Predicting MoRFs in protein sequences using HMM profiles
作者: Ronesh SharmaShiu KumarTatsuhiko TsunodaAshwini PatilAlok Sharma
作者单位: 1Fiji National University
2The University of the South Pacific
4RIKEN Center for Integrative Medical Science
5Medical Research Institute, Tokyo Medical and Dental University
6The University of Tokyo
刊名: BMC Bioinformatics, 2016, Vol.17 (19)
来源数据库: Springer Nature Journal
DOI: 10.1186/s12859-016-1375-0
关键词: Molecular recognition featuresHidden Markov model profilesIntrinsically disordered proteinsIntrinsically disordered regionsSupport vector machines
原始语种摘要: Abstract(#br) Background(#br)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.(#br) Methods(#br)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...
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影响因子:3.024 (2012)

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