Modeling of cassava-cassava mosaic vrus interactions with computational biology and bioinformatics approach

Abstract

Every year pathogenic organisms cause billions of dollars’ worth damage to crops and livestock. In agriculture, study of plant-microbe interactions is demanding a special attention to develop management strategies for the destructive pathogen induced diseases that cause huge crop losses every year worldwide. Cassava Mosaic Virus (CMV) is a major viral leaf pathogen that causes disease in cassava. Protein-Protein Interactions (PPIs) play a critical role in initiating pathogenesis and maintaining infection. Understanding the PPI network between a host and pathogen is a critical step for studying the molecular basis of pathogenesis. The experimental study of PPIs at a large scale is very scarce and also the high throughput experimental results show high false positive rate. Hence, there is a need for developing efficient computational models to predict the interaction between host and pathogen in a genome scale, and find novel candidate effectors and/or their targets.

In this study, interacting proteins in cassava-CMV interaction is predicted using interolog-based method. The interolog method relies on protein sequence similarity to conduct the PPI prediction. Using this method, 114 PPIs have been predicted between 114 proteins of cassava and 10 proteins of CMV. Functional annotation of the predicted proteins showed the presence of 10 disease resistance protein in cassava that interacts with CMV. The subcellular location of the predicted proteins was found and it showed that major interactions occur in nucleus and chloroplast region. This can be a useful resource to the plant community to characterize the host-pathogen interaction in cassava and CMV. Further, these prediction models can be applied to the agriculturally relevant crops.