Computational structural genomics unravels common folds and predicted functions in the secretome of fungal phytopathogenMagnaporthe oryzae

Abstract

Magnaporthe oryzae relies on a diverse collection of secreted effector proteins to reprogram the host metabolic and immune responses for the pathogen’s benefit. Characterization of the effectors is thus critical for understanding the biology and host infection mechanisms of this phytopathogen. In rapid, divergent effector evolution, structural information has the potential to illuminate the unknown aspects of effectors that sequence analyses alone cannot reveal. It has recently become feasible to reliably predict the protein structures without depending on homologous templates. In this study, we tested structure modeling on 1854 secreted proteins from M. oryzae and evaluated success and obstacles involved in effector structure prediction. With sensitive homology search and structure-based clustering, we defined both distantly related homologous groups and structurally related analogous groups. With this dataset, we propose sequence-unrelated, structurally similar effectors are a common theme in M. oryzae and possibly in other phytopathogens. We incorporated the predicted models for structure-based annotations, molecular docking and evolutionary analyses to demonstrate how the predicted structures can deepen our understanding of effector biology. We also provide new experimentally testable structure-derived hypotheses of effector functions. Collectively, we propose that computational structural genomic approaches can now be an integral part of studying effector biology and provide valuable resources that were inaccessible before the advent of reliable, machine learning-based structure prediction.

Maintenance of divergent lineages of the Rice Blast Fungus Pyricularia oryzae through niche separation, loss of sex and post-mating genetic incompatibilities

“…None of the orthogroups with π N /π S > 1 or high inter-lineage F ST corresponded to effectors previously characterized as being involved in P . oryzae virulence on rice or other Poaceae hosts [ 61 ].…”

EffectorO: motif-independent prediction of effectors in oomycete genomes using machine learning and lineage-specificity

“…. Recently, trRosetta was used to predict effectors based on structure for the fungal species Magnaporthe oryzae(Seong and Krasileva 2021) and a similar analysis should be done with EffectorO candidates.…”

Impacts, Tolerance, Adaptation, and Mitigation of Heat Stress on Wheat under Changing Climates

“…Genomics evolved in diverse stages from first generation (1970s) to next generation (20th Century) to third generation sequencing (21st Century) [ 203 ]. Structural genomics helps to identify structure of a gene, prompter sequences, and other regulatory sequences located upstream or downstream [ 204 ], whereas functional genomics facilitate gene function at a molecular level and the type of tolerance (HS, salt, drought) it provides [ 205 , 206 ]. Therefore, genomics aids in providing genome wide network information of genes and their interaction with other complex stress-resilient traits [ 207 ].…”