Identification of potential ACAT-2 selective inhibitors using pharmacophore, SVM and SVR from Chinese herbs

Abstract

Acyl-coenzyme A cholesterol acyltransferase (ACAT) plays an important role in maintaining cellular and organismal cholesterol homeostasis. Two types of ACAT isozymes with different functions exist in mammals, named ACAT-1 and ACAT-2. Numerous studies showed that ACAT-2 selective inhibitors are effective for the treatment of hypercholesterolemia and atherosclerosis. However, as a typical endoplasmic reticulum protein, ACAT-2 protein has not been purified and revealed, so combinatorial ligand-based methods might be the optimal strategy for discovering the ACAT-2 selective inhibitors. In this study, selective pharmacophore models of ACAT-1 inhibitors and ACAT-2 inhibitors were built, respectively. The optimal pharmacophore model for each subtype was identified and utilized as queries for the Traditional Chinese Medicine Database screening. A total of 180 potential ACAT-2 selective inhibitors were obtained, which were identified using an ACAT-2 pharmacophore and not by our ACAT-1 model. Selective SVM model and bioactive SVR model were generated for further identification of the obtained ACAT-2 inhibitors. Ten compounds were finally obtained with predicted inhibitory activities toward ACAT-2. Hydrogen bond acceptor, 2D autocorrelations, GETAWAY descriptors, and BCUT descriptors were identified as key structural features for selectivity and activity of ACAT-2 inhibitors. This study provides a reasonable ligand-based approach to discover potential ACAT-2 selective inhibitors from Chinese herbs, which could help in further screening and development of ACAT-2 selective inhibitors.

Discovery of Potential Inhibitors of Squalene Synthase from Traditional Chinese Medicine Based on Virtual Screening and In Vitro Evaluation of Lipid-Lowering Effect

“…The structure, ID numbers, and biological activity (IC 50 ) values of the compounds are shown in Figure 7 . Then, 154 active compounds and 462 inactive compounds [ 38 ], which selected randomly from the Binding Database, were regarded as the test set in order to validate the pharmacophore model. The 3D structures of all the compounds were generated using the ‘Prepare Ligands’ module and minimized in CHARMm force field [ 39 ].…”

“…The corresponding “MaxOmitFeat value” is set to 2 to suggest that all features can be ignored for these compounds [ 38 ]. The maximum excluded volumes (Ev) value was set to 5, and all the other parameters were set at default values.…”

Using Coexpression Protein Interaction Network Analysis to Identify Mechanisms of Danshensu Affecting Patients with Coronary Heart Disease

“…Based on more than 100 pharmacophore models constructed previously by our laboratory [37,38,39,40,41], the ones matched with DSS were searched and their Fit values compared to the chemical structure of DSS were calculated. The proteins corresponding to pharmacophore models with Fit value >0.7 were selected as source proteins used in the following study [42].…”

A Novel Method for Screening the Effective Compatibility Fractions of Naomaitong for the Treatment of Cerebral Ischemic Stroke Using Support Vector Machine Model

“…The support vector machine (SVM) prediction model is a method that relies on statistical learning theory to provide optimization [9,10]. The advantage of SVM is the small errors obtained using a limited number of training sets still guarantee small errors in the independent test set [11].…”

“…To obtain pharmacological data to be used as one of the output vectors of the SVM training set, cerebral ischemia-reperfusion experiments were performed in Sprague-Dawley (SD) rats. The content of representative ingredients in the samples, determined by high-performance liquid chromatography (HPLC), served as another output vector of the SVM training set [9]. Finally, the effectiveness of the optimized formulation of NMT was veri ed in animal experiments.…”