Supervised learning classification models for prediction of plant virus encoded RNA silencing suppressors

• Published in: PloS one Vol. 9; no. 5; p. e97446
• Main Authors: Jagga, Zeenia, Gupta, Dinesh
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.05.2014; Public Library of Science (PLoS)
• Subjects: Acids; Algorithms; Amino acid composition; Amino acids; Amino Acids - genetics; Artificial intelligence; Bayes Theorem; Bayesian analysis; Bioinformatics; Biology; Biology and life sciences; Biotechnology; Classification; Classifiers; Coding; Correlation; Correlation coefficient; Correlation coefficients; Data mining; Datasets; Forest management; Gene therapy; Genetic engineering; Health aspects; Host plants; Immunity; Internet; Learning algorithms; Machine learning; Machinery; Machinery and equipment; Mathematical models; MicroRNAs; Next-generation sequencing; Performance prediction; Physicochemical properties; Plant Diseases - virology; Plant viruses; Plant Viruses - genetics; Plants - virology; Prediction models; Proteins; Proteomics; Ribonucleic acid; RNA; RNA Interference - physiology; RNA viruses; RNA, Viral - genetics; RNA-mediated interference; Servers; Supervised learning; Suppressors; Taxonomy; Viral infections; Viral Proteins - genetics; Virus diseases; Viruses
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0097446

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 in silico 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 http://bioinfo.icgeb.res.in/pvsup/.