Methodology flow.

<p>Framework of the computational method used for development of predictions models for plant virus encoded RNA silencing suppressors.</p

Supervised Learning Classification Models for Prediction of Plant Virus Encoded RNA Silencing Suppressors

<div><p>Viral encoded RNA silencing suppressor proteins interfere with the host RNA silencing machinery, facilitating viral infection by evading host immunity. In plant hosts, the viral proteins have several basic science implications and biotechnology applications. However <i>in silico</i> identification of these proteins is limited by their high sequence diversity. In this study we developed supervised learning based classification models for plant viral RNA silencing suppressor proteins in plant viruses. We developed four classifiers based on supervised learning algorithms: J48, Random Forest, LibSVM and Naïve Bayes algorithms, with enriched model learning by correlation based feature selection. Structural and physicochemical features calculated for experimentally verified primary protein sequences were used to train the classifiers. The training features include amino acid composition; auto correlation coefficients; composition, transition, and distribution of various physicochemical properties; and pseudo amino acid composition. Performance analysis of predictive models based on 10 fold cross-validation and independent data testing revealed that the Random Forest based model was the best and achieved 86.11% overall accuracy and 86.22% balanced accuracy with a remarkably high area under the Receivers Operating Characteristic curve of 0.95 to predict viral RNA silencing suppressor proteins. The prediction models for plant viral RNA silencing suppressors can potentially aid identification of novel viral RNA silencing suppressors, which will provide valuable insights into the mechanism of RNA silencing and could be further explored as potential targets for designing novel antiviral therapeutics. Also, the key subset of identified optimal features may help in determining compositional patterns in the viral proteins which are important determinants for RNA silencing suppressor activities. The best prediction model developed in the study is available as a freely accessible web server pVsupPred at <a href="http://bioinfo.icgeb.res.in/pvsup/" target="_blank">http://bioinfo.icgeb.res.in/pvsup/</a>.</p></div

Number of positive and negative instances in testing and training dataset.

<p>Number of positive and negative instances in testing and training dataset.</p

Sensitivity and Specificity plot.

<p>The plot compares sensitivity and specificity of the developed predictive models, to determine effective classifier for identifying positive and negative instances. Random Forest classifier has the highest sensitivity and specificity values as compared to J48, LibSVM and Naïve Bayes.</p

Feature distribution of optimal feature subset generated by correlation based feature selection.

<p>Feature distribution of optimal feature subset generated by correlation based feature selection.</p

ROC curves for the predictive performance of different cost sensitive classifiers.

<p>ROC plot depicts a relative trade-off between true positive rate and false positive rate of the predictions. The diagonal value represents a completely random guess. The corresponding scalar values of area under curve are given as auROC in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097446#pone-0097446-t004" target="_blank">table 4</a>.</p

Number of protein sequences after removing redundant proteins at thresholds of 90%, 70% and 40% using CD-HIT.

<p>Number of protein sequences after removing redundant proteins at thresholds of 90%, 70% and 40% using CD-HIT.</p

Schematic representation of the dataset generation and filtration steps.

<p>The figure shows flowchart for the steps followed for sequence collection, filtering and redundancy removal for training dataset generation.</p

Number of protein sequences after removing redundant proteins at thresholds of 90%, 70% and 40% using CD-HIT.

<p>Number of protein sequences after removing redundant proteins at thresholds of 90%, 70% and 40% using CD-HIT.</p

Summary of statistical measures of the best classifiers on re-evaluation with independent dataset.

<p>Summary of statistical measures of the best classifiers on re-evaluation with independent dataset.</p