The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
InterPro in 2022
Open accessAbstract
The InterPro database (https://www.ebi.ac.uk/interpro/) provides an integrative classification of protein sequences into families, and identifies functionally important domains and conserved sites. Here, we report recent developments with InterPro (version 90.0) and its associated software, including updates to data content and to the website. These developments extend and enrich the information provided by InterPro, and provide a more user friendly access to the data. Additionally, we have worked on adding Pfam website features to the InterPro website, as the Pfam website will be retired in late 2022. We also show that InterPro's sequence coverage has kept pace with the growth of UniProtKB. Moreover, we report the development of a card game as a method of engaging the non-scientific community. Finally, we discuss the benefits and challenges brought by the use of artificial intelligence for protein structure prediction.
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Citations
Cites
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“A Google Research team is working on the development of ProtENN, a deep-learning method predicting functional annotations for unaligned amino acid sequences using Pfam families as a training set (31).”
Self-citation
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Cited by
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
Self-citationUniProt: the Universal Protein Knowledgebase in 2025
The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Adesina, A., Ahmad, S., Bowler-Barnett, E. H., Bye-A-Jee, H., Carpentier, D., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1010 · Cited by 2,537
Self-citationVaradi, M., Bertoni, D., Magana, P., Paramval, U., Pidruchna, I., Radhakrishnan, M., Tsenkov, M., Nair, S., Mirdita, M., Yeo, J., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad1011 · Cited by 2,052
Simulating 500 million years of evolution with a language model
Hayes, T., Rao, R., Akin, H., Sofroniew, N. J., Oktay, D., Lin, Z., Verkuil, R., Tran, V. Q., Deaton, J., Wiggert, M., et al. (2025) · Science · doi:10.1126/science.ads0018 · Cited by 937
Mining the CRBN target space redefines rules for molecular glue-induced neosubstrate recognition
Petzold, G., Gainza, P., Annunziato, S., Lamberto, I., Trenh, P., McAllister, L. A., DeMarco, B., Schwander, L., Bunker, R. D., Zlotosch, M., et al. (2025) · Science · doi:10.1126/science.adt6736 · Cited by 117
Metadata
- Authors
- Paysan-Lafosse, T., Blum, M., Chuguransky, S., Grego, T., Pinto, B. L., Salazar, G. A., Bileschi, M. L., Bork, P., Bridge, A., Colwell, L., Gough, J., Haft, D. H., Letunić, I., Marchler-Bauer, A., Mi, H., Natale, D. A., Orengo, C. A., Pandurangan, A. P., Rivoire, C., Sigrist, C. J. A., Sillitoe, I., Thanki, N., Thomas, P. D., Tosatto, S. C. E., Wu, C. H., & Bateman, A.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-11-09
- Volume
- 51
- Issue
- D1
- Pages
- D418-D427
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W4308616064
- Cited by
- 2,399
- References
- 7
- FWCI
- 150.47
- Type
- journal article
- Language
- en
- Open access
- gold
- Funders
- Wellcome Trust (108433/Z/15/Z, 221320/Z/20/Z)
- Biotechnology and Biological Sciences Research Council (BB/T010541/1, BB/S020381/1)
- National Institutes of Health (R35GM141873)
- National Human Genome Research Institute (U24HG012212, U41HG002273)
- National Science Foundation, Division of Biological Infrastructure (1661543, 1917302)
- ELIXIR
- Open Targets
- European Molecular Biology Laboratory core funds
- Wellcome Genome Campus
- National Center for Biotechnology Information of the National Library of Medicine, National Institutes of Health
- German Network for Bioinformatics Infrastructure
- Swiss Institute of Bioinformatics
- Swiss node of ELIXIR
- State Secretariat for Education, Research and Innovation
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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.
