Modeling Sequence-Dependent Peptide Fluctuations in Immunologic Recognition

In cellular immunity, T cells recognize peptide antigens bound and presented by major histocompatibility complex (MHC) proteins. The motions of peptides bound to MHC proteins play a significant role in determining immunogenicity. However, existing approaches for investigating peptide/MHC motional dynamics are challenging or of low throughput, hindering the development of algorithms for predicting immunogenicity from large databases, such as those of tumor or genetically unstable viral genomes. We addressed this by performing extensive molecular dynamics simulations on a large structural database of peptides bound to the most commonly expressed human class-I MHC protein, HLA-A*0201. The simulations reproduced experimental indicators of motion and were used to generate simple models for predicting site-specific, rapid motions of bound peptides through differences in their sequence and chemical composition alone. The models can easily be applied on their own or incorporated into immunogenicity prediction algorithms. Beyond their predictive power, the models provide insight into how amino acid substitutions can influence peptide and protein motions and how dynamic information is communicated across peptides. They also indicate a link between peptide rigidity and hydrophobicity, two features known to be important in influencing cellular immune responses.在细胞免疫过程中，T细胞可识别由主要组织相容性复合体（major histocompatibility complex, MHC）蛋白结合并呈递的肽抗原。MHC蛋白结合的肽段的运动状态，在决定免疫原性方面发挥着关键作用。然而，当前用于研究肽/MHC复合物运动动力学的方法，要么操作难度大，要么实验通量较低，这极大阻碍了从大型数据库（如肿瘤或基因不稳定的病毒基因组数据库）中开发免疫原性预测算法的进程。为此，我们针对最常见的人类I类MHC蛋白——人类白细胞抗原A*0201（HLA-A*0201）结合的肽段所构建的大型结构数据库，开展了大规模分子动力学模拟。该模拟复现了肽段运动的实验观测特征，并被用于构建简易预测模型：仅通过肽段的序列差异与化学组成，即可预测其结合后发生的位点特异性快速运动。该模型既可独立直接应用，也可整合至免疫原性预测算法中。除预测能力外，这些模型还揭示了氨基酸替换如何影响肽段与蛋白质的运动，以及动态信息如何在肽段间传递的机制。此外，研究还发现肽段刚性与疏水性之间存在关联——这两种特征均被证实对细胞免疫应答具有重要影响。