AbLang: an antibody language model for completing antibody sequences
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Abstract
Motivation: General protein language models have been shown to summarize the semantics of protein sequences into representations that are useful for state-of-the-art predictive methods. However, for antibody specific problems, such as restoring residues lost due to sequencing errors, a model trained solely on antibodies may be more powerful. Antibodies are one of the few protein types where the volume of sequence data needed for such language models is available, e.g. in the Observed Antibody Space (OAS) database. Results: Here, we introduce AbLang, a language model trained on the antibody sequences in the OAS database. We demonstrate the power of AbLang by using it to restore missing residues in antibody sequence data, a key issue with B-cell receptor repertoire sequencing, e.g. over 40% of OAS sequences are missing the first 15 amino acids. AbLang restores the missing residues of antibody sequences better than using IMGT germlines or the general protein language model ESM-1b. Further, AbLang does not require knowledge of the germline of the antibody and is seven times faster than ESM-1b. Availability and implementation: AbLang is a python package available at https://github.com/oxpig/AbLang. Supplementary information: online.
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The AbLang model showed strong performance in predicting missing amino acids in the OAS database.
“AbLang model [60] showed strong performance in predicting missing amino acids in the OAS database where 40% of the sequences are missing 15 or more amino acids.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [60]
In the OAS database, 40% of sequences are missing 15 or more amino acids.
“AbLang model [60] showed strong performance in predicting missing amino acids in the OAS database where 40% of the sequences are missing 15 or more amino acids.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [60]
The AbLang model showed strong performance in predicting missing amino acids in the OAS database where 40% of the sequences are missing 15 or more amino acids.
“The AbLang model [60] showed strong performance in predicting missing amino acids in the OAS database where 40% of the sequences are missing 15 or more”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [60]
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- Authors
- Olsen, T. H., Moal, I. H., & Deane, C. M.
- Affiliations
- University of Oxford (GB)
- GlaxoSmithKline (United Kingdom) (GB)
- Corresponding author
- Deane, Charlotte M
- Venue
- Bioinformatics Advances
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-01-01
- Volume
- 2
- Issue
- 1
- Pages
- vbac046-vbac046
- ISSN
- 2635-0041 (ISSN-L)
- PMC
- PMC9710568
- OpenAlex
- W4283068487
- Cited by
- 198
- References
- 16
- Citations by year
- 2026: 43 · 2025: 66 · 2024: 46 · 2023: 37 · 2022: 6
- FWCI
- 22.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Bioinformatics Advances (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/S024093/1)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- antibody language models, B cell receptor repertoire, protein language models
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