Insight Mechanism of the Selective Lanosterol Synthase Inhibitor: Molecular Modeling, Docking and Density Functional Theory Approaches.

BACKGROUND: Lanosterol synthase (Oxidosqualene cyclase) is an enzyme, which plays a central role in cholesterol and sterols biosynthesis. Lanosterol synthase drugs are used to lower the level of cholesterol in the blood and treat wide variety of diseases like atherosclerosis, coronary heart diseases etc. OBJECTIVE: There is a great interest in the identification of drugs that target this enzyme for anticholesteraemic agent using in silico tools. METHODS: Ligand based pharmacophore model was developed using Discovery Studio 2.5. The best model was used as a tool to retrieve suitable molecule for Lanosterol synthase inhibitor from commercial database and Virtual screening of large commercially available databases to retrieve the best mole of Hypo1 using. Molecular docking was done using three different tools named as GOLD, GLIDE and AUTODOCK 4.0. Density functional theory approach and Density of State spectrum were carried out using Gaussian 09 and GAUSS SUM 3.0. Contribution of these methods in the selection of anticholesteraemic compounds has been discussed. RESULTS: The best pharmacophore model was used to screen the commercial database. Totally 8 compounds were showed with the best orientation, binding mode and binging energy in the docking analyses. The orbital energies such as HOMO, LUMO and DOS spectrum for 8 hit compounds showed the energy gap that results in charge transfer and stability in the active site region. The results showed that our 8 potent leads could serve for further findings. CONCLUSION: In silico approaches, our 8 hit compounds could serve as the better understanding to design the novel lanosterol synthase inhibitors as anticholesteraemic activity.

Atom Based 3D-QSAR, Molecular Docking and Density Functional Theory Approaches to Identify Novel JNK-1 Inhibitor.

AIM AND OBJECTIVE: C-Jun-N-terminal kinase -1 (JNK -1) is a seriene/threonine kinase protein and a member of mitogen activated protein family (MAP- Kinase). The activation of JNK-1 leads to cell proliferation, cell death, DNA repair and metabolism. In our study we aim in creating a novel JNK-1 inhibitor. MATERIAL AND METHOD: Various computational techniques like 3D-atom based QSAR analysis; pharmacophore based virtual screening; molecular docking and Density functional theory approaches are utilised to obtain novel JNK-1 inhibitor. RESULT: Pharmacophores with pharmacophoric features as two hydrogen bond acceptors (A), one hydrogen bond donor (D), one hydrophobic (H) and one aromatic ring (R) are generated. Amongst the generated pharmacophore hypothesis, AADHR.6 was found to have good survival score of 3.214 and is used to derive atom based 3D - QSAR model. The obtained 3D - QSAR model has excellent squared correlation coefficient value (R2= 0.9272) and a good fisher ratio (F= 273.9). The reliability and robustness of the chosen model is validated both internally and externally to obtain good statistical results. The model AADHR.6 is used in virtual screening of Zinc and NCI databases for potential inhibitors. Resulting hit compounds from virtual screening are then subjected to docking and Density Functional Theory (DFT) studies. CONCLUSION: Both docking and DFT studies brings out two lead compounds with good inhibitory activity against the receptor. Thus the work presents a novel JNK - 1 inhibitor that can serve as potential therapeutics for the treatment of various diseases associated with abnormal JNK -1 functioning.

Investigation on the isoform selectivity of novel kinesin-like protein 1 (KIF11) inhibitor using chemical feature based pharmacophore, molecular docking, and quantum mechanical studies.

Kinesin-like protein (KIF11) is a molecular motor protein that is essential in mitosis. Removal of KIF11 prevents centrosome migration and causes cell arrest in mitosis. KIF11 defects are linked to the disease of microcephaly, lymph edema or mental retardation. The human KIF11 protein has been actively studied for its role in mitosis and its potential as a therapeutic target for cancer treatment. Pharmacophore modeling, molecular docking and density functional theory approaches was employed to reveal the structural, chemical and electronic features essential for the development of small molecule inhibitor for KIF11. Hence we have developed chemical feature based pharmacophore models using Discovery Studio v 2.5 (DS). The best hypothesis (Hypo1) consisting of four chemical features (two hydrogen bond acceptor, one hydrophobic and one ring aromatic) has exhibited high correlation co-efficient of 0.9521, cost difference of 70.63 and low RMS value of 0.9475. This Hypo1 is cross validated by Cat Scramble method; test set and decoy set to prove its robustness, statistical significance and predictability respectively. The well validated Hypo1 was used as 3Dquery to perform virtual screening. The hits obtained from the virtual screening were subjected to various scrupulous drug-like filters such as Lipinski's rule of five and ADMET properties. Finally, six hit compounds were identified based on the molecular interaction and its electronic properties. Our final lead compound could serve as a powerful tool for the discovery of potent inhibitor as KIF11 agonists.

Pharmacophore modeling, virtual screening, molecular docking studies and density functional theory approaches to identify novel ketohexokinase (KHK) inhibitors.

Fructose catabolism starts with phosphorylation of d-fructose to fructose 1-phosphate, which is performed by ketohexokinase (KHK). Fructose metabolism may be the key to understand the long-term consumption of fructose in human's obesity, diabetes and metabolic states in western populations. The inhibition of KHK has medicinally potential roles in fructose metabolism and the metabolic syndrome. To identify the essential chemical features for KHK inhibition, a three-dimensional (3D) chemical-feature-based QSAR pharmacophore model was developed for the first time by using Discovery Studio v2.5 (DS). The best pharmacophore hypothesis (Hypo1) consisting two hydrogen bond donor, two hydrophobic features and has exhibited high correlation co-efficient (0.97), cost difference (76.1) and low RMS (0.66) value. The robustness and predictability of Hypo1 was validated by fisher's randomization method, test set, and the decoy set. Subsequently, chemical databases like NCI, Chembridge and Maybridge were screened for validated Hypo1. The screened compounds were further analyzed by applying drug-like filters such as Lipinski's rule of five, ADME properties, and molecular docking studies. Further, the highest occupied molecular orbital, lowest unoccupied molecular orbital and energy gap values were calculated for the hits compounds using density functional theory. Finally, 3 hit compounds were selected based on their good molecular interactions with key amino acids in the KHK active site, GOLD fitness score, and lowest energy gaps.

Pharmacophore design, virtual screening, molecular docking and optimization approaches to discover potent thrombin inhibitors.

Thrombin plays a key role in the regulation of hemostasis and thrombosis. Inhibition of thrombin is therefore an effective therapeutic target to prevent the formation of blood clots and related thromboembolism disorders. Hence, we have developed chemical feature based pharmacophore models of thrombin inhibitors. The best hypothesis, Hypo1, is characterized with two hydrogen bond acceptors (A), one hydrophobic (H) and one ring aromatic (R) feature. Hypo1 was cross validated using several techniques to prove its validity and statistical significance. The well validated model Hypo1 was used as a 3D query to perform virtual screening. The scores obtained from virtual screening were sorted by applying drug-like filters and molecular docking studies. Finally, 4 compounds were obtained as drug-like leads based on scoring functions, binding modes and molecular interactions at the active site. These 4 molecules were further optimized by adding different substitutions in their side chains. When compared to the original database hits, optimized molecules showed high scoring function, good binding modes and molecular interactions. Hence, we suggest that, upon optimization, these four database hits can act as potential virtual leads to design novel thrombin inhibitors. Also, our model could be useful to retrieve the structurally diverse compounds from various databases.

Discovery of potent inhibitors for interleukin-2-inducible T-cell kinase: structure-based virtual screening and molecular dynamics simulation approaches.

In our study, a structure-based virtual screening study was conducted to identify potent ITK inhibitors, as ITK is considered to play an important role in the treatment of inflammatory diseases. We developed a structure-based pharmacophore model using the crystal structure (PDB ID: 3MJ2) of ITK complexed with BMS-50944. The most predictive model, SB-Hypo1, consisted of six features: three hydrogen-bond acceptors (HBA), one hydrogen-bond donor (HBD), one ring aromatic (RA), and one hydrophobic (HY). The statistical significance of SB-Hypo1 was validated using wide range of test set molecules and a decoy set. The resulting well-validated model could then be confidently used as a 3D query to screen for drug-like molecules in a database, in order to retrieve new chemical scaffolds that may be potent ITK inhibitors. The hits retrieved from this search were filtered based on the maximum fit value, drug-likeness, and ADMET properties, and the hits that were retained were used in a molecular docking study to find the binding mode and molecular interactions with crucial residues at the active site of the protein. These hits were then fed into a molecular dynamics simulation to study the flexibility of the activation loop of ITK upon ligand binding. This combination of methodologies is a valuable tool for identifying structurally diverse molecules with desired biological activities, and for designing new classes of selective ITK inhibitors.

Identification of important chemical features of 11ß-hydroxysteroid dehydrogenase type1 inhibitors: application of ligand based virtual screening and density functional theory.

11ß-Hydroxysteroid dehydrogenase type1 (11ßHSD1) regulates the conversion from inactive cortisone to active cortisol. Increased cortisol results in diabetes, hence quelling the activity of 11ßHSD1 has been thought of as an effective approach for the treatment of diabetes. Quantitative hypotheses were developed and validated to identify the critical chemical features with reliable geometric constraints that contribute to the inhibition of 11ßHSD1 function. The best hypothesis, Hypo1, which contains one-HBA; one-Hy-Ali, and two-RA features, was validated using Fischer's randomization method, a test and a decoy set. The well validated, Hypo1, was used as 3D query to perform a virtual screening of three different chemical databases. Compounds selected by Hypo1 in the virtual screening were filtered by applying Lipinski's rule of five, ADMET, and molecular docking. Finally, five hit compounds were selected as virtual novel hit molecules for 11ßHSD1 based on their electronic properties calculated by Density functional theory.