Deciphering novel potential antibacterial targets in tomato pathogen Ralstonia solanacearum GMI1000 through integration of in silico subtractive genomics, codon usage and protein–protein interaction analyses

Subramanian, Gurunathan; Vetrivel, Umashankar; Mohamedyousuff, Mohamed Imran

doi:10.1007/s13313-021-00845-6

Cited by 1 publication

(1 citation statement)

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“…Accurate determination of protein subcellular localization is a critical aspect for effective pesticide delivery to target proteins. In contrast to previous studies limited to one (Mandal and Sinha, 2021; Dineshkumar and Antony, 2022), or two computational methods (Subramanian et al, 2022), the present study employs a consensus approach from eight distinct computational tools, including Arti cial Intelligence (AI) and deep learning, to enhance the precision of protein location prediction. Recognizing the signi cance of cytoplasmic proteins as suitable targets for pathogens (Almeida et al, 2023;Sabzi et al, 2023), only proteins predicted to reside in the cytoplasm, as indicated by the consensus, were considered for subsequent analysis.…”

Section: Target Prioritization Protein Localization Predictionmentioning

confidence: 99%

Comparative Omics Analysis for Novel Target Discovery in Plant Pathogens: A Case Study for Magnaporthe oryzae

Luthfi,

Piapukiew,

Pandey

et al. 2023

Preprint

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The central concern surrounding chemical pesticide application is its potential adverse effects on non-target organisms. For fungal pathogens, the search for specific targets has been complicated by the similarities in pathways shared between these pathogens and humans. We present a comprehensive strategy, integrating comparative omics and bioinformatics, to pinpoint precise targets for fungicides effective against the fungal pathogen Magnaporthe oryzae(M. oryzae), responsible for rice blast disease. Our approach involves subtractive metabolic pathways, homology screening and target prioritization. Through subtractive metabolic analysis, we identified three unique M. oryzaepathways, distinct from human and rice. Non-redundant protein sequences were subsequently subjected to BLASTP screening against human and rice, as well as other databases from diverse organisms. Target subcellular localization was predicted using eight tools, including Artificial Intelligence and a deep learning method. A comprehensive examination of biological processes was conducted, including gene expression, protein-protein interactions, network enrichment, broad-spectrum activity, and physicochemical analysis. Glutamate 5-kinase (G5K) emerged as the prime candidate for targeted fungicide development, promising progress in precision-oriented solutions.

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Section: Target Prioritization Protein Localization Predictionmentioning

confidence: 99%