Department of Physics, University of Houston, Houston, Texas 77204, USA.
J Chem Phys. 2010 May 7;132(17):175101. doi: 10.1063/1.3404401.
We developed a multiscale approach (MultiSCAAL) that integrates the potential of mean force obtained from all-atomistic molecular dynamics simulations with a knowledge-based energy function for coarse-grained molecular simulations in better exploring the energy landscape of a small protein under chemical interference such as chemical denaturation. An excessive amount of water molecules in all-atomistic molecular dynamics simulations often negatively impacts the sampling efficiency of some advanced sampling techniques such as the replica exchange method and it makes the investigation of chemical interferences on protein dynamics difficult. Thus, there is a need to develop an effective strategy that focuses on sampling structural changes in protein conformations rather than solvent molecule fluctuations. In this work, we address this issue by devising a multiscale simulation scheme (MultiSCAAL) that bridges the gap between all-atomistic molecular dynamics simulation and coarse-grained molecular simulation. The two key features of this scheme are the Boltzmann inversion and a protein atomistic reconstruction method we previously developed (SCAAL). Using MultiSCAAL, we were able to enhance the sampling efficiency of proteins solvated by explicit water molecules. Our method has been tested on the folding energy landscape of a small protein Trp-cage with explicit solvent under 8M urea using both the all-atomistic replica exchange molecular dynamics and MultiSCAAL. We compared computational analyses on ensemble conformations of Trp-cage with its available experimental NOE distances. The analysis demonstrated that conformations explored by MultiSCAAL better agree with the ones probed in the experiments because it can effectively capture the changes in side-chain orientations that can flip out of the hydrophobic pocket in the presence of urea and water molecules. In this regard, MultiSCAAL is a promising and effective sampling scheme for investigating chemical interference which presents a great challenge when modeling protein interactions in vivo.
我们开发了一种多尺度方法（MultiSCAAL），该方法将全原子分子动力学模拟中获得的平均力势能与基于知识的粗粒化分子模拟能量函数相结合，以更好地探索小分子蛋白质在化学干扰（如化学变性）下的能量景观。在全原子分子动力学模拟中，过多的水分子通常会对某些高级采样技术（如复制交换方法）的采样效率产生负面影响，并且使对蛋白质动力学的化学干扰的研究变得困难。因此，需要开发一种有效的策略，重点是采样蛋白质构象的结构变化，而不是溶剂分子的波动。在这项工作中，我们通过设计一种多尺度模拟方案（MultiSCAAL）来解决此问题，该方案弥补了全原子分子动力学模拟和粗粒化分子模拟之间的差距。该方案的两个关键特征是我们之前开发的 Boltzmann 反演和蛋白质原子重建方法（SCAAL）。使用 MultiSCAAL，我们能够提高由显式水分子溶剂化的蛋白质的采样效率。我们的方法已在含有 8M 脲的小分子 Trp-cage 折叠能量景观的全原子复制交换分子动力学和 MultiSCAAL 中进行了测试。我们比较了 Trp-cage 的构象集合与可用实验 NOE 距离的计算分析。分析表明，MultiSCAAL 探索的构象与实验中探测到的构象更吻合，因为它可以有效地捕获在脲和水分子存在下可以翻转出疏水性口袋的侧链取向的变化。在这方面，MultiSCAAL 是一种很有前途和有效的采样方案，可用于研究化学干扰，这在体内建模蛋白质相互作用时是一个巨大的挑战。
