02422nas a2200217   4500008004100000022001400041245013900055210006900194260000900263490000800272520162400280653002501904653002001929653002301949653001801972653002101990653002202011100002002033700002002053856013102073       2024                        eng d  a0167-732200aTranscriptome analysis and Structure-Based drug discovery identifies potential biofungicides for controlling Fusarium wilt in chickpea0 aTranscriptome analysis and StructureBased drug discovery identif  cAPR 0 v3993 a<p>
	Fusarium wilt caused by the fungal pathogen Fusarium oxysporum f.sp. ciceri (Foc) is a devastating chickpea disease. Foc is a soil-borne pathogen that invades plants through roots and can kill them in three to four weeks. Although Fusarium wilt can be controlled by soil solarization or fumigation with chemical fungicides, these are not always effective. Soil fumigation is also hazardous to the beneficial soil microflora, deteriorates soil health, and causes pollution. Hence, there is an urgent need to identify potent and environment-friendly biofungicides to control fungal pathogens. We employed transcriptome analysis and structure-based drug discovery approaches to identify potential biofungicides from four widely used medicinal plants: Lantana camara, Piper betel, Ricinus communis, and Azadirachta indica. Fusarium wilt-resistant and susceptible chickpea varieties were pathogeninoculated and grown under controlled conditions in a greenhouse. Transcriptome analysis was performed to identify pathogenicity-related differentially expressed genes (DEGs). Over 600 phytochemicals from the four medicinal plants and four chemical fungicides were considered for molecular docking against the predicted protein structures of the four most expressed pathogen DEGs. The phytochemicals with the best docking scores and the lowest predicted toxicity risk were considered for molecular dynamics (MD) simulation at 100 ns timescale, and 15 potential biofungicides were identified. This study paves the way for developing biofungicides with enhanced efficacy and safety to manage Fusarium wilt in chickpea.</p>
10aAntifungal molecules10aCicer arietinum10afusarium oxysporum10aMD Simulation10amedicinal plants10aMolecular docking1 aSahoo, Rosaleen1 aKadoo, Narendra  uhttp://library.ncl.res.in/content/transcriptome-analysis-and-structure-based-drug-discovery-identifies-potential-biofungicides02890nas a2200229   4500008004100000022001400041245011200055210006900167260000800236300000800244490000800252520211300260653000902373653001202382653002002394653001802414653003302432653002002465100002002485700002402505856013102529       2025                        eng d  a0032-093500aIntegrative multi-omics and computer-aided biofungicide design approach to combat fusarium wilt of chickpea0 aIntegrative multiomics and computeraided biofungicide design app  cSEP  a1070 v2623 a<p>
	Main conclusionIntegrating multi-omics and computer-aided drug discovery approaches can overcome the limitations of traditional methods and help develop highly effective, specific, and environmentally safe biofungicides to control crop diseases.AbstractChickpea is a valuable legume crop in terms of nutrition, food security, economic sustainability, and environmental benefits. Fusarium wilt caused by the soil-borne fungus Fusarium oxysporum f.sp. ciceri is one of the most important diseases affecting chickpea. Several disease management methods, including crop rotation, soil fumigation with chemical fungicides, soil solarization, etc., are practiced to manage the disease. However, these methods have various limitations and cannot completely control the disease. Moreover, chemical fungicides indiscriminately kill even the beneficial soil microbes, pollute groundwater, and enter the food chain. Hence, modern approaches emphasizing innovative strategies and technologies need to be explored to manage the disease effectively. In this review, we propose integrating multi-omics (genomics, proteomics, metabolomics, etc.) and computer-aided drug discovery (CADD) approaches to develop biofungicides targeting vital pathogen proteins. Multi-omics approaches can delve deeper into the plant-pathogen interaction and reveal essential pathogen genes or proteins. These proteins could be targeted using CADD to identify phytochemical-based potential biofungicides, either using structure- or ligand-based drug design approaches. The potential biofungicides can be subjected to the prediction of carcinogenicity, hepatotoxicity, mutagenicity, etc., to identify biofungicides that are safe to use and are highly specific to the target pathogen. In vivo and in vitro validation studies can be followed to establish the efficacy and safety of the identified biofungicides for their practical application. This integrated approach can reduce the time and cost compared to the traditional methods and accelerate the discovery of highly effective biofungicides to protect crops from various diseases.</p>
10aCADD10adocking10aFungal diseases10aMD Simulation10aProtein structure prediction10aTranscriptomics1 aSahoo, Rosaleen1 aKadoo, Narendra, Y.  uhttp://library.ncl.res.in/content/integrative-multi-omics-and-computer-aided-biofungicide-design-approach-combat-fusarium-wilt