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.
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 .
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>
Background: The major concern of today's time is the developing resistance in the most of the clinically derived pathogenic micro-organisms for the available drugs through several mechanisms. So, there is an acute need to develop novel molecules with drug like properties that can be effective against the otherwise resistant micro-organisms. Methods : New drugs can be developed using several methods like structure based drug design, ligand based drug design or by developing analogues of the available drugs to improve their effect further. But the smartness is to opt for the techniques that involve lower expenditure, lower failure rates and faster discovery rates. Results: Analogue based drug design (ABDD) is one such technique that researchers worldwide are opting to develop new drug like molecules with comparatively lower market values.They start by first designing the analogues sharing structural and pharmacological similarities to the existing drugs. This method embark on scaffold structures of available drugs already approved by the clinical trials, but are left ineffective because of resistance developed by the pathogens. Conclusion: In this review we have stated some recent examples of anti-fungal and anti-bacterial (antimicrobial) drugs that were designed based on ABDD technique. Also we have tried to focus on the in silico tools and techniques that can contribute for the designing and computational screening of the analogues, so that these can be further taken for in vitro screening to validate their better biological activities against the pathogens with comparatively reduced rates of failure.
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