CoV-AbDab: the Coronavirus Antibody Database

By Matthew I. J. Raybould, Aleksandr Kovaltsuk, Claire Marks, Charlotte M. Deane. bioRxiv, 2020

349 citations recorded by the source corpus.

The emergence of a novel strain of betacoronavirus, SARS-CoV-2, has led to a pandemic that has been associated with hundreds of thousands of deaths. 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 380 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 and other betacoronaviruses such as SARS-CoV-1 and MERS-CoV. We supply relevant metadata such as 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. Our preliminary analysis exemplifies a spectrum of potential applications for the database, including identifying characteristic germline usage biases in receptor-binding domain antibodies and contextualising the diagnostic value of the SARS-CoV binding CDRH3s through comparison to over 500 million antibody sequences from SARS-CoV serologically naive individuals. Community submissions are invited to ensure CoV-AbDab is efficiently updated with the growing body of data analysing SARS-CoV-2. CoV-AbDab is freely available and downloadable on our website at http://opig.stats.ox.ac.uk/webapps/coronavirus.

Subfields: Animal Science and Zoology, Infectious Diseases, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cf020e5706b95b28f84c469622227169aded0228

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In Molecular Biology

Citations in the corpus

Modern Hopfield Networks and Attention for Immune Repertoire Classification (2020, background): “…factors), and (d) increased GPU memory and increased computing power, it will be possible to identify discriminating immune receptor motifs for many diseases, potentially even for the current SARS-CoV-2 (COVID-19) pandemic (Raybould et al., 2020; Minervina et al., 2020; Galson et al., 2020).”
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design (2021, background): “Lastly, we propose an antibody optimization task which aims to redesign CDR-H3 of antibodies in the Coronavirus Antibody Database (Raybould et al., 2021) to improve their neutralization against SARS-CoV-2.”
COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas (2021, positive): “Only one nonclonal BCR had an identical CDR3 amino acid sequence in its heavy chain with a comprehensive compendium containing 1,505 SARS-CoV-2-specific antibodies (Raybould et al., 2020).”