Deciphering the language of antibodies using self-supervised learning.
| Autor: | Leem J; Alchemab Therapeutics, Ltd., East Side, Office 1.02, Kings Cross, London N1C 4AX, UK., Mitchell LS; Alchemab Therapeutics, Ltd., East Side, Office 1.02, Kings Cross, London N1C 4AX, UK., Farmery JHR; Alchemab Therapeutics, Ltd., East Side, Office 1.02, Kings Cross, London N1C 4AX, UK., Barton J; Alchemab Therapeutics, Ltd., East Side, Office 1.02, Kings Cross, London N1C 4AX, UK., Galson JD; Alchemab Therapeutics, Ltd., East Side, Office 1.02, Kings Cross, London N1C 4AX, UK. |
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| Jazyk: | angličtina |
| Zdroj: | Patterns (New York, N.Y.) [Patterns (N Y)] 2022 May 18; Vol. 3 (7), pp. 100513. Date of Electronic Publication: 2022 May 18 (Print Publication: 2022). |
| DOI: | 10.1016/j.patter.2022.100513 |
| Abstrakt: | An individual's B cell receptor (BCR) repertoire encodes information about past immune responses and potential for future disease protection. Deciphering the information stored in BCR sequence datasets will transform our understanding of disease and enable discovery of novel diagnostics and antibody therapeutics. A key challenge of BCR sequence analysis is the prediction of BCR properties from their amino acid sequence alone. Here, we present an antibody-specific language model, Antibody-specific Bidirectional Encoder Representation from Transformers (AntiBERTa), which provides a contextualized representation of BCR sequences. Following pre-training, we show that AntiBERTa embeddings capture biologically relevant information, generalizable to a range of applications. As a case study, we fine-tune AntiBERTa to predict paratope positions from an antibody sequence, outperforming public tools across multiple metrics. To our knowledge, AntiBERTa is the deepest protein-family-specific language model, providing a rich representation of BCRs. AntiBERTa embeddings are primed for multiple downstream tasks and can improve our understanding of the language of antibodies. Competing Interests: The authors declare no competing interests. (© 2022 The Authors.) |
| Databáze: | MEDLINE |
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