USP Electronic Research Repository

Predicting MoRFs in protein sequences using HMM profiles

Sharma, Ronesh and Kumar, Shiu and Tsunoda, T. and Patil, A and Sharma, Alokanand (2016) Predicting MoRFs in protein sequences using HMM profiles. BMC Bioinformatics, 17 (19). pp. 504-512. ISSN 1471-2105

[img]
Preview
PDF - Published Version
Available under License Creative Commons Attribution.

Download (412Kb) | Preview

    Abstract

    Background: Intrinsically Disordered Proteins (IDPs) lack an ordered three-dimensional structure and are enriched in various biological processes. The Molecular Recognition Features (MoRFs) are functional regions within IDPs that undergo a disorder-to-order transition on binding to a partner protein. Identifying MoRFs in IDPs using computational methods is a challenging task. Methods: In this study, we introduce hidden Markov model (HMM) profiles to accurately identify the location of MoRFs in disordered protein sequences. Using windowing technique, HMM profiles are utilised to extract features from protein sequences and support vector machines (SVM) are used to calculate a propensity score for each residue. Two different SVM kernels with high noise tolerance are evaluated with a varying window size and the scores of the SVM models are combined to generate the final propensity score to predict MoRF residues. The SVM models are designed to extract maximal information between MoRF residues, its neighboring regions (Flanks) and the remainder of the sequence (Others). Results: To evaluate the proposed method, its performance was compared to that of other MoRF predictors; MoRFpred and ANCHOR. The results show that the proposed method outperforms these two predictors. Conclusions: Using HMM profile as a source of feature extraction, the proposed method indicates improvement in predicting MoRFs in disordered protein sequences

    Item Type: Journal Article
    Additional Information: Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
    Subjects: Q Science > Q Science (General)
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
    Depositing User: Fulori Nainoca
    Date Deposited: 08 Mar 2017 16:14
    Last Modified: 11 Sep 2017 11:58
    URI: http://repository.usp.ac.fj/id/eprint/9661
    UNSPECIFIED

    Actions (login required)

    View Item

    Document Downloads

    More statistics for this item...