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  • Enhanced Feature Extraction from Evolutionary Profiles for Protein Fold Recognition

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    Lyons_2016_01Thesis.pdf (768.4Kb)
    Author(s)
    Lyons, James
    Primary Supervisor
    Paliwal, Kuldip
    Other Supervisors
    So, Stephen
    Year published
    2016
    Metadata
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    Abstract
    Proteins are important biological macromolecules that play important roles in al- most all biological reactions. The function of a protein is dependent on the shape it folds in to, which is in turn dependent on the protein’s amino acid sequence. Ex- perimental approaches for determining a protein’s 3D structure are expensive and time consuming, so computational methods for determining the structure from the amino acid sequence are desired. Methods for directly computing the 3D structure of a protein exist, however they are impractical for large proteins and high resolution models due to the large search space. Instead of ...
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    Proteins are important biological macromolecules that play important roles in al- most all biological reactions. The function of a protein is dependent on the shape it folds in to, which is in turn dependent on the protein’s amino acid sequence. Ex- perimental approaches for determining a protein’s 3D structure are expensive and time consuming, so computational methods for determining the structure from the amino acid sequence are desired. Methods for directly computing the 3D structure of a protein exist, however they are impractical for large proteins and high resolution models due to the large search space. Instead of trying to directly find the 3D struc- ture from first principles, the primary structure can be compared to proteins with known 3D structure. A ‘fold’ is a way of classifying proteins with the same major secondary structures in the same arrangement and with the same topological con- nections. Protein Fold Recognition (PFR) is an important step towards determining a protein’s structure, simplifying the protein structure prediction problem. This is a multi-class classification problem solvable using machine learning techniques. The PFR problem has been widely studied in the past, with feature extraction approaches including using counts of amino acids and pairs of amino acids, physic- ochemical information, evolutionary information from the Position Specific Scoring Matrix (PSSM), and structural information from its predicted secondary structure. These approaches do work, but with limited success. Current state of the art features use information from the PSSM as well as the predicted secondary structure.
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    Thesis Type
    Thesis (PhD Doctorate)
    Degree Program
    Doctor of Philosophy (PhD)
    School
    Griffith School of Engineering
    DOI
    https://doi.org/10.25904/1912/2421
    Copyright Statement
    The author owns the copyright in this thesis, unless stated otherwise.
    Item Access Status
    Public
    Subject
    Proteins
    protein amino acid sequence
    Protein Fold Recognition (PFR)
    Position Specific Scoring Matrix (PSSM),
    Publication URI
    http://hdl.handle.net/10072/365732
    Collection
    • Theses - Higher Degree by Research

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