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MoRFPred-plus: Computational Identification of MoRFs in Protein Sequence using physicochemical properties and HMM profiles

Sharma, Ronesh and Bayarjargal, M. and Tsunoda, T. and Patil, A. and Sharma, Alokanand (2018) MoRFPred-plus: Computational Identification of MoRFs in Protein Sequence using physicochemical properties and HMM profiles. Journal of Theoretical Biology, 437 . pp. 9-16. ISSN 0022-5193

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    Abstract

    Intrinsically Disordered Proteins (IDPs) lack stable tertiary structure and they actively participate in performing various biological functions. These IDPs expose short binding regions called Molecular Recognition Features (MoRFs) that permit interaction with structured protein regions. Upon interaction they undergo a disorder-to-order transition as a result of which their functionality arises. Predicting these MoRFs in disordered protein sequences is a challenging task. In this study, we present MoRFpred-plus, an improved predictor over our previous proposed predictor to identify MoRFs in disordered protein sequences. Two separate independent propensity scores are computed via incorporating physicochemical properties and HMM profiles, these scores are combined to predict final MoRF propensity score for a given residue. The first score reflects the characteristics of a query residue to be part of MoRF region based on the composition and similarity of assumed MoRF and flank regions. The second score reflects the characteristics of a query residue to be part of MoRF region based on the properties of flanks associated around the given residue in the query protein sequence. The propensity scores are processed and common averaging is applied to generate the final prediction score of MoRFpred-plus. Performance of the proposed predictor is compared with available MoRF predictors, MoRFchibi, MoRFpred, and ANCHOR. Using previously collected training and test sets used to evaluate the mentioned predictors, the proposed predictor outperforms these predictors and generates lower false positive rate. In addition, MoRFpred-plus is a downloadable predictor, which makes it useful as it can be used as input to other computational tools.

    Item Type: Journal Article
    Subjects: Q Science > Q Science (General)
    T Technology > T Technology (General)
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
    Depositing User: Alokanand Sharma
    Date Deposited: 09 Jun 2018 14:57
    Last Modified: 09 Jun 2018 14:57
    URI: http://repository.usp.ac.fj/id/eprint/10773
    UNSPECIFIED

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