Multiple target-based pharmacophore design from active site structures

Structure based Drug Designing Approaches in SARS-CoV-2 Spike Inhibitor Design

The COVID-19 outbreak and the pandemic situation have hastened the research community to design a novel drug and vaccine against its causative organism, the SARS-CoV-2. The spike glycoprotein present on the surface of this pathogenic organism plays an immense role in viral entry and antigenicity. Hence, it is considered an important drug target in COVID-19 drug design. Several three-dimensional crystal structures of this SARS-CoV-2 spike protein have been identified and deposited in the Protein DataBank during the pandemic period. This accelerated the research in computer- aided drug designing, especially in the field of structure-based drug designing. This review summarizes various structure-based drug design approaches applied to this SARS-CoV-2 spike protein and its findings. Specifically, it is focused on different structure-based approaches such as molecular docking, high-throughput virtual screening, molecular dynamics simulation, drug repurposing, and target-based pharmacophore modelling and screening. These structural approaches have been applied to different ligands and datasets such as FDA-approved drugs, small molecular chemical compounds, chemical libraries, chemical databases, structural analogs, and natural compounds, which resulted in the prediction of spike inhibitors, spike-ACE-2 interface inhibitors, and allosteric inhibitors.

Computer-aided discovery of pleiotropic effects: Anti-inflammatory action of dithioloquinolinethiones as a case study

Most of pharmaceutical agents exhibit several or even many biological activities. It is clear that testing even one compound for thousands of biological activities is a practically not feasible task. Therefore, computer-aided prediction is the method-of-the-choice to select the most promising bioassays for particular compounds. Using PASS Online software, we determined the likely anti-inflammatory action of the 13 dithioloquinolinethione derivatives with antimicrobial activities. Chemical similarity search in the Cortellis Drug Discovery Intelligence database did not reveal close structural analogues with anti-inflammatory action. Experimental testing of anti-inflammatory activity of the synthesized compounds in carrageenan-induced inflammation mouse model confirmed the computational predictions. The anti-inflammatory activity of the studied compounds was comparable with or higher than the reference drug Indomethacin. Thus, based on the in silico predictions, novel class of the anti-inflammatory agents was discovered.

Development of a Novel Pharmacophore Model Guided by the Ensemble of Waters and Small Molecule Fragments Bound to SARS-CoV-2 Main Protease

Recent fragment‐based drug design efforts have generated huge amounts of information on water and small molecule fragment binding sites on SARS‐CoV‐2 M^pro and preference of the sites for various types of chemical moieties. However, this information has not been effectively utilized to develop automated tools for in silico drug discovery which are routinely used for screening large compound libraries. Utilization of this information in the development of pharmacophore models can help in bridging this gap. In this study, information on water and small molecule fragments bound to M^pro has been utilized to develop a novel Water Pharmacophore (Waterphore) model. The Waterphore model can also implicitly represent the conformational flexibilities of binding pockets in terms of pharmacophore features. The Waterphore model derived from 173 apo‐ or small molecule fragment‐bound structures of M^pro has been validated by using a dataset of 68 known bioactive inhibitors and 78 crystal structure bound inhibitors of SARS‐CoV‐2 M^pro. It is encouraging to note that, even though no inhibitor data has been used in developing the Waterphore model, it could successfully identify the known inhibitors from a library of decoys with a ROC‐AUC of 0.81 and active hit rate (AHR) of 70 %. The Waterphore model is also general enough for potential applications for other drug targets.

Elucidation of endocrine-disrupting polychlorinated biphenyls binding potency with steroidogenic genes: Integration of in silico methods and ensemble docking approaches

A myriad of polychlorinated biphenyls (PCBs) may have potential to reproductive axis and endocrine disruptions. PCBs mostly breach the cholesterol biotransformation in mitochondria through interfering with steroidogenic genes and lead to adverse consequences in steroidogenesis; however, studies are scanty. In this examination, the combinations of quantitative structure-activity relationship (QSAR) modeling and ensemble docking approaches was performed to envisage structural properties of PCBs that influence the developmental toxicity, estrogen receptor-mediated impacts, and to provide a better comprehension of binding levels between PCBs, steroidogenic acute regulatory protein (StAR) and cholesterol side-chain cleavage enzyme (CYP11A1). Prognostic QSAR models were illustrated with good robustness and predictive ability. Models provide extensive data on applicability domain and similarities between PCBs and training set compounds was used to implement for clustering and classification of toxic PCBs by employing Self-Organizing Maps. Docking and interaction profiles interpretations provided various insights into the structural features of PCBs that influence the cholesterol binding to StAR and CYP11A1 domains. The non-polar, atomic π - stacking and halogen bonds of PCBs with novel hotspots residues of StAR and CYP11A1 was indicated subtle conformational changes that barrier to cholesterol binding and/or locks to cholesterol ejection from Ω1-loop of StAR, and inhibits cholesterol to pregnenolone biosynthesis by CYP11A1; thus, these are probably revealed as block-cluster mechanisms. These outcomes are potential to be useful to predict developmental toxicity, endocrine disruption potencies and anti-steroidogenic activities of other environmental pollutants and provided sorted pinpoints for further evaluation of interaction mechanisms of PCBs with other sterodogenic genes.