Identification of Magnaporthe oryzae candidate secretory effector proteins through standardizing the filtering process of the canonical parameters

Abstract The virulence of Magnaporthe oryzae largely hinges on its secretory effectors. Therefore, identification and thorough understanding of the effector functionality is crucial for unravelling the pathogenicity of the pathogen. In the present study, we employed a modified computational pipeline with deep machine learning techniques with an integration of Magnaporthe effector reference datasets (MOED) that predicted 434 M . oryzae candidate secretory effector proteins (MoCSEPs) from the genomic data. The reliability of the modified CSEP prediction workflow through utilization of precise parametric filtering is considered valid as it predicted 100 functional effectors (97.08%) out of 103 previously identified effector proteins within the Magnaporthe genus. Insights into secretion patterns and subcellular localization elucidated the role of these proteins in host cell recognition. Furthermore, structural classification of MoCSEPs, based on conserved motifs, combined with an exploration of their biological functions, revealed their significance in host adaptability and localization. Experimental validation done through examining expression of the MoCSEPs revealed varied secretion patterns in the resistant (40 expressed) and susceptible (92 expressed) rice cultivars at different time intervals after pathogen inoculation owing to different degrees of resistance by the host cultivars. The present work thus provides the strategic model of canonical parametric evaluation within the MOED and deepens the understanding on the role of secretory proteins of M . oryzae in establishing successful parasitic infection in rice. The predicted MoCSEPs could be used as biomarkers for disease diagnosis and tracking evolutionary shifts in M . oryzae .