Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China.
NYU-ECNU Center for Computational Chemistry and Shanghai Frontiers Science Center of AI and DL, NYU Shanghai, 567 West Yangsi Road, Shanghai 200126, China.
Molecules. 2024 Feb 16;29(4):881. doi: 10.3390/molecules29040881.
The major histocompatibility complex (MHC) can recognize and bind to external peptides to generate effective immune responses by presenting the peptides to T cells. Therefore, understanding the binding modes of peptide-MHC complexes (pMHC) and predicting the binding affinity of pMHCs play a crucial role in the rational design of peptide vaccines. In this study, we employed molecular dynamics (MD) simulations and free energy calculations with an Alanine Scanning with Generalized Born and Interaction Entropy (ASGBIE) method to investigate the protein-peptide interaction between HLA-A*02:01 and the G9 peptide derived from the melanoma antigen gp100. The energy contribution of individual residue was calculated using alanine scanning, and hotspots on both the MHC and the peptides were identified. Our study shows that the pMHC binding is dominated by the van der Waals interactions. Furthermore, we optimized the ASGBIE method, achieving a Pearson correlation coefficient of 0.91 between predicted and experimental binding affinity for mutated antigens. This represents a significant improvement over the conventional MM/GBSA method, which yields a Pearson correlation coefficient of 0.22. The computational protocol developed in this study can be applied to the computational screening of antigens for the MHC1 as well as other protein-peptide binding systems.
主要组织相容性复合体 (MHC) 可以识别和结合外来肽，通过将肽呈递给 T 细胞来产生有效的免疫反应。因此，了解肽-MHC 复合物 (pMHC) 的结合模式和预测 pMHC 的结合亲和力，在合理设计肽疫苗方面起着至关重要的作用。在这项研究中，我们采用分子动力学 (MD) 模拟和自由能计算与丙氨酸扫描与广义 Born 和相互作用熵 (ASGBIE) 方法，研究了 HLA-A*02:01 与源自黑色素瘤抗原 gp100 的 G9 肽之间的蛋白质-肽相互作用。使用丙氨酸扫描计算了单个残基的能量贡献，并确定了 MHC 和肽上的热点。我们的研究表明，pMHC 结合主要由范德华相互作用主导。此外，我们优化了 ASGBIE 方法，对于突变抗原，预测和实验结合亲和力的 Pearson 相关系数达到 0.91。与传统的 MM/GBSA 方法相比，这是一个显著的改进，传统 MM/GBSA 方法的 Pearson 相关系数为 0.22。本研究中开发的计算方案可应用于 MHC1 以及其他蛋白质-肽结合系统的抗原计算筛选。
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