Department of Chemical Engineering, University of Washington, Seattle, WA 98105, United States.
Department of Chemical Engineering, University of Washington, Seattle, WA 98105, United States.
Biochem Biophys Res Commun. 2018 Mar 29;498(2):274-281. doi: 10.1016/j.bbrc.2017.07.066. Epub 2017 Jul 15.
Many proteins exhibit strong binding affinities to surfaces, with binding energies much greater than thermal fluctuations. When modelling these protein-surface systems with classical molecular dynamics (MD) simulations, the large forces that exist at the protein/surface interface generally confine the system to a single free energy minimum. Exploring the full conformational space of the protein, especially finding other stable structures, becomes prohibitively expensive. Coupling MD simulations with metadynamics (enhanced sampling) has fast become a common method for sampling the adsorption of such proteins. In this paper, we compare three different flavors of metadynamics, specifically well-tempered, parallel-bias, and parallel-tempering in the well-tempered ensemble, to exhaustively sample the conformational surface-binding landscape of model peptide GGKGG. We investigate the effect of mobile ions and ion charge, as well as the choice of collective variable (CV), on the binding free energy of the peptide. We make the case for explicitly biasing ions to sample the true binding free energy of biomolecules when the ion concentration is high and the binding free energies of the solute and ions are similar. We also make the case for choosing CVs that apply bias to all atoms of the solute to speed up calculations and obtain the maximum possible amount of information about the system.
许多蛋白质与表面表现出很强的结合亲和力,其结合能远大于热波动。在使用经典分子动力学 (MD) 模拟来模拟这些蛋白质-表面系统时,蛋白质/表面界面上存在的巨大力通常将系统限制在单个自由能最小值内。探索蛋白质的完整构象空间,特别是找到其他稳定结构,变得非常昂贵。将 MD 模拟与元动力学(增强采样)相结合,已经迅速成为采样此类蛋白质吸附的常用方法。在本文中,我们比较了三种不同类型的元动力学,特别是在调谐系综中的调谐良好、平行偏置和平行调温,以详尽地采样模型肽 GGKGG 的构象表面结合景观。我们研究了可移动离子和离子电荷以及选择集体变量 (CV) 对肽结合自由能的影响。当离子浓度高且溶质和离子的结合自由能相似时,我们提出了明确偏向离子以采样生物分子真实结合自由能的观点。我们还提出了选择 CV 的观点,该 CV 对溶质的所有原子施加偏置,以加快计算速度并获得有关系统的最大信息量。