Repurposing benzimidazole and benzothiazole derivatives as potential inhibitors of SARS-CoV-2: DFT, QSAR, molecular docking, molecular dynamics simulation, and in-silico pharmacokinetic and toxicity studies.
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ABSTRACT:
SUBMITTER: Mohapatra RK
PROVIDER: S-EPMC8496942 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A new series of 1,2,3-triazole derivatives 5a-f based on benzothiazole were synthesized by the 1,3-dipolar cycloaddition reaction of S-propargyl mercaptobenzothiazole and α-halo ester/amide in moderate to good yields (47-75%). The structure of all products was characterized by 1H NMR, 13C NMR, and CHN elemental data. This protocol is easy and green and proceeds under mild and green reaction conditions with available starting materials. The structural and electronic analysis and 1H and 13C chemical shifts of the characterized structure of 5e were also calculated by applying the B3LYP/6-31 + G(d, p) level of density functional theory (DFT) method. In the final section, all the synthesized compounds were evaluated for their anti-inflammatory activity by biochemical COX-2 inhibition, antifungal inhibition with CYP51, anti-tuberculosis target protein ENR, DPRE1, pks13, and Thymidylate kinase by molecular docking studies. The ADMET analysis of the molecules 5a-f revealed that 5d and 5a are the most-promising drug-like molecules out of the six synthesized molecules.
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Project description:Treatment of several autoimmune diseases and types of cancer has been an intense area of research over the past two decades. Many signaling pathways that regulate innate and/or adaptive immunity, as well as those that induce overexpression or mutation of protein kinases, have been targeted for drug discovery. One of the serine/threonine kinases, Interleukin-1 Receptor Associated Kinase 4 (IRAK4) regulates signaling through various Toll-like receptors (TLRs) and interleukin-1 receptor (IL1R). It controls diverse cellular processes including inflammation, apoptosis, and cellular differentiation. MyD88 gain-of-function mutations or overexpression of IRAK4 has been implicated in various types of malignancies such as Waldenström macroglobulinemia, B cell lymphoma, colorectal cancer, pancreatic ductal adenocarcinoma, breast cancer, etc. Moreover, over activation of IRAK4 is also associated with several autoimmune diseases. The significant role of IRAK4 makes it an interesting target for the discovery and development of potent small molecule inhibitors. A few potent IRAK4 inhibitors such as PF-06650833, RA9 and BAY1834845 have recently entered phase I/II clinical trial studies. Nevertheless, there is still a need of selective inhibitors for the treatment of cancer and various autoimmune diseases. A great need for the same intrigued us to perform molecular modeling studies on 4,6-diaminonicotinamide derivatives as IRAK4 inhibitors. We performed molecular docking and dynamics simulation of 50 ns for one of the most active compounds of the dataset. We also carried out MM-PBSA binding free energy calculation to identify the active site residues, interactions of which are contributing to the total binding energy. The final 50 ns conformation of the most active compound was selected to perform dataset alignment in a 3D-QSAR study. Generated RF-CoMFA (q2 = 0.751, ONC = 4, r2 = 0.911) model revealed reasonable statistical results. Overall results of molecular dynamics simulation, MM-PBSA binding free energy calculation and RF-CoMFA model revealed important active site residues of IRAK4 and necessary structural properties of ligand to design more potent IRAK4 inhibitors. We designed few IRAK4 inhibitors based on these results, which possessed higher activity (predicted pIC50) than the most active compounds of the dataset selected for this study. Moreover, ADMET properties of these inhibitors revealed promising results and need to be validated using experimental studies.
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Project description:We selected fifty one drugs already known for their potential disease treatment roles in various studies and subjected to docking and molecular docking simulation (MDS) analyses. Five of them showed promising features that are discussed and suggested as potential candidates for repurposing for COVID-19. These top five compounds were boswellic acid, pimecrolimus, GYY-4137, BMS-345541 and triamcinolone hexacetonide that interacted with the chosen receptors 1R42, 4G3D, 6VW1, 6VXX and 7MEQ, respectively with binding energies of -9.2 kcal/mol, -9.1 kcal/mol, -10.3 kcal/mol, -10.1 kcal/mol and -8.7 kcal/mol, respectively. The MDS studies for the top 5 best complexes revealed binding features for the chosen receptor, human NF-kappa B transcription factor as an important drug target in COVID-19-based drug development strategies.
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Project description:A micro-molecule of dimension 125 nm has caused around 479 million human infections (80 M for the USA) and 6.1 million human deaths (977,000 for the USA) worldwide and slashed the global economy by US$ 8.5 Trillion over two years period. The only other events in recent history that caused comparative human life loss through direct usage (either by human or nature, respectively) of structure-property relations of 'nano-structures' (either human-made or nature, respectively) were nuclear bomb attacks during World War II and 1918 Flu Pandemic. This molecule is called SARS-CoV-2, which causes a disease known as COVID-19. The high liability cost of the pandemic had incentivized various private, government, and academic entities to work towards finding a cure for this and emerging diseases. As an outcome, multiple vaccine candidates are discovered to avoid the infection in the first place. But so far, there has been no success in finding fully effective therapeutic candidates. In this paper, we attempted to provide multiple therapy candidates based upon a sophisticated multi-scale in-silico framework, which increases the probability of the candidates surviving an in-vivo trial. We have selected a group of ligands from the ZINC database based upon previously partially successful candidates, i.e., Hydroxychloroquine, Lopinavir, Remdesivir, Ritonavir. We have used the following robust framework to screen the ligands; Step-I: high throughput molecular docking, Step-II: molecular dynamics analysis, Step-III: density functional theory analysis. In total, we have analyzed 242,000(ligands)*9(proteins) = 2.178 million unique protein binding site/ligand combinations. The proteins were selected based on recent experimental studies evaluating potential inhibitor binding sites. Step-I had filtered that number down to 10 ligands/protein based on molecular docking binding energy, further screening down to 2 ligands/protein based on drug-likeness analysis. Additionally, these two ligands per protein were analyzed in Step-II with a molecular dynamic modeling-based RMSD filter of less than 1Å. It finally suggested three ligands (ZINC001176619532, ZINC000517580540, ZINC000952855827) attacking different binding sites of the same protein(7BV2), which were further analyzed in Step-III to find the rationale behind comparatively higher ligand efficacy.Supplementary informationThe online version contains supplementary material available at 10.1007/s10853-022-07195-8.
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Project description:In the present work we have calculated several DFT reactivity descriptors for 1,2,4,5-Tetrazine at the B3LYP/6-311++G(d,p) level of theory in order to analyze its reactivity in vacuum and solvent phases. Whereas, the influence of the solvent was taken into account employing the PCM model. DFT-based descriptors such as (electronic chemical potential, electrophilicity, condensed Fukui function….) have been determined to predict the reactivity of 1,2,4,5-Tetrazine. A series of eighteen 1,2,4,5-Tetrazine derivatives was studied by using two computational techniques, namely, quantitative structure activity relationship (QSAR) and molecular docking. QSAR models of the antitumor activity of some 1,2,4,5-Tetrazine derivatives were established in gas and solvent phases which exhibited good statistical values for both cases. Whereas, multiple linear regression (MLR) procedure was used to obtain the best QSAR models and the leave-one-out (LOO) method to estimate the predictivity of our models. The most and the least active compounds were docked with the protein (3C4E) to confirm those obtained results from QSAR models and elucidate the binding mode between this type of compounds and corresponding protein.
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Project description:The COVID-19 pandemic outbreak demands the designing of potential drugs as there is no specific treatment available. Thanks to their safety and effectiveness, phytochemicals have been used to treat various diseases, including antiviral therapeutics. Molecular docking is a simple, quick, and effective way to screen a variety of molecules for structure-based drug design. Here, we investigate molecular docking experiments on compounds present in plant species, Cocculus hirsutus and Rhodiola rosea and show their potential for the treatment of COVID-19. Almost all the components showed higher binding affinity than the built-in ligand, and those with significantly higher binding affinity were explored further. Molecular mechanics-based generalized born surface area calculations were used to re-rank the top candidates, rhodionidin and cocsoline, and their stability in association with viral protease was confirmed. Density functional theory was used for detailed investigations of the geometries, and electrical properties of rhodionidin and cocsoline. Using the frontier molecular orbitals method, the charge transfer within the molecule was calculated. Chemical reactivity and intermolecular interactions were studied using molecular electrostatic potential maps. These in silico discoveries will simulate the identification of powerful COVID-19 inhibitors, and similar research is likely to make a significant contribution to antiviral drug discovery.Supplementary informationThe online version contains supplementary material available at 10.1007/s11224-022-01982-4.
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