UniProt: the universal protein knowledgebase in 2021
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 article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.
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Only 35% of human proteins map to a PDB entry, and in many cases the structure covers only a fragment of the sequence.
“Despite this intensive study, only 35% of human proteins map to a PDB entry, and in many cases the structure covers only a fragment of the sequence6.”
From Highly accurate protein structure prediction for the human proteome (2021) · cites this paper as [6]
The Data Integration section discusses mapping datasets to protein sequences, but not the proportion of human proteins with PDB entries or how much of each sequence a structure covers. Searches for “35% of human proteins,” “PDB,” and fragmentary structure coverage found no passage addressing either claim. (LLM, conf=0.75)
Experimental — accuracy not yet measuredAlthough the number of known genes involved in ATG is increasing, a systematic experimental method for identifying ATG-related genes has yet to be established.
“Although the number of known genes involved in ATG is increasing (71), a systematic experimental method for identifying ATG-related genes has yet to be established (72).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [71]
The citation is attached to the assertion that no systematic experimental method for identifying ATG-related genes has been established. The paper’s “Automatic annotation” section discusses computational annotation of proteins, but neither it nor the passages retrieved for ATG and autophagy-related gene discovery address that assertion. The supplied copy gives no page number for these passages. (LLM, conf=0.76)
Experimental — accuracy not yet measuredUniProt archives almost 220 million unique protein sequences.
“However, while the Universal Protein Resource (UniProt) archives almost 220 million unique protein sequences, the Protein Data Bank (PDB) holds only just over 180 000 3D structures for over 55 000 distinct proteins, thus severely limiting the coverage of the sequence space to support biomolecular research globally (5–7).”
From AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models (2022) · cites this paper as [5]
The coverage of the sequence space by experimental structures is severely limited.
“thus severely limiting the coverage of the sequence space to support biomolecular research globally (5–7).”
From AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models (2022) · cites this paper as [5]
The PDB holds just over 180,000 3D structures for over 55,000 distinct proteins.
“However, while the Universal Protein Resource (UniProt) archives almost 220 million unique protein sequences, the Protein Data Bank (PDB) holds only just over 180 000 3D structures for over 55 000 distinct proteins, thus severely limiting the coverage of the sequence space to support biomolecular research globally (5–7).”
From AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models (2022) · cites this paper as [5]
Each protein has a dedicated structure page that shows basic information and three separate outputs of the AlphaFold model.
“Each protein has a dedicated structure page that shows basic information (drawn from UniProt (5) and PDBe (6)) and three separate outputs of the AlphaFold model.”
From AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models (2022) · cites this paper as [5]
UniProt, Pfam, InterPro and PDBe-KB use the AlphaFold DB API to display AlphaFold models on their web pages.
“UniProt (5), Pfam (18), InterPro (19) and PDBe-KB (7) use this API to display AlphaFold models on their web pages.”
From AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models (2022) · cites this paper as [5]
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Novel machine learning approaches revolutionize protein knowledge
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Construction and Analysis of an Enzyme-Constrained Metabolic Model of Corynebacterium glutamicum
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Phosphoregulation of DSB-1 mediates control of meiotic double-strand break activity
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Computed cancer interactome explains the effects of somatic mutations in cancers
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Vision-related convergent gene losses reveal SERPINE3’s unknown role in the eye
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Data integration across conditions improves turnover number estimates and metabolic predictions
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- Authors
- The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Agivetova, R., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bursteinas, B., Bye-A-Jee, H., Coetzee, R., Cukura, A., Da Silva, A., Denny, P., Dogan, T., Ebenezer, T., Fan, J., Castro, L. G., Garmiri, P., Georghiou, G., Gonzales, L., Hatton-Ellis, E., Hussein, A., Ignatchenko, A., Insana, G., Ishtiaq, R., Jokinen, P., Joshi, V., Jyothi, D., Lock, A., Lopez, R., Luciani, A., Luo, J., Lussi, Y., MacDougall, A., Madeira, F., Mahmoudy, M., Menchi, M., Mishra, A., Moulang, K., Nightingale, A., Oliveira, C. S., Pundir, S., Qi, G., Raj, S., Rice, D., Lopez, M. R., Saidi, R., Sampson, J., Sawford, T., Speretta, E., Turner, E., Tyagi, N., Vasudev, P., Volynkin, V., Warner, K., Watkins, X., Zaru, R., Zellner, H., Bridge, A., Poux, S., Redaschi, N., Aimo, L., Argoud-Puy, G., Auchincloss, A., Axelsen, K., Bansal, P., Baratin, D., Blatter, M.-C., Bolleman, J., Boutet, E., Breuza, L., Casals-Casas, C., de Castro, E., Echioukh, K. C., Coudert, E., Cuche, B., Doche, M., Dornevil, D., Estreicher, A., Famiglietti, M. L., Feuermann, M., Gasteiger, E., Gehant, S., Gerritsen, V., Gos, A., Gruaz-Gumowski, N., Hinz, U., Hulo, C., Hyka-Nouspikel, N., Jungo, F., Keller, G., Kerhornou, A., Lara, V., Le Mercier, P., Lieberherr, D., Lombardot, T., Martin, X., Masson, P., Morgat, A., Neto, T. B., Paesano, S., Pedruzzi, I., Pilbout, S., Pourcel, L., Pozzato, M., Pruess, M., Rivoire, C., Sigrist, C., Sonesson, K., Stutz, A., Sundaram, S., Tognolli, M., Verbregue, L., Wu, C. H., Arighi, C. N., Arminski, L., Chen, C., Chen, Y., Garavelli, J. S., Huang, H., Laiho, K., McGarvey, P., Natale, D. A., Ross, K., Vinayaka, C. R., Wang, Q., Wang, Y., Yeh, L.-S., Zhang, J., Ruch, P., & Teodoro, D.
- ORCID
- Bateman, Alex
- Martin, Maria-Jesus
- Orchard, Sandra
- Magrane, Michele
- Ahmad, Shadab
- Alpi, Emanuele
- Bowler-Barnett, Emily H
- Britto, Ramona
- Bursteinas, Borisas
- Denny, Paul
- Ebenezer, ThankGod
- Fan, Jun
- Garmiri, Penelope
- Georghiou, George
- Gonzales, Leonardo
- Hatton-Ellis, Emma
- Hussein, Abdulrahman
- Ignatchenko, Alexandr
- Insana, Giuseppe
- Ishtiaq, Rizwan
- Joshi, Vishal
- Jyothi, Dushyanth
- Lock, Antonia
- Luo, Jie
- Lussi, Yvonne
- Menchi, Manuela
- Mishra, Alok
- Nightingale, Andrew
- Pundir, Sangya
- Qi, Guoying
- Raj, Shriya
- Rice, Daniel
- Saidi, Rabie
- Sawford, Tony
- Speretta, Elena
- Turner, Edward
- Tyagi, Nidhi
- Volynkin, Vladimir
- Warner, Kate
- Watkins, Xavier
- Zaru, Rossana
- Bridge, Alan
- Poux, Sylvain
- Redaschi, Nicole
- Aimo, Lucila
- Auchincloss, Andrea
- Axelsen, Kristian
- Bansal, Parit
- Baratin, Delphine
- Blatter, Marie-Claude
- Bolleman, Jerven
- Boutet, Emmanuel
- Breuza, Lionel
- Coudert, Elisabeth
- Cuche, Beatrice
- Estreicher, Anne
- Famiglietti, Maria Livia
- Feuermann, Marc
- Gasteiger, Elisabeth
- Gos, Arnaud
- Gruaz-Gumowski, Nadine
- Hinz, Ursula
- Hulo, Chantal
- Jungo, Florence
- Kerhornou, Arnaud
- Lieberherr, Damien
- Lombardot, Thierry
- Masson, Patrick
- Morgat, Anne
- Neto, Teresa Batista
- Pedruzzi, Ivo
- Pourcel, Lucille
- Pozzato, Monica
- Rivoire, Catherine
- Sigrist, Christian
- Sundaram, Shyamala
- Tognolli, Michael
- Wu, Cathy H
- Arighi, Cecilia N
- Chen, Chuming
- Garavelli, John S
- Huang, Hongzhan
- McGarvey, Peter
- Natale, Darren A
- Ross, Karen
- Wang, Qinghua
- Wang, Yuqi
- Zhang, Jian
- Ruch, Patrick
- Teodoro, Douglas
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-11-25
- Volume
- 49
- Issue
- D1
- Pages
- D480-D489
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC7778908
- OpenAlex
- W3112376646
- Cited by
- 6,372
- References
- 45
- Citations by year
- 2026: 328 · 2025: 607 · 2024: 1076 · 2023: 1718 · 2022: 1963 · 2021: 663 · 2020: 10 · 2019: 3 · 2018: 1
- FWCI
- 360.43
- 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)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion, other-oa)
- Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
- ArODES (HES-SO (https://www.hes-so.ch/)) (submittedVersion, cc-by)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Funders
- National Eye Institute
- National Heart, Lung, and Blood Institute
- National Institute of Allergy and Infectious Diseases
- National Institute of Diabetes and Digestive and Kidney Diseases
- National Cancer Institute
- National Institutes of Health (U24HG007822)
- National Human Genome Research Institute (U41HG002273)
- National Institute of General Medical Sciences (R01GM080646, P20GM103446)
- Biotechnology and Biological Sciences Research Council (BB/T010541/1)
- British Heart Foundation (RG/13/5/30112)
- Open Targets
- Swiss Federal Government
- 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 › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- protein sequence annotation, automatic annotation, expert curation, COVID-19 pandemic, protein databases
- MeSH terms
- COVID-19, SARS-CoV-2, Humans, User-Computer Interface, Viral Proteins, Computational Biology, Internet, Proteome, Databases, Protein, Proteomics, Knowledge Bases, Pandemics, Molecular Sequence Annotation, Data Curation
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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