Dataset Information

Molecular Docking and In Silico ADMET Study Reveals Acylguanidine 7a as a Potential Inhibitor of ?-Secretase.

ABSTRACT: Amyloidogenic pathway in Alzheimer's disease (AD) involves breakdown of APP by ?-secretase followed by ?-secretase and results in formation of amyloid beta plaque. ?-secretase has been a promising target for developing novel anti-Alzheimer drugs. To test different molecules for this purpose, test ligands like acylguanidine 7a, rosiglitazone, pioglitazone, and tartaric acid were docked against our target protein ?-secretase enzyme retrieved from Protein Data Bank, considering MK-8931 (phase III trial, Merck) as the positive control. Docking revealed that, with respect to their free binding energy, acylguanidine 7a has the lowest binding energy followed by MK-8931 and pioglitazone and binds significantly to ?-secretase. In silico ADMET predictions revealed that except tartaric acid all other compounds had minimal toxic effects and had good absorption as well as solubility characteristics. These compounds may serve as potential lead compound for developing new anti-Alzheimer drug.

SUBMITTER: Nisha CM

PROVIDER: S-EPMC4842033 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Molecular Docking and In Silico ADMET Study Reveals Acylguanidine 7a as a Potential Inhibitor of β-Secretase.

Nisha Chaluveelaveedu Murleedharan CM Kumar Ashwini A Nair Prateek P Gupta Nityasha N Silakari Chitrangda C Tripathi Timir T Kumar Awanish A

Advances in bioinformatics 20160410

Amyloidogenic pathway in Alzheimer's disease (AD) involves breakdown of APP by β-secretase followed by γ-secretase and results in formation of amyloid beta plaque. β-secretase has been a promising target for developing novel anti-Alzheimer drugs. To test different molecules for this purpose, test ligands like acylguanidine 7a, rosiglitazone, pioglitazone, and tartaric acid were docked against our target protein β-secretase enzyme retrieved from Protein Data Bank, considering MK-8931 (phase III t ...[more]

PMID: 27190510

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Project description:mPGES-1 is an enzyme, which, when activated by inflammatory factors, can cause prostaglandin E synthesis. Traditional non-steroidal anti-inflammatory drugs are capable of inhibiting prostaglandin production, yet they can also cause gastrointestinal reactions and coagulation disorders. mPGES-1, the enzyme at the conclusion of prostaglandin production, does not cause any adverse reactions when inhibited. Numerous studies have demonstrated that mPGES-1 is more abundant in cancerous cells than in healthy cells, indicating that decreasing the expression of mPGES-1 could be a potential therapeutic strategy for cancer. Consequently, the invention of mPGES-1 inhibitors presents a fresh avenue for the treatment of inflammation and cancer. Incorporating a database of TCM compounds, we collected a batch of compounds that had an inhibitory effect on mPGES-1 and possessed IC50 value. Firstly, a pharmacophore model was constructed, and the TCM database was screened, and the compounds with score cut-off values of more than 1 were retained. Then, the compounds retained after being screened via the pharmacodynamic model were screened for docking at the mPGES-1 binding site, followed by high-throughput virtual screening [HTVS] and standard precision [SP] and super-precision [XP] docking, and the compounds in the top 20% of the XP docking score were selected to calculate the total free binding energy of MM-GBSA. The best ten compounds were chosen by comparing their score against the reference ligand 4U9 and the MM-GBSA_dG_Bind score. ADMET analysis resulted in the selection of ten compounds, three of which had desirable medicinal properties. Finally, the binding energy of the target protein mPGES-1 and the candidate ligand compound was analyzed using a 100 ns molecular dynamics simulation of the reference ligand 4U9 and three selected compounds. After a gradual screening study and analysis, we identified a structure that is superior to the reference ligand 4U9 in all aspects, namely compound 15643. Taken together, the results of this study reveal a structure that can be used to inhibit mPGES-1 compound 15643, thereby providing a new option for anti-inflammatory and anti-tumor drugs.

Project description:IntroductionIn modern drug design, in silico methods are largely used to understand drug-receptor interactions and quantum chemical properties. In the present study, a computational de novo design approach was used to confirm mode of binding for antibacterial activity, elucidating quantum chemical properties and ADMET-drug-likeness of carbazole alkaloid (1) and three coumarins (2-4) isolated from roots of Clausena anisata.MethodsDocking studies were performed with DNA-Gyrase (6F86) and LasR binding domain (2UV0) employing a flexible ligand docking approach using AutoDock Vina. SwissADME prediction and toxicological predictions were performed by ADMET. The optimized structures and molecular electrostatic potential surface of the isolated compounds were predicted by DFT analysis using B3LYP/6-31G basis levels.Results and discussionThe docking results revealed that compound 3 showed better docking scores against both DNA gyrase B and LasR binding domain compared with ciprofloxacin with potential as an inhibitor of bacterial DNA gyrase and quorum sensing LasR binding domain. The SwissADME prediction results showed that all the isolated compounds (1-4) satisfy Lipinski's rule of five with zero violations. Toxicological prediction results suggested that all compounds and ciprofloxacin are non-hepatotoxic, non-carcinogenic, non-irritant, immunogenic, and non-cytotoxic. The DFT analysis results revealed that compound 3 has large electronegativity (χeV), global softness (σ eV-1), global electrophilicity (ωeV), and mutagenicity value closer to ciprofloxacin (with LD50 value of 480 mg/kg) suggesting better bioactivity and chemical reactivity with considerable intra-molecular charge transfer between electron-donor to electron-acceptor groups.ConclusionOverall, compound 3 may serve as a lead molecule that could be developed into a potent E. coli DNA gyrase B inhibitor and efficient inhibitor for quorum sensing auto-inducer LasR binding domain of Pseudomonas aeruginosa.

Dataset Information

Molecular Docking and In Silico ADMET Study Reveals Acylguanidine 7a as a Potential Inhibitor of ?-Secretase.

Publications

Molecular Docking and In Silico ADMET Study Reveals Acylguanidine 7a as a Potential Inhibitor of β-Secretase.

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