Prediction of backbone dihedral angles and protein secondary structure using support vector machines

Abstract

BackgroundThe prediction of the secondary structure of a protein is a critical step in the prediction of its tertiary structure and, potentially, its function. Moreover, the backbone dihedral angles, highly correlated with secondary structures, provide crucial information about the local three-dimensional structure.ResultsWe predict independently both the secondary structure and the backbone dihedral angles and combine the results in a loop to enhance each prediction reciprocally. Support vector machines, a state-of-the-art supervised classification technique, achieve secondary structure predictive accuracy of 80% on a non-redundant set of 513 proteins, significantly higher than other methods on the same dataset. The dihedral angle space is divided into a number of regions using two unsupervised clustering techniques in order to predict the region in which a new residue belongs. The performance of our method is comparable to, and in some cases more accurate than, other multi-class dihedral prediction methods.ConclusionsWe have created an accurate predictor of backbone dihedral angles and secondary structure. Our method, called DISSPred, is available online at http://comp.chem.nottingham.ac.uk/disspred/.

CONCORD: a consensus method for protein secondary structure prediction via mixed integer linear optimization

“…Table S2 of electronic supplementary material presents the list of proteins. We compared the performance of our secondary structure prediction method with another set of 10 secondary structure prediction methods, YASSPP (Karypis 2006), PORTER (Pollastri & McLysaght 2005), Jpred (Cuff et al 1998), gorV (Kloczkowski et al 2002), CDM (Cheng et al 2007), symPred (Lin et al 2010), FDM (Cheng et al 2005), DISSpred (Kountouris & Hirst 2009), PCISS (Green et al 2009) and PROTEUS (Montgomerie et al 2006). By testing the performance of the secondary structure prediction methods on CASP9 targets, the problem of overestimating the performance can be avoided (Rost 2005).…”

Estimating the Class Posterior Probabilities in Protein Secondary Structure Prediction

“…Until the end of the nineties, the classifiers at the basis of most of the prediction methods implementing the cascade treatment were neural networks [6], either feed-forward, like the multilayer perceptron (MLP) [1,2,4] or recurrent [3]. During the last decade, they were gradually replaced with bi-class support vector machines (SVMs) [7,5] and multi-class SVMs (M-SVMs) [8,9,10]. This resulted in a slight increase of the prediction accuracy.…”

“…Since the pioneering work of Qian and Sejnowski [1], state-of-the-art methods are machine learning ones [2,3,4,5]. Furthermore, a majority of them shares the original architecture implemented by Qian and Sejnowski.…”

Predicting the Secondary Structure of Proteins by Cascading Neural Networks

“…Further improvement in preformance of PSSP were also acheived by exploiting evolutioary information via multiple sequence alignments (MSAs) profiles [10], Position-Specific Score Matrices (PSSM) [16], homology detection using hidden markov models [17] and PSI-BLAST [18]. Other approaches have been applied to PSS prediction are Support Vector Machines (SVM) [19], [20], [21] and ensemble methods that combine some machine learning methods [22] via the majority voting or weighted majority voting techniques. These methods could give up to a 3% improvement in Q 3 accuracy over the best individual method.…”