping and mutagenesis of human transcriptional effector domains
DelRosso, N., Tycko, J., Suzuki, P., Andrews, C., Aradhana, Mukund, A., Liongson, I., Ludwig, C., Spees, K., Fordyce, P., et al. (2023) · Nature · doi:10.1038/s41586-023-05906-y · Cited by 130 · see citing works
- Authors
- Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., Žídek, A., Green, T., Tunyasuvunakool, K., Petersen, S., Jumper, J., Clancy, E., Green, R., Vora, A., Lutfi, M., Figurnov, M., Cowie, A., Hobbs, N., Kohli, P., Kleywegt, G., Birney, E., Hassabis, D., & Velankar, S.
- Affiliations
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
- European Bioinformatics Institute (GB)
- Google DeepMind (United Kingdom) (GB)
- Corresponding author
- Hassabis, Demis
- Velankar, Sameer
- ORCID
- Varadi, Mihaly
- Anyango, Stephen
- Deshpande, Mandar
- Nair, Sreenath
- Yordanova, Galabina
- Yuan, David
- Stroe, Oana
- Laydon, Agata
- Žídek, Augustin
- Green, Tim
- Tunyasuvunakool, Kathryn
- Petersen, Stig
- Jumper, John
- Clancy, Ellen
- Green, Richard
- Vora, Ankur
- Figurnov, Michael
- Kohli, Pushmeet
- Kleywegt, Gerard
- Birney, Ewan
- Hassabis, Demis
- Velankar, Sameer
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-11-17
- Volume
- 50
- Issue
- D1
- Pages
- D439-D444
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC8728224
- OpenAlex
- W3211795435
- Semantic Scholar
- 86c048c4d91066610beba9099b7d23bdae29caab
- Cited by
- 7,850
- References
- 24
- Citations by year
- 2026: 1070 · 2025: 1540 · 2024: 2150 · 2023: 2142 · 2022: 925 · 2021: 13 · 2020: 1 · 2019: 1 · 2017: 1 · 2014: 1 · 2013: 1 · 2012: 1
- FWCI
- 416.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- DeepMind
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- AlphaFold, protein structure prediction, protein sequence space, structural coverage, model confidence, atomic coordinates, proteome, DeepMind
- MeSH terms
- Protein Conformation, alpha-Helical, Protein Conformation, beta-Strand, Amino Acid Sequence, Animals, Bacteria, Dictyostelium, Fungi, Humans, Models, Molecular, Plants, Proteins, Software, Trypanosoma cruzi, Protein Folding, Internet, Databases, Protein, Datasets as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-07.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-07.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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