Protein Structure Prediction using Feature-Based Resampling Techniques

Description

Trent Higgs. 2013. Protein Structure Prediction using Feature-Based Resampling Techniques.Thesis (PhD Doctorate), Griffith University, Brisbane.

Abstract

A protein is formed by a string of amino acids folding into a specic three-dimensional shape. Experimental approaches used to determine a protein's three-dimensional structure are time consuming and resource demanding. Therefore, computational methods that predict protein structure have been introduced. Computational protein structure predition (PSP) methods can be grouped into three main categories: comparative modelling, threading, and ab initio. Ab initio methods try to predict a protein's three-dimensional structure from its sequence alone. This is based on `Ansen's Thermodynamic Hypothesis' that a protein's native conformation is at its free-energy minimum. However, the biggest problem that the ab initio eld faces is that the free-energy landscape is very irregular and high-dimensional. To minimise this problem the concept of fragments was introduced to limit the number of conformations considered for a particular segment of the protein chain. Fragments can be applied to most PSP search techniques. A promising search method in PSP is the Genetic Algorithm (GA), as they allow for a generic search strategy. GAs provide a way of recombining good genetic traits from generation to generation, which allows them to easily be applied to feature-based resampling. Feature-based resampling focuses on taking an already sampled search space and recombining features from it, in order to improve the nal solution. In the literature feature-based resampling using GAs has been mentioned, primarily concerning the use of strings of torsion angles, and low-resolution PSP models to represent the protein structure. The biggest problem that can be seen from this is that torsion angles are unable to capture larger-scale elements of protein structure.

Subject Keywords
Protein structure, Feature-Based Resampling Techniques, Ansen's Thermodynamic Hypothesis
Thesis Type
Thesis (PhD Doctorate)
Degree Program
Doctor of Philosophy (PhD)
School
School of Information and Communication Technology
Year
2013
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Primary Supervisor
Bela Stantic, Abdul Sattar
Other Supervisors
Tamjidul Hoque
Identifier
gu1416291893238
ADT Shelf Number
ADT0
Item Access Status
Public
Rights
Copyright © 2013 Trent Higgs.
Copyright Disclaimer
This thesis is protected by copyright. Copyright in the thesis remains with the author. The Griffith University Higher Degree Theses Repository has a non-exclusive licence to archive, publish and communicate this thesis online.

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  • Owner: Pamela Tonkin
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