TonB-Dependent Receptors Activity Reduction Strategy in Xanthomonas oryzae pv. oryzae Pathogenicity: A Computational Approach

Abstract

Introduction: Rice, a vital staple crop globally, feeding over 3.5 billion people and providing about 20% of global caloric intake. In India, cultivated on 44 million hectares, contributing 20-25% to GDP and employing over 50% of the workforce. Xanthomonas oryzae pv. oryzae (Xoo) is a Gram-negative bacterium responsible for bacterial blight (BB), one of the most devastating diseases of Rice crop, worldwide. Xoo enters rice leaves through hydathodes or wounds, multiplies in the xylem, and spreads systemically, leading to leaf wilting, reduced photosynthesis, and significant yield losses, particularly in Asia and parts of Africa. The pathogenicity of Xoo relies on a suite of virulence factors, including the type III secretion system (T3SS) for effector delivery, extracellular polysaccharides, cell wall-degrading enzymes, cell membrane associated transport proteins. The aim is to study cell membrane associated transport proteins responsible for secretion using computational tools and screen come best performing compounds from Zinc15 database to mitigate their efficiency.

Materials and Methods: The study includes sequences of Xanthomonas oryzae pv. oryzae PXO99A, virulence factors identification using advanced machine learning tools like (SVMs, HMMs), three dimensional structure modelling using AlphaFold2 server, energy-minimization using YASARA, and validation using most efficient online tools i.e. ModFOLD, ERRAT, VERIFY3D, VADAR, PROCHECK, Draggability and ligand-binding sites mapping with CavityPlus server, CavPharmer tool and Virtual screening and docking of over 80,000 ZINC15 ligands (Secondary metabolites) through MTiOpenScreen and DockThor tool and Molecular interaction analysis with Swiss Dock-Vina and ADMET properties analysis using Artificial Intelligence driven tools of S.M.University’s AI Drug Lab.

Results and Discussion: Computational analysis of ACD59730.1 TonB-dependent outer membrane Receptor and ACD60174.1 TonB-dependent Receptor/Oar-like protein done using AlphaFold2 for 3D structure prediction. Both the proteins showed higher model quality with both structures validated as stable and accurate predicted by YASARA, ModFOLD, ERRAT, VERIFY3D, VADAR, and PROCHECK.

Conclusion: A small group of ligands were identified by docking studies from a vast dataset with good potential as inhibitors to these proteins, which can be used to mitigate the infection of Xoo. The study provides a thorough understanding in membrane associated proteins in Xoo’s pathogenicity and identifying potential therapeutic targets through advanced computational tools.