Cited by 7,210Genomics and Phylogenetic StudiesBioinformatics and Genomic NetworksMachine Learning in Bioinformatics

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UniProt: the Universal Protein Knowledgebase in 2023

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The UniProt Consortium, Bateman, Alex, Martin, Maria-Jesus, Orchard, Sandra, Magrane, Michele, Ahmad, Shadab, Alpi, Emanuele, Bowler-Barnett, Emily H, Britto, Ramona, Bye-A-Jee, Hema, Cukura, Austra, Denny, Paul, Dogan, Tunca, Ebenezer, ThankGod, Fan, Jun, Garmiri, Penelope, da Costa Gonzales, Leonardo Jose, Hatton-Ellis, Emma, Hussein, Abdulrahman, Ignatchenko, Alexandr, Insana, Giuseppe, Ishtiaq, Rizwan, Joshi, Vishal, Jyothi, Dushyanth, Kandasaamy, Swaathi, Lock, Antonia, Luciani, Aurelien, Lugaric, Marija, Luo, Jie, Lussi, Yvonne, MacDougall, Alistair, Madeira, Fabio, Mahmoudy, Mahdi, Mishra, Alok, Moulang, Katie, Nightingale, Andrew, Pundir, Sangya, Qi, Guoying, Raj, Shriya, Raposo, Pedro, Rice, Daniel L, Saidi, Rabie, Santos, Rafael, Speretta, Elena, Stephenson, James, Totoo, Prabhat, Turner, Edward, Tyagi, Nidhi, Vasudev, Preethi, Warner, Kate, Watkins, Xavier, Zaru, Rossana, Zellner, Hermann, Bridge, Alan J, Aimo, Lucila, Argoud-Puy, Ghislaine, Auchincloss, Andrea H, Axelsen, Kristian B, Bansal, Parit, Baratin, Delphine, Batista Neto, Teresa M, Blatter, Marie-Claude, Bolleman, Jerven T, Boutet, Emmanuel, Breuza, Lionel, Gil, Blanca Cabrera, Casals-Casas, Cristina, Echioukh, Kamal Chikh, Coudert, Elisabeth, Cuche, Beatrice, de Castro, Edouard, Estreicher, Anne, Famiglietti, Maria L, Feuermann, Marc, Gasteiger, Elisabeth, Gaudet, Pascale, Gehant, Sebastien, Gerritsen, Vivienne, Gos, Arnaud, Gruaz, Nadine, Hulo, Chantal, Hyka-Nouspikel, Nevila, Jungo, Florence, Kerhornou, Arnaud, Le Mercier, Philippe, Lieberherr, Damien, Masson, Patrick, Morgat, Anne, Muthukrishnan, Venkatesh, Paesano, Salvo, Pedruzzi, Ivo, Pilbout, Sandrine, Pourcel, Lucille, Poux, Sylvain, Pozzato, Monica, Pruess, Manuela, Redaschi, Nicole, Rivoire, Catherine, Sigrist, Christian J A, Sonesson, Karin, Sundaram, Shyamala, Wu, Cathy H, Arighi, Cecilia N, Arminski, Leslie, Chen, Chuming, Chen, Yongxing, Huang, Hongzhan, Laiho, Kati, McGarvey, Peter, Natale, Darren A, Ross, Karen, Vinayaka, C R, Wang, Qinghua, Wang, Yuqi, Zhang, Jian·2023·Nucleic Acids Research

DOI 10.1093/nar/gkac1052PMC PMC9825514

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Abstract

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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Database resources of the National Center for Biotechnology Information: update
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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).”
Background
UniProt: 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
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Method
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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
DOI: 10.1093/nar/gkac1052
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: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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

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