(9d) Soft MATERIAL Design USING PRINCIPAL Component Analysis

Tao Wu¹, Sheldon Wang², and Barry Cohen³

^1,3Computer Science Department, New Jersey Institute of Technology, Newark, NJ

²McCoy School of Engineering, Midwestern State University, Wichita Falls, TX

tw37@njit.edu, Sheldon.wang@mwsu.edu, barry.cohen@njit.edu

ABSTRACT

    This paper presents a new computational framework for calculating the normal modes and interactions of proteins, macromolecular assemblies and surrounding solvents. The framework employs a combination of molecular dynamics simulation (MD) and principal component analysis (PCA). It enables the capture and visualization of the molecules' normal modes and interactions over time scales that are computationally challenging, providing a starting point for experimental and further computational studies of protein conformational changes. PCA reduces the dimensionality of MD atomic trajectory data and provides a concise way to visualize, analyze, and compare the motions observed over the course of a simulation. PCA involves diagonalization of the positional covariance matrix to identify an orthogonal set of eigenvectors or “modes'' describing the direction of maximum variation in the observed conformational distribution. Slow conformational changes can be identified by projecting these dominant modes back to original trajectory data. In this work, the new multiscale methodology was applied to a relatively small mutant T4 phage lysozyme, establishing its equilibrium atomic thermal fluctuations and its inter-residue fluctuation correlations. These results were compared with published data obtained by NMA, by finite element methods, and by experiment. The eigenmodes captured are in quantitative agreement with previously published results.