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dc.contributor.authorSharma, Ronesh
dc.contributor.authorRaicar, Gaurav
dc.contributor.authorTsunoda, Tatsuhiko
dc.contributor.authorPatil, Ashwini
dc.contributor.authorSharma, Alok
dc.date.accessioned2019-07-04T12:38:37Z
dc.date.available2019-07-04T12:38:37Z
dc.date.issued2018
dc.identifier.issn1367-4803
dc.identifier.doi10.1093/bioinformatics/bty032
dc.identifier.urihttp://hdl.handle.net/10072/379824
dc.description.abstractMotivation: Intrinsically disordered proteins lack stable 3-dimensional structure and play a crucial role in performing various biological functions. Key to their biological function are the molecular recognition features (MoRFs) located within long disordered regions. Computationally identifying these MoRFs from disordered protein sequences is a challenging task. In this study, we present a new MoRF predictor, OPAL, to identify MoRFs in disordered protein sequences. OPAL utilizes two independent sources of information computed using different component predictors. The scores are processed and combined using common averaging method. The first score is computed using a component MoRF predictor which utilizes composition and sequence similarity of MoRF and non-MoRF regions to detect MoRFs. The second score is calculated using half-sphere exposure (HSE), solvent accessible surface area (ASA) and backbone angle information of the disordered protein sequence, using information from the amino acid properties of flanks surrounding the MoRFs to distinguish MoRF and non-MoRF residues. Results: OPAL is evaluated using test sets that were previously used to evaluate MoRF predictors, MoRFpred, MoRFchibi and MoRFchibi-web. The results demonstrate that OPAL outperforms all the available MoRF predictors and is the most accurate predictor available for MoRF prediction. It is available at http://www.alok-ai-lab.com/tools/opal/.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherOxford University Press
dc.publisher.placeUnited Kingdom
dc.relation.ispartofpagefrom1850
dc.relation.ispartofpageto1858
dc.relation.ispartofissue11
dc.relation.ispartofjournalBioinformatics
dc.relation.ispartofvolume34
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchBiological sciences
dc.subject.fieldofresearchOther biological sciences not elsewhere classified
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode31
dc.subject.fieldofresearchcode319999
dc.titleOPAL: prediction of MoRF regions in intrinsically disordered protein sequences
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2018 Oxford University Press. This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version OPAL: prediction of MoRF regions in intrinsically disordered protein sequences, Bioinformatics, Volume 34, Issue 11, 1 June 2018, Pages 1850–1858 is available online at: https://doi.org/10.1093/bioinformatics/bty032
gro.hasfulltextFull Text
gro.griffith.authorSharma, Alok


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