CoV-AbDab: the coronavirus antibody database
Open accessPublished version of a preprint
Abstract
Motivation: The emergence of a novel strain of betacoronavirus, SARS-CoV-2, has led to a pandemic that has been associated with over 700 000 deaths as of August 5, 2020. Research is ongoing around the world to create vaccines and therapies to minimize rates of disease spread and mortality. Crucial to these efforts are molecular characterizations of neutralizing antibodies to SARS-CoV-2. Such antibodies would be valuable for measuring vaccine efficacy, diagnosing exposure and developing effective biotherapeutics. Here, we describe our new database, CoV-AbDab, which already contains data on over 1400 published/patented antibodies and nanobodies known to bind to at least one betacoronavirus. This database is the first consolidation of antibodies known to bind SARS-CoV-2 as well as other betacoronaviruses such as SARS-CoV-1 and MERS-CoV. It contains relevant metadata including evidence of cross-neutralization, antibody/nanobody origin, full variable domain sequence (where available) and germline assignments, epitope region, links to relevant PDB entries, homology models and source literature. Results: On August 5, 2020, CoV-AbDab referenced sequence information on 1402 anti-coronavirus antibodies and nanobodies, spanning 66 papers and 21 patents. Of these, 1131 bind to SARS-CoV-2. Availabilityand implementation: CoV-AbDab is free to access and download without registration at http://opig.stats.ox.ac.uk/webapps/coronavirus. Community submissions are encouraged. Supplementary information: Supplementary data are available at Bioinformatics online.
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?
Participants under 80 years of age had a higher frequency of convergent clones with the CoV-AbDab database than the older group.
“We explored the convergence between BCR clones in our study and the CoV-AbDab database 12 and found that participants under 80 years of age had a higher frequency of convergent clones, in keeping with increased neutralization, when compared with the older group (Fig. 3e).”
From Age-related immune response heterogeneity to SARS-CoV-2 vaccine BNT162b2 (2021) · cites this paper as [12]
There are more focused repositories of antibodies, such as CoV-AbDab for anticoronavirus antibodies, and sdAb-DB for single-domain antibodies and related classes of proteins.
“Furthermore, there are more focused repositories of antibodies, such as CoV-AbDab [19] for anticoronavirus antibodies, and sdAb-DB [20] for single-domain antibodies and related classes of proteins.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [19]
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
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation
Wrapp, D., Wang, N., Corbett, K. S., Goldsmith, J. A., Hsieh, C.-L., Abiona, O., Graham, B. S., & McLellan, J. S. (2020) · Science · doi:10.1126/science.abb2507 · Cited by 8,897 · see citing works
The trinity of COVID-19: immunity, inflammation and intervention
Tay, M. Z., Poh, C. M., Rénia, L., MacAry, P. A., & Ng, L. F. P. (2020) · Nature Reviews Immunology · doi:10.1038/s41577-020-0311-8 · Cited by 4,360 · see citing works
Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19
Liao, M., Liu, Y., Yuan, J., Wen, Y., Xu, G., Zhao, J., Cheng, L., Li, J., Wang, X., Wang, F., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0901-9 · Cited by 2,614 · see citing works
Antibody Responses to SARS-CoV-2 in Patients With Novel Coronavirus Disease 2019
Zhao, J., Yuan, Q., Wang, H., Liu, W., Liao, X., Su, Y., Wang, X., Yuan, J., Li, T., Li, J., et al. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa344 · Cited by 2,607 · see citing works
Convergent Antibody Responses to SARS-CoV-2 in Convalescent Individuals
Robbiani, D. F., Gaebler, C., Muecksch, F., Lorenzi, J. C. C., Wang, Z., Cho, A., Agudelo, M., Barnes, C. O., Gazumyan, A., Finkin, S., et al. (2020) · Nature · doi:10.1038/s41586-020-2456-9 · Cited by 1,853 · see citing works
Human neutralizing antibodies elicited by SARS-CoV-2 infection
Ju, B., Zhang, Q., Ge, J., Wang, R., Sun, J., Ge, X., Yu, J., Shan, S., Zhou, B., Song, S., et al. (2020) · Nature · doi:10.1038/s41586-020-2380-z · Cited by 1,667 · see citing works
A highly conserved cryptic epitope in the receptor binding domains of SARS-CoV-2 and SARS-CoV
Yuan, M., Wu, N. C., Zhu, X., Lee, C.-C. D., So, R. T. Y., Lv, H., Mok, C. K. P., & Wilson, I. A. (2020) · Science · doi:10.1126/science.abb7269 · Cited by 1,405 · see citing works
Studies in humanized mice and convalescent humans yield a SARS-CoV-2 antibody cocktail
Hansen, J., Baum, A., Pascal, K. E., Russo, V., Giordano, S., Wloga, E., Fulton, B. O., Yan, Y., Koon, K., Patel, K., et al. (2020) · Science · doi:10.1126/science.abd0827 · Cited by 1,210 · see citing works
Immunogenicity and structures of a rationally designed prefusion MERS-CoV spike antigen
Pallesen, J., Wang, N., Corbett, K. S., Wrapp, D., Kirchdoerfer, R. N., Turner, H. L., Cottrell, C. A., Becker, M. M., Wang, L., Shi, W., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707304114 · Cited by 1,073 · see citing works
Lefranc, M., Pommie, C., Ruíz, M., Giudicelli, V., Foulquier, E., Truong, L., Contet, V., & Lefranc, G. (2002) · Developmental & Comparative Immunology · doi:10.1016/s0145-305x(02)00039-3 · Cited by 823 · see citing works
Lefranc, M., Pommie, C., Kaas, Q., Duprat, É., Bosc, N., Guiraudou, D., Jean, C., Ruíz, M., da Piedade, I., Rouard, M., et al. (2004) · Developmental & Comparative Immunology · doi:10.1016/j.dci.2004.07.003 · Cited by 808 · see citing works
Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing
Wen, W., Su, W., Tang, H., Le, W., Zhang, X., Zheng, Y., Liu, X., Xie, L., Li, J., Ye, J., et al. (2020) · Cell Discovery · doi:10.1038/s41421-020-0168-9 · Cited by 730 · see citing works
SAbDab: the structural antibody database
Dunbar, J., Krawczyk, K., Leem, J., Baker, T., Fuchs, A., Georges, G., Shi, J., & Deane, C. M. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1043 · Cited by 674 · see citing works
Self-citationUnexpected Receptor Functional Mimicry Elucidates Activation of Coronavirus Fusion
Walls, A. C., Xiong, X., Park, Y.-J., Tortorici, M. A., Snijder, J., Quispe, J., Cameroni, E., Gopal, R., Dai, M., Lanzavecchia, A., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.028 · Cited by 582 · see citing works
The potential danger of suboptimal antibody responses in COVID-19
Iwasaki, A., & Yang, Y.-X. (2020) · Nature reviews. Immunology · doi:10.1038/s41577-020-0321-6 · Cited by 510 · see citing works
ANARCI: antigen receptor numbering and receptor classification
Dunbar, J., & Deane, C. M. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv552 · Cited by 421 · see citing works
Self-citationMERS-CoV spike protein: a key target for antivirals
Du, L., Yang, Y., Zhou, Y., Lu, L., Li, F., & Jiang, S. (2017) · Expert Opinion on Therapeutic Targets · doi:10.1080/14728222.2017.1271415 · Cited by 301 · see citing works
Kovaltsuk, A., Leem, J., Kelm, S., Snowden, J., Deane, C. M., & Krawczyk, K. (2018) · The Journal of Immunology · doi:10.4049/jimmunol.1800708 · Cited by 247 · see citing works
Self-citationABodyBuilder: automated antibody structure prediction with data-driven accuracy estimation
Leem, J., Dunbar, J., Georges, G., Shi, J., & Deane, C. M. (2016) · mAbs · doi:10.1080/19420862.2016.1205773 · Cited by 238 · see citing works
Self-citationTang, X.-C., Agnihothram, S. S., Jiao, Y., Stanhope, J., Graham, R. L., Peterson, E. C., Avnir, Y., Tallarico, A. S. C., Sheehan, J., Zhu, Q., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1402074111 · Cited by 232 · see citing works
Convergent Antibody Responses to SARS-CoV-2 Infection in Convalescent IndividualsPreprint
Robbiani, D. F., Gaebler, C., Muecksch, F., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.13.092619 · Cited by 138 · see citing works
Rockx, B., Corti, D., Donaldson, E., Sheahan, T., Stadler, K., Lanzavecchia, A., & Baric, R. (2008) · Journal of Virology · doi:10.1128/jvi.02377-07 · Cited by 134 · see citing works
B cell clonal expansion and convergent antibody responses to SARS-CoV-2Preprint
Nielsen, S. C. A., Yang, F., Hoh, R. A., et al. (2020) · Research Square · doi:10.21203/rs.3.rs-27220/v1 · Cited by 32 · see citing works
Galson, J. D., Schaetzle, S., Bashford-Rogers, R. J. M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.20.106294 · Cited by 28 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Omicron escapes the majority of existing SARS-CoV-2 neutralizing antibodies
Cao, Y., Wang, J., Jian, F., Xiao, T., Song, W., Yisimayi, A., Huang, W., Li, Q., Wang, P., An, R., et al. (2022) · Nature · doi:10.1038/s41586-021-04385-3 · Cited by 2,006 · see citing works
COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas
Ren, X., Wen, W., Fan, X., Hou, W., Su, B., Cai, P., Li, J., Liu, Y., Tang, F., Zhang, F., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.053 · Cited by 865 · see citing works
Structural and functional ramifications of antigenic drift in recent SARS-CoV-2 variants
Yuan, M., Huang, D., Lee, C.-C. D., Wu, N. C., Jackson, A. M., Zhu, X., Liu, H., Peng, L., van Gils, M. J., Sanders, R. W., et al. (2021) · doi:10.1101/2021.02.16.430500 · Cited by 364 · see citing works
A blood atlas of COVID-19 defines hallmarks of disease severity and specificity
Ahern, D. J., Ai, Z., Ainsworth, M., Allan, C., Allcock, A., Angus, B., Ansari, M. A., Arancibia-Cárcamo, C. V., Aschenbrenner, D., Attar, M., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.012 · Cited by 328 · see citing works
Computational and artificial intelligence-based methods for antibody development
Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.12.005 · Cited by 178 · see citing works
DLAB-deep learning methods for structure-based virtual screening of antibodies
Schneider, C., Buchanan, A., Taddese, B., & Deane, C. M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab660 · Cited by 102 · see citing works
Self-citationSingle-cell profiling of proteins and chromatin accessibility using PHAGE-ATAC
Fiskin, E., Lareau, C. A., Ludwig, L. S., Eraslan, G., Liu, F., Ring, A. M., Xavier, R. J., & Regev, A. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01065-5 · Cited by 68 · see citing works
- Authors
- Raybould, M. I. J., Kovaltsuk, A., Marks, C., & Deane, C. M.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Deane, Charlotte M
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-08-17
- Volume
- 37
- Issue
- 5
- Pages
- 734-735
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC7558925
- OpenAlex
- W3049166744
- Cited by
- 383
- References
- 25
- Citations by year
- 2026: 34 · 2025: 51 · 2024: 68 · 2023: 85 · 2022: 73 · 2021: 58 · 2020: 14
- FWCI
- 7.53
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- Medical Research Council (EP/L016044/1)
- Biotechnology and Biological Sciences Research Council (BB/M011224/1)
- GlaxoSmithKline plc
- UCB Pharma Ltd.
- AstraZeneca plc
- F. Hoffmann-La Roche AG
- Topics
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Keywords
- SARS-CoV-2, neutralizing antibodies, nanobodies, betacoronaviruses, SARS-CoV-1, MERS-CoV, cross-neutralization, antibody variable domains, antigenic determinants, vaccine development
- MeSH terms
- COVID-19, SARS-CoV-2, Antibodies, Viral, Humans, Antibodies, Neutralizing, Spike Glycoprotein, Coronavirus, Middle East Respiratory Syndrome Coronavirus
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