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This creates a significant bottleneck in the development of T\u2010cell\u2010based immunotherapies. To address this, we established a sequential screening framework that first employs NetMHC\u2010based prediction to rapidly identify potential HBV binders, followed by template\u2010guided molecular dynamics (MD) refinement to evaluate their biophysical stability and eliminate false positives. This integrated approach enabled a proteome\u2010wide discovery of high\u2010confidence HBV T\u2010cell epitopes with superior binding affinities. Subsequent experimental validation confirmed their exceptional potency, with a remarkable nine newly identified peptides exhibiting binding affinities superior to a clinically validated positive control. Notably, the HBsAg candidate FLGGTTVCL emerged as the top binder, demonstrating a relative affinity several\u2010fold higher than the control, highlighting its significant therapeutic potential. The success of this discovery was enabled by the framework's ability to provide a multi\u2010faceted biophysical assessment, accurately filtering false positives by identifying underlying dynamic instabilities or non\u2010physiological binding modes, which dramatically increased screening precision. Our work not only delivers a set of high\u2010value HBV vaccine candidates but also establishes a robust blueprint for the rational discovery of elite epitopes, accelerating the development of next\u2010generation vaccines and immunotherapies.<\/jats:p>","DOI":"10.1002\/pro.70503","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T12:16:24Z","timestamp":1771503384000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Discovery of novel, high\u2010affinity\n                    <scp>HBV<\/scp>\n                    T\u2010cell epitopes by molecular dynamics\u2010augmented proteome\u2010wide screening"],"prefix":"10.1002","volume":"35","author":[{"given":"Mingchen","family":"Yan","sequence":"first","affiliation":[{"name":"School of Pharmacy, Faculty of Medicine Macau University of Science and Technology  Macau China"},{"name":"Faculty of Synthetic Biology Shenzhen University of Advanced Technology  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meiling","family":"Jin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Quantitative Synthetic Biology Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Xia","sequence":"additional","affiliation":[{"name":"NYU\u2010ECNU Center for Computational Chemistry and NYU Shanghai Center for Data Science NYU Shanghai  Shanghai China"},{"name":"Department of Chemistry New York University  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Quantitative Synthetic Biology Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihao","family":"Zhong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Quantitative Synthetic Biology Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoyue","family":"Wan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Quantitative Synthetic Biology Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"Binary Biotechnology Ltd.  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