InterPro: the protein sequence classification resource in 2027
11 affiliations
- 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus , Hinxton CB10 1SD ,
- 2Google DeepMind , Europaallee 36, Zürich 8004 ,
- 3Google DeepMind , 14 Rue de Londres, Paris 75009 ,
- 4Google DeepMind , 325 Main Street, Cambridge, MA 02142 ,
- 5National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , Bethesda, MD 20894 ,
- 6Department of Structural and Molecular Biology, University College London , Gower St, Bloomsbury, London WC1E 6BT ,
- 7Department of Biomedical Sciences, University of Padova , Padova 35121 ,
- 8Swiss-Prot Group, Swiss Institute of Bioinformatics, CMU , 1 rue Michel Servet, CH-1211, Geneva 4 ,
- 9Biobyte Solutions GmbH , Bothestr 142, Heidelberg 69126 ,
- 10Department of Population and Public Health Sciences, University of Southern California , Los Angeles, CA 90033 ,
- 11Protein Information Resource, Georgetown University Medical Center , Washington, DC 20007 ,
Abstract
InterPro (https://www.ebi.ac.uk/interpro) is a freely accessible resource for the classification of protein sequences. It integrates predictive models, known as signatures, from multiple member databases to classify sequences into families and to predict the presence of domains and functionally important sites. The InterPro database provides annotations for hundreds of millions of sequences, ensuring extensive coverage of UniProtKB, the standard repository of protein sequences, and includes mapping to several other major resources, such as Gene Ontology, the Protein Data Bank in Europe (PDBe), and the AlphaFold Protein Structure Database. In this publication we report on the status of InterPro (version 110.0), detailing new developments in the database, its associated web interface, and software. Notable updates include InterPro-N, a deep-learning method that increases sequence coverage of UniProtKB to over 90%; the transition from InterProScan 5 to the completely re-engineered InterProScan 6; the integration of structure-based domain classifications and predicted structures from AlphaFold; a redesigned protein viewer offering a concise summary view; and a new Model Context Protocol (MCP) server that allows AI assistants to query InterPro programmatically. Over the past two years, 9484 new InterPro entries have been created, bringing the total to 54 922. InterPro data, software and tools are freely available.
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BibTeX
@article{blum2026interpro,
title = {{InterPro: the protein sequence classification resource in 2027}},
author = {Matthias Blum and Antonina Andreeva and Sara Chuguransky and Laise Cavalcanti Florentino and Nicole Morveli Flores and Tiago Grego and Emma Elizabeth Mary Hobbs and Beatriz Lazaro Pinto and Alessandro Polignano and Laetitia Meng-Papaxanthos and Felipe Llinares-López and Lucy Colwell and Aron Marchler-Bauer and Nicola Bordin and Alan Bridge and Daniel H Haft and William Klimke and Ivica Letunic and Huaiyu Mi and Darren A Natale and Christine A Orengo and Damiano Piovesan and Catherine Rivoire and Christian J A Sigrist and Paul D Thomas and Silvio C E Tosatto and Cathy H Wu and Alex Bateman},
journal = {Nucleic Acids Research},
year = {2026},
month = nov,
doi = {10.1093/nar/gkag1114},
url = {https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkag1114/9006192},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - InterPro: the protein sequence classification resource in 2027
AU - Matthias Blum
AU - Antonina Andreeva
AU - Sara Chuguransky
AU - Laise Cavalcanti Florentino
AU - Nicole Morveli Flores
AU - Tiago Grego
AU - Emma Elizabeth Mary Hobbs
AU - Beatriz Lazaro Pinto
AU - Alessandro Polignano
AU - Laetitia Meng-Papaxanthos
AU - Felipe Llinares-López
AU - Lucy Colwell
AU - Aron Marchler-Bauer
AU - Nicola Bordin
AU - Alan Bridge
AU - Daniel H Haft
AU - William Klimke
AU - Ivica Letunic
AU - Huaiyu Mi
AU - Darren A Natale
AU - Christine A Orengo
AU - Damiano Piovesan
AU - Catherine Rivoire
AU - Christian J A Sigrist
AU - Paul D Thomas
AU - Silvio C E Tosatto
AU - Cathy H Wu
AU - Alex Bateman
PY - 2026
DA - 2026/11/02
JO - Nucleic Acids Research
DO - 10.1093/nar/gkag1114
UR - https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkag1114/9006192
AB - InterPro (https://www.ebi.ac.uk/interpro) is a freely accessible resource for the classification of protein sequences. It integrates predictive models, known as signatures, from multiple member databases to classify sequences into families and to predict the presence of domains and functionally important sites. The InterPro database provides annotations for hundreds of millions of sequences, ensuring extensive coverage of UniProtKB, the standard repository of protein sequences, and includes mapping to several other major resources, such as Gene Ontology, the Protein Data Bank in Europe (PDBe), and the AlphaFold Protein Structure Database. In this publication we report on the status of InterPro (version 110.0), detailing new developments in the database, its associated web interface, and software. Notable updates include InterPro-N, a deep-learning method that increases sequence coverage of UniProtKB to over 90%; the transition from InterProScan 5 to the completely re-engineered InterProScan 6; the integration of structure-based domain classifications and predicted structures from AlphaFold; a redesigned protein viewer offering a concise summary view; and a new Model Context Protocol (MCP) server that allows AI assistants to query InterPro programmatically. Over the past two years, 9484 new InterPro entries have been created, bringing the total to 54 922. InterPro data, software and tools are freely available.
SN - 0305-1048
SN - 1362-4962
ER -