SEQUENCE BASED PREDICTION OF PHYTOPHTHORA- HOST INTERACTION USING MACHINE LEARNING METHODS

Phytophthora is a genus of oomycetes that cause extensive crop damage and economic loss. Specific proteins from the host and pathogen facilitate their interaction and mediate a multifaceted mechanism in infection. In this study, we utilized published protein protein interactions between Phytophthora and its hosts for developing a model for prediction. We applied supervised learning algorithms- Support vector machine (SVM) and Ensemble methods to predict interactions.Different features of proteins in host and pathogen proteins like amino acid composition, dipeptide composition, pseudo amino acid composition, amphiphilic pseudo amino acid composition, C/T/D, conjoint triads, autocorrelation, sequence order coupling number, quasi-sequence order descriptors were utilized to develop the model for the binary classification, whether the proteins interact or not. The relative importance of different protein features in the training model were also evaluated. SVM with radial kernel had an accuracy of 75%. Bagging algorithm Random Forest showed an accuracy of 84.6%. A GLM ensemble of K-Nearest Neighbour (KNN), SVM (radial), rpart and random forest gave an accuracy of 70.1%. The model developed can be trained with more experimentally validated interactions to improve the accuracy. Furthermore we constructed a protein-protein interaction network of host and pathogen proteins to depict the interaction network operating during pathogenesis and evaluated the network topology. The results of the study may be taken forward for experimental validation.