Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 1,062

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

InterPro: the protein sequence classification resource in 2025

Open access
Blum, Matthias, Andreeva, Antonina, Florentino, Laise Cavalcanti, Chuguransky, Sara Rocio, Grego, Tiago, Hobbs, Emma, Pinto, Beatriz Lazaro, Orr, Ailsa, Paysan-Lafosse, Typhaine, Ponamareva, Irina, Salazar, Gustavo A, Bordin, Nicola, Bork, Peer, Bridge, Alan, Colwell, Lucy, Gough, Julian, Haft, Daniel H, Letunic, Ivica, Llinares-López, Felipe, Marchler-Bauer, Aron, Meng-Papaxanthos, Laetitia, Mi, Huaiyu, Natale, Darren A, Orengo, Christine A, Pandurangan, Arun P, Piovesan, Damiano, Rivoire, Catherine, Sigrist, Christian J A, Thanki, Narmada, Thibaud-Nissen, Françoise, Thomas, Paul D, Tosatto, Silvio C E, Wu, Cathy H, Bateman, Alex·2025·Nucleic Acids Research
DOI 10.1093/nar/gkae1082
Open this paper in CoChat — free account

Abstract

InterPro (https://www.ebi.ac.uk/interpro) is a freely accessible resource for the classification of protein sequences into families. It integrates predictive models, known as signatures, from multiple member databases to classify sequences into families and predict the presence of domains and significant sites. The InterPro database provides annotations for over 200 million sequences, ensuring extensive coverage of UniProtKB, the standard repository of protein sequences, and includes mappings to several other major resources, such as Gene Ontology (GO), Protein Data Bank in Europe (PDBe) and the AlphaFold Protein Structure Database. In this publication, we report on the status of InterPro (version 101.0), detailing new developments in the database, associated web interface and software. Notable updates include the increased integration of structures predicted by AlphaFold and the enhanced description of protein families using artificial intelligence. Over the past two years, more than 5000 new InterPro entries have been created. The InterPro website now offers access to 85 000 protein families and domains from its member databases and serves as a long-term archive for retired databases. InterPro data, software and tools are freely available.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/nar/gkae1082

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 8 references

Research works printed but not linked: 45

  1. Sillitoe, 2021, CATH: increased structural coverage of functional space, Nucleic Acids Res., 49, D266, 10.1093/nar/gkaa1079 · DOI · Paper page
  2. Wang, 2023, The conserved domain database in 2023, Nucleic Acids Res., 51, D384, 10.1093/nar/gkac1096 · DOI · Paper page
  3. Pedruzzi, 2015, HAMAP in 2015: updates to the protein family classification and annotation system, Nucleic Acids Res., 43, D1064, 10.1093/nar/gku1002 · DOI
  4. Haft, 2024, RefSeq and the prokaryotic genome annotation pipeline in the age of metagenomes, Nucleic Acids Res., 52, D762, 10.1093/nar/gkad988 · DOI
  5. Thomas, 2022, PANTHER: making genome-scale phylogenetics accessible to all, Protein Sci., 31, 8, 10.1002/pro.4218 · DOI · Paper page
  6. Nikolskaya, 2007, PIRSF family classification system for protein functional and evolutionary analysis, Evol. Bioinform. Online, 2, 197
  7. Mistry, 2021, Pfam: the protein families database in 2021, Nucleic Acids Res., 49, D412, 10.1093/nar/gkaa913 · DOI · Paper page
  8. Attwood, 2012, The PRINTS database: a fine-grained protein sequence annotation and analysis resource–its status in 2012, Database, 2012, bas019, 10.1093/database/bas019 · DOI
  9. Sigrist, 2013, New and continuing developments at PROSITE, Nucleic Acids Res., 41, D344, 10.1093/nar/gks1067 · DOI · Paper page
  10. Akiva, 2013, The structure–function linkage database, Nucleic Acids Res., 42, D521, 10.1093/nar/gkt1130 · DOI
  11. Letunic, 2021, SMART: recent updates, new developments and status in 2020, Nucleic Acids Res., 49, D458, 10.1093/nar/gkaa937 · DOI · Paper page
  12. Oates, 2014, The SUPERFAMILY 1.75 database in 2014: a doubling of data, Nucleic Acids Res., 43, D227, 10.1093/nar/gku1041 · DOI
  13. Ontology Consortium, 2023, The gene ontology knowledgebase in 2023, Genetics, 224, iyad031, 10.1093/genetics/iyad031 · DOI · Paper page
  14. Burge, 2012, Manual GO annotation of predictive protein signatures: the InterPro approach to GO curation, Database, 2012, bar068, 10.1093/database/bar068 · DOI
  15. Necci, 2021, MobiDB-lite 3.0: fast consensus annotation of intrinsic disorder flavors in proteins, Bioinformatics, 36, 5533, 10.1093/bioinformatics/btaa1045 · DOI
  16. Nielsen, 2017, Predicting secretory proteins with SignalP, Methods Mol. Biol., 1611, 59, 10.1007/978-1-4939-7015-5_6 · DOI · Paper page
  17. Käll, 2007, Advantages of combined transmembrane topology and signal peptide prediction–the Phobius web server, Nucleic Acids Res., 35, W429, 10.1093/nar/gkm256 · DOI · Paper page
  18. Krogh, 2001, Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes, J. Mol. Biol., 305, 567, 10.1006/jmbi.2000.4315 · DOI · Paper page
  19. Das, 2015, Functional classification of CATH superfamilies: a domain-based approach for protein function annotation, Bioinformatics, 31, 3460, 10.1093/bioinformatics/btv398 · DOI
  20. Eberhardt, 2012, AntiFam: a tool to help identify spurious ORFs in protein annotation, Database, 2012, bas003, 10.1093/database/bas003 · DOI
  21. Drysdale, 2018, The process for the selection of ELIXIR core data resources, F1000Res., 7, 1712
  22. Anderson, 2017, A global coalition to sustain core data, Nature, 543, 179, 10.1038/543179a · DOI
  23. Madeira, 2024, The EMBL-EBI Job Dispatcher sequence analysis tools framework in 2024, Nucleic Acids Res., 52, W521, 10.1093/nar/gkae241 · DOI · Paper page
  24. MacDougall, 2021, UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase, Bioinformatics, 36, 5562, 10.1093/bioinformatics/btaa663 · DOI
  25. Jones, 2014, InterProScan 5: genome-scale protein function classification, Bioinformatics, 30, 1236, 10.1093/bioinformatics/btu031 · DOI · Paper page
  26. Harrison, 2024, Ensembl 2024, Nucleic Acids Res., 52, D891, 10.1093/nar/gkad1049 · DOI · Paper page
  27. Yates, 2022, Ensembl Genomes 2022: an expanding genome resource for non-vertebrates, Nucleic Acids Res., 50, D996, 10.1093/nar/gkab1007 · DOI · Paper page
  28. Armstrong, 2020, PDBe: improved findability of macromolecular structure data in the PDB, Nucleic Acids Res., 48, D335
  29. Conesa, 2008, Blast2GO: a comprehensive suite for functional analysis in plant genomics, Int. J. Plant Genomics, 2008, 619832, 10.1155/2008/619832 · DOI · Paper page
  30. Richardson, 2019, Genome properties in 2019: a new companion database to InterPro for the inference of complete functional attributes, Nucleic Acids Res., 47, D564, 10.1093/nar/gky1013 · DOI · Paper page
  31. Richardson, 2023, MGnify: the microbiome sequence data analysis resource in 2023, Nucleic Acids Res., 51, D753, 10.1093/nar/gkac1080 · DOI · Paper page
  32. Gurbich, 2023, MGnify genomes: a resource for biome-specific microbial genome catalogues, J. Mol. Biol., 435, 168016, 10.1016/j.jmb.2023.168016 · DOI
  33. Paysan-Lafosse, 2023, InterPro in 2022, Nucleic Acids Res., 51, D418, 10.1093/nar/gkac993 · DOI
  34. Sehnal, 2021, Mol* viewer: modern web app for 3D visualization and analysis of large biomolecular structures, Nucleic Acids Res., 49, W431, 10.1093/nar/gkab314 · DOI · Paper page
  35. Gymnopoulos, 2007, Rare cancer-specific mutations in PIK3CA show gain of function, Proc. Natl. Acad. Sci., 104, 5569, 10.1073/pnas.0701005104 · DOI · Paper page
  36. Kumar, 2021, The Eukaryotic Linear Motif resource: 2022 release, Nucleic Acids Res., 50, D497, 10.1093/nar/gkab975 · DOI
  37. Aspromonte, 2023, DisProt in 2024: improving function annotation of intrinsically disordered proteins, Nucleic Acids Res., 52, D434, 10.1093/nar/gkad928 · DOI
  38. Paladin, 2020, RepeatsDB in 2021: improved data and extended classification for protein tandem repeat structures, Nucleic Acids Res., 49, D452, 10.1093/nar/gkaa1097 · DOI
  39. Nightingale, 2017, The Proteins API: accessing key integrated protein and genome information, Nucleic Acids Res., 45, W539, 10.1093/nar/gkx237 · DOI
  40. Salazar, 2023, Nightingale: web components for protein feature visualization, Bioinform. Adv., 3, vbad064, 10.1093/bioadv/vbad064 · DOI
  41. Tang, 2018, TreeGrafter: phylogenetic tree-based annotation of proteins with gene ontology terms and other annotations, Bioinformatics, 35, 518, 10.1093/bioinformatics/bty625 · DOI
  42. Gaudet, 2011, Phylogenetic-based propagation of functional annotations within the gene ontology consortium, Brief Bioinform, 12, 449, 10.1093/bib/bbr042 · DOI · Paper page
  43. Linding, 2003, Protein disorder prediction: implications for structural proteomics, Structure, 11, 1453, 10.1016/j.str.2003.10.002 · DOI · Paper page
  44. Walsh, 2012, ESpritz: accurate and fast prediction of protein disorder, Bioinformatics, 28, 503, 10.1093/bioinformatics/btr682 · DOI · Paper page
  45. Mészáros, 2018, IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding, Nucleic Acids Res., 46, W329, 10.1093/nar/gky384 · DOI · Paper page

Cited by

See all 1,062

Metadata

Authors
Blum, M., Andreeva, A., Florentino, L. C., Chuguransky, S. R., Grego, T., Hobbs, E., Pinto, B. L., Orr, A., Paysan-Lafosse, T., Ponamareva, I., Salazar, G. A., Bordin, N., Bork, P., Bridge, A., Colwell, L., Gough, J., Haft, D. H., Letunic, I., Llinares-López, F., Marchler-Bauer, A., Meng-Papaxanthos, L., Mi, H., Natale, D. A., Orengo, C. A., Pandurangan, A. P., Piovesan, D., Rivoire, C., Sigrist, C. J. A., Thanki, N., Thibaud-Nissen, F., Thomas, P. D., Tosatto, S. C. E., Wu, C. H., & Bateman, A.
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2024-11-20
Volume
53
Issue
D1
Pages
D444-D456
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gkae1082
OpenAlex
W4404531735
Cited by
1,062
References
8
FWCI
123.39
Type
journal article
Language
en
Open access
gold
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • Wellcome Trust (221 320/Z/20/Z)
  • Biotechnology and Biological Sciences Research Council (BB/S020381/1, BB/X012492/1, BB/X018660/1)
  • European Molecular Biology Laboratory
  • Italiadomani-PNRR (IR0000010)
  • National Center for Biotechnology
  • National Library of Medicine
  • National Institutes of Health

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.

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 ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: InterPro: the protein sequence classification resource in 2025. https://app.cochat.ai/papers/10.1093/nar/gkae1082
The paper
Blum, M., Andreeva, A., Florentino, L. C., Chuguransky, S. R., Grego, T., Hobbs, E., Pinto, B. L., Orr, A., Paysan-Lafosse, T., Ponamareva, I., Salazar, G. A., Bordin, N., Bork, P., Bridge, A., Colwell, L., Gough, J., Haft, D. H., Letunic, I., Llinares-López, F., . . . Bateman, A. (2025). InterPro: the protein sequence classification resource in 2025. Nucleic Acids Research. https://doi.org/10.1093/nar/gkae1082

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.