Robust Biased Brownian Dynamics for Rate Constant Calculation

Zou, Gang

This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.

Permalink

https://hdl.handle.net/2142/81614

Description

Title

Robust Biased Brownian Dynamics for Rate Constant Calculation

Author(s)

Zou, Gang

Issue Date

2002

Doctoral Committee Chair(s)

Skeel, Robert D.

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

Biophysics, General

Language

eng

Abstract

A reaction probability is required to calculate the rate constant of a diffusion-dominated reaction. Due to the complicated geometry and potentially high dimension of the reaction probability problem, it is usually solved by Brownian dynamics simulations, which is also known as a random walk or path integral method, instead of solving the equivalent partial differential equation by a discretization method. In this thesis, a robust importance sampling algorithm for Brownian dynamics, biased Brownian dynamics with weight control, is developed to overcome the high energy and entropy barriers in biomolecular association reactions. The biased Brownian dynamics steers sampling by a biasing force, and the weight control algorithm controls sampling by a target weight. This algorithm is optimal if the biasing force and the target weight is constructed from the solution of the reaction probability. In reality, an approximate reaction probability has to be used to construct the biasing force and the target weight. Thus, how close the algorithm is to optimal depends on the quality of the approximate solution. Here, an a priori method and an a posteriori method are given to calculate the approximate solution. The a priori method is based on the selection of a reaction coordinate and the variational formulation of the reaction probability problem. The a posteriori method improves the approximate solution by collecting reaction probability information during the Brownian dynamics simulations. The algorithm is proved to be effective by numerical tests. The numerical tests for bovine SOD, Escherichia coli SOD, and anti-sweetener antibody NC6.8 show speedups of 16, 36, and 42, respectively. The numerical test for reactions between two model proteins with orientations shows speedups of 2578 for one set of configurations and 3484 for another set of configurations.

Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)

IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.