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  • Protein Structure Prediction using Feature-Based Resampling Techniques

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    Higgs_2013_02Thesis.pdf (14.38Mb)
    Author(s)
    Higgs, Trent B.
    Primary Supervisor
    Stantic, Bela
    Sattar, Abdul
    Other Supervisors
    Hoque, Tamjidul
    Year published
    2013
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    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 ...
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    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.
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    Thesis Type
    Thesis (PhD Doctorate)
    Degree Program
    Doctor of Philosophy (PhD)
    School
    School of Information and Communication Technology
    DOI
    https://doi.org/10.25904/1912/2144
    Copyright Statement
    The author owns the copyright in this thesis, unless stated otherwise.
    Item Access Status
    Public
    Subject
    Protein structure
    Feature-Based Resampling Techniques
    Ansen's Thermodynamic Hypothesis
    Publication URI
    http://hdl.handle.net/10072/365543
    Collection
    • Theses - Higher Degree by Research

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