Collaborative Research: Computational Tools for Biomolecular Electrostatics

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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 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.