{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T09:01:21Z","timestamp":1775379681647,"version":"3.50.1"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"vor","delay-in-days":236,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202186"],"award-info":[{"award-number":["62202186"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hubei Provincial Natural Science Foundation Project","award":["2023AFB342"],"award-info":[{"award-number":["2023AFB342"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Predicting the binding affinity between antigens and antibodies accurately is crucial for assessing therapeutic antibody effectiveness and enhancing antibody engineering and vaccine design. Traditional machine learning methods have been widely used for this purpose, relying on interfacial amino acids\u2019 structural information. Nevertheless, due to technological limitations and high costs of acquiring structural data, the structures of most antigens and antibodies are unknown, and sequence-based methods have gained attention. Existing sequence-based approaches designed for protein-protein affinity prediction exhibit a significant drop in performance when applied directly to antibody\u2013antigen affinity prediction due to imbalanced training data and lacking design in the model framework specifically for antibody\u2013antigen, hindering the learning of key features of antibodies and antigens. Therefore, we propose MVSF-AB, a Multi-View Sequence Feature learning for accurate Antibody\u2013antigen Binding affinity prediction.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>MVSF-AB designs a multi-view method that fuses semantic features and residue features to fully utilize the sequence information of antibody\u2013antigen and predicts the binding affinity. Experimental results demonstrate that MVSF-AB outperforms existing approaches in predicting unobserved natural antibody\u2013antigen affinity and maintains its effectiveness when faced with mutant strains of antibodies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Datasets we used and source code are available on our public GitHub repository https:\/\/github.com\/TAI-Medical-Lab\/MVSF-AB.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae579","type":"journal-article","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T06:37:18Z","timestamp":1728023838000},"source":"Crossref","is-referenced-by-count":11,"title":["MVSF-AB: accurate antibody\u2013antigen binding affinity prediction via multi-view sequence feature 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