The therapeutic challenges in the treatment of tuberculosis demand multidisciplinary approaches for the identification of potential drug targets as well as fast and accurate techniques to screen huge chemical libraries. Mycobacterial cyclopropane synthase (CmaA1) has been shown to be essential for the survival of the bacteria due to its critical role in the synthesis of mycolic acids. The present study proposes pharmacophore models based on the structure of CmaA1 taking into account its various states in the cyclopropanation process, and their dynamic nature as assessed using molecular dynamics (MD) simulations. The qualities of these pharmacophore models were validated by mapping 23 molecules that have been previously reported to exhibit inhibitory activities on CmaA1. Additionally, 1398 compounds that have been shown to be inactive for tuberculosis were collected from the ChEMBL database and were screened against the models for validation. The models were further validated by comparing the results from pharmacophore mapping with the results obtained from docking these molecules with the respective protein structures. The best models are suggested by validating all the models based on their screening abilities and by comparing with docking results. The models generated from the MD trajectories were found to perform better than the one generated based on the crystal structure demonstrating the importance of incorporating receptor flexibility in drug design.

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The recent pandemic of severe acute respiratory syndrome-coronavirus2 (SARS-CoV-2) infection (COVID-19) has put the world on serious alert. The main protease of SARS-CoV-2 (SARS-CoV-2-M Pro ) cleaves the long polyprotein chains to release functional proteins required for replication of the virus and thus is a potential drug target to design new chemical entities in order to inhibit the viral replication in human cells. The current study employs state of art computational methods to design novel molecules by linking molecular fragments which specifically bind to different constituent sub-pockets of the SARS-CoV-2-M Pro binding site. A huge library of 191678 fragments was screened against the binding cavity of SARS-CoV-2-M Pro and high affinity fragments binding to adjacent sub-pockets were tailored to generate new molecules. These newly formed molecules were further subjected to molecular docking, ADMET filters and MM-GBSA binding energy calculations to select 17 best molecules (named as MP-In1 to MP-In17), which showed comparable binding affinities and interactions with the key binding site residues as the reference ligand. The complexes of these 17 molecules and the reference molecule with SARS-CoV-2-M Pro , were subjected to molecular dynamics simulations, which assessed the stabilities of their binding with SARS-CoV-2-M Pro . Fifteen molecules were found to form stable complexes with SARS-CoV-2-M Pro . These novel chemical entities designed specifically according to the pharmacophoric requirements of SARS-CoV-2-M Pro binding pockets showed good synthetic feasibility and returned no exact match when searched against chemical databases. Considering their interactions, binding efficiencies and novel chemotypes, they can be further evaluated as potential starting points for SARS-CoV-2 drug discovery.

This study critically examines the role of conceptual DFT descriptors and docking scores on a diverse set of 156 inhibitors of HIV proteases. Five QSAR models were developed on the basis of available experimental IC(50) values (HIV-I and HIV-IIIB infected MT4 and CEMSS cells and HIV-I infected C8166 cells) and sixth QSAR model was generated by combining the inhibitors of all five models. B3LYP/6-31G(d) optimizations were carried out on all considered inhibitors, and the results are compared with more economic semi-empirical SCF AM1 results in order to find out the best and efficient way of descriptor calculations. Interestingly semi-empirical results appear to be satisfactory for this class of inhibitors. Selected QSAR models were validated by taking about 20% of inhibitors in the test sets. The effect of the number of descriptors on the R(2) and R(2)(cv) values was tested and three to four orthogonal descriptors based models were selected to be the optimum ones to avoid over correlation.