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CoV-UniBind: a unified antibody binding database for SARS-CoV-2
Aryan Bhasin1, Francesco Saccon1, Callum Canavan1
1Applied AI, InstaDeep Ltd, London, W2 1AY, United Kingdom.
Bioinformatics Advances
|January 16, 2026
Summary
We created CoV-UniBind, a comprehensive database of SARS-CoV-2 antibody-antigen interactions, to aid machine learning in antibody design. This unified resource supports vaccine development against SARS-CoV-2 variants.
Area of Science:
- Immunology
- Structural Biology
- Bioinformatics
Background:
- Numerous studies investigated SARS-CoV-2 antibody interactions with viral variants.
- Existing antibody-antigen data are fragmented, hindering machine learning model development.
- There is a need for a unified, standardized dataset for computational antibody research.
Purpose of the Study:
- To present CoV-UniBind, a unified database of SARS-CoV-2 antibody-antigen data.
- To facilitate the development and validation of machine learning models for antibody-antigen interaction prediction.
- To support antibody and vaccine development against SARS-CoV-2.
Main Methods:
- Integrated and standardized data from three public sources and peer-reviewed publications.
- Compiled over 75,000 entries of antibody-antigen sequence, binding, and structural data for SARS-CoV-2.
- Benchmarked protein folding, inverse folding, and language models using the CoV-UniBind database.
Main Results:
- CoV-UniBind provides a unified resource of SARS-CoV-2 antibody-antigen data.
- The database facilitates benchmarking of computational models for antibody design.
- Demonstrated the utility of the database for tasks relevant to vaccine development.
Conclusions:
- CoV-UniBind is a valuable resource for computational antibody research.
- The database is expected to accelerate the development of antibodies and vaccines against SARS-CoV-2.
- Standardized data integration is crucial for advancing machine learning applications in immunology.
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