The DOI matches its Crossref record; 40 of the 41 references it cites were found in a registry or an index.
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.
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.
Integrity signals
Registry
Cites
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.
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 cited source says what the claim attributes to it.
In the citing paper
“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]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“of the 51 human PX domain-containing proteins, 26 possess a serine or threonine at positions corresponding to SNX17 S38 or SNX3 S72 (38, 42)”
From Phosphorylation of SNX17 impedes activation of Retriever-mediated sorting (2025) · cites this paper as [42]
Experimental — accuracy not yet measured
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
Database resources of the National Center for Biotechnology Information: update
Wheeler, D. L. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh073 · Cited by 10,695
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×
“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
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
Paysan‐Lafosse, T., Blum, M., Chuguransky, S., Grego, T., Pinto, B. L., Salazar, G. A., Bileschi, M. L., Bork, P., Bridge, A. J., Colwell, L. J., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac993 · Cited by 2,457
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
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
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
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
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
Flagged first, then by citations
1 of the 41 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Paysan-Lafosse, 2022, InterProin 2022, Nucleic Acids Res.
Cited by
Sorted by citations
Items 101–200 of 1,020
Sequence-based prediction of intermolecular interactions driven by disordered regions
Ginell, G. M., Emenecker, R. J., Lotthammer, J. M., Keeley, A. T., Plassmeyer, S. P., Razo, N. J., Usher, E. T., Pelham, J. F., & Holehouse, A. S. (2025) · Science · doi:10.1126/science.adq8381 · Cited by 123
What Can Ribo-Seq, Immunopeptidomics, and Proteomics Tell Us About the Noncanonical Proteome?
Prensner, J. R., Abelin, J. G., Kok, L. W., Clauser, K. R., Mudge, J. M., Ruiz‐Orera, J., Bassani‐Sternberg, M., Moritz, R. L., Deutsch, E. W., & van Heesch, S. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100631 · Cited by 114
Tesson, F., Planel, R., Egorov, A. A., Georjon, H., Vaysset, H., Brancotte, B., Néron, B., Mordret, E., Atkinson, G. C., Bernheim, A., et al. (2024) · Peer Community Journal · doi:10.24072/pcjournal.470 · Cited by 120
DeepSTABp: A Deep Learning Approach for the Prediction of Thermal Protein Stability
Jung, F., Frey, K. L., Zimmer, D., & Mühlhaus, T. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24087444 · Cited by 98
PLMSearch: Protein language model powers accurate and fast sequence search for remote homology
Liu, W., Wang, Z., You, R., Xie, C., Wei, H., Xiong, Y., Yang, J., & Zhu, S. (2024) · Nature Communications · doi:10.1038/s41467-024-46808-5 · Cited by 96
Diverse anti-defence systems are encoded in the leading region of plasmids
Samuel, B., Mittelman, K., Croitoru, S. Y., Haim, M. B., & Burstein, D. (2024) · Nature · doi:10.1038/s41586-024-07994-w · Cited by 97
Sage: An Open-Source Tool for Fast Proteomics Searching and Quantification at Scale
Lazear, M. R. (2023) · Journal of Proteome Research · doi:10.1021/acs.jproteome.3c00486 · Cited by 93
Brouwer, H., Porbahaie, M., Boeren, S. A., Busch, M., & Bouwmeester, H. (2024) · Particle and Fibre Toxicology · doi:10.1186/s12989-024-00563-z · Cited by 99
The molecular architecture of the nuclear basket
(2024) · doi:10.1016/j.cell.2024.07.020 · Cited by 94
Pathophysiology of chikungunya virus infection associated with fatal outcomes
de Souza, W. M., Fumagalli, M. J., de Lima, S. T. S., Parise, P. L., Carvalho, D. C. M., Hernandez, C., de Jesus, R., Delafiori, J., Cândido, D. D. S., Carregari, V. C., et al. (2024) · Cell Host & Microbe · doi:10.1016/j.chom.2024.02.011 · Cited by 96
Yao, L., Guan, J., Xie, P., Chung, C., Zhao, Z., Dong, D., Guo, Y., Zhang, W., Deng, J., Pang, Y., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1019 · Cited by 101
Cryo-EM structures reveal native GABAA receptor assemblies and pharmacology
Sun, C. L., Zhu, H., Clark, S., & Gouaux, E. (2023) · Nature · doi:10.1038/s41586-023-06556-w · Cited by 95
Deciphering the impact of genomic variation on function
Writing group (ordered by contribution), Engreitz, J., Lawson, H. A., Singh, H., Starita, L. M., Hon, G. C., Carter, H. K., Sahni, N., Reddy, T. E., Lin, X., et al. (2024) · Nature · doi:10.1038/s41586-024-07510-0 · Cited by 105
Valdés-Mas, R., Leshem, A., Zheng, D., Cohen, Y., Kern, L., Zmora, N., He, Y., Katina, C., Eliyahu-Miller, S., Yosef-Hevroni, T., et al. (2025) · Cell · doi:10.1016/j.cell.2024.12.016 · Cited by 89
Zhang, X., Ibrahim, Z., Khaskheli, M. B., Raza, H., Zhou, F., & Shamsi, I. H. (2024) · Sustainability · doi:10.3390/su16177651 · Cited by 93
Bispecific antibodies in the treatment of multiple myeloma
Devasia, A. J., Chari, A., & Lancman, G. (2024) · Blood Cancer Journal · doi:10.1038/s41408-024-01139-y · Cited by 94
Herrera, L. P. T., Andreassen, S. N., Caroli, J., Rodríguez‐Espigares, I., Kermani, A. A., Keserű, G. M., Kooistra, A. J., Pándy‐Szekeres, G., & Gloriam, D. E. I. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1065 · Cited by 88
BFVD—a large repository of predicted viral protein structures
Kim, R. S., Levy Karin, E., Mirdita, M., Chikhi, R., & Steinegger, M. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1119 · Cited by 86
Critical assessment of protein intrinsic disorder prediction ( CAID ) ‐ Results of round 2
Conte, A. D., Mehdiabadi, M., Bouhraoua, A., Miguel Monzon, A., Tosatto, S. C. E., & Piovesan, D. (2023) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26582 · Cited by 86
Ding, K., Chin, M., Zhao, Y., Huang, W., Mai, B. K., Wang, H., Liu, P., Yang, Y., & Luo, Y. (2024) · Nature Communications · doi:10.1038/s41467-024-50698-y · Cited by 86
Bergquist, T., Stenton, S. L., Nadeau, E. A. W., Byrne, A. B., Greenblatt, M. S., Harrison, S. M., Tavtigian, S. V., O’Donnell‐Luria, A., Biesecker, L. G., Predrag Radivojac, A., et al. (2025) · Genetics in Medicine · doi:10.1016/j.gim.2025.101402 · Cited by 92
Bardet-Biedl Syndrome: Current Perspectives and Clinical Outlook
Melluso, A., Secondulfo, F., Capolongo, G., Capasso, G., & Zacchia, M. (2023) · Therapeutics and Clinical Risk Management · doi:10.2147/tcrm.s338653 · Cited by 97
Bioorthogonal photocatalytic proximity labeling in primary living samples
Liu, Z., Guo, F., Zhu, Y., Qin, S., Hou, Y., Guo, H., Lin, F., Chen, P. R., & Fan, X. (2024) · Nature Communications · doi:10.1038/s41467-024-46985-3 · Cited by 91
Artificial intelligence for drug discovery and development in Alzheimer's disease
Qiu, Y., & Cheng, F. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102776 · Cited by 83
Advancing microbial production through artificial intelligence-aided biology
Gong, X., Zhang, J., Gan, Q., Teng, Y., Hou, J., Lyu, Y., Liu, Z., Wu, Z., Dai, R., Zou, Y., et al. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108399 · Cited by 85
Self-citationPiovesan, D., Conte, A. D., Mehdiabadi, M., Aspromonte, M. C., Blum, M., Tesei, G., von Bülow, S., Lindorff‐Larsen, K., & Tosatto, S. C. E. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae969 · Cited by 85
Unlocking de novo antibody design with generative artificial intelligencePreprint
Shanehsazzadeh, A., McPartlon, M., Kasun, G. W., Steiger, A. K., Sutton, J. M., Yassine, E., McCloskey, C. M., Haile, R. G., Shuai, R. W., Alverio, J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.08.523187 · Cited by 87
Expanding the human proteome with microproteins and peptideins
Deutsch, E. W., Kok, L. W., Mudge, J. M., Valls, C., Jungreis, I., Ruiz‐Orera, J., Sun, Z., Kusebauch, U., Fierro-Monti, I., Abelin, J. G., et al. (2026) · Nature · doi:10.1038/s41586-026-10459-x · Cited by 98
Self-citationYang, Y. R., Gao, D., Xie, X., Qin, J., Li, J., Lin, H., Yan, D., & Deng, K. (2022) · Clinical Pharmacokinetics · doi:10.1007/s40262-022-01180-9 · Cited by 80
García‐Moreno, P. J., Jacobsen, C., Marcatili, P., Echers, S. G., Overgaard, M. T., Andersen, M. L., Sørensen, A. M., & Hansen, E. B. (2019) · Food Hydrocolloids · doi:10.1016/j.foodhyd.2019.105529 · Cited by 79
Pramanik, S. K., Mahmud, S., Paul, G. K., Jabin, T., Naher, K., Uddin, M. S., Zaman, S., & Saleh, M. A. (2020) · Current Research in Microbial Sciences · doi:10.1016/j.crmicr.2020.100013 · Cited by 79
López‐Cerdán, A., Andreu, Z., Hidalgo, M. R., Grillo-Risco, R., Català-Senent, J. F., Soler-Sáez, I., Neva-Alejo, A., Gordillo, F., de la Iglesia-Vayá, M., & Garcia‐García, F. (2022) · Biology of Sex Differences · doi:10.1186/s13293-022-00477-5 · Cited by 77
Kumar, B., Lorusso, E., Fosso, B., & Pesole, G. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1343572 · Cited by 93
He, Y., Zhou, X., Chang, C. H., Chen, G., Liu, W., Li, G., Fan, X., Sun, M., Miao, C., Huang, Q., et al. (2024) · Molecular Cell · doi:10.1016/j.molcel.2024.01.021 · Cited by 79
The microbiologist's guide to metaproteomics
Bossche, T. V. D., Armengaud, J., Benndorf, D., Blakeley‐Ruiz, J. A., Brauer, M., Cheng, K., Creskey, M., Figeys, D., Grenga, L., Griffin, T. J., et al. (2025) · iMeta · doi:10.1002/imt2.70031 · Cited by 82
Chung, K. Y., Xu, B., Tan, D., Yang, Q., Li, Z., & Fu, H. C. (2024) · Nano-Micro Letters · doi:10.1007/s40820-024-01362-z · Cited by 77
Molecular mimicry as a mechanism of viral immune evasion and autoimmunity
Maguire, C. P., Wang, C., Ramasamy, A., Fonken, C., Morse, B. A., Lopez, N., Wylie, D., & Melamed, E. (2024) · Nature Communications · doi:10.1038/s41467-024-53658-8 · Cited by 96
Hong, Y., Wang, D., Lin, Y., Yang, Q., Wang, Y., Xie, Y., Shu, W., Gao, S., & Hua, C. (2024) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2024.117352 · Cited by 79
Self-citationTemplate and target-site recognition by human LINE-1 in retrotransposition
Thawani, A., Ariza, A. J. F., Nogales, E., & Collins, K. (2023) · Nature · doi:10.1038/s41586-023-06933-5 · Cited by 82
Predicting the subcellular location of prokaryotic proteins with DeepLocPro
Moreno, J., Nielsen, H., Winther, O., & Teufel, F. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae677 · Cited by 76
PHI-base – the multi-species pathogen–host interaction database in 2025
Urban, M., Cuzick, A., Seager, J. W., Nonavinakere, N., Sahoo, J., Sahu, P., Iyer, V. L., Khamari, L., Martinez, M. C., & Hammond-Kosack, K. E. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1084 · Cited by 82
BioASQ-QA: A manually curated corpus for Biomedical Question Answering
Krithara, A., Nentidis, A., Bougiatiotis, K., & Παλιούρας, Γ. (2023) · Scientific Data · doi:10.1038/s41597-023-02068-4 · Cited by 75
Disrupted degradative sorting of TLR7 is associated with human lupus
Mishra, H., Schlack-Leigers, C., Lim, E. L., Thieck, O., Magg, T., Raedler, J., Wolf, C., Klein, C., Ewers, H., Lee‐Kirsch, M. A., et al. (2024) · Science Immunology · doi:10.1126/sciimmunol.adi9575 · Cited by 74
Cheng, P., Mao, C., Tang, J., Yang, S., Cheng, Y., Wang, W., Gu, Q., Han, W., Chen, H., Li, S., et al. (2024) · Cell Research · doi:10.1038/s41422-024-00989-2 · Cited by 76
Self-citationMachine Learning‐Enabled Drug‐Induced Toxicity Prediction
Bai, C., Wu, L., Li, R., Cao, Y., He, S., & Bo, X. (2025) · Advanced Science · doi:10.1002/advs.202413405 · Cited by 88
Starke, R., Jehmlich, N., & Bastida, F. (2018) · Journal of Proteomics · doi:10.1016/j.jprot.2018.11.011 · Cited by 71
Accurate modeling of peptide-MHC structures with AlphaFold
Mikhaylov, V., Brambley, C. A., Keller, G. L. J., Arbuiso, A. G., Weiss, L. I., Baker, B. M., & Levine, A. J. (2024) · Structure · doi:10.1016/j.str.2023.11.011 · Cited by 72
The origin and structural evolution of de novo genes in Drosophila
Peng, J., & Zhao, L. (2024) · Nature Communications · doi:10.1038/s41467-024-45028-1 · Cited by 77
Martiniaková, M., Bíro, R., Kováčová, V., Babikova, M., Zemanová, N., Mondočková, V., & Omelka, R. (2024) · Journal of Molecular Medicine · doi:10.1007/s00109-024-02418-8 · Cited by 80
Updates to the Alliance of Genome Resources central infrastructure
Aleksander, S., Anagnostopoulos, A. V., Antonazzo, G., Arnaboldi, V., Attrill, H. L., Becerra, A., Bello, S. M., Blodgett, O., Bradford, Y. M., Bult, C. J., et al. (2024) · Genetics · doi:10.1093/genetics/iyae049 · Cited by 78
Self-citationRecruitment of FBXO22 for targeted degradation of NSD2
Nie, Y., Tabor, J. R., Li, J., Kutera, M., St‐Germain, J. R., Hanley, R. P., Wolf, E., Paulаkonis, E., Kenney, T. M. G., Duan, S., et al. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01660-y · Cited by 72
Asian diversity in human immune cells
Kock, K. H., Tan, L. M., Han, K. Y., Ando, Y., Jevapatarakul, D., Chatterjee, A., Lin, Q. X. X., Buyamin, E. V., Sonthalia, R., Rajagopalan, D., et al. (2025) · Cell · doi:10.1016/j.cell.2025.02.017 · Cited by 85
A survey of geometric graph neural networks: data structures, models and applications
Han, J., Cen, J., Wu, L., Li, Z., Kong, X., Jiao, R., Yu, Z., Xu, T., Wu, F., Wang, Z., et al. (2025) · Frontiers of Computer Science · doi:10.1007/s11704-025-41426-w · Cited by 104
ASD2023: towards the integrating landscapes of allosteric knowledgebase
He, J., Liu, X., Zhu, C., Zha, J., Li, Q., Zhao, M., Wei, J., Li, M., Wu, C., Wang, J., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad915 · Cited by 69
Self-citationSimpson, J. B., Walker, M. E., Sekela, J. J., Ivey, S. M., Jariwala, P. B., Storch, C. M., Kowalewski, M. E., Graboski, A. L., Lietzan, A. D., Walton, W. G., et al. (2024) · Cell Host & Microbe · doi:10.1016/j.chom.2024.04.018 · Cited by 75
Zheng, L., Shi, S., Lu, M., Fang, P., Pan, Z., Zhang, H., Zhou, Z., Zhang, H., Mou, M., Huang, S., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03166-1 · Cited by 73
Self-citationPeng, H., Wang, H., Kong, W., Li, J., & Goh, W. W. B. (2024) · Nature Communications · doi:10.1038/s41467-024-47899-w · Cited by 69
Protein language models are biased by unequal sequence sampling across the tree of life
Ding, F., & Steinhardt, J. (2024) · doi:10.1101/2024.03.07.584001 · Cited by 57
Díaz‐Holguín, A., Saarinen, M., Vo, D. D., Sturchio, A., Branzell, N., de Vaca, I. C., Hu, H., Mitjavila-Domènech, N., Lindqvist, A., Baranczewski, P., et al. (2024) · Science Advances · doi:10.1126/sciadv.adn1524 · Cited by 73
Machine learning prediction of enzyme optimum pH
Gado, J. E., Knotts, M., Shaw, A. Y., Marks, D. S., Gauthier, N. P., Sander, C., & Beckham, G. T. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01026-6 · Cited by 78
Dual-inhibitory domain iCARs improve the efficiency of the AND-NOT gate CAR T strategy
Bangayan, N. J., Wang, L., Sojo, G. B., Noguchi, M., Cheng, D., Ta, L. H., Gunn, D., Mao, Z., Liu, S., Yin, Q., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2312374120 · Cited by 68
The ProteomeXchange consortium in 2026: making proteomics data FAIR
Deutsch, E. W., Bandeira, N., Pérez‐Riverol, Y., Sharma, V., Carver, J., Mendoza, L., Kundu, D. J., Bandla, C., Kamatchinathan, S., Hewapathirana, S., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1146 · Cited by 81
Hammer, L., Moretti, D., Abbühl, L., Kandiah, P., Hilaj, N., Portmann, R., & Egger, L. (2023) · Frontiers in Nutrition · doi:10.3389/fnut.2023.1150581 · Cited by 86
AI-Based Prediction of Protein Corona Composition on DNA Nanostructures
Huzar, J., Coreas, R., Landry, M. P., & Tikhomirov, G. A. (2025) · ACS Nano · doi:10.1021/acsnano.4c12259 · Cited by 73
Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution.
Bisardi, M., Rodriguez-Rivas, J., Zamponi, F., & Weigt, M. (2022) · Molecular Biology and Evolution · doi:10.1093/molbev/msab321
Petrov, P. B., Considine, J. M., Izzi, V., & Naba, A. (2023) · Journal of Cell Science · doi:10.1242/jcs.261255 · Cited by 74
Wang, B., Pozarickij, A., Mazidi, M., Wright, N., Yao, P., Said, S., Iona, A., Kartsonaki, C., Fry, H., Lin, K., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56935-2 · Cited by 64
Self-citationChu, S., Xie, X., Payan, C., & Stochaj, U. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00639-y · Cited by 58
Exploring new galaxies: Perspectives on the discovery of novel PET-degrading enzymes
Mičan, J., Jaradat, D. M. M., Liu, W., Weber, G., Mazurenko, S., Bornscheuer, U. T., Damborský, J., Wei, R., & Bednář, D. (2023) · Applied Catalysis B: Environmental · doi:10.1016/j.apcatb.2023.123404 · Cited by 65
DTI-LM: language model powered drug–target interaction prediction
Ahmed, K. T., Ansari, M. I., & Zhang, W. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae533 · Cited by 65
Machine Learning for Sequence and Structure-Based Protein–Ligand Interaction Prediction
Zhang, Y., Li, S., Meng, K., & Sun, S. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01841 · Cited by 63
Large scale paired antibody language models
Kenlay, H., Dreyer, F. A., Kovaltsuk, A., Miketa, D., Pires, D. E. V., & Deane, C. M. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012646 · Cited by 70
Merecz-Sadowska, A., Sadowski, A., Zielińska‐Bliźniewska, H., Zajdel, K., & Zajdel, R. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26146678 · Cited by 70
P450Rdb: A manually curated database of reactions catalyzed by cytochrome P450 enzymes
Zhang, Y., Pan, X., Shi, T., Gu, Z.-F., Yang, Z., Liu, M., Xu, Y. M., Yang, Y., Ren, L., Song, X., et al. (2023) · Journal of Advanced Research · doi:10.1016/j.jare.2023.10.012 · Cited by 61
Schäfer, P. S. L., Dimitrov, D., Villablanca, E. J., & Sáez-Rodríguez, J. (2024) · Nature Immunology · doi:10.1038/s41590-024-01768-2 · Cited by 71
Izadifar, Z., Cotton, J., Chen, S., Bustos, N. A., Horvath, V., Stejskalová, A., Wu, C. M., Gulati, A., LoGrande, N., Budnik, B., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48910-0 · Cited by 64
Self-citationGain-of-function human UNC93B1 variants cause systemic lupus erythematosus and chilblain lupus
David, C., Arango-Franco, C. A., Badonyi, M., Fouchet, J., Rice, G., Didry‐Barca, B., Maisonneuve, L., Seabra, L., Kechiche, R., Masson, C., et al. (2024) · The Journal of Experimental Medicine · doi:10.1084/jem.20232066 · Cited by 63
Transferable deep generative modeling of intrinsically disordered protein conformations
Janson, G., & Feig, M. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012144 · Cited by 62
Glycine and aging: Evidence and mechanisms
Johnson, A. A., & Cuellar, T. L. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.101922 · Cited by 65
Burley, S. K., Piehl, D. W., Vallat, B. K., & Zardecki, C. (2024) · IUCrJ · doi:10.1107/s2052252524002604 · Cited by 64
Artificial intelligence in peptide-based drug design
Zhai, S., Liu, T., Lin, S., Li, D., Liu, H., Yao, X., & Hou, T. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104300 · Cited by 67
MaizeGDB's new data types, resources and activities
Lawrence‐Dill, C. J., Schaeffer, M. L., Seigfried, T. E., Campbell, D. A., & Harper, L. C. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkl1048 · Cited by 60
Cao, Z., Fan, D., Sun, Y., Huang, Z., Li, Y., Su, R., Zhang, F., Li, Q., Yang, H., Zhang, F., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45794-y · Cited by 62
Immunopeptidomics-based identification of naturally presented non-canonical circRNA-derived peptides
Ferreira, H. J., Stevenson, B. J., Pak, H., Yu, F., Oliveira, J., Huber, F., Taillandier-Coindard, M., Michaux, J., Altimiras, E. R., Kraemer, A. I., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46408-3 · Cited by 61
Understanding the genetic complexity of puberty timing across the allele frequency spectrum
Kentistou, K. A., Kaisinger, L. R., Stankovic, S., Vaudel, M., de Oliveira, E. M., Messina, A., Walters, R. G., Liu, X., Busch, A. S., Helgason, H., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01798-4 · Cited by 81
Lin, B., Luo, X., Liu, Y., & Jin, X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae289 · Cited by 63
Simple and effective embedding model for single-cell biology built from ChatGPT
Chen, Y. T., & Zou, J. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01284-6 · Cited by 205
Self-citationCXCL2: a key player in the tumor microenvironment and inflammatory diseases
Lv, Y., Chen, C., Han, M., Tian, C., Song, F., Feng, S., Xu, M., Zhao, Z., Zhou, H., Su, W., et al. (2025) · Cancer Cell International · doi:10.1186/s12935-025-03765-3 · Cited by 59
Self-citationLalis, M., Hladiš, M., Khalil, S. A., Briand, L. V., Fiorucci, S., & Topin, J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad886 · Cited by 58
Herrera-Quiterio, G. A., & Encarnación-Guevara, S. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1244740 · Cited by 55
An overview on polyurethane-degrading enzymes
Raczyńska, A., Góra, A., & André, I. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108439 · Cited by 66
Evaluating generalizability of artificial intelligence models for molecular datasets
Ektefaie, Y., Shen, A. N., Bykova, D., Marin, M. G., Žitnik, M., & Farhat, M. R. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00931-6 · Cited by 69
Kettel, P., & Karagöz, G. E. (2024) · The International Journal of Biochemistry & Cell Biology · doi:10.1016/j.biocel.2024.106598 · Cited by 60
Deep contrastive learning enables genome-wide virtual screening
Jia, Y., Gao, B., Tan, J., Zheng, J., Hong, X., Zhu, W., Tan, H., Xiao, Y., Tan, L., Cai, H., et al. (2026) · Science · doi:10.1126/science.ads9530 · Cited by 66
The fork protection complex promotes parental histone recycling and epigenetic memory
Charlton, S. J., Flury, V., Kanoh, Y., Genzor, A. V., Kollenstart, L., Ao, W., Brøgger, P., Weisser, M. B., Adamus, M., Alcaraz, N., et al. (2024) · Cell · doi:10.1016/j.cell.2024.07.017 · Cited by 56
Benchmarking AI-powered docking methods from the perspective of virtual screening
Gu, S., Shen, C., Zhang, X., Sun, H., Cai, H., Luo, H., Zhao, H., Liu, B., Du, H., Zhao, Y., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-00993-0 · Cited by 57
Roothans, N., Pabst, M., van Diemen, M., Mexicano, C. H., Zandvoort, M. H., Abeel, T., van Loosdrecht, M. C. M., & Laureni, M. (2025) · Nature Water · doi:10.1038/s44221-025-00430-x · Cited by 55
Pei, Q., Zhang, W., Zhu, J., Wu, K., Gao, K., Wu, L., Xia, Y., & Yan, R. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.70 · Cited by 108
Epithelial-derived interleukin-23 promotes oral mucosal immunopathology
Kim, T. S., Ikeuchi, T., Theofilou, V. I., Williams, D. W., Greenwell‐Wild, T., June, A., Adade, E. E., Li, L., Abusleme, L., Dutzan, N., et al. (2024) · Immunity · doi:10.1016/j.immuni.2024.02.020 · Cited by 56
Metadata
- 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
- de Castro, Edouard
- 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
- 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
