Advanced Electrostatic Computation in Molecular Dynamics

描述（由申请人提供）：计算硬件和分子建模技术的进步彻底改变了我们模拟静电相互作用的能力，静电相互作用在生物分子的稳定性、结构、折叠和功能中起着重要作用。目前，基于泊松-玻尔兹曼方程（PBE）的连续介质静电描述能较好地结合建模保真度和计算成本。虽然含有数十万个原子的核糖体等非常大的生物分子组件已经使用PBE求解器成功求解，但当这些求解器被纳入分子动力学（MD）和能量最小化（EM）代码时，遇到了主要困难。分子表面预测静电场的准确性降低，破坏了电磁模拟的稳定性，阻碍了电磁计算的收敛。所提出的努力将提供新的计算工具，以准确和有效地预测所需的静电性质附近的分子表面。以前的技术，特别是一种新的自适应笛卡尔网格结构，可以最大限度地提高大规模生物分子的计算性能，将被修改以实现表面的高精度。由此产生的计算工具将被证明是非常宝贵的：(i)通过有效地提供高保真度，pbe质量的静电能量梯度来进行MD和EM计算；（ii）对将静电表面特性（例如电位和感应电荷）与生物分子功能联系起来感兴趣的研究人员。

DESCRIPTION (provided by applicant): Advances in computational hardware and molecular modeling techniques have revolutionized our ability to simulate electrostatic interactions which play a fundamental role in the stability, structure, folding and function of bio-molecules. Currently, the continuum electrostatic description based upon the Poisson- Boltzmann Equation (PBE) offers the best combination of modeling fidelity and computational cost. While very large bio-molecular assemblies such as ribosomes, containing hundreds of thousands of atoms, have been successfully solved using PBE solvers, major difficulties are encountered when these solvers are incorporated into molecular dynamics (MD) and energy minimization (EM) codes. Impaired accuracy of predicted electrostatic field at molecular surfaces destabilizes MD simulations and hinders convergence in EM computations. The proposed effort will provide new computational tools to accurately and efficiently predict the required electrostatic properties near the molecular surface. Previous technology, notably a novel adaptive Cartesian grid structure that maximizes computational performance for large-scale bio-molecules, will be modified to achieve high accuracy at the surface. The resulting computational tool should prove invaluable to: (i) MD and EM calculations by efficiently providing high fidelity, PBE-quality electrostatic energy gradients and (ii) researchers interested in relating electrostatic surface properties (e.g., potential and induced charge) to bio-molecular function.