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Probing an optimal class distribution for enhancing prediction and feature characterization of plant virus-encoded RNA-silencing suppressors.

ABSTRACT: To counter the host RNA silencing defense mechanism, many plant viruses encode RNA silencing suppressor proteins. These groups of proteins share very low sequence and structural similarities among them, which consequently hamper their annotation using sequence similarity-based search methods. Alternatively the machine learning-based methods can become a suitable choice, but the optimal performance through machine learning-based methods is being affected by various factors such as class imbalance, incomplete learning, selection of inappropriate features, etc. In this paper, we have proposed a novel approach to deal with the class imbalance problem by finding the optimal class distribution for enhancing the prediction accuracy for the RNA silencing suppressors. The optimal class distribution was obtained using different resampling techniques with varying degrees of class distribution starting from natural distribution to ideal distribution, i.e., equal distribution. The experimental results support the fact that optimal class distribution plays an important role to achieve near perfect learning. The best prediction results are obtained with Sequential Minimal Optimization (SMO) learning algorithm. We could achieve a sensitivity of 98.5 %, specificity of 92.6 % with an overall accuracy of 95.3 % on a tenfold cross validation and is further validated using leave one out cross validation test. It was also observed that the machine learning models trained on oversampled training sets using synthetic minority oversampling technique (SMOTE) have relatively performed better than on both randomly undersampled and imbalanced training data sets. Further, we have characterized the important discriminatory sequence features of RNA-silencing suppressors which distinguish these groups of proteins from other protein families.

SUBMITTER: Nath A

PROVIDER: S-EPMC4801844 | biostudies-other | 2016 Jun

REPOSITORIES: biostudies-other

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Probing an optimal class distribution for enhancing prediction and feature characterization of plant virus-encoded RNA-silencing suppressors.

Nath Abhigyan A Subbiah Karthikeyan K

3 Biotech 20160321 1

To counter the host RNA silencing defense mechanism, many plant viruses encode RNA silencing suppressor proteins. These groups of proteins share very low sequence and structural similarities among them, which consequently hamper their annotation using sequence similarity-based search methods. Alternatively the machine learning-based methods can become a suitable choice, but the optimal performance through machine learning-based methods is being affected by various factors such as class imbalance ...[more]

PMID: 28330163

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Project description: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/.

Project description:Primary infection of a plant with a pathogen that causes high accumulation of salicylic acid in the plant typically via a hypersensitive response confers enhanced resistance against secondary infection with a broad spectrum of pathogens, including viruses. This phenomenon is called systemic acquired resistance (SAR), which is a plant priming for adaption to repeated biotic stress. However, the molecular mechanisms of SAR-mediated enhanced inhibition, especially of virus infection, remain unclear. Here, we show that SAR against cucumber mosaic virus (CMV) in tobacco plants (Nicotiana tabacum) involves a calmodulin-like protein, rgs-CaM. We previously reported the antiviral function of rgs-CaM, which binds to and directs degradation of viral RNA silencing suppressors (RSSs), including CMV 2b, via autophagy. We found that rgs-CaM-mediated immunity is ineffective against CMV infection in normally growing tobacco plants but is activated as a result of SAR induction via salicylic acid signaling. We then analyzed the effect of overexpression of rgs-CaM on salicylic acid signaling. Overexpressed and ectopically expressed rgs-CaM induced defense reactions, including cell death, generation of reactive oxygen species, and salicylic acid signaling. Further analysis using a combination of the salicylic acid analogue benzo-(1,2,3)-thiadiazole-7-carbothioic acid S-methyl ester (BTH) and the Ca2+ ionophore A23187 revealed that rgs-CaM functions as an immune receptor that induces salicylic acid signaling by simultaneously perceiving both viral RSS and Ca2+ influx as infection cues, implying its autoactivation. Thus, secondary infection of SAR-induced tobacco plants with CMV seems to be effectively inhibited through 2b recognition and degradation by rgs-CaM, leading to reinforcement of antiviral RNA silencing and other salicylic acid-mediated antiviral responses.IMPORTANCE Even without an acquired immune system like that in vertebrates, plants show enhanced whole-plant resistance against secondary infection with pathogens; this so-called systemic acquired resistance (SAR) has been known for more than half a century and continues to be extensively studied. SAR-induced plants strongly and rapidly express a number of antibiotics and pathogenesis-related proteins targeted against secondary infection, which can account for enhanced resistance against bacterial and fungal pathogens but are not thought to control viral infection. This study showed that enhanced resistance against cucumber mosaic virus is caused by a tobacco calmodulin-like protein, rgs-CaM, which detects and counteracts the major viral virulence factor (RNA silencing suppressor) after SAR induction. rgs-CaM-mediated SAR illustrates the growth versus defense trade-off in plants, as it targets the major virulence factor only under specific biotic stress conditions, thus avoiding the cost of constitutive activation while reducing the damage from virus infection.

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Dataset Information

Probing an optimal class distribution for enhancing prediction and feature characterization of plant virus-encoded RNA-silencing suppressors.

Publications

Probing an optimal class distribution for enhancing prediction and feature characterization of plant virus-encoded RNA-silencing suppressors.

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