Repurposing small molecules of Tephrosia purpurea against SARS-CoV-2 main protease - Sahoo, Rosaleen; Sahu, Parameswar; Swargam, Sandeep; Kumari, Indu; Behera, Banshidhar (Journal of Biomolecular Structure and Dynamics, 2023)
Coronavirus infection is a communicable disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which emerged as a global pandemic with deteriorating effect on the world's population. Main protease (M-pro) of SARS-CoV-2 plays a significant role in the viral replication, transcription and disease propagation as well as a potential candidate for drug discovery and development for COVID-19 infection. The current study employed state of art structure-based drug discovery to decipher the role of phytochemicals of Tephrosia purpurea against M-pro. Tephrosia purpurea is being used as a traditional medicinal plant for the treatment of cough, breathlessness and fever as per the Indian Materia Medica. Screening of the phytochemicals of Tephrosia purpurea against M-pro was performed using molecular docking approach to identify the top 5 hits (+)-tephrorin B, deguelin, vitamin p, lanceolarin and 3beta-hydroxy-20(29)-lupene with binding energy of -8.4, -8.1, -8.0, -7.8, and -7.8 kcal/mol, respectively. Furthermore, identified top 5 hits were subjected to drug-likeness and toxicity prediction as well as MM-GBSA calculation. Out of the five molecules four molecules were predicted not to comprise any mutagenic and carcinogenic effects. Top two molecules based on the drug-likeness properties for oral bio-availability were further analysed by molecular dynamics simulation at 100 ns timescale. It was observed from the dynamic behaviour of the two complexes that the addition of these molecules changed the conformation and stability of the apo protein; thus may act as inhibitors for M-pro. Communicated by Ramaswamy H. Sarma
Keywords: molecular dynamics; SARS-CoV-2; Tephrosia purpurea

Repurposing phytochemicals of Citrullus colocynthis against maltase-glucoamylase using molecular docking, MMGBSA, MD simulation and linear regression to identify potential anti-diabetic compounds - Sahu, Parameswar; Sahoo, Rosaleen; Sahu, Abhishek Kumar; Saluja, Sundeep Singh; Behera, Banshidhar (Journal of biomolecular structure and dynamics, 2024)
Diabetes is a common lifestyle disorder found in populations of different age groups. Maltase-glucoamylase catalyses the release of the glucose molecule in the final enzymatic reaction of starch digestion; therefore, inhibition of maltase-glucoamylase is one of the approaches in the development of therapeutics for diabetes. Citrullus colocynthis is commonly recommended in Ayurveda for the treatment of diabetes. The current study applied a structure-based drug design approach to repurpose the phytochemicals of Citrullus colocynthis to identify potential inhibitors for maltase-glucoamylase. 70 phytochemicals of Citrullus colocynthis were screened against maltase-glucoamylase and top 5 molecules 8-p-hydroxybenzylisovitexin, isoorientin, cucurbitacin B, cucurbitacin E, and cucurbitacin I with significant binding energy of -10 kcal/mol, -9.9 kcal/mol, -9.6 kcal/mol, -9.2 kcal/mol, and -7.7 kcal/mol were identified. Furthermore, MMGBSA, pharmacokinetics properties and toxicity prediction were performed on the five identified molecules and top 3 molecules were selected for molecular dynamics (MD) simulation. It was observed from the structural flexibility and dynamic behaviour of the systems that conformational changes were noticed in the complexes as compared to its native state, which suggests that the 3 molecules, namely 8-p-hydroxybenzylisovitexin, isoorientin, and cucurbitacin I of Citrullus colocynthis may act as inhibitors for maltase-glucoamylase.Communicated by Ramaswamy H. Sarma
Keywords: Citrullus colocynthis; Diabetes; linear regression; molecular dynamics simulation; phytochemicals

Transcriptome analysis and Structure-Based drug discovery identifies potential biofungicides for controlling Fusarium wilt in chickpea - Sahoo, Rosaleen; Kadoo, Narendra (Journal of Molecular Liquids, 2024)
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.
Keywords: Antifungal molecules; Cicer arietinum; fusarium oxysporum; MD Simulation; medicinal plants; Molecular docking

Comprehensive genomic analysis of bipolaris sorokiniana strains: insights into genetic diversity and pathogenicity - Shukla, Anand Kumar; Sahoo, Rosaleen; Kadoo, Narendra (Plant Pathology, 2025)
Bipolaris sorokiniana is an important plant-pathogenic fungus that primarily infects cereals, such as wheat and barley, causing diseases such as spot blotch, common root rot and seedling blight. This study presents a comprehensive comparative genomic analysis of nine B. sorokiniana strains based on 16 genomic parameters, including genome completeness, virulence factors, secondary metabolite biosynthesis, effector proteins and CAZymes, to explore their genetic diversity, genome assembly quality and pathogenic potential. Genomic assemblies of the strains LK93 and ND93-1 exhibited higher N50, L50, BUSCO completeness scores and gene annotations, and were designated as high-quality. Similarly, ND90Pr and BS112 demonstrated a rich arsenal of CAZymes and effector proteins, which indicated their greater infection potential. Variability in biosynthetic gene clusters, especially the presence of isocyanide-NRPS and fungal RiPP clusters, highlights the ecological adaptability and metabolic diversity of these strains. The study also revealed distinct protein family distributions and effector protein repertoires, supporting strain-specific pathogenic strategies. Phylogenetic analyses grouped the strains into three clusters, indicating evolutionary divergence and potential ecological specialisation. The findings of the study underscore the importance of high-quality genomic data and propose LK93 and ND93-1 as more reliable reference genomes for B. sorokiniana. Integrating transcriptomics, proteomics and pathogenicity data, along with expanded strain sampling and unified assembly pipelines, can enhance understanding of the molecular basis of pathogenicity and help develop suitable disease management strategies to control this agriculturally important pathogen.
Keywords: Cochliobolus sativus; comparative genomics; effector proteins; pathogen variability; Secondary metabolites; spot blotch

Integrative multi-omics and computer-aided biofungicide design approach to combat fusarium wilt of chickpea - Sahoo, Rosaleen; Kadoo, Narendra Y. (Planta, 2025)
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.
Keywords: CADD; docking; Fungal diseases; MD Simulation; Protein structure prediction; Transcriptomics