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Improving protein fold recognition and structural class prediction accuracies using physicochemical properties of amino acids

Raicar, Gaurav and Saini, Harsh and Dehzangi, Abdollah and Lal, Sunil P. and Sharma, Alokanand (2016) Improving protein fold recognition and structural class prediction accuracies using physicochemical properties of amino acids. Journal of Theoretical Biology, 402 . pp. 117-128. ISSN 0022-5193

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    Abstract

    Predicting the three-dimensional (3-D) structure of a protein is an important task in the field of bioinformatics and biological sciences. However, directly predicting the 3-D structure from the primary structure is hard to achieve. Therefore, predicting the fold or structural class of a protein sequence is generally used as an intermediate step in determining the protein's 3-D structure. For protein fold recognition (PFR) and structural class prediction (SCP), two steps are required – feature extraction step and classification step. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physicochemical-based information to extract features. In this study, we explore the importance of utilizing the physicochemical properties of amino acids for improving PFR and SCP accuracies. For this, we propose a Forward Consecutive Search (FCS) scheme which aims to strategically select physicochemical attributes that will supplement the existing feature extraction techniques for PFR and SCP. An exhaustive search is conducted on all the existing 544 physicochemical attributes using the proposed FCS scheme and a subset of physicochemical attributes is identified. Features extracted from these selected attributes are then combined with existing syntactical-based and evolutionary-based features, to show an improvement in the recognition and prediction performance on benchmark datasets.

    Item Type: Journal Article
    Subjects: Q Science > Q Science (General)
    Divisions: Office of the PVC (R&I)
    Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
    Faculty of Science, Technology and Environment (FSTE)
    Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
    Depositing User: Harsh Saini
    Date Deposited: 18 May 2016 16:51
    Last Modified: 13 Jul 2017 16:28
    URI: http://repository.usp.ac.fj/id/eprint/8854
    UNSPECIFIED

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