UniProt: the Universal Protein Knowledgebase in 2023
Open accessAbstract
The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this publication we describe enhancements made to our data processing pipeline and to our website to adapt to an ever-increasing information content. The number of sequences in UniProtKB has risen to over 227 million and we are working towards including a reference proteome for each taxonomic group. We continue to extract detailed annotations from the literature to update or create reviewed entries, while unreviewed entries are supplemented with annotations provided by automated systems using a variety of machine-learning techniques. In addition, the scientific community continues their contributions of publications and annotations to UniProt entries of their interest. Finally, we describe our new website (https://www.uniprot.org/), designed to enhance our users' experience and make our data easily accessible to the research community. This interface includes access to AlphaFold structures for more than 85% of all entries as well as improved visualisations for subcellular localisation of proteins.
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.
Integrity signals
Registry
Cites
Statistics
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.
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 impact of predicted structures is enhanced by their seamless integration into several core data resources, including PDBe-KB, UniProt, Ensembl, InterPro, Genecards and MobiDB.
“The impact of the predicted structures is further enhanced by their seamless integration into several core data resources, including PDBe-KB (17), UniProt (6), Ensembl (16), InterPro (15), Genecards (24) and MobiDB (25), among others.”
From AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences (2024) · cites this paper as [6]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Database resources of the National Center for Biotechnology Information: update
Wheeler, D. L. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh073 · Cited by 10,691 · see citing works
Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1061 · Cited by 7,850 · cited 1× · 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).”
BackgroundUniProt: the universal protein knowledgebase in 2021
The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Agivetova, R., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1100 · Cited by 6,372 · see citing works
Self-citationDatabase resources of the national center for biotechnology information
Sayers, E. W., Bolton, E. E., Brister, J. R., Canese, K., Chan, J., Comeau, D. C., Connor, R., Funk, K., Kelly, C., Kim, S., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1112 · Cited by 2,812 · see citing works
Paysan‐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
Self-citationCunningham, F., Allen, J. E., Allen, J., Alvarez-Jarreta, J., Amode, M. R., Armean, I. M., Austine-Orimoloye, O., Azov, A. G., Barnes, I., Bennett, R., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1049 · Cited by 1,923 · see citing works
Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V., Christie, C. H., Dalenberg, K., Di Costanzo, L., Duarte, J. M., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1038 · Cited by 1,581 · see citing works
ChEBI in 2016: Improved services and an expanding collection of metabolites
Hastings, J., Owen, G., Dekker, A., Ennis, M., Kale, N., Muthukrishnan, V., Turner, S., Swainston, N., Mendes, P., & Steinbeck, C. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1031 · Cited by 1,083 · see citing works
Self-citationMetabolomics: A New Tool to Reveal the Nature of Diabetic Kidney Disease
Huang, G., Li, M., Li, Y. J., & Mao, Y. (2022) · Laboratory Medicine · doi:10.1093/labmed/lmac041 · Cited by 933 · see citing works
BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions
Yang, J., Roy, A., & Zhang, Y. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks966 · Cited by 749 · see citing works
The International Nucleotide Sequence Database Collaboration
Karsch‐Mizrachi, I., Nakamura, Y., & Cochrane, G. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr1006 · Cited by 717 · see citing works
A Peptide Encoded by a Putative lncRNA HOXB-AS3 Suppresses Colon Cancer Growth
Huang, J., Chen, M., Chen, D., Gao, X.-C., Zhu, S., Huang, H., Hu, M., Zhu, H., & Yan, G. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.09.015 · Cited by 711 · see citing works
Self-citationThe ProteomeXchange consortium in 2020: enabling ‘big data’ approaches in proteomics
Deutsch, E. W., Bandeira, N., Sharma, V., Pérez‐Riverol, Y., Carver, J., Kundu, D. J., García‐Seisdedos, D., Jarnuczak, A. F., Hewapathirana, S., Pullman, B. S., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz984 · Cited by 700 · see citing works
A joint NCBI and EMBL-EBI transcript set for clinical genomics and research
Morales, J., Pujar, S., Loveland, J. E., Astashyn, A., Bennett, R., Berry, A., Cox, E., Davidson, C., Ermolaeva, O., Farrell, C. M., et al. (2022) · Nature · doi:10.1038/s41586-022-04558-8 · Cited by 571 · cited 1× · see citing works
“Efforts are also being made to ensure consistency with the Matched Annotation from NCBI and EMBL-EBI (MANE) protein set (24).”
MethodWu, Q., Peng, Z., Zhang, Y., & Yang, J. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky439 · Cited by 307 · see citing works
The international nucleotide sequence database collaboration
Arita, M., Karsch‐Mizrachi, I., & Cochrane, G. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa967 · Cited by 261 · see citing works
Rhea, the reaction knowledgebase in 2022
Bansal, P., Morgat, A., Axelsen, K. B., Muthukrishnan, V., Coudert, E., Aimo, L., Hyka‐Nouspikel, N., Gasteiger, E., Kerhornou, A., Neto, T. B., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1016 · Cited by 251 · see citing works
Self-citationMetalPDB in 2018: a database of metal sites in biological macromolecular structures
Putignano, V., Rosato, A., Banci, L., & Andreini, C. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx989 · Cited by 251 · see citing works
Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set
Khadake, J., Meldal, B., Panni, S., Thorneycroft, D., Roey, K. V., Abbani, S., Salwiński, Ł., Pellegrini, M. A., Iannuccelli, M., Licata, L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07709-6 · Cited by 247 · see citing works
Self-citationMetalPDB: a database of metal sites in biological macromolecular structures
Andreini, C., Cavallaro, G., Lorenzini, S., & Rosato, A. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1063 · Cited by 227 · see citing works
PDBe: improved findability of macromolecular structure data in the PDB
Armstrong, D. R., Berrisford, J. M., Conroy, M. J., Gutmanas, A., Anyango, S., Choudhary, P., Clark, A. R., Dana, J. M., Deshpande, M., Dunlop, R., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz990 · Cited by 210 · see citing works
A crowdsourcing open platform for literature curation in UniProt
Wang, Y., Wang, Q., Huang, H., Huang, W., Chen, Y., McGarvey, P. B., Wu, C. H., & Arighi, C. N. (2021) · PLoS Biology · doi:10.1371/journal.pbio.3001464 · Cited by 149 · see citing works
Self-citationHuman Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0
Deutsch, E. W., Lane, L., Overall, C. M., Bandeira, N., Baker, M. S., Pineau, C., Moritz, R. L., Corrales, F., Orchard, S., Van Eyk, J. E., et al. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00542 · Cited by 122 · see citing works
Self-citationThe European Nucleotide Archive in 2021
Cummins, C. A., Ahamed, A., Aslam, R., Burgin, J., Devraj, R., Edbali, O., Gupta, D., Harrison, P. W., Haseeb, M., Holt, S., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1051 · Cited by 111 · see citing works
Self-citationEnzyme annotation in UniProtKB using Rhea
Morgat, A., Lombardot, T., Coudert, E., Axelsen, K. B., Neto, T. B., Géhant, S., Bansal, P., Bolleman, J. T., Gasteiger, E., de Castro, E., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz817 · Cited by 101 · see citing works
Self-citationThe European Bioinformatics Institute (EMBL-EBI) in 2021
Cantelli, G., Bateman, A. G., Brooksbank, C., Petrov, A. I., Malik‐Sheriff, R. S., Ide‐Smith, M., Hermjakob, H., Flicek, P., Apweiler, R., Birney, E., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1127 · Cited by 93 · see citing works
Self-citationTowards a unified open access dataset of molecular interactions
Porras, P., Barrera, E., Bridge, A. J., del‐Toro, N., Cesareni, G., Duesbury, M., Hermjakob, H., Iannuccelli, M., Jurišica, I., Kotlyar, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19942-z · Cited by 82 · see citing works
Self-citationDDBJ update: streamlining submission and access of human data
Fukuda, A., Kodama, Y., Mashima, J., Fujisawa, T., & Ogasawara, O. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa982 · Cited by 72 · see citing works
UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase
MacDougall, A., Volynkin, V., Saidi, R., Poggioli, D., Zellner, H., Hatton-Ellis, E., Joshi, V., O’Donovan, C., Orchard, S. E., Auchincloss, A. H., et al. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa485 · Cited by 71 · see citing works
Self-citationProtVista: visualization of protein sequence annotations
Watkins, X., Garcia, L. J., Pundir, S., & Martin, M. J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx120 · Cited by 69 · see citing works
Self-citationLitSuggest: a web-based system for literature recommendation and curation using machine learning
Allot, A., Lee, K., Chen, Q., Luo, L., & Lu, Z. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab326 · Cited by 61 · cited 1× · see citing works
“Our curation efforts around small molecule chemistry are guided by LitSuggest, an interactive platform to train and deploy advanced machine learning approaches for literature recommendation (20).”
MethodUniProt genomic mapping for deciphering functional effects of missense variants
McGarvey, P. B., Nightingale, A., Luo, J., Huang, H., Martin, M. J., & Wu, C. H. (2019) · Human Mutation · doi:10.1002/humu.23738 · Cited by 49 · see citing works
Self-citationWehrspan, Z. J., McDonnell, R. T., & Elcock, A. H. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167377 · Cited by 45 · see citing works
Li, M., Shao, F., Qian, Q., Yu, W., Zhang, Z., Chen, B., Su, D., Guo, Y., Phan, A.-V., Song, L., et al. (2021) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2021.06.027 · Cited by 33 · see citing works
Finding enzyme cofactors in Protein Data Bank
Mukhopadhyay, A., Borkakoti, N., Pravda, L., Tyzack, J. D., Thornton, J. M., & Velankar, S. S. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz115 · Cited by 27 · see citing works
Minimizing proteome redundancy in the UniProt Knowledgebase
Bursteinas, B., Britto, R., Bely, B., Auchincloss, A. H., Rivoire, C., Redaschi, N., O’Donovan, C., & Martin, M. J. (2016) · Database · doi:10.1093/database/baw139 · Cited by 26 · see citing works
Self-citationFireDB: a compendium of biological and pharmacologically relevant ligands
Maietta, P., López, G., Carro, A., Pingilley, B. J., León, L., Valencia, A., & Tress, M. (2013) · Nucleic Acids Res. · doi:10.1093/nar/gkt1127 · Cited by 26 · see citing works
le Mercier, P., Bolleman, J. T., de Castro, E., Gasteiger, E., Bansal, P., Auchincloss, A., Boutet, E., Breuza, L., Casals-Casas, C., Estreicher, A., et al. (2022) · Database J. Biol. Databases Curation · doi:10.1093/database/baac026 · Cited by 19 · see citing works
Self-citationUniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase
MacDougall, A., Volynkin, V., Saidi, R., Poggioli, D., Zellner, H., Hatton-Ellis, E., Joshi, V., O’Donovan, C., Orchard, S. E., Auchincloss, A. H., et al. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa663 · Cited by 13 · see citing works
Self-citation
Flagged first, then by citations
1 of the 41 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
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
UniProt: 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 · see citing works
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 · see citing works
The PRIDE database at 20 years: 2025 update
Pérez‐Riverol, Y., Bandla, C., Kundu, D. J., Kamatchinathan, S., Bai, J., Hewapathirana, S., John, N. S., Prakash, A., Walzer, M., Wang, S., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1011 · Cited by 2,075 · see citing works
The EMBL-EBI Job Dispatcher sequence analysis tools framework in 2024
Madeira, F., Madhusoodanan, N., Lee, J., Eusebi, A., Niewielska, A., Tivey, A., Lopez, R., & Butcher, S. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae241 · Cited by 1,666 · see citing works
Self-citationThe Reactome Pathway Knowledgebase 2024
Orlic-Milacic, M., Beavers, D., Conley, P., Gong, C., Gillespie, M. E., Griss, J., Haw, R., Jassal, B., Matthews, L. R., May, B., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1025 · Cited by 1,511 · see citing works
InterPro: the protein sequence classification resource in 2025
Blum, M., Andreeva, A. E., Florentino, L. C., Chuguransky, S., Grego, T., Hobbs, E., Pinto, B. L., Orr, A., Paysan‐Lafosse, T., Ponamareva, I., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1082 · Cited by 1,180 · see citing works
Self-citationGeneralized 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
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 · see citing works
TTD: Therapeutic Target Database describing target druggability information
Zhou, Y., Zhang, Y., Zhao, D., Yu, X., Shen, X., Zhou, Y., Wang, S., Qiu, Y., Chen, Y., & Zhu, F. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad751 · Cited by 667 · see citing works
Self-citationThe 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 656 · see citing works
Proximity labeling in mammalian cells with TurboID and split-TurboID
Cho, K. F., Branon, T. C., Udeshi, N. D., Myers, S. A., Carr, S. A., & Ting, A. Y. (2020) · Nature Protocols · doi:10.1038/s41596-020-0399-0 · Cited by 534 · see citing works
Yarnall, M. T. N., Ioannidi, E. I., Schmitt-Ulms, C., Krajeski, R. N., Lim, J., Villiger, L., Zhou, W., Jiang, K., Garushyants, S. K., Roberts, N., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01527-4 · Cited by 490 · see citing works
The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity
Cerezo, M., Sollis, E., Ji, Y., Lewis, E., Abid, A., Bircan, K. O., Hall, P., Hayhurst, J. D., John, S., Mosaku, A., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1070 · Cited by 434 · see citing works
GENCODE 2025: reference gene annotation for human and mouse
Mudge, J. M., Carbonell-Sala, S., Diekhans, M., Martínez, J. G., Hunt, T., Jungreis, I., Loveland, J., Arnan, C., Barnes, I., Bennett, R., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1078 · Cited by 379 · see citing works
Self-citationUsing evolutionary data to make sense of macromolecules with a “face‐lifted” ConSurf
Yariv, B., Yariv, E., Kessel, A., Masrati, G., Chorin, A. B., Martz, E., Mayrose, I., Pupko, T., & Ben-Tal, N. (2023) · Protein Science · doi:10.1002/pro.4582 · Cited by 387 · see citing works
Müller‐Dott, S., Tsirvouli, E., Vázquez, M., Flores, R. O. R., Badia-i-Mompel, P., Fallegger, R., Türei, D., Lægreid, A., & Sáez-Rodríguez, J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad841 · Cited by 404 · see citing works
OrthoDB and BUSCO update: annotation of orthologs with wider sampling of genomes
Tegenfeldt, F., Kuznetsov, D. A., Manni, M., Berkeley, M., Zdobnov, E. M., & Kriventseva, E. V. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae987 · Cited by 376 · 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
Self-citationMechanisms of antigen escape from BCMA- or GPRC5D-targeted immunotherapies in multiple myeloma
Lee, H., Ahn, S., Maity, R., Leblay, N., Ziccheddu, B., Truger, M. S., Chojnacka, M., Cirrincione, A. M., Durante, M. A., Tilmont, R., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02491-5 · Cited by 370 · see citing works
Burley, S. K., Bhatt, R., Bhikadiya, C., Bi, C., Biester, A., Biswas, P., Bittrich, S., Blaumann, S., Brown, R., Chao, H., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1091 · Cited by 342 · see citing works
LIPID MAPS: update to databases and tools for the lipidomics community
Conroy, M. J., Andrews, R., Andrews, S., Cockayne, L., Dennis, E. A., Fahy, E., Gaud, C., Griffiths, W. J., Jukes, G., Kolchin, M., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad896 · Cited by 335 · see citing works
Mechanisms of Assembly and Remodeling of the Extracellular Matrix
Naba, A. (2024) · Nature reviews. Molecular cell biology · doi:10.1038/s41580-024-00767-3 · Cited by 311 · 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
<scp>BepiPred</scp> ‐3.0: Improved B‐cell epitope prediction using protein language models
Clifford, J. N., Høie, M. H., Deleuran, S., Peters, B., Nielsen, M., & Marcatili, P. (2022) · Protein Science · doi:10.1002/pro.4497 · Cited by 296 · see citing works
iPath3.0: interactive pathways explorer v3
Darzi, Y., Letunić, I., Peer Bork, B., & Yamada, T. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky299 · Cited by 293 · see citing works
Sanches, P. H. G., de Melo, N. C., Porcari, A. M., & de Carvalho, L. M. (2024) · Biology · doi:10.3390/biology13110848 · Cited by 269 · see citing works
The Immune Epitope Database (IEDB): 2024 update
Vita, R., Blazeska, N., Marrama, D., IEDB Curation Team Members, Shackelford, D., Zalman, L., Foos, G., Zarebski, L., Chan, K., Reardon, B., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1092 · Cited by 274 · see citing works
Self-citationA review of large language models and autonomous agents in chemistry
Ramos, M. C., Collison, C. J., & White, A. D. (2025) · Chemical Science · doi:10.1039/d4sc03921a · Cited by 274 · see citing works
Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering
Yang, J., Li, F.-Z., & Arnold, F. H. (2024) · ACS Central Science · doi:10.1021/acscentsci.3c01275 · Cited by 257 · see citing works
Huang, S. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.167904 · Cited by 251 · see citing works
BindingDB in 2024: a FAIR knowledgebase of protein-small molecule binding data
Liu, T., Hwang, L., Burley, S. K., Nitsche, C. I., Southan, C., Walters, W. P., & Gilson, M. K. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1075 · Cited by 246 · see citing works
Mouse Genome Informatics: an integrated knowledgebase system for the laboratory mouse
Baldarelli, R. M., Smith, C. L., Ringwald, M., Richardson, J. E., Bult, C. J., Mouse Genome Informatics Group, Anagnostopoulos, A. V., Begley, D. A., Bello, S. M., Christie, K., et al. (2024) · Genetics · doi:10.1093/genetics/iyae031 · Cited by 240 · 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
DeepLoc 2.1: multi-label membrane protein type prediction using protein language models
Ødum, M. T., Teufel, F., Thumuluri, V., Armenteros, J. J. A., Johansen, A. R., Winther, O., & Nielsen, H. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae237 · Cited by 230 · see citing works
UniKP: a unified framework for the prediction of enzyme kinetic parameters
Yu, H., Deng, H., He, J., Keasling, J. D., & Luo, X. (2023) · Nature Communications · doi:10.1038/s41467-023-44113-1 · Cited by 226 · see citing works
The IUPHAR/BPS Guide to PHARMACOLOGY in 2024
Harding, S. D., Armstrong, J. F., Faccenda, E., Southan, C., Alexander, S. P. H., Davenport, A. P., Spedding, M., & Davies, J. A. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad944 · Cited by 226 · see citing works
Damme, E. V., Laukens, K., Dang, T. H., & Ostade, X. V. (2010) · International Journal of Biological Sciences · doi:10.7150/ijbs.6.51 · Cited by 220 · see citing works
Tumour circular RNAs elicit anti-tumour immunity by encoding cryptic peptides
Huang, D., Zhu, X., Ye, S., Zhang, J., Liao, J., Zhang, N., Zeng, X., Wang, J., Yang, B., Zhang, Y., et al. (2023) · Nature · doi:10.1038/s41586-023-06834-7 · Cited by 218 · see citing works
Self-citationThe Arabidopsis Information Resource in 2024
Reiser, L., Bakker, E., Subramaniam, S., Chen, X., Sawant, S., Khosa, K., Prithvi, T., & Berardini, T. Z. (2023) · doi:10.1101/2023.11.06.565838 · Cited by 202 · see citing works
A cross-platform approach identifies genetic regulators of human metabolism and health
Lotta, L. A., Pietzner, M., Stewart, I. D., Wittemans, L. B. L., Li, C., Bonelli, R., Raffler, J., Biggs, E. K., Oliver‐Williams, C., Auyeung, V. P. W., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00751-5 · Cited by 204 · see citing works
Nitrite Oxidation in Wastewater Treatment: Microbial Adaptation and Suppression Challenges
Su, Z., Liu, T., Guo, J., & Zheng, M. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c00636 · Cited by 199 · see citing works
The Arabidopsis Information Resource in 2024
Reiser, L., Bakker, E., Subramaniam, S., Chen, X., Sawant, S., Khosa, K., Prithvi, T., & Berardini, T. Z. (2024) · GENETICS · doi:10.1093/genetics/iyae027 · Cited by 202 · see citing works
Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry
Jiang, Y., Balaya, R. D. A., Schuster, D., Neely, B. A., Rosano, G. L., Volkmar, N., Momenzadeh, A., Peters-Clarke, T. M., Egbert, S., Kreimer, S. D., et al. (2024) · ACS Measurement Science Au · doi:10.1021/acsmeasuresciau.3c00068 · Cited by 200 · see citing works
Rfam 15: RNA families database in 2025
Ontiveros‐Palacios, N., Cooke, E. J., Nawrocki, E. P., Triebel, S., Marz, M., Rivas, E., Griffiths‐Jones, S., Petrov, A. I., Bateman, A. G., & Sweeney, B. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1023 · Cited by 189 · see citing works
Self-citationErdős, G., & Dosztányi, Z. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae385 · Cited by 188 · see citing works
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
Han, R., Yoon, H., Kim, G., Lee, H., & Lee, Y. (2023) · Pharmaceuticals · doi:10.3390/ph16091259 · Cited by 192 · see citing works
CellPhoneDB v5: inferring cell–cell communication from single-cell multiomics data
Troulé, K., Petryszak, R., Çakır, B., Cranley, J. J., Harasty, A., Prete, M., Tuong, Z. K., Teichmann, S. A., García‐Alonso, L., & Vento-Tormo, R. (2025) · Nature Protocols · doi:10.1038/s41596-024-01137-1 · Cited by 199 · see citing works
Goles, M., Sanchez–Daza, A., Cabas-Mora, G., Varón, L. S., Sepúlveda-Yáñez, J., Anvari-Kazemabad, H., Davari, M. D., Uribe-Paredes, R., Olivera‐Nappa, Á., Navarrete, M. A., et al. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae275 · Cited by 180 · see citing works
Beck, L. C., Masi, A. C., Young, G. R., Vatanen, T., Lamb, C. A., Smith, R., Coxhead, J., Butler, A., Marsland, B. J., Embleton, N. D., et al. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01213-w · Cited by 164 · see citing works
Navigating the landscape of enzyme design: from molecular simulations to machine learning
Zhou, J., & Huang, M. (2024) · Chemical Society Reviews · doi:10.1039/d4cs00196f · Cited by 164 · see citing works
Zheng, H., An, M., Luo, Y., Diao, X., Zhong, W., Pang, M., Lin, Y., Chen, J., Li, Y., Kong, Y., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.02.002 · Cited by 159 · see citing works
Self-citationUncovering 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
Self-citationGalperin, M. Y., Alvarez, R. V., Karamycheva, S. A., Makarova, K. S., Wolf, Y. I., Landsman, D., & Koonin, E. V. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae983 · Cited by 154 · see citing works
Bac Dive in 2025: the core database for prokaryotic strain data
Schober, I., Koblitz, J., Carbasse, J. S., Ebeling, C., Schmidt, M. L., Podstawka, A., Gupta, R., Ilangovan, V., Chamanara, J., Overmann, J., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae959 · Cited by 152 · see citing works
Lian, Q., Hüettel, B., Walkemeier, B., Mayjonade, B., Lopez‐Roques, C., Gil, L., Roux, F., Schneeberger, K., & Mercier, R. (2024) · Nature Genetics · doi:10.1038/s41588-024-01715-9 · Cited by 152 · see citing works
Structural variation in the pangenome of wild and domesticated barley
Jayakodi, M., Lu, Q., Pidon, H., Rabanus‐Wallace, M. T., Bayer, M., Lux, T. M., Guo, Y., Jaegle, B., Badea, A., Bekele, W. A., et al. (2024) · Nature · doi:10.1038/s41586-024-08187-1 · Cited by 174 · see citing works
Analysis of AlphaMissense data in different protein groups and structural context
Tordai, H., Torres, O., Csepi, M., Padányi, R., Lukács, G. L., & Hegedűs, T. (2024) · Scientific Data · doi:10.1038/s41597-024-03327-8 · Cited by 144 · see citing works
Harmonizome 3.0: integrated knowledge about genes and proteins from diverse multi-omics resources
Diamant, I., Clarke, D. J. B., Evangelista, J. E., Lingam, N., & Ma’ayan, A. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1080 · Cited by 149 · see citing works
DiscoTope-3.0: improved B-cell epitope prediction using inverse folding latent representations
Høie, M. H., Gade, F. S., Johansen, J. M., Würtzen, C., Winther, O., Nielsen, M., & Marcatili, P. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1322712 · Cited by 151 · see citing works
Accurately identifying hemagglutinin using sequence information and machine learning methods
Zou, X., Ren, L., Cai, P., Zhang, Y., Ding, H., Deng, K., Yu, X., Lin, H., & Huang, C. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1281880 · Cited by 140 · see citing works
AI-Driven Deep Learning Techniques in Protein Structure Prediction
Chen, L., Li, Q., Nasif, K. F. A., Xie, Y., Deng, B., Niu, S., Pouriyeh, S., Dai, Z., Chen, J., & Xie, Y. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25158426 · Cited by 148 · see citing works
UniProt Tools: BLAST, Align, Peptide Search, and ID Mapping
Zaru, R., & Orchard, S. E. (2023) · Current Protocols · doi:10.1002/cpz1.697 · Cited by 138 · see citing works
Self-citationExploring structural diversity across the protein universe with The Encyclopedia of Domains
Lau, A. M., Bordin, N., Kandathil, S. M., Sillitoe, I., Waman, V. P., Wells, J., Orengo, C. A., & Jones, D. T. (2024) · Science · doi:10.1126/science.adq4946 · Cited by 140 · see citing works
Zheng, W. H., Wuyun, Q., Li, Y., Zhang, C., Freddolino, L., & Zhang, Y. (2024) · Nature Methods · doi:10.1038/s41592-023-02130-4 · Cited by 133 · see citing works
Protenix - Advancing Structure Prediction Through a Comprehensive AlphaFold3 ReproductionPreprint
ByteDance AML AI4Science Team, Chen, X., Zhang, Y., Lu, C., Ma, W., Guan, J., Gong, C., Yang, J., Zhang, H., Zhang, K., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.08.631967 · Cited by 150 · see citing works
The impact of AlphaFold Protein Structure Database on the fields of life sciences
Váradi, M., & Velankar, S. S. (2022) · PROTEOMICS · doi:10.1002/pmic.202200128 · Cited by 132 · see citing works
Recombinant protein expression: Challenges in production and folding related matters
Beygmoradi, A., Homaei, A. A., Hemmati, R., & Fernandes, P. C. D. B. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.123407 · Cited by 125 · see citing works
Xu, J., Li, F., Li, C., Guo, X., Landersdorfer, C. B., Shen, H., Peleg, A. Y., Li, J., Imoto, S., Yao, J., et al. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad240 · Cited by 125 · see citing works
Scientific Large Language Models: A Survey on Biological & Chemical Domains
Zhang, Q., Ding, K., Lv, T., Wang, X., Yin, Q., Zhang, Y., Jing, Y., Wang, Y., Li, X., Xiang, Z., et al. (2025) · ACM Computing Surveys · doi:10.1145/3715318 · Cited by 155 · see citing works
Self-citationCheng, M., Li, M., Zhang, Y., Gu, X., Gao, W., Zhang, S., & Liu, J. (2025) · Environment International · doi:10.1016/j.envint.2025.109324 · Cited by 126 · see citing works
Protein function prediction as approximate semantic entailment
Kulmanov, M., Guzmán-Vega, F. J., Duek Roggli, P., Lane, L., Arold, S. T., & Hoehndorf, R. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00795-w · Cited by 131 · see citing works
Multistate and functional protein design using RoseTTAFold sequence space diffusion
Lisanza, S. L., Gershon, J. M., Tipps, S. W. K., Sims, J. N., Arnoldt, L., Hendel, S. J., Simma, M. K., Liu, G., Yase, M., Wu, H., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02395-w · Cited by 172 · see citing works
Oncofetal protein IGF2BPs in human cancer: functions, mechanisms and therapeutic potential
Zhu, T., Hong, L., & Ling, Z. (2023) · Biomarker Research · doi:10.1186/s40364-023-00499-0 · Cited by 121 · see citing works
Genomic language models: opportunities and challenges
Benegas, G., Ye, C., Albors, C., Li, J. C., & Song, Y. S. (2025) · Trends in Genetics · doi:10.1016/j.tig.2024.11.013 · Cited by 133 · see citing works
RetroRules: a database of reaction rules for engineering biology
Duigou, T., Du, M., Carbonell, P., & Faulon, J. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky940 · Cited by 119 · see citing works
Cesaro, A., Hoffman, S. C., Das, P., & de la Fuente‐Núñez, C. (2025) · npj Antimicrobials and Resistance · doi:10.1038/s44259-024-00068-x · Cited by 132 · see citing works
Robust enzyme discovery and engineering with deep learning using CataPro
Wang, Z.-C., Xie, D., Wu, D., Luo, X.-Z., Wang, S., Li, Y., Yang, Y.-M., Li, W.-F., & Zheng, L.-Z. (2025) · Nature Communications · doi:10.1038/s41467-025-58038-4 · Cited by 128 · see citing works
Foundation models in bioinformatics
Guo, F., Guan, R., Li, Y., Liu, Q., Wang, X., Yang, C., & Wang, J. (2025) · National Science Review · doi:10.1093/nsr/nwaf028 · Cited by 121 · see citing works
CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters
Boorla, V. S., & Maranas, C. D. (2025) · Nature Communications · doi:10.1038/s41467-025-57215-9 · Cited by 114 · see citing works
Global organelle profiling reveals subcellular localization and remodeling at proteome scale
Hein, M. Y., Peng, D., Todorova, V., McCarthy, F., Kim, K., Liu, C., Savy, L., Januel, C., Baltazar-Nunez, R., Sekhar, M., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.028 · Cited by 113 · see citing works
Jiang, Z., Feng, J., Wang, F., Wang, J., Wang, N., Zhang, M., Hsieh, C., Hou, T., Cui, W., & Ma, L. (2025) · Advanced Materials · doi:10.1002/adma.202500043 · Cited by 113 · see citing works
Western, D., Timsina, J., Wang, L., Wang, C., Yang, C., Phillips, B., Wang, Y., Liu, M., Ali, M., Beric, A., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01972-8 · Cited by 111 · see citing works
Galba: genome annotation with miniprot and AUGUSTUS
Brůna, T., Li, H., Guhlin, J. G., Honsel, D., Herbold, S., Stanke, M., Nenasheva, N., Ebel, M., Gabriel, L., & Hoff, K. J. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05449-z · Cited by 108 · see citing works
De novo design of self-assembling peptides with antimicrobial activity guided by deep learning
Liu, H., Song, Z., Zhang, Y. L., Wu, B., Chen, D., Zhou, Z., Zhang, H., Li, S., Feng, X., Huang, J., et al. (2025) · Nature Materials · doi:10.1038/s41563-025-02164-3 · Cited by 118 · see citing works
Prediction of peptide mass spectral libraries with machine learning
Cox, J. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01424-w · Cited by 105 · see citing works
DrugMap: A quantitative pan-cancer analysis of cysteine ligandability
Takahashi, M., Chong, H. B., Zhang, S., Yang, T.-Y., Lazarov, M. J., Harry, S. A., Maynard, M., Hilbert, B. J., White, R. D., Murrey, H. E., et al. (2024) · Cell · doi:10.1016/j.cell.2024.03.027 · Cited by 107 · see citing works
Self-citationAI-Driven Antimicrobial Peptide Discovery: Mining and Generation
Szymczak, P., Zarzecki, W., Wang, J., Duan, Y., Wang, J., Coelho, L. P., de la Fuente‐Núñez, C., & Szczurek, E. (2025) · Accounts of Chemical Research · doi:10.1021/acs.accounts.0c00594 · Cited by 113 · see citing works
GenePT: A Simple But Effective Foundation Model for Genes and Cells Built From ChatGPTPreprint
Chen, Y. T., & Zou, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.16.562533 · Cited by 106 · see citing works
Self-citationxiVIEW: Visualisation of Crosslinking Mass Spectrometry Data
Combe, C., Graham, M., Kolbowski, L., Fischer, L., & Rappsilber, J. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168656 · Cited by 99 · see citing works
From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2
Bret, H., Gao, J., Zea, D. J., Andreani, J., & Guerois, R. (2024) · Nature Communications · doi:10.1038/s41467-023-44288-7 · Cited by 103 · see citing works
Reconstructing growth and dynamic trajectories from single-cell transcriptomics data
Sha, Y., Qiu, Y., Zhou, P., & Nie, Q. H. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00763-w · Cited by 107 · see citing works
Bell, E. L., Rosetto, G., Ingraham, M. A., Ramirez, K. J., Lincoln, C. L., Clarke, R. W., Gado, J. E., Lilly, J. L., Kucharzyk, K. H., Erickson, E., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45523-5 · Cited by 103 · see citing works
Functional and evolutionary significance of unknown genes from uncultivated taxa
del Río, Á. R., Giner‐Lamia, J., Cantalapiedra, C. P., Botas, J., Deng, Z., Hernández-Plaza, A., Palmer, M. M., Santamaría‐Hernando, S., Rodríguez‐Herva, J. J., Ruscheweyh, H., et al. (2023) · Nature · doi:10.1038/s41586-023-06955-z · Cited by 110 · see citing works
PomBase: a Global Core Biodata Resource—growth, collaboration, and sustainability
Rutherford, K., Lera-Ramírez, M., & Wood, V. (2024) · Genetics · doi:10.1093/genetics/iyae007 · Cited by 101 · see citing works
Omidi, A., Møller, M. H., Malhis, N., Bui, J. M., & Gsponer, J. A. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2406407121 · Cited by 103 · see citing works
- Authors
- 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., Cukura, A., Denny, P., Dogan, T., Ebenezer, T., Fan, J., Garmiri, P., da Costa Gonzales, L. J., Hatton-Ellis, E., Hussein, A., Ignatchenko, A., Insana, G., Ishtiaq, R., Joshi, V., Jyothi, D., Kandasaamy, S., Lock, A., Luciani, A., Lugaric, M., Luo, J., Lussi, Y., MacDougall, A., Madeira, F., Mahmoudy, M., Mishra, A., Moulang, K., Nightingale, A., Pundir, S., Qi, G., Raj, S., Raposo, P., Rice, D. L., Saidi, R., Santos, R., Speretta, E., Stephenson, J., Totoo, P., Turner, E., Tyagi, N., Vasudev, P., Warner, K., Watkins, X., Zaru, R., Zellner, H., Bridge, A. J., Aimo, L., Argoud-Puy, G., Auchincloss, A. H., Axelsen, K. B., Bansal, P., Baratin, D., Batista Neto, T. M., Blatter, M.-C., Bolleman, J. T., Boutet, E., Breuza, L., Gil, B. C., Casals-Casas, C., Echioukh, K. C., Coudert, E., Cuche, B., de Castro, E., Estreicher, A., Famiglietti, M. L., Feuermann, M., Gasteiger, E., Gaudet, P., Gehant, S., Gerritsen, V., Gos, A., Gruaz, N., Hulo, C., Hyka-Nouspikel, N., Jungo, F., Kerhornou, A., Le Mercier, P., Lieberherr, D., Masson, P., Morgat, A., Muthukrishnan, V., Paesano, S., Pedruzzi, I., Pilbout, S., Pourcel, L., Poux, S., Pozzato, M., Pruess, M., Redaschi, N., Rivoire, C., Sigrist, C. J. A., Sonesson, K., Sundaram, S., Wu, C. H., Arighi, C. N., Arminski, L., Chen, C., Chen, Y., Huang, H., Laiho, K., McGarvey, P., Natale, D. A., Ross, K., Vinayaka, C. R., Wang, Q., Wang, Y., & Zhang, J.
- ORCID
- Bateman, Alex
- Martin, Maria-Jesus
- Orchard, Sandra
- Magrane, Michele
- Ahmad, Shadab
- Alpi, Emanuele
- Bowler-Barnett, Emily H
- Britto, Ramona
- Denny, Paul
- Ebenezer, ThankGod
- Fan, Jun
- Garmiri, Penelope
- Hatton-Ellis, Emma
- Hussein, Abdulrahman
- Ignatchenko, Alexandr
- Insana, Giuseppe
- Ishtiaq, Rizwan
- Joshi, Vishal
- Jyothi, Dushyanth
- Kandasaamy, Swaathi
- Lock, Antonia
- Luo, Jie
- Lussi, Yvonne
- Nightingale, Andrew
- Pundir, Sangya
- Qi, Guoying
- Raj, Shriya
- Raposo, Pedro
- Rice, Daniel L
- Saidi, Rabie
- Santos, Rafael
- Speretta, Elena
- Stephenson, James
- Totoo, Prabhat
- Tyagi, Nidhi
- Warner, Kate
- Watkins, Xavier
- Zaru, Rossana
- Bridge, Alan J
- Aimo, Lucila
- Auchincloss, Andrea H
- Axelsen, Kristian B
- Bansal, Parit
- Baratin, Delphine
- Blatter, Marie-Claude
- Bolleman, Jerven T
- Boutet, Emmanuel
- Breuza, Lionel
- Gil, Blanca Cabrera
- Coudert, Elisabeth
- Cuche, Beatrice
- Estreicher, Anne
- Famiglietti, Maria L
- Feuermann, Marc
- Gasteiger, Elisabeth
- Gaudet, Pascale
- Gos, Arnaud
- Hulo, Chantal
- Jungo, Florence
- Kerhornou, Arnaud
- Lieberherr, Damien
- Masson, Patrick
- Morgat, Anne
- Muthukrishnan, Venkatesh
- Pedruzzi, Ivo
- Pourcel, Lucille
- Poux, Sylvain
- Pozzato, Monica
- Redaschi, Nicole
- Rivoire, Catherine
- Sigrist, Christian J A
- Sundaram, Shyamala
- Wu, Cathy H
- Arighi, Cecilia N
- Chen, Chuming
- Huang, Hongzhan
- McGarvey, Peter
- Natale, Darren A
- Ross, Karen
- Wang, Qinghua
- Wang, Yuqi
- Zhang, Jian
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-11-21
- Volume
- 51
- Issue
- D1
- Pages
- D523-D531
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC9825514
- OpenAlex
- W4309506674
- Cited by
- 7,210
- References
- 41
- Citations by year
- 2026: 1511 · 2025: 2294 · 2024: 2343 · 2023: 887 · 2022: 78 · 2021: 35 · 2020: 23 · 2019: 21 · 2018: 7 · 2017: 1 · 2015: 1 · 2012: 1
- FWCI
- 464.54
- 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
- National Human Genome Research Institute (OD/DPCPSI/ODSS)
- National Institute of Allergy and Infectious Diseases
- National Institute on Aging
- National Institute of General Medical Sciences
- National Institute of Diabetes and Digestive and Kidney Diseases
- National Eye Institute
- National Cancer Institute
- National Heart, Lung, and Blood Institute
- National Institutes of Health (U24HG007822)
- NHGRI NIH (HG002273)
- Biotechnology and Biological Sciences Research Council (BB/T010541/1, BB/S01781X/1)
- Open Targets
- SERI
- European Molecular Biology Laboratory
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein sequence annotation, reference proteomes, machine learning annotation, AlphaFold protein structure, subcellular localization, data pipelines
- MeSH terms
- Machine Learning, Amino Acid Sequence, Proteome, Databases, Protein, Knowledge Bases
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
