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dc.contributor.authorSharma, Ronesh
dc.contributor.authorKumar, Shiu
dc.contributor.authorTsunoda, Tatsuhiko
dc.contributor.authorPatil, Ashwini
dc.contributor.authorSharma, Alok
dc.date.accessioned2021-09-02T03:35:12Z
dc.date.available2021-09-02T03:35:12Z
dc.date.issued2016
dc.identifier.issn1471-2105
dc.identifier.doi10.1186/s12859-016-1375-0
dc.identifier.urihttp://hdl.handle.net/10072/407505
dc.description.abstractBackground: 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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherSpringer
dc.relation.ispartofpagefrom251
dc.relation.ispartofpageto258
dc.relation.ispartofissueSupplement 19
dc.relation.ispartofjournalBMC Bioinformatics
dc.relation.ispartofvolume17
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchBiological sciences
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode31
dc.subject.keywordsScience & Technology
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsBiochemical Research Methods
dc.subject.keywordsBiotechnology & Applied Microbiology
dc.subject.keywordsMathematical & Computational Biology
dc.titlePredicting MoRFs in protein sequences using HMM profiles
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationSharma, R; Kumar, S; Tsunoda, T; Patil, A; Sharma, A, Predicting MoRFs in protein sequences using HMM profiles, BMC Bioinformatics, 2016, 17 (Supplement 19), pp. 251-258
dc.date.updated2021-09-02T03:31:30Z
gro.hasfulltextNo Full Text
gro.griffith.authorSharma, Alok


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