计算生物学
表位
结合亲和力
药物发现
生物
假阳性悖论
计算机科学
乙型肝炎表面抗原
集合(抽象数据类型)
表位定位
生物信息学
生物信息学
化学
合理设计
病毒学
血浆蛋白结合
瓶颈
作者
Mingchen Yan,Meiling Jin,Wei Xia,Jing Sun,Zhihao Zhong,Guoyue Wan,Jian Wang,Jun Huang,John Z. H. Zhang
摘要
Accurate prediction of peptide-MHC (pMHC) binding is critical for vaccine design, yet conventional sequence-based methods alone lack the quantitative accuracy required to prioritize the most potent high-affinity epitopes. This creates a significant bottleneck in the development of T-cell-based immunotherapies. To address this, we established a sequential screening framework that first employs NetMHC-based prediction to rapidly identify potential HBV binders, followed by template-guided molecular dynamics (MD) refinement to evaluate their biophysical stability and eliminate false positives. This integrated approach enabled a proteome-wide discovery of high-confidence HBV T-cell 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-fold 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-faceted biophysical assessment, accurately filtering false positives by identifying underlying dynamic instabilities or non-physiological binding modes, which dramatically increased screening precision. Our work not only delivers a set of high-value HBV vaccine candidates but also establishes a robust blueprint for the rational discovery of elite epitopes, accelerating the development of next-generation vaccines and immunotherapies.
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