Identification of novel and safe fungicidal molecules against fusarium oxysporum from plant essential oils: in vitro and computational approaches.

Phytopathogenic fungi are serious threats in the agriculture sector especially in fruit and vegetable production. The use of plant essential oil as antifungal agents has been in practice from many years. Plant essential oils (PEOs) of Cuminum cyminum, Trachyspermum ammi, Azadirachta indica, Syzygium aromaticum, Moringa oleifera, Mentha spicata, Eucalyptus grandis, Allium sativum, and Citrus sinensis were tested against Fusarium oxysporum. Three phase trials consist of lab testing (MIC and MFC), field testing (seed treatment and foliar spray), and computer-aided fungicide design (CAFD). Two concentrations (25 and 50 μl/ml) have been used to asses MIC while MFC was assessed at four concentrations (25, 50, 75, and 100 μl/ml). C. sinensis showed the largest inhibition zone (47.5 and 46.3 m2) for both concentrations. The lowest disease incidence and disease severity were recorded in treatments with C. sinensis PEO. Citrus sinensis that qualified in laboratory and field trials was selected for CAFD. The chemical compounds of C. sinensis PEO were docked with polyketide synthase beta-ketoacyl synthase domain of F. oxysporum by AutoDock Vina. The best docked complex was formed by nootkatone with -6.0 kcal/mol binding affinity. Pharmacophore of the top seven C. sinensis PEO compounds was used for merged pharmacophore generation. The best pharmacophore model with 0.8492 score was screened against the CMNP database. Top hit compounds from screening were selected and docked with polyketide synthase beta-ketoacyl synthase domain. Four compounds with the highest binding affinity and hydrogen bonding were selected for confirmation of lead molecule by doing MD simulation. The polyketide synthase-CMNPD24498 showed the highest stability throughout 80 ns run of MD simulation. CMNPD24498 (FW054-1) from Verrucosispora was selected as the lead compound against F. oxysporum

Genetic Variability and Population Structure of Pakistani Potato Genotypes Using Retrotransposon-Based Markers

Molecular germplasm characterization is essential for gathering information on favorable attributes and varietal improvement. The current study evaluated the genetic divergence and population structure of 80 potato genotypes collected from Punjab, Pakistan, using polymorphic retrotransposon-DNA-based markers (iPBS). A total of 11 iPBS primers generated 787 alleles with a mean value of 8.9 alleles per primer, of which ~95% were polymorphic across the 80 genotypes. Different variation attributes, such as mean expected heterozygosity (H = 0.21), mean unbiased expected heterozygosity (µHe = 0.22), and mean Shannon’s information index (I = 0.32), showed the existence of sufficient genetic diversity in the studied potato genotypes. Analysis of molecular variance (AMOVA) showed that genetic variation within the population was higher (84%) than between populations (16%). A neighbor-joining tree was constructed based on the distance matrices that arranged the 80 genotypes into five distinct groups, and the genotypes FD61-3 and potato 2 had the highest genetic distance. A STRUCTURE analysis corroborated the dendrogram results and distributed the 80 genotypes also into five clusters. Our results determined that retrotransposon-based markers are highly polymorphic and could be used to evaluate genetic diversity between local and exotic potato genotypes. The genotypic data and population structure dissection analysis reported in this study will enhance potato varietal improvement and development

Virtual screening and molecular dynamics simulation analysis of Forsythoside A as a plant-derived inhibitor of SARS-CoV-2 3CLpro

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a more severe strain of coronavirus (CoV) that was first emerged in China in 2019. Available antiviral drugs could be repurposed and natural compounds with antiviral activity could be safer and cheaper source of medicine for SARS-CoV-2. 78 natural antiviral compounds database was identified from literature and virtual screening technique was applied to identify potential 3-chymotrypsin-like protease (3CLpro) inhibitors. Molecular docking studies were conducted to analyze the main protease (3CLpro) and inhibitors interactions with key residues of active site of target protein (PDB ID: 6LU7), active site constitute the part of active domain I and II of 3CLpro. 10 compounds with highest dock score were subjected to calculate ADMET parameters to figure out drug-likeness. Molecular dynamic (MD) simulation of the selected lead was performed by Amber simulation package to understand the conformational changes in docked complex. MD simulations analysis (RMSD, RMSF, Rg, BF, HBs, and SASA plots) of lead bounded with 3CLpro, hence revealed the important structural turns and twists during MD simulations from 0 to 100 ns. MM-PBSA/GBSA methods has also been applied for the estimation binding free energy (BFE) of the selected lead-complex. The present study has identified lead compound “Forsythoside A” an active extract of Forsythia suspense as SARS-CoV-2 3CLpro inhibitor that can block the viral replication and translation. Structural analysis of target protein and lead compound performed in this study could contribute to the development of potential drug against SARS-CoV-2 infection

Structural-Dynamics and Binding Analysis of RBD from SARS-CoV-2 Variants of Concern (VOCs) and GRP78 Receptor Revealed Basis for Higher Infectivity

Glucose-regulated protein 78 (GRP78) might be a receptor for SARS-CoV-2 to bind and enter the host cell. Recently reported mutations in the spike glycoprotein unique to the receptor-binding domain (RBD) of different variants might increase the binding and pathogenesis. However, it is still not known how these mutations affect the binding of RBD to GRP78. The current study provides a structural basis for the binding of GRP78 to the different variants, i.e., B.1.1.7, B.1.351, B.1.617, and P.1 (spike RBD), of SARS-CoV-2 using a biomolecular simulation approach. Docking results showed that the new variants bound stronger than the wild-type, which was further confirmed through the free energy calculation results. All-atom simulation confirmed structural stability, which was consistent with previous results by following the global stability trend. We concluded that the increased binding affinity of the B.1.1.7, B.1.351, and P.1 variants was due to a variation in the bonding network that helped the virus induce a higher infectivity and disease severity. Consequently, we reported that the aforementioned new variants use GRP78 as an alternate receptor to enhance their seriousness

Study of <i>MDM2</i> as Prognostic Biomarker in Brain-LGG Cancer and Bioactive Phytochemicals Inhibit the p53-MDM2 Pathway: A Computational Drug Development Approach

An evaluation of the expression and predictive significance of the MDM2 gene in brain lower-grade glioma (LGG) cancer was carried out using onco-informatics pipelines. Several transcriptome servers were used to measure the differential expression of the targeted MDM2 gene and search mutations and copy number variations. GENT2, Gene Expression Profiling Interactive Analysis, Onco-Lnc, and PrognoScan were used to figure out the survival rate of LGG cancer patients. The protein–protein interaction networks between MDM2 gene and its co-expressed genes were constructed by Gene-MANIA tool. Identified bioactive phytochemicals were evaluated through molecular docking using Schrödinger Suite Software, with the MDM2 (PDB ID: 1RV1) target. Protein–ligand interactions were observed with key residues of the macromolecular target. A molecular dynamics simulation of the novel bioactive compounds with the targeted protein was performed. Phytochemicals targeting MDM2 protein, such as Taxifolin and (-)-Epicatechin, have been shown with more highly stable results as compared to the control drug, and hence, concluded that phytochemicals with bioactive potential might be alternative therapeutic options for the management of LGG patients. Our once informatics-based designed pipeline has indicated that the MDM2 gene may have been a predictive biomarker for LGG cancer and selected phytochemicals possessed outstanding interaction results within the macromolecular target’s active site after utilizing in silico approaches. In vitro and in vivo experiments are recommended to confirm these outcomes