Computational models for prediction of protein–protein interaction in rice and Magnaporthe grisea - Karan, Biswajit; Mahapatra, Satyajit; Sahu, Sitanshu Sekhar; Pandey, Dev Mani; Chakravarty, Sumit (Frontiers in Plant Science, 2023)
Plant-microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice, Oryza sativa (O. sativa) due to Magnaporthe grisea (M. grisea) fungus. Protein-protein interactions (PPIs) between rice and fungus plays a key role in causing rice blast disease. In this paper, four genomic information based models such as (i) the interolog (ii) the domain (iii) the gene ontology and (iv) the phylogenetic are developed for predicting the interaction between Oryza sativa and Magnaporthe grisea in a whole genome scale. Total 59430 interacting pairs between 1801 rice proteins and 135 blast fungus proteins are obtained from the four models. Further, a machine learning model, support vector machine is developed to assess the predicted interactions. An accuracy of 88% and 89% is achieved respectively using composition-based amino acid composition (AAC) and conjoint triad (CT) features. When tested on the experimental dataset, the CT features provide highest accuracy of 95 %. Further, the specificity of the model is verified with other pathogen-host datasets where less accuracy is obtained which confirmed that the model is specific to Oryzasativa and M. grisea. The predicted PPIs will provide useful information to the research community for further investigation and analysis.