Izadi Saeed, Anandakrishnan Ramu, Onufriev Alexey V
Department of Biomedical Engineering and Mechanics, ‡Biomedical Division, Edward Via College of Osteopathic Medicine, ¶Department of Computer Science and Physics, and §Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute and State University , Blacksburg, Virginia 24061, United States.
J Chem Theory Comput. 2016 Dec 13;12(12):5946-5959. doi: 10.1021/acs.jctc.6b00712. Epub 2016 Nov 7.
Molecular dynamics (MD) simulations based on the implicit solvent generalized Born (GB) models can provide significant computational advantages over the traditional explicit solvent simulations. However, the standard GB becomes prohibitively expensive for all-atom simulations of large structures; the model scales poorly, ∼n, with the number of solute atoms. Here we combine our recently developed optimal point charge approximation (OPCA) with the hierarchical charge partitioning (HCP) approximation to present an ∼n log n multiscale, yet fully atomistic, GB model (GB-HCPO). The HCP approximation exploits the natural organization of biomolecules (atoms, groups, chains, and complexes) to partition the structure into multiple hierarchical levels of components. OPCA approximates the charge distribution for each of these components by a small number of point charges so that the low order multipole moments of these components are optimally reproduced. The approximate charges are then used for computing electrostatic interactions with distant components, while the full set of atomic charges are used for nearby components. We show that GB-HCPO can deliver up to 2 orders of magnitude speedup compared to the standard GB, with minimal impact on its accuracy. For large structures, GB-HCPO can approach the same nominal speed, as in nanoseconds per day, as the highly optimized explicit-solvent simulation based on particle mesh Ewald (PME). The increase in the nominal simulation speed, relative to the standard GB, coupled with substantially faster sampling of conformational space, relative to the explicit solvent, makes GB-HCPO a suitable candidate for MD simulation of large atomistic systems in implicit solvent. As a practical demonstration, we use GB-HCPO simulation to refine a ∼1.16 million atom structure of 30 nm chromatin fiber (40 nucleosomes). The refined structure suggests important details about spatial organization of the linker DNA and the histone tails in the fiber: (1) the linker DNA fills the core region, allowing the H3 histone tails to interact with the linker DNA, which is consistent with experiment; (2) H3 and H4 tails are found mostly in the core of the structure, closer to the helical axis of the fiber, while H2A and H2B are mostly solvent exposed. Potential functional consequences of these findings are discussed. GB-HCPO is implemented in the open source MD software NAB in Amber 2016.
基于隐式溶剂广义玻恩（GB）模型的分子动力学（MD）模拟相较于传统的显式溶剂模拟具有显著的计算优势。然而，对于大型结构的全原子模拟，标准GB模型的计算成本过高；该模型的计算量随着溶质原子数量呈∼n的比例增长，扩展性较差。在此，我们将最近开发的最佳点电荷近似（OPCA）与分层电荷划分（HCP）近似相结合，提出了一种计算量为∼n log n的多尺度、但完全基于原子的GB模型（GB - HCPO）。HCP近似利用生物分子（原子、基团、链和复合物）的自然组织方式，将结构划分为多个层次的组件。OPCA通过少量点电荷近似每个组件的电荷分布，从而最佳地再现这些组件的低阶多极矩。然后，使用近似电荷计算与远距离组件的静电相互作用，而使用全套原子电荷计算与近距离组件的静电相互作用。我们表明，与标准GB相比，GB - HCPO的速度可提高多达2个数量级，且对其准确性的影响最小。对于大型结构，GB - HCPO可以达到与基于粒子网格埃瓦尔德（PME）的高度优化显式溶剂模拟相同的名义速度，即每天纳秒级。相对于标准GB，名义模拟速度的提高，以及相对于显式溶剂而言显著更快的构象空间采样，使得GB - HCPO成为隐式溶剂中大型原子系统MD模拟的合适候选者。作为一个实际示例，我们使用GB - HCPO模拟对30纳米染色质纤维（40个核小体）的约116万个原子的结构进行了优化。优化后的结构揭示了纤维中连接DNA和组蛋白尾巴空间组织的重要细节：（1）连接DNA填充核心区域，使得H3组蛋白尾巴能够与连接DNA相互作用，这与实验结果一致；（2）发现H3和H4尾巴大多位于结构的核心，更靠近纤维的螺旋轴，而H2A和H2B大多暴露于溶剂中。我们讨论了这些发现可能产生的潜在功能后果。GB - HCPO已在开源MD软件NAB的Amber 2016版本中实现。
