Collaborative Research: Implicit Solvent Modeling and Fast Algorithm Development for Simulating Solutes with Atomic Polarizable Multipoles

合作研究：使用原子极化多极子模拟溶质的隐式溶剂建模和快速算法开发

• 批准号: 2110914
• 负责人: Shan Zhao
• 金额: $ 24.9万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110914&HistoricalAwards=false
• 关键词: Collaborative; Research; Implicit; Solvent; Modeling

This collaborative research project aims to improve the implicit solvent modeling for studying electrostatic interaction between solutes, such as proteins, DNA, and RNA, and their surrounding solvent environment. The research will improve on current approaches and will formulate a new polarizable multipole implicit solvent model with improved and enhanced modeling accuracy. Furthermore, efficient and accurate numerical algorithms will be developed to meet computational challenges of the new model. This research will provide biophysicists a new tool for analyzing electrostatic interactions of solvated biomolecules in the form of models and algorithms implemented in a freely available software package. In addition, this project will offer interdisciplinary research and training opportunities for undergraduate and graduate students in biological modeling, computation, and mathematical analysis.The project will address limitations in the existing implicit solvent models for studying electrostatic interaction between solutes. These include the facts that the solute charge sources are often modeled as point charges located at atomic centers, and this rough approximation to the quantum mechanical charge density is known to be a major source of the modeling errors. Moreover, polarization, an important physical phenomenon account for the redistribution of the electron density in the presence of an external electric field is missing in this point charge model. The project will develop a novel nonlinear Poisson-Boltzmann (PB) model associated with an atomic polarizable multipole (PM) force field to describe the self-consistent polarization process and study electrostatic interactions among permanent multipoles, induced dipoles, and reaction-field potential. The coupling of PM source with the nonlinear PB (NPB) equation, as opposed to the linearized PB, is challenging in many aspects involving modeling and numerical difficulties such as charge singularities, geometric complexity, interface jumps, nonlinearity, polarization, as well as high computational cost. To overcome such difficulties, a set of efficient, accurate, and seamlessly coupled numerical methods will be developed to resolve numerical challenges associated with the PM-NPB model. In particular, multipole charge singularities are regularized using Green’s function based decomposition; self-consistent polarization, which involves repeatedly solving an NPB equation across the molecular interface, is efficiently realized by a linearized iterative algorithm coupled with a fast 3D Augmented Matched Interface and Boundary (AMIB) method. Finally, model benchmarking and biological applications will be carried out to ensure that the research results provide a robust tool for simulating electrostatic interactions.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.

该合作研究项目旨在改进隐式溶剂模型，用于研究溶质（如蛋白质，DNA和RNA）及其周围溶剂环境之间的静电相互作用。该研究将改进现有的方法，并将制定一个新的极化多极隐式溶剂模型，提高和增强建模精度。此外，将开发高效和准确的数值算法，以满足新模型的计算挑战。这项研究将为生物化学家提供一个新的工具，用于分析溶剂化生物分子的静电相互作用的模型和算法的形式，在一个免费提供的软件包。此外，本计划将提供跨学科的研究和培训机会，为本科生和研究生在生物建模，计算和数学分析。该项目将解决现有的隐式溶剂模型的局限性，研究溶质之间的静电相互作用。这些包括溶质电荷源通常被建模为位于原子中心的点电荷的事实，并且已知这种对量子力学电荷密度的粗略近似是建模误差的主要来源。此外，极化，一个重要的物理现象，在存在外电场的情况下的电子密度的重新分布的帐户是失踪在这个点电荷模型。该项目将开发一种新的非线性Poisson-Boltzmann（PB）模型，该模型与原子极化多极（PM）力场相关联，以描述自洽极化过程，并研究永久多极，诱导偶极子和反应场电位之间的静电相互作用。与线性化PB方程相比，PM源与非线性PB（NPB）方程的耦合在建模和数值计算方面具有挑战性，如电荷奇异性、几何复杂性、界面跳跃、非线性、极化以及高计算成本等。为了克服这些困难，一组高效，准确，无缝耦合的数值方法将开发解决与PM-NPB模型的数值挑战。特别是，多极电荷奇点正则化使用绿色的功能为基础的分解;自洽的极化，其中涉及反复求解NPB方程的分子界面，有效地实现了线性化的迭代算法，加上一个快速的三维增广匹配界面和边界（AMIB）方法。最后，将进行模型基准测试和生物应用，以确保研究结果为模拟静电相互作用提供了一个强大的工具。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

A regularization approach for solving the super-Gaussian Poisson-Boltzmann model with heterogeneous dielectric functions

• DOI: 10.1016/j.jcp.2022.111340
• 发表时间: 2022-05
• 期刊: J. Comput. Phys.
• 作者: Siwen Wang;Yuanzhen Shao;E. Alexov;Shan Zhao

A Fast Sine Transform Accelerated High-Order Finite Difference Method for Parabolic Problems over Irregular Domains

• DOI: 10.1007/s10915-023-02177-7
• 发表时间: 2023-03
• 期刊: Journal of Scientific Computing
• 影响因子: 2.5
• 作者: Chuan Li;Y. Ren;Guangqing Long;Eric Boerman;Shan Zhao

A FFT accelerated fourth order finite difference method for solving three-dimensional elliptic interface problems

• DOI: 10.1016/j.jcp.2023.111924
• 发表时间: 2023-01-19
• 期刊: JOURNAL OF COMPUTATIONAL PHYSICS
• 影响因子: 4.1
• 作者: Ren，Yiming;Zhao，Shan
• 通讯作者: Zhao，Shan

Benchmarking electrostatic free energy of the nonlinear Poisson–Boltzmann model for the Kirkwood sphere

柯克伍德球非线性泊松玻尔兹曼模型的静电自由能基准测试

• DOI: 10.4310/cis.2022.v22.n3.a1
• 发表时间: 2022
• 期刊: Communications in Information and Systems
• 影响因子: 0.9
• 作者: Amihere, Sylvia;Geng, Weihua;Zhao, Shan
• 通讯作者: Zhao, Shan

Physics-guided multiple regression analysis for calculating electrostatic free energies of proteins in different reference states

物理引导的多元回归分析用于计算不同参考状态下蛋白质的静电自由能

• DOI: 10.4310/cis.2022.v22.n2.a2
• 发表时间: 2022
• 期刊: Communications in Information and Systems
• 影响因子: 0.9
• 作者: Hazra, Tania;Zhao, Shan
• 通讯作者: Zhao, Shan