Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions

Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions

Abstract Background Molecular Recognition Features (MoRFs) are short protein regions present in intrinsically disordered protein (IDPs) sequences. MoRFs interact with structured partner protein and upon interaction, they undergo a disorder-to-order transition to perform various biological functions....

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Main Authors: Ronesh Sharma, Alok Sharma, Ashwini Patil, Tatsuhiko Tsunoda
Format: Article
Language:English
Published: BMC 2019-02-01
Series:BMC Bioinformatics
Online Access:http://link.springer.com/article/10.1186/s12859-018-2396-7
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spelling doaj-4b9a0bd6efaf42aea7c0f6071cc904252020-11-25T02:43:24ZengBMCBMC Bioinformatics1471-21052019-02-0119S1315516310.1186/s12859-018-2396-7Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regionsRonesh Sharma0Alok Sharma1Ashwini Patil2Tatsuhiko Tsunoda3School of Engineering and Physics, The University of the South PacificSchool of Engineering and Physics, The University of the South PacificHuman Genome Center, The Institute of Medical Science, The University of TokyoLaboratory of Medical Science Mathematics, RIKEN Center for Integrative Medical SciencesAbstract Background Molecular Recognition Features (MoRFs) are short protein regions present in intrinsically disordered protein (IDPs) sequences. MoRFs interact with structured partner protein and upon interaction, they undergo a disorder-to-order transition to perform various biological functions. Analyses of MoRFs are important towards understanding their function. Results Performance is reported using the MoRF dataset that has been previously used to compare the other existing MoRF predictors. The performance obtained in this study is equivalent to the benchmarked OPAL predictor, i.e., OPAL achieved AUC of 0.815, whereas the model in this study achieved AUC of 0.819 using TEST set. Conclusion Achieving comparable performance, the proposed method can be used as an alternative approach for MoRF prediction.http://link.springer.com/article/10.1186/s12859-018-2396-7
collection DOAJ
language English
format Article
sources DOAJ
author Ronesh Sharma
Alok Sharma
Ashwini Patil
Tatsuhiko Tsunoda
spellingShingle Ronesh Sharma
Alok Sharma
Ashwini Patil
Tatsuhiko Tsunoda
Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions
BMC Bioinformatics
author_facet Ronesh Sharma
Alok Sharma
Ashwini Patil
Tatsuhiko Tsunoda
author_sort Ronesh Sharma
title Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions
title_short Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions
title_full Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions
title_fullStr Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions
title_full_unstemmed Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions
title_sort discovering morfs by trisecting intrinsically disordered protein sequence into terminals and middle regions
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2019-02-01
description Abstract Background Molecular Recognition Features (MoRFs) are short protein regions present in intrinsically disordered protein (IDPs) sequences. MoRFs interact with structured partner protein and upon interaction, they undergo a disorder-to-order transition to perform various biological functions. Analyses of MoRFs are important towards understanding their function. Results Performance is reported using the MoRF dataset that has been previously used to compare the other existing MoRF predictors. The performance obtained in this study is equivalent to the benchmarked OPAL predictor, i.e., OPAL achieved AUC of 0.815, whereas the model in this study achieved AUC of 0.819 using TEST set. Conclusion Achieving comparable performance, the proposed method can be used as an alternative approach for MoRF prediction.
url http://link.springer.com/article/10.1186/s12859-018-2396-7
work_keys_str_mv AT roneshsharma discoveringmorfsbytrisectingintrinsicallydisorderedproteinsequenceintoterminalsandmiddleregions
AT aloksharma discoveringmorfsbytrisectingintrinsicallydisorderedproteinsequenceintoterminalsandmiddleregions
AT ashwinipatil discoveringmorfsbytrisectingintrinsicallydisorderedproteinsequenceintoterminalsandmiddleregions
AT tatsuhikotsunoda discoveringmorfsbytrisectingintrinsicallydisorderedproteinsequenceintoterminalsandmiddleregions
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