Computational Studies of Novel Chymase Inhibitors Against Cardiovascular and Allergic Diseases: Mechanism and Inhibition

Abstract

To provide a new idea for drug design, a computational investigation is performed on chymase and its novel 1,4-diazepane-2,5-diones inhibitors that explores the crucial molecular features contributing to binding specificity. Molecular docking studies of inhibitors within the active site of chymase were carried out to rationalize the inhibitory properties of these compounds and understand their inhibition mechanism. The density functional theory method was used to optimize molecular structures with the subsequent analysis of highest occupied molecular orbital, lowest unoccupied molecular orbital, and molecular electrostatic potential maps, which revealed that negative potentials near 1,4-diazepane-2,5-diones ring are essential for effective binding of inhibitors at active site of enzyme. The Bayesian model with receiver operating curve statistic of 0.82 also identified arylsulfonyl and aminocarbonyl as the molecular features favoring and not favoring inhibition of chymase, respectively. Moreover, genetic function approximation was applied to construct 3D quantitative structure-activity relationships models. Two models (genetic function approximation model 1 r 2 = 0.812 and genetic function approximation model 2 r 2 = 0.783) performed better in terms of correlation coefficients and crossvalidation analysis. In general, this study is used as example to illustrate how combinational use of 2D ⁄ 3D quantitative structure-activity relationships modeling techniques, molecular docking, frontier molecular orbital density fields (highest occupied molecular orbital and lowest unoccupied molecular orbital), and molecular electrostatic potential analysis may be useful to gain an insight into the binding mechanism between enzyme and its inhibitors.Key words: Bayesian categorization, chymase, genetic function approximation, molecular docking, quantitative structure-activity relationship Abbreviations: ACE, angiotensin-converting enzyme; Ang-I, angiotensin-I; Ang-II, angiotensin-II; DFT, density functional theory; DS, discovery studio; GFA, genetic function approximation; GOLD, genetic optimization for ligand docking; His, histidine; HOMO, highest occupied molecular orbital; LUMO, lowest unoccupied molecular orbital; MESP, molecular electrostatic potential; PDB, protein data bank; Phe, phenylalanine; RMSD, root mean square deviation; SAR, structure-activity relationship; TGF-b, transforming growth factor-b; Trp, tryptophan; Tyr, tyrosine. Received 3 April 2012, revised 30 May 2012 and accepted for publication 4 July 2012Assessing ligand orientation and the strength of ligand…receptor interactions is befitted as an essential component of modern drug discovery and prolific approaches, and programs are available to model ligand at the respective active site. In the course of investigating ligand binding, acquiring the best conformation and orientation of the ligand in the binding pocket along with the accurate estimates (or score) of binding affinities (generally referred to as the 'docking problem') are normally distinguished as t...

Identification of 4‐hydroxyphenylpyruvate dioxygenase inhibitors by virtual screening, molecular docking, molecular dynamic simulation

“…Topomer CoMFA calculations provide optimization strategies for common skeletons from electrostatic and three‐dimensional fields, respectively 37 . Bayesian classification is a simple and fast method for predicting samples 38 . GFA has a strong ability to avoid the algorithm falling into local optimal search through mutation mechanism 39 .…”

“…37 Bayesian classification is a simple and fast method for predicting samples. 38 GFA has a strong ability to avoid the algorithm falling into local optimal search through mutation mechanism. 39 It introduces the probabilistic idea of natural selection, and individual selection has randomicity and strong scalability, making it easy to combine with other algorithms.…”