Protein Disorder Prediction using Information Theory Measures on the Distribution of the Dihedral Torsion Angles from Ramachandran Plots

This paper addresses the problem of order/disorder prediction in protein sequences from alignment free methods. The proposed approach is based on a set of 11 information theory measures estimated from the distribution of the dihedral torsion angles in the amino acid chain. The aim is to characterize the energetically allowed regions for amino acids in the protein structures, as a way of measuring the rigidity/flexibility of every amino acid in the chain, and the effect of such rigidity on the disorder propensity. The features are estimated from empirical Ramachandran Plots obtained using the Protein Geometry Database. The proposed features are used in conjunction with well-established features in the state of the art for disorder prediction. The classification is performed using two different strategies: one based on conventional supervised methods and the other one based on structural learning. The performance is evaluated in terms of AUC (Area Under the ROC Curve), and three suitable performance metrics for unbalanced classification problems. The results show that the proposed scheme using conventional supervised methods is able to achieve results similar than well-known alignment free methods for disorder prediction. Moreover, the scheme based on structural learning outperforms the results obtained for all the methods evaluated, including three alignment-based methods.

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