Predicting MoRFs in protein sequences using HMM profiles.
BACKGROUND
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.
School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.|School of Engineering and Physics, The University of the South Pacific, Suva, Fiji.
Ronesh
R
Sharma
School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.|School of Engineering and Physics, The University of the South Pacific, Suva, Fiji.
Shiu
S
Kumar
CREST, JST, Yokohama, 230-0045, Japan.|RIKEN Center for Integrative Medical Science, Yokohama, 230-0045, Japan.|Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan.
Tatsuhiko
T
Tsunoda
Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan. ashwini@hgc.jp.
Ashwini
A
Patil
School of Engineering and Physics, The University of the South Pacific, Suva, Fiji. alok.fj@gmail.com.|CREST, JST, Yokohama, 230-0045, Japan. alok.fj@gmail.com.|RIKEN Center for Integrative Medical Science, Yokohama, 230-0045, Japan. alok.fj@gmail.com.|Medical Research Institute, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan. alok.fj@gmail.com.
Alok
A
Sharma
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D064267:Intrinsically Disordered Proteins / Q000737:chemistry*; D008390:Markov Chains; D008962:Models, Theoretical; D060388:Support Vector Machine
D064267:Intrinsically Disordered Proteins
Hidden Markov model profiles; Intrinsically disordered proteins; Intrinsically disordered regions; Molecular recognition features; Support vector machines