Coronavirus-Binding Antibody Sequences & Structures

The Oxford Protein Informatics Group (Dept. of Statistics, University of Oxford) is collaborating in efforts to understand the immune response to SARS-CoV2 infection and vaccination. As part of our investigations, we are releasing and maintaining this public database to document all published/patented antibodies and nanobodies able to bind to coronaviruses, including SARS-CoV2, SARS-CoV1, and MERS-CoV.

Explanations and a preliminary analysis of the database contents can be found in our Applications Note in Bioinformatics. Please consider citing it if you are making use of our database in your research. BibTex Reference.

If you have recently released a preprint, paper, or publication with SARS-CoV-2 binding antibodies, please let us know by emailing opig [at]

CoV-AbDab currently contains 12,916 entries (last updated: 8th February, 2024).

Please note that the data contained within CoV-AbDab is available under a CC-BY 4.0 license.

>   Search Database by Attribute

To view all entries, leave all search fields as 'All' and click 'Search'.

Binds to:
Doesn't bind to:
Neutralising against:
Not neutralising against:
Heavy V Gene:
Heavy J Gene:
Light V Gene:
Light J Gene:
Added since:
Updated since:
>   Search Database by Sequence

Please enter an amino acid sequence (either a full-length variable domain sequence or an IMGT CDR3 sequence -- NB: you should trim the C104 and W118 if using AIRR-seq data, so -CARRGDGLYYTGMDVW- should be searched as "ARRGDGLYYTGMDV"). Then click Submit to search our database for similar sequences to your query.

Only database entries that are the same length as your query are considered.

If your query contains a non-standard amino acid character (e.g. B, X...), you will be returned to the main search page. If this is happening, please check your submitted sequence.

Query sequence:

Matthew I. J. Raybould, Aleksandr Kovaltsuk, Claire Marks, Charlotte M. Deane (2021) CoV-AbDab: the Coronavirus Antibody Database. Bioinformatics. 37(5):734-735. doi = 10.1093/bioinformatics/btaa739.

Download BibTex Reference

Header image credit: David Goodsell