InterPro: the protein sequence classification resource in 2027

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ID: 338111
2026
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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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Authors Matthias Blum, Antonina E. Andreeva, Sara Chuguransky, Laise Cavalcanti Florentino, Noemí Flores, Tiago Grego, Emma Hobbs, Beatriz Lázaro, Alessandro Polignano, Laetitia Meng-Papaxanthos, Felipe Llinares-López, Lucy J. Colwell, Aron Marchler‐Bauer, Nicola Bordin, Alan James Bridge, Daniel H. Haft, William A. Klimke, Ivica Letunić, Huaiyu Mi, Darren A. Natale, Christine Anne Orengo, Damiano Piovesan, Catherine Rivoire, Christian J A Sigrist, Paul D. Thomas, Silvio C. E. Tosatto, Cathy H. Wu, Alex G. Bateman
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Journal Nucleic Acids Research
Year 2026
DOI
10.1093/nar/gkag1114
URL
https://doi.org/10.1093/nar/gkag1114
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