Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction.
Antibodies play a crucial role in the adaptive immune response, with their specificity to antigens being a fundamental determinant of immune function. Accurate prediction of antibody-antigen specificity is vital for understanding immune responses, guiding vaccine design, and developing antibody-base...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012153 |
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| _version_ | 1849711323632369664 |
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| author | Meng Wang Jonathan Patsenker Henry Li Yuval Kluger Steven H Kleinstein |
| author_facet | Meng Wang Jonathan Patsenker Henry Li Yuval Kluger Steven H Kleinstein |
| author_sort | Meng Wang |
| collection | DOAJ |
| description | Antibodies play a crucial role in the adaptive immune response, with their specificity to antigens being a fundamental determinant of immune function. Accurate prediction of antibody-antigen specificity is vital for understanding immune responses, guiding vaccine design, and developing antibody-based therapeutics. In this study, we present a method of supervised fine-tuning for antibody language models, which improves on pre-trained antibody language model embeddings in binding specificity prediction to SARS-CoV-2 spike protein and influenza hemagglutinin. We perform supervised fine-tuning on four pre-trained antibody language models to predict specificity to these antigens and demonstrate that fine-tuned language model classifiers exhibit enhanced predictive accuracy compared to classifiers trained on pre-trained model embeddings. Additionally, we investigate the change of model attention activations after supervised fine-tuning to gain insights into the molecular basis of antigen recognition by antibodies. Furthermore, we apply the supervised fine-tuned models to BCR repertoire data related to influenza and SARS-CoV-2 vaccination, demonstrating their ability to capture changes in repertoire following vaccination. Overall, our study highlights the effect of supervised fine-tuning on pre-trained antibody language models as valuable tools to improve antigen specificity prediction. |
| format | Article |
| id | doaj-art-189a7a6eadab4c6bb1ea1653c5463dc7 |
| institution | DOAJ |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-189a7a6eadab4c6bb1ea1653c5463dc72025-08-20T03:14:39ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-03-01213e101215310.1371/journal.pcbi.1012153Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction.Meng WangJonathan PatsenkerHenry LiYuval KlugerSteven H KleinsteinAntibodies play a crucial role in the adaptive immune response, with their specificity to antigens being a fundamental determinant of immune function. Accurate prediction of antibody-antigen specificity is vital for understanding immune responses, guiding vaccine design, and developing antibody-based therapeutics. In this study, we present a method of supervised fine-tuning for antibody language models, which improves on pre-trained antibody language model embeddings in binding specificity prediction to SARS-CoV-2 spike protein and influenza hemagglutinin. We perform supervised fine-tuning on four pre-trained antibody language models to predict specificity to these antigens and demonstrate that fine-tuned language model classifiers exhibit enhanced predictive accuracy compared to classifiers trained on pre-trained model embeddings. Additionally, we investigate the change of model attention activations after supervised fine-tuning to gain insights into the molecular basis of antigen recognition by antibodies. Furthermore, we apply the supervised fine-tuned models to BCR repertoire data related to influenza and SARS-CoV-2 vaccination, demonstrating their ability to capture changes in repertoire following vaccination. Overall, our study highlights the effect of supervised fine-tuning on pre-trained antibody language models as valuable tools to improve antigen specificity prediction.https://doi.org/10.1371/journal.pcbi.1012153 |
| spellingShingle | Meng Wang Jonathan Patsenker Henry Li Yuval Kluger Steven H Kleinstein Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction. PLoS Computational Biology |
| title | Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction. |
| title_full | Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction. |
| title_fullStr | Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction. |
| title_full_unstemmed | Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction. |
| title_short | Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction. |
| title_sort | supervised fine tuning of pre trained antibody language models improves antigen specificity prediction |
| url | https://doi.org/10.1371/journal.pcbi.1012153 |
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