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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.
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“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]
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“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]
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“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
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Prediction of protein assemblies by structure sampling followed by interface‐focused scoring
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Protein structure-informed bacteriophage genome annotation with Phold
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Nucleophilic covalent ligand discovery for the cysteine redoxome
Fu, L., Jung, Y., Tian, C., Ferreira, R. B., Cheng, R., He, F., Yang, J. H., & Carroll, K. S. (2023) · Nature Chemical Biology · doi:10.1038/s41589-023-01330-5 · Cited by 46
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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 ›
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