Collaborative Research: Computational Tools for Biomolecular Electrostatics

生物分子是生命的基本组成部分，静电力在决定其性质方面起着关键作用。为了补充物理实验，这些力的计算机模拟对于促进对生物分子功能的理解至关重要。该项目将为溶剂化生物分子的静电学提供新的计算工具。该研究将集中于离子通道蛋白中的电荷转运，目的是协助研究神经元相关的自身免疫性疾病。开发的软件将以开放源码的形式提供给科学界，并将安装在公共软件包中。项目研究的要素将包括在一个pi教授的数学生物学课程中。研究结果将在科学会议和学术研讨会上传播，并将在科学期刊上发表。该项目将培养一名博士后学者、一名研究生和几名本科生，为国家科技队伍做出贡献。提出的研究开发了改进的计算工具溶剂化生物分子的静电。该项目有两个组成部分，(1)改进现有的泊松-玻尔兹曼（PB）和3D-RISM（参考相互作用点模型）隐式溶剂模型的计算工具，(2)开发泊松-能斯特-普朗克（PNP）模型的边界元方法。在第一个组件中，pi将使用更有效的技术升级之前的树码加速边界积分（TABI） PB求解器，包括节点补丁离散化，NanoShaper分子表面三角测量，新的预处理策略和gpu加速重心拉格朗日对偶树遍历（BLDTT）快速多极方法。改进的TABI-PB求解器将应用于3D-RISM中对溶剂化病毒的静电自由能和远程渐近相关函数的加速计算。在第二部分，pi将开发一种新的基于积分方程的PNP模型方法，应用于嵌入在膜中的溶剂化离子通道蛋白。该方法将结合计算静电势的边界/体积元方法和计算溶解离子漂移扩散的粒子方法。目标是应用新的计算PNP工具来研究乙酰胆碱受体（AChR），这是一种离子通道蛋白，在神经元相关的自身免疫性疾病（如重症肌无力，也可能是Covid-19冠状病毒）中起着重要作用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Biological molecules are basic components of life and electrostatic forces play a key role in determining their properties. In order to complement physical experiments, computer simulation of these forces is critical to advance the understanding of how biological molecules function. This project will contribute new computational tools for electrostatics of solvated biomolecules. The research will focus on charge transport in ion channel proteins with the goal of assisting in the study of neuron-related autoimmune disease. The software developed will be made available in open source format to the scientific community and will be installed in public software packages. Elements of the project research will be included in a mathematical biology course taught by one of the PIs. The results will be disseminated at scientific conferences and academic seminars, and will be published in scientific journals. The project will contribute to the national scientific workforce by training a postdoctoral scholar, as well as a graduate student and several undergraduate students. The proposed research develops improved computational tools for electrostatics of solvated biomolecules. The project has two components, (1) improving existing computational tools for the Poisson-Boltzmann (PB) and 3D-RISM (Reference Interaction Site Model) implicit solvent models, and (2) developing a boundary element method for the Poisson-Nernst-Planck (PNP) model. In the first component the PIs will upgrade their previous treecode-accelerated boundary integral (TABI) PB solver with more efficient techniques including node patch discretization, NanoShaper molecular surface triangulation, a new preconditioning strategy, and the GPU-accelerated barycentric Lagrange dual tree traversal (BLDTT) fast multipole method. The improved TABI-PB solver will be applied to accelerate computation of the electrostatic free energy of solvated viruses and the long-range asymptotic correlation functions in 3D-RISM. In the second component, the PIs will develop a novel integral equation based method for the PNP model applied to solvated ion channel proteins embedded in a membrane. The method will combine a boundary/volume element approach for the electrostatic potential with a particle method for the drift-diffusion of dissolved ions. The goal is to apply the new computational PNP tool to study the Acetylcholine receptor (AChR), an ion channel protein that plays a significant role in neuron-related autoimmune diseases such as myasthenia gravis and possibly also the Covid-19 coronavirus.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.