State Key Laboratory of Chemical Oncogenomics, Guangdong Provincial Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
Institute of Chemical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China.
J Chem Inf Model. 2022 Jun 13;62(11):2744-2760. doi: 10.1021/acs.jcim.2c00066. Epub 2022 May 13.
Atomic descriptions of peptide self-assembly are crucial to an understanding of disease-related peptide aggregation and the design of peptide-assembled materials. Obtaining these descriptions through computer simulation is challenging because current force fields, which were not designed for this process and are often unable to describe correctly peptide self-assembly behavior and the sequence dependence. Here, we developed a framework using dipeptide aggregation as a model system to improve force fields for simulations of self-assembly. Aggregation-related structural properties were designed and used to guide the optimization of peptide-peptide and peptide-solvent interactions. With this framework, we developed a self-assembly force field, termed PACE-ASM, by reoptimizing a hybrid-resolution force field that was originally developed for folding simulation. With its applicability in folding simulations, the new PACE was used to simulate the self-assembly of two disease-related short peptides, Aβ and PHF6, into β-sheet-rich cross-β amyloids. These simulations reproduced the crystal structures of Aβ and PHF6 amyloids at near-atomic resolution and captured the difference in packing orientations between the two sequences, a task which is challenging even with all-atom force fields. Apart from cross-β amyloids, the self-assembly of emerging helix-rich cross-α amyloids by another peptide PSMα3 can also be correctly described with the new PACE, manifesting the versatility of the force field. We demonstrated that the ability of the PACE-ASM to model peptide self-assembly is based largely on its improved description of peptide-peptide and peptide-solvent interactions. This was achieved with our optimization framework that can readily identify and address the deficiency in describing these interactions.
肽自组装的原子描述对于理解与疾病相关的肽聚集以及设计肽组装材料至关重要。通过计算机模拟获得这些描述具有挑战性，因为当前的力场不是为此过程设计的，并且通常无法正确描述肽自组装行为和序列依赖性。在这里，我们开发了一个使用二肽聚集作为模型系统的框架，以改进用于自组装模拟的力场。设计了与聚集相关的结构特性，并将其用于指导肽-肽和肽-溶剂相互作用的优化。使用该框架，我们通过重新优化最初为折叠模拟开发的混合分辨率力场，开发了一种自组装力场，称为 PACE-ASM。由于其在折叠模拟中的适用性，新的 PACE 被用于模拟两种与疾病相关的短肽 Aβ和 PHF6 自组装成富含β-折叠的交叉-β淀粉样蛋白。这些模拟以近原子分辨率再现了 Aβ和 PHF6 淀粉样蛋白的晶体结构，并捕获了两种序列之间的不同堆积取向，即使使用全原子力场，这也是一项具有挑战性的任务。除了交叉-β淀粉样蛋白外，另一种肽 PSMα3 形成的新兴富含螺旋的交叉-α淀粉样蛋白也可以用新的 PACE 正确描述，这表明了力场的多功能性。我们证明了 PACE-ASM 模拟肽自组装的能力在很大程度上基于其对肽-肽和肽-溶剂相互作用的改进描述。这是通过我们的优化框架实现的，该框架可以快速识别和解决描述这些相互作用的缺陷。
