Current progress on the computational methods for prediction of host-pathogen protein-protein interaction in the Ganoderma boninense-oil palm pathosystem.

Ganoderma boninense is a major pathogen for basal stem rot disease that can depolymerize lignocellulosic materials of the oil palm plant by secreting the cell wall-degrading enzymes. The management of this disease is complicated by the asymptomatic phase mediated by the protein-protein interaction (PPI) between the pathogen's effector and plant proteins. There is a lack of network-wide studies on the PPI to elucidate the action of effectors on plant pathways from a systemic perspective as the computational prediction of host-pathogen PPI (HP PPI) is focused on human interaction. Hence, this minireview explores the current computational methods for predicting HP PPI and proposes an approach to predict PPI between G. boninense and oil palm. [Display omitted] • Prediction of host-pathogen protein-protein interaction (PPI) is focused on humans and their pathogens. • Understanding of the early- and late-stage infection stages of G. boninense is limited. • Omics resources for G. boninense and oil palm support the computational prediction of their PPIs. • The structure-based machine learning method is suggested to predict the G. boninense -oil palm PPIs. [ABSTRACT FROM AUTHOR]