HMM-Based Prediction for Protein Structural Motifs’ Two Local Properties: Solvent Accessibility and Backbone Torsion Angles

Jianyong      Yu; Leijun      Xiang; Jiang      Hong; Weidong      Zhang

doi:10.2174/0929866511320020006

Abstract

Protein structure prediction is often assisted by predicting one-dimensional structural properties including relative solvent accessibility (RSA) surface and backbone torsion angles (BTA) of residues, and these two properties are continuously varying variables because proteins can move freely in a three-dimensional space. Instead of subdividing them into a few arbitrarily defined states that many popular approaches used, this paper proposes an integrated system for realvalue prediction of protein structural motifs’ two local properties, based on the modified Hidden Markov Model that we previously presented. The model was used to capture the relevance of RSA and the dependency of BTA between adjacent residues along the local protein chain in motifs with definite probabilities. These two properties were predicted according to their own probability distribution. The method was applied to a protein fragment library. For nine different classes of motifs, real values of RSA were predicted with mean absolute error (MAE) of 0.122-0.175 and Pearson’s correlation coefficient (PCC) of 0.623-0.714 between predicted and actual RSA. Meanwhile, real values of BTA were obtained with MAE of 8.50-29.40 for Φ angles, 11.20-38.50 for ψ angles and PCC of 0.601-0.716 for Φ, 0.597-0.713 for ψ. The results were compared with well-known Real-SPINE Server, and indicate the proposed method may at least serve as the foundation to obtain better local properties from structural motifs for protein structure prediction.

Keywords: Protein structure predication, structural motifs, hidden Markov model, solvent accessibility surface, backbone torsion angles, directional statistics distribution

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