Computational Sciences and Engineering Division & Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
Bredesen Center for Interdisciplinary Research and Graduate Education, The University of Tennessee, Knoxville, TN, USA.
Methods Mol Biol. 2020;2114:149-161. doi: 10.1007/978-1-0716-0282-9_10.
Classical force fields are essential for computer simulations of proteins and are typically parameterized to reproduce secondary and tertiary structure of isolated proteins. However, while protein-protein interactions are ubiquitous in nature, they are not considered in parameterization efforts and are far less understood than isolated proteins. A better characterization of intermolecular interactions is widely recognized as a key to revolutionizing drug and therapeutic developments with high-throughput computational screening. Urgently needed is a critical assessment of the performance of modern protein force fields against first-principles electronic structure methods and experiments. In a daring step toward this goal, we here describe a comparison of peptide folding dynamics as predicted by a molecular mechanics force field on the one hand and by an approximate electronic structure quantum mechanical (QM) method based on density-functional tight-binding (DFTB) on the other. We further compare the dynamics from straightforward DFTB simulations with a near-linear scaling version of DFTB for massively parallel computation based on the fragment molecular orbital (FMO-DFTB) method. We illustrate differences between the phenomenology of the folding dynamics from these three methods for a small model peptide, as well as charge polarization and dynamic fluctuations, point out possible correlations and implications for force field developers, and discuss the lessons learned that might become applicable to future predictive high-throughput computer screening for personalized neoantigen cancer therapy.
经典力场对于蛋白质的计算机模拟至关重要,通常经过参数化处理以再现孤立蛋白质的二级和三级结构。然而,尽管蛋白质-蛋白质相互作用在自然界中普遍存在,但在参数化过程中并未考虑这些相互作用,并且对其了解远不如孤立蛋白质深入。更准确地描述分子间相互作用被广泛认为是通过高通量计算筛选彻底改变药物和治疗开发的关键。迫切需要对现代蛋白质力场针对第一性原理电子结构方法和实验的性能进行批判性评估。在此方面迈出的大胆一步,我们在此描述了一方面由基于密度泛函紧束缚(DFTB)的近似电子结构量子力学(QM)方法,另一方面由分子力学力场预测的肽折叠动力学之间的比较。我们进一步比较了基于片段分子轨道(FMO-DFTB)方法的大规模并行计算的直接 DFTB 模拟与 DFTB 的近线性标度版本的动力学。我们说明了这三种方法的折叠动力学现象之间的差异,这些方法适用于一个小模型肽,以及电荷极化和动态波动,指出了可能的相关性和对力场开发人员的影响,并讨论了可能适用于未来用于个性化新抗原癌症治疗的高通量计算机筛选的经验教训。