Efficient Simulation of Protein-Membrane Interactions by Implicit Solvent Algorithms

University Of Wisconsin-Milwaukee, Milwaukee WI

Investigators

Abstract

This project aims to develop a new computational model for studying protein conformation changes upon membrane interactions. Its objectives are to (1) develop a robust and efficient parallel iterative algorithm for solving the Poisson-Boltzmann equation; (2) define a new biomolecular potential-energy function through a large reduction of the number of conformation-freedom variables for a family of particular proteins; (3) develop a new sparse matrix compress scheme for programming an efficient application-tailored preconditioner in an optimal order of memory locations; (4) develop a parallel iterative algorithm for constrained molecular dynamics simulations; and (5) train undergraduate and graduate researchers for the post-genomic era. Specifically, the new algorithms and the new model will be applied to studying the molecular mechanism of membrane damage induced by the protein toxin Cyt1A upon membrane interactions. The theoretical results will also be compared to laboratory data produced by biochemical and biophysical methods such as fluorescence spectroscopy, surface tensiometry, and electron microscopy. Synthesis of the computational and experimental data may bring new insights to the manner of membrane permeabilization and damage by proteins that do not transverse the lipid bilayer. Efficient algorithms and modeling of protein-membrane interactions can save considerable amounts of time, money, and laboratory effort in biotechnology, medicine, agriculture, and the food, pharmaceutical, or defense industries. The new algorithms and the new model developed in this project can significantly simplify computing complexity in simulations of large protein-water-membrane systems. They can also be applied to various biomolecular simulations. In fact, numerical solution of the Poisson-Boltzmann equation plays a central role in implicit solvent models. The molecular potential energy function is a cornerstone of molecular modeling, and the minimization of the molecular potential energy function and the molecular dynamics simulation are two fundamental tasks in computational biology. Furthermore, the chosen model protein, the toxin Cyt1A, has been used as an environmentally-safe insecticide specific against mosquitoes and blackflies. Elucidating its mode of action may help the industry increase the toxin's efficacy by targeting specificity and synergism with other insecticides. Additionally, through the collaboration between a mathematician/computer scientist and a biochemist/biophysicist, students involved in this project will be trained in a virtual hub of mathematics, computer science, and the life sciences. This grant is made under the Joint DMS/NIGMS Initiative to Support Research Grants in the Area of Mathematical Biology. This is a joint competition sponsored by the Division of Mathematical Sciences (DMS) at the National Science Foundation and the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health.