3D QSAR Pharmacophore Modeling, in Silico Screening, and Density Functional Theory (DFT) Approaches for Identification of Human Chymase Inhibitors

Abstract

Human chymase is a very important target for the treatment of cardiovascular diseases. Using a series of theoretical methods like pharmacophore modeling, database screening, molecular docking and Density Functional Theory (DFT) calculations, an investigation for identification of novel chymase inhibitors, and to specify the key factors crucial for the binding and interaction between chymase and inhibitors is performed. A highly correlating (r = 0.942) pharmacophore model (Hypo1) with two hydrogen bond acceptors, and three hydrophobic aromatic features is generated. After successfully validating “Hypo1”, it is further applied in database screening. Hit compounds are subjected to various drug-like filtrations and molecular docking studies. Finally, three structurally diverse compounds with high GOLD fitness scores and interactions with key active site amino acids are identified as potent chymase hits. Moreover, DFT study is performed which confirms very clear trends between electronic properties and inhibitory activity (IC50) data thus successfully validating “Hypo1” by DFT method. Therefore, this research exertion can be helpful in the development of new potent hits for chymase. In addition, the combinational use of docking, orbital energies and molecular electrostatic potential analysis is also demonstrated as a good endeavor to gain an insight into the interaction between chymase and inhibitors.

Tubulin inhibitors: pharmacophore modeling, virtual screening and molecular docking

“…This method generates pharmacophore hypotheses by www.chinaphar.com Niu MM et al Acta Pharmacologica Sinica npg randomizing the activity data of these compounds while using the same parameters and features used to generate the original pharmacophore hypothesis. For the Fischer's randomization test, a 95% confidence level was chosen for this validation study, and 19 random spreadsheets were constructed [19,20] . During the pharmacophore generation process, if the randomized data set generates similar or better cost values, RMSD and correlation, the original hypothesis were generated by chance [21] .…”

“…The cost difference between the null and fixed cost values should be larger for a significant pharmacophore model. A value of 40-60 bits in a model implies that it has 75%-90% probability of representing a true correlation within the data [19,20] . The hypotheses are also evaluated based on other cost components.…”

“…All compounds in the Specs database were further filtered based on Lipinski's rule of five [35][36][37] . A Lipinski-positive compound has the following qualities: (i) a molecular weight <500; (ii) <5 hydrogen bond donor groups; (iii) <10 hydrogen bond acceptor groups; (iv) an octanol/water partition coefficient (Log P) value <5 [19,20] . To identify any novel hit compounds, the validated pharmacophore model was used as a 3D query to screen the drug-like compounds in the Specs database.…”

“…Hydrogen bond donor (HBD), hydrogen bond acceptor (HBA), hydrophobic (HY) and ring aromatic (RA) features were used to generate ten pharmacophore models. All other parameters used in the HypoGen module were kept at their default settings [19,20] . In this study, the top 10 hypothetical structures returned by the generation process were selected for further calculations.…”

“…To identify any novel hit compounds, the validated pharmacophore model was used as a 3D query to screen the drug-like compounds in the Specs database. A Search 3D Database protocol with Best/Flexible search option was applied during the database screening [19,20] . Finally, these compounds were retrieved for further analysis and were selected based on the ligand conformations; these conformations can satisfy the binding free energy and molecular interactions with the key amino acids in the active site.…”