Date of Award

Spring 5-15-2015

Degree Type

Thesis

Degree Name

M.S.

Degree Program

Computer Science

Department

Computer Science

Major Professor

Hoque, Md Tamjidul

Abstract

Energy functions are found to be a key of protein structure prediction. In this work, we propose a novel 3-dimensional energy function based on hydrophobic-hydrophilic properties of amino acid where we consider at least three different possible interaction of amino acid in a 3-dimensional sphere categorized as hydrophilic versus hydrophilic, hydrophobic versus hydrophobic and hydrophobic versus hydrophilic. Each of these interactions are governed by a 3-dimensional parameter alpha used to model the interaction and 3-dimensional parameter beta used to model weight of contribution. We use Genetic Algorithm (GA) to optimize the value of alpha, beta and Z-score. We obtain three energy scores libraries from a database of 4332 protein structures obtained from Protein Data Bank (PDB) server. Proposed energy function is found to outperform nearest competitor by 40.9% for the most challenging Rosetta decoy as well as better in terms of the Z-score based on Moulder and Rosetta decoy sets.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.