Date of Award
Summer 8-2019
Degree Type
Dissertation
Degree Name
Ph.D.
Degree Program
Engineering and Applied Science
Department
Computer Science
Major Professor
Hoque Md Tamjidul
Second Advisor
Summa Christopher
Third Advisor
Tu Shengru
Fourth Advisor
Atallah Joel
Fifth Advisor
Chen Huimin
Abstract
Proteins are an important component of living organisms, composed of one or more polypeptide chains, each containing hundreds or even thousands of amino acids of 20 standard types. The structure of a protein from the sequence determines crucial functions of proteins such as initiating metabolic reactions, DNA replication, cell signaling, and transporting molecules. In the past, proteins were considered to always have a well-defined stable shape (structured proteins), however, it has recently been shown that there exist intrinsically disordered proteins (IDPs), which lack a fixed or ordered 3D structure, have dynamic characteristics and therefore, exist in multiple states. Based on this, we extend the mapping of protein sequence not only to a fixed stable structure but also to an ensemble of protein conformations, which help us explain the complex interaction within a cell that was otherwise obscured. The objective of this dissertation is to develop effective ab initio methods and tools for protein un/structure prediction by developing effective statistical energy function, conformational search method, and disulfide connectivity patterns predictor.
The key outcomes of this dissertation research are: i) a sequence and structure-based energy function for structured proteins that includes energetic terms extracted from hydrophobic-hydrophilic properties, accessible surface area, torsion angles, and ubiquitously computed dihedral angles uPhi and uPsi, ii) an ab initio protein structure predictor that combines optimal energy function derived from sequence and structure-based properties of proteins and an effective conformational search method which includes angular rotation and segment translation strategies, iii) an SVM with RBF kernel-based framework to predict disulfide connectivity pattern, iv) a hydrophobic-hydrophilic property based energy function for unstructured proteins, and v) an ab initio conformational ensemble generator that combines energy function and conformational search method for unstructured proteins which can help understand the biological systems involving IDPs and assist in rational drugs design to cure critical diseases such as cancer or cardiovascular diseases caused by challenging states of IDPs.
Recommended Citation
Mishra, Avdesh, "Effective Statistical Energy Function Based Protein Un/Structure Prediction" (2019). University of New Orleans Theses and Dissertations. 2674.
https://scholarworks.uno.edu/td/2674
Included in
Algebraic Geometry Commons, Amino Acids, Peptides, and Proteins Commons, Bioinformatics Commons, Biological and Chemical Physics Commons, Computational Biology Commons, Other Computer Sciences Commons, Statistical Models Commons, Structural Biology Commons
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.