Predicting MoRFs in protein sequences using HMM profiles

BMC Bioinformatics

Research

Tatsuhiko Tsunoda¹  Ashwini Patil²  Shiu Kumar³  Ronesh Sharma³  Alok Sharma⁴ 

[1] CREST, JST, 230-0045, Yokohama, Japan;RIKEN Center for Integrative Medical Science, 230-0045, Yokohama, Japan;Medical Research Institute, Tokyo Medical and Dental University, 113-8510, Tokyo, Japan;Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan;School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji;School of Engineering and Physics, The University of the South Pacific, Suva, Fiji;School of Engineering and Physics, The University of the South Pacific, Suva, Fiji;CREST, JST, 230-0045, Yokohama, Japan;RIKEN Center for Integrative Medical Science, 230-0045, Yokohama, Japan;Medical Research Institute, Tokyo Medical and Dental University, 113-8510, Tokyo, Japan;

关键词: Molecular recognition features;    Hidden Markov model profiles;    Intrinsically disordered proteins;    Intrinsically disordered regions;    Support vector machines;   

DOI  :  10.1186/s12859-016-1375-0

来源: Springer

【 摘 要 】

BackgroundIntrinsically 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.MethodsIn 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).ResultsTo 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.ConclusionsUsing HMM profile as a source of feature extraction, the proposed method indicates improvement in predicting MoRFs in disordered protein sequences.

【 授权许可】

CC BY   
© The Author(s). 2016

【 图 表 】

12864_2016_2796_Article_IEq9.gif

12864_2017_3600_Article_IEq1.gif

12864_2016_2913_Article_IEq9.gif