Molecular Docking is used to positioning the computer-generated 3D structure of small ligands into a receptor structure in a variety of orientations, conformations and positions. This method is useful in drug discovery and medicinal chemistry providing insights into molecular recognition. Docking has become an integral part of Computer-Aided Drug Design and Discovery (CADDD). Traditional docking methods suffer from limitations of semi-flexible or static treatment of targets and ligand. Over the last decade, advances in the field of computational, proteomics and genomics have also led to the development of different docking methods which incorporate protein-ligand flexibility and their different binding conformations. Receptor flexibility accounts for more accurate binding pose predictions and a more rational depiction of protein binding interactions with the ligand. Protein flexibility has been included by generating protein ensembles or by dynamic docking methods. Dynamic docking considers solvation, entropic effects and also fully explores the drug-receptor binding and recognition from both energetic and mechanistic point of view. Though in the fast-paced drug discovery program, dynamic docking is computationally expensive but is being progressively used for screening of large compound libraries to identify the potential drugs. In this review, a quick introduction is presented to the available docking methods and their application and limitations in drug discovery.

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<P>Background: Protein-Protein interaction (PPI) network analysis of virulence proteins of Aspergillus fumigatus is a prevailing strategy to understand the mechanism behind the virulence of A. fumigatus. The identification of major hub proteins and targeting the hub protein as a new antifungal drug target will help in treating the invasive aspergillosis. </P><P> Materials & Method: In the present study, the PPI network of 96 virulence (drug target) proteins of A. fumigatus were investigated which resulted in 103 nodes and 430 edges. Topological enrichment analysis of the PPI network was also carried out by using STRING database and Network analyzer a cytoscape plugin app. The key enriched KEGG pathway and protein domains were analyzed by STRING. Conclusion: Manual curation of PPI data identified three proteins (PyrABCN-43, AroM-34, and Glt1- 34) of A. fumigatus possessing the highest interacting partners. Top 10% hub proteins were also identified from the network using cytohubba on the basis of seven algorithms, i.e. betweenness, radiality, closeness, degree, bottleneck, MCC and EPC. Homology model and the active pocket of top three hub proteins were also predicted.</P>

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Type II topoisomerases like DNA gyrase initiate ATP-dependent negative supercoils in bacterial DNA. It is critical in all of the bacteria but is missing from eukaryotes, making it a striking target for antibacterials. Ciprofloxacin is a clinically approved drug, but its clinical effectiveness is affected by the emergence of resistance in both Gram-positive and Gram-negative bacteria. Thus, it is vital to identify novel compounds that can efficiently inhibit DNA gyrase, and quantitative structure–activity relationship (QSAR) modeling is a quick and economical means to do so. A QSAR-based virtual screening approach was applied to identify new gyrase inhibitors using an in-house -generated combinatorial library of 29828 compounds from seven ciprofloxacin scaffold structures. QSAR was built using a data set of 271 compounds, which were identified as positive and negative inhibitors from existing data reported in in vitro studies. The best QSAR model was developed using the 5-fold cross-validation Neural Network in Orange, and it was based on five PaDEL descriptors with an accuracy and sensitivity of 83%. As a result of screening of an in-house -built combinatorial library with the best-developed QSAR model, 675 compounds were identified as potential inhibitors of DNA gyrase. These inhibitors were further docked with DNA gyrase using AutoDock to compare the binding mode and score of the selected/screened compounds, and 615 compounds exhibited a docking score comparable to or lower than that of ciprofloxacin. Out of these, the top five analogues 902b, 9699f, 4419f, 5538f, and 898b reported in our study have binding scores of −13.81, −12.95, −12.52, −12.43, and −12.41 kcal/mol, respectively. The MD simulations of these five analogues for 100 ns supported the interaction stability of analogues with Escherichia coli DNA gyrase. Ninety-one per cent of the analogues screened by the QSAR model displayed better binding energy than ciprofloxacin, demonstrating the efficacy of the generated model. The NN-QSAR model proposed in this manuscript can be downloaded from .

Results from this study recommend that plant rhizosphere remains a rich hotspot for delivering a novel antifungal compounds.

Background: Chikungunya infection flare-ups have manifested in nations of Africa, Asia, and Europe including Indian and Pacific seas. It causes fever and different side effects include muscle torment, migraine, sickness, exhaustion and rash. It has turned into another, startling general medical issue in numerous tropical African and Asian countries and is presently being viewed as a genuine risk. No antiviral treatment or vaccine is yet available for this ailment. The current treatment is centered just on mitigating its side effects. Objective: The objective was to encourage the study on this viral pathogen, by the development of a database dedicated to Chikungunya Virus, that annotates and unifies the related data from various resources. associations while known disease-lncRNA associations are required only. Method: It undertook a consolidated approach for Chikungunya Virus genomic, proteomic, phylogenetics and therapeutic learning, involving the entire genome sequences and their annotation utilizing different in silico tools. Annotation included the information for CpG Island, usage bias, codon context and phylogenetic analysis at both the genome and proteome levels. Results: This database incorporates information of 41 strains of virus causing Chikungunya infection that can be accessed conveniently as well as downloaded effortlessly. Therapeutics section of this database contains data about B and T cell Epitopes, siRNAs and miRNAs that can be used as potential therapeutic targets. Conclusion: This database can be explored by specialists and established researchers around the world to assist their research on this non-treatable virus. It is a public database available from “www.chkv.in”.</P>

Objective: The objective of this study is to evaluate the in vitro antibacterial and antioxidant prospective of Terminalia arjuna (leaves). The most active extracts were examined for their chemical composition and cytotoxicity.Methods: The antibacterial activity of five different extracts were examined against 8 bacterial strains (5 Gram-positive and 3 Gram-negative) using resazurin-based microtiter dilution assay (RMDA) and disk-diffusion assay. The antioxidant potential of five extracts was demonstrated using 1, 1-diphenyl-2-picrylhydrazyl (DPPH) assay and superoxide radical scavenging assay. Chemical composition and cytotoxicity were assessed using gas chromatography-mass spectrometry (GC-MS) and hemolytic assay, respectively.Results: According to RMDA, the acetone extract (AE) exhibited highest antibacterial activity. The AE showed highest activity against Salmonella enterica ser. typhi and Bacillus cereus with minimum inhibitory concentration, i.e., 195.31 μg/ml. In DPPH assay, AE showed the highest radical scavenging activity with inhibition concentration 50 23.09 μg/ml. In GC-MS analysis, the principal compound in AE was celidoniol (8.72 %). According to the results of hemolytic assay, the AE showed non-toxic behavior upto 500 μg/ml. Conclusion:The present investigation represents T. arjuna as an incredible herb. The AE was found to possess promising antibacterial and antioxidant properties.