Predicting MoRFs in protein sequences using HMM profiles.

Ashwini Patil,Ronesh Sharma,Alok Sharma,Tatsuhiko Tsunoda,Shiu Kumar

doi:10.1186/s12859-016-1375-0

Abstract

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.

Highlights

Disordered Proteins (IDPs) lack an ordered three-dimensional structure and are enriched in various biological processes
To guarantee unbiased prediction different sets of non-Molecular Recognition Features (MoRFs) residue segments are randomly selected for each model with different window size
support vector machines (SVM) model and feature selection The dataset used in this study has more non-MoRF residues compared to the number of MoRF residues present in the sequences resulting in a biased prediction

Summary

Introduction

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. IDRs lack a fixed three-dimensional structure under physiological conditions and can adopt an ensemble of conformations. They are associated with important cellular processes, such as signal transduction and transcriptional regulation [2, 3]. MoRFs are short binding regions of length 5 to 25 residues present within longer disordered protein sequences [4, 5]. They undergo a disorder-to-order transition on binding. The second approach addresses long interaction segments present in IDPs called MoRFs, The Author(s) BMC Bioinformatics 2016, 17(Suppl 19):504 which are conserved but vary in size, can be up to 70 amino acids and are often described as disordered domains

Methods

Results

Discussion

Conclusion