Identification of Potential Inhibitors of Three NDM Variants of Klebsiella Species from Natural Compounds: A Molecular Docking, Molecular Dynamics Simulation, and MM-PBSA Study.

BACKGROUND: Klebsiella species have emerged as well-known opportunistic pathogens causing nosocomial infections with ß-lactamase-mediated resistance as a prevalent antibiotic resistance mechanism. The discovery and emergence of metallo-ß-lactamases, mainly new- Delhi metallo-ß-lactamases (NDMs), have increased the threat and challenges in healthcare facilities. OBJECTIVE: A computational screening was conducted using 570 natural compounds from Dr. Duke's Phytochemical and Ethnobotanical data to discover promising inhibitors for NDM-6, NDM-9, and NDM-23 of the Klebsiella species. METHODS: Using homology modeling on the Raptor-X web server, the structures of the three NDM variants were predicted. The structures were validated using various computational tools and MD simulation for 50 ns. Lipinski - Vebers' Filter and ADMET Screening were used to screen 570 compounds, followed by docking in Biovia Discovery Studio 2019 using the CDOCKER module. GROMACS was used to simulate the compounds with the highest scores with the proteins for 50 ns. Using the MM-PBSA method and g_mmpbsa tool, binding free energies were estimated and per-residue decomposition analysis was conducted. RESULTS: The three structures predicted were found stable after the 50 ns MD Simulation run. The compounds Budmunchiamine-A and Rhamnocitrin were found to have the best binding energy towards NDM-6, NDM-9, and NDM-23, respectively. From the results of MD Simulation, MM-PBSA binding free energy calculations, and per-residue decomposition analysis, the Protein-ligand complex of NDM-6 with Budmunchiamine A and NDM-9 with Rhamnocitrin was relatively more stable than the complex of NDM-23 and Rhamnocitrin. CONCLUSION: The study suggests that Budmunchiamine-A and Rhamnocitrin are potential inhibitors of NDM-6 and NDM-9, respectively, and may pave a path for in-vivo and in-vitro studies in the future.

Antidiabetic potential of Amomum dealbatum Roxb. flower and isolation of three bioactive compounds with molecular docking and in vivo study.

Amomum dealbatum Roxb. parts have been traditionally used as remedies for joint pain, diabetes, muscular rheumatism, antiseptic, and abscesses in Arunachal Pradesh, Assam, and Tripura. Ethyl acetate sub-fraction E3 had significantly inhibited the α-glucosidase (IC50 5.385 µg/mL). The molecular docking revealed quercetin-3-O-galactoside to be the most potent α-glucosidase inhibitor (binding energy -43.214 kcal/mol). Using the QSAR model, the pIC50 values of myricetin, gallic acid, quercetin-3-O-galactoside, and acarbose were predicted to be 5.65235, 4.39858, 5.65235, and 6.03058, respectively. For the first time, quercetin-3-O-galactoside, myricetin, and gallic acid have been isolated from the flowers of A. dealbatum (ADF). E3 decreased blood glucose level to a near-normal concentration (100.60 ± 2.94 mg/dL) in comparison to diabetic control rats (575.20 ± 24.80 mg/dL). The results have strongly suggested the potential of ADF in treating diabetes. This lesser-known plant has the potential to uncover its full medicinal properties through further in-depth research.

Arenga westerhoutii Griff.: bioactive constituents, nutraceuticals, antioxidant and anti-diabetic potential of stem extract and an insight into molecular docking analysis.

Herein, we reported a systematic scientific study of Arenga westerhoutii Griff. by evaluating its bioactive components, nutraceuticals, antioxidant and anti-diabetic properties. Three major bioactive compounds were identified using HPLC and HRMS. Quantification of the components through HPLC yielded the presence of 75.67 ± 0.05, 38.19 ± 0.10 and 13.11 ± 0.02 µg/mL of chlorogenic acid, ferulic acid and epicatechin respectively in 1 mg/ml of the extract. 50% MeOH hydro-alcoholic extract was found to show lowest IC50 value in both in-vitro antioxidant (IC50 = 2.925 ± 0.12 µg/mL, DPPH assay) and anti-diabetic assays (IC50 = 18.03 ± 0.18 µg/mL, α-glucosidase assay). Further analysis by molecular docking study suggested the interaction of components towards α-glucosidase enzyme.

Bioassay-guided isolation of potent α-glucosidase inhibitory compounds from the fruit of Piper mullesua Buch-Ham ex D Don. and their in silico screening.

Two bioactive compounds caffeic and sinapic acid were isolated from the fruit of the Piper mullesua Buch-Ham ex D Don using bioassay guided approach. These compounds were isolated from water fraction using column chromatography followed by semi preparative HPLC. These compounds showed very potent anti-diabetic and antioxidant activities. The molecular docking was carried out to predict the mode of interaction of the isolated compounds with α-glucosidase. The in vitro α-glucosidase inhibitory activity of caffeic and sinapic acid was determined, and their IC50 values were found 0.67 and 0.82 µg/ml, respectively. A QSAR equation was generated with an R2 value of 84.81%, which is suitable enough for predicting the IC50 values of test molecules. The aforementioned finding confirms the isolated compounds show very significant anti-diabetic potential which is supported by the molecular docking and QSAR study. So, it has ample scope for drug development with further in vivo and clinical study.

Virtual high-throughput screening and simulation studies of compounds from selected traditionally important medicinal plants for the identification of potential inhibitors of AcrB.

One of the significant human health concerns today is the emergence of drug-resistant bacteria and their propagation worldwide, rendering all available treatment options useless. One of the molecular mechanisms behind the evolution and dissemination of multidrug-resistant species is the overexpression of efflux pumps. AcrB is a major component of the AcrAB-TolC efflux pump belonging to the RND division and responsible for the extrusion of antibiotics from the bacterial cell leading to resistance. In this study, we applied the reverse chemogenomics technique to find inhibitors of AcrB to combat the issue of drug resistance. A total of 102 compounds from five different plants having medicinal properties were passed through Lipinski filter and ADMET screening was done to check their drug likeliness before docking against the AcrB protein and based on the -cDocker energy scores and toxicity analysis report, the compounds with best values were analyzed. A comparison was made between them and known inhibitors as well as antibiotics. Heat maps, frequency histograms, 2 D diagrams were generated, and the molecules were simulated for 60 ns using GROMACS. From the study, we have found Dihydrocapsaicin and Garcinexanthone-A to be a potential efflux pump inhibitors having all the characters of a promising drug candidate.Communicated by Ramaswamy H. Sarma.

Identification of potential plant-based inhibitor against viral proteases of SARS-CoV-2 through molecular docking, MM-PBSA binding energy calculations and molecular dynamics simulation.

The Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus, SARS-CoV-2, has recently emerged as a pandemic. Here, an attempt has been made through in-silico high throughput screening to explore the antiviral compounds from traditionally used plants for antiviral treatments in India namely, Tea, Neem and Turmeric, as potential inhibitors of two widely studied viral proteases, main protease (Mpro) and papain-like protease (PLpro) of the SARS-CoV-2. Molecular docking study using BIOVIA Discovery Studio 2018 revealed, (-)-epicatechin-3-O-gallate (ECG), a tea polyphenol has a binding affinity toward both the selected receptors, with the lowest CDocker energy - 46.22 kcal mol-1 for SARS-CoV-2 Mpro and CDocker energy - 44.72 kcal mol-1 for SARS-CoV-2 PLpro, respectively. The SARS-CoV-2 Mpro complexed with (-)-epicatechin-3-O-gallate, which had shown the best binding affinity was subjected to molecular dynamics simulations to validate its binding affinity, during which, the root-mean-square-deviation values of SARS-CoV-2 Mpro-Co-crystal ligand (N3) and SARS-CoV-2 Mpro- (-)-epicatechin-3-O-gallate systems were found to be more stable than SARS-CoV-2 Mpro system. Further, (-)-epicatechin-3-O-gallate was subjected to QSAR analysis which predicted IC50 of 0.3281 nM against SARS-CoV-2 Mpro. Overall, (-)-epicatechin-3-O-gallate showed a potential binding affinity with SARS-CoV-2 Mpro and could be proposed as a potential natural compound for COVID-19 treatment.

NoSQL data model for semi-automatic integration of ethnomedicinal plant data from multiple sources.

INTRODUCTION: Sharing traditional knowledge with the scientific community could refine scientific approaches to phytochemical investigation and conservation of ethnomedicinal plants. As such, integration of traditional knowledge with scientific data using a single platform for sharing is greatly needed. However, ethnomedicinal data are available in heterogeneous formats, which depend on cultural aspects, survey methodology and focus of the study. Phytochemical and bioassay data are also available from many open sources in various standards and customised formats. OBJECTIVE: To design a flexible data model that could integrate both primary and curated ethnomedicinal plant data from multiple sources. MATERIALS AND METHODS: The current model is based on MongoDB, one of the Not only Structured Query Language (NoSQL) databases. Although it does not contain schema, modifications were made so that the model could incorporate both standard and customised ethnomedicinal plant data format from different sources. RESULTS: The model presented can integrate both primary and secondary data related to ethnomedicinal plants. Accommodation of disparate data was accomplished by a feature of this database that supported a different set of fields for each document. It also allowed storage of similar data having different properties. CONCLUSION: The model presented is scalable to a highly complex level with continuing maturation of the database, and is applicable for storing, retrieving and sharing ethnomedicinal plant data. It can also serve as a flexible alternative to a relational and normalised database.