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      CoV-AbDab: the Coronavirus Antibody Database

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          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 5 th August 2020. Research is ongoing around the world to create vaccines and therapies to minimise rates of disease spread and mortality. Crucial to these efforts are molecular characterisations of neutralising 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-neutralisation, 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 5 th August 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.

          Availability

          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
          btaa739_Supplementary_Data

          Supplementary data are available at Bioinformatics online.

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          Author and article information

          Contributors
          Role: Associate Editor
          Journal
          Bioinformatics
          Bioinformatics
          bioinformatics
          Bioinformatics
          Oxford University Press
          1367-4803
          1367-4811
          17 August 2020
          : btaa739
          Affiliations
          Oxford Protein Informatics Group, Department of Statistics, University of Oxford , 24-29 St. Giles’, Oxford, OX1 3LB, United Kingdom
          Author notes
          To whom correspondence should be addressed. deane@ 123456stats.ox.ac.uk
          Article
          btaa739
          10.1093/bioinformatics/btaa739
          7558925
          32805021
          6b230f46-52f4-4276-83f8-a92ea93b3e25
          © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

          This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

          This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

          History
          : 25 June 2020
          : 10 August 2020
          : 10 August 2020
          Page count
          Pages: 2
          Categories
          Applications Note
          AcademicSubjects/SCI01060
          Custom metadata
          accepted-manuscript
          PAP

          Bioinformatics & Computational biology
          Bioinformatics & Computational biology

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