Ludi Jiang

Virtual Screening and Molecular Dynamics Study of Potential Negative Allosteric Modulators of mGluR1 from Chinese Herbs

The metabotropic glutamate subtype 1 (mGluR1), a member of the metabotropic glutamate receptors, is a therapeutic target for neurological disorders. However, due to the lower subtype selectivity of mGluR1 orthosteric compounds, a new targeted strategy, known as allosteric modulators research, is needed for the treatment of mGluR1-related diseases. Recently, the structure of the seven-transmembrane domain (7TMD) of mGluR1 has been solved, which reveals the binding site of allosteric modulators and provides an opportunity for future subtype-selectivity drug design. In this study, a series of computer-aided drug design methods were utilized to discover potential mGluR1 negative allosteric modulators (NAMs). Pharmacophore models were constructed based on three different structure types of mGluR1 NAMs. After validation using the built-in parameters and test set, the optimal pharmacophore model of each structure type was selected and utilized as a query to screen the Traditional Chinese Medicine Database (TCMD). Then, three different hit lists of compounds were obtained. Molecular docking was used based on the latest crystal structure of mGluR1-7TMD to further filter these hits. As a compound with high QFIT and LibDock Score was preferred, a total of 30 compounds were retained. MD simulation was utilized to confirm the stability of potential compounds binding. From the computational results, thesinine-4ʹ-O-β-d-glucoside, nigrolineaxanthone-P and nodakenin might exhibit negative allosteric moderating effects on mGluR1. This paper indicates the applicability of molecular simulation technologies for discovering potential natural mGluR1 NAMs from Chinese herbs.

Virtual Screening for Potential Allosteric Inhibitors of Cyclin-Dependent Kinase 2 from Traditional Chinese Medicine

Cyclin-dependent kinase 2 (CDK2), a member of Cyclin-dependent kinases (CDKs), plays an important role in cell division and DNA replication. It is regarded as a desired target to treat cancer and tumor by interrupting aberrant cell proliferation. Compared to lower subtype selectivity of CDK2 ATP-competitive inhibitors, CDK2 allosteric inhibitor with higher subtype selectivity has been used to treat CDK2-related diseases. Recently, the first crystal structure of CDK2 with allosteric inhibitor has been reported, which provides new opportunities to design pure allosteric inhibitors of CDK2. The binding site of the ATP-competition inhibitors and the allosteric inhibitors are partially overlapped in space position, so the same compound might interact with the two binding sites. Thus a novel screening strategy was essential for the discovery of pure CDK2 allosteric inhibitors. In this study, pharmacophore and molecular docking were used to screen potential CDK2 allosteric inhibitors and ATP-competition inhibitors from Traditional Chinese Medicine (TCM). In the docking result of the allosteric site, the compounds which can act with the CDK2 ATP site were discarded, and the remaining compounds were regarded as the potential pure allosteric inhibitors. Among the results, prostaglandin E1 and nordihydroguaiaretic acid (NDGA) were available and their growth inhibitory effect on human HepG2 cell lines was determined by MTT assay. The two compounds could substantially inhibit the growth of HepG2 cell lines with an estimated IC50 of 41.223 μmol/L and 45.646 μmol/L. This study provides virtual screening strategy of allosteric compounds and a reliable method to discover potential pure CDK2 allosteric inhibitors from TCM. Prostaglandin E1 and NDGA could be regarded as promising candidates for CDK2 allosteric inhibitors.

Discovery of potential novel microsomal triglyceride transfer protein inhibitors via virtual screening of pharmacophore modelling and molecular docking

Abstract In order to identify potential natural inhibitors against the microsomal triglyceride transfer protein (MTP), HipHop models were generated using 20 known inhibitors from the Binding Database. Using evaluation indicators, the best hypothesis model, Hypo1, was selected and utilised to screen the Traditional Chinese Medicine Database, which resulted in a hit list of 58 drug-like compounds. A homology model of MTP was built by MODELLER and was minimised by CHARMm force field. It was then validated by Ramachandran plot and Verify-3D so as to obtain a stable structure, which was further used to refine the 58 hits using molecular docking studies. Then, five compounds with higher docking scores which satisfied the docking requirements were discovered. Among them, Ginkgetin and Dauricine were most likely to be candidates that exhibition inhibiting effect on MTP. The screening strategy in this study is relatively new to the discovery of MTP inhibitors in medicinal chemistry. Moreover, it is important to note that, lomitapide, an approved MTP inhibitor, fits well with Hypo1 as well as our homology model of MTP, which confirmed the rationality of our studies. The results indicated the applicability of molecular modeling for the discovery of potential natural MTP inhibitors from traditional Chinese herbs.

A method to predict different mechanisms for blood–brain barrier permeability of CNS activity compounds in Chinese herbs using support vector machine

The blood-brain barrier (BBB), a highly selective barrier between central nervous system (CNS) and the blood stream, restricts and regulates the penetration of compounds from the blood into the brain. Drugs that affect the CNS interact with the BBB prior to their target site, so the prediction research on BBB permeability is a fundamental and significant research direction in neuropharmacology. In this study, we combed through the available data and then with the help of support vector machine (SVM), we established an experiment process for discovering potential CNS compounds and investigating the mechanisms of BBB permeability of them to advance the research in this field four types of prediction models, referring to CNS activity, BBB permeability, passive diffusion and efflux transport, were obtained in the experiment process. The first two models were used to discover compounds which may have CNS activity and also cross the BBB at the same time; the latter two were used to elucidate the mechanism of BBB permeability of those compounds. Three optimization parameter methods, Grid Search, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), were used to optimize the SVM models. Then, four optimal models were selected with excellent evaluation indexes (the accuracy, sensitivity and specificity of each model were all above 85%). Furthermore, discrimination models were utilized to study the BBB properties of the known CNS activity compounds in Chinese herbs and this may guide the CNS drug development. With the relatively systematic and quick approach, the application rationality of traditional Chinese medicines for treating nervous system disease in the clinical practice will be improved.

Discovery of Potential Orthosteric and Allosteric Antagonists of P2Y1R from Chinese Herbs by Molecular Simulation Methods

P2Y1 receptor (P2Y1R), which belongs to G protein-coupled receptors (GPCRs), is an important target in ADP-induced platelet aggregation. The crystal structure of P2Y1R has been solved recently, which revealed orthosteric and allosteric ligand-binding sites with the details of ligand-protein binding modes. And it suggests that P2Y1R antagonists, which recognize two distinct sites, could potentially provide an efficacious and safe antithrombotic profile. In present paper, 2D similarity search, pharmacophore based screening, and molecular docking were used to explore the potential natural P2Y1R antagonists. 2D similarity search was used to classify orthosteric and allosteric antagonists of P2Y1R. Based on the result, pharmacophore models were constructed and validated by the test set. Optimal models were selected to discover potential P2Y1R antagonists of orthosteric and allosteric sites from Traditional Chinese Medicine (TCM). And the hits were filtered by Lipinskis rule. Then molecular docking was used to refine the results of pharmacophore based screening and analyze the binding mode of the hits and P2Y1R. Finally, two orthosteric and one allosteric potential compounds were obtained, which might be used in future P2Y1R antagonists design. This work provides a reliable guide for discovering natural P2Y1R antagonists acting on two distinct sites from TCM.

Prediction of hepatotoxicity of traditional Chinese medicine compounds by support vector machine approach

In this study, based on literatures and web databases, 490 hepatotoxic compounds and 598 non-hepatotoxic compounds were selected as a data set for hepatotoxicity discriminative model generation. 1664 molecular descriptors, including physicochemical, charge distribution and geometrical descriptors, were calculated to characterize the molecular structure of liver toxic compounds. The combination of CfsSubsetEval valuation and BestFirst searching was used to choose molecular descriptors for model construction. With the help of support vector machine (SVM), a discriminative model with high accuracy was built. Meanwhile, the accuracy, sensitivity and specificity of this model were all above 80%. Besides, 23 traditional Chinese medicine compounds with hepatotoxicity were regarded as external validation, so as to further verify the model accuracy. Then, the present model was utilized to identify hepatotoxic compounds in Qingkailing injection. The results demonstrated that present study provides a reliable utility for the hepatotoxic compounds prediction in Chinese Medicinal Materials studies.

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

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.

A combination of pharmacophore modeling, molecular docking and virtual screening for NPC1L1 receptor inhibitors from Chinese herbs

NPC1L1, a protein localized in jejunal enterocytes, is critical for cholesterol absorption. As the receptor inhibitors are effective solutions for hyperlipidaemia, NPC1L1 receptor is becoming a hot spot in drug targets. In this study, pharmacophore modeling and molecular docking were combined to discover potential NPC1L1 inhibitors from traditional Chinese medicine. The best pharmacophore model, Hypo1, which was generated by 9 known inhibitors, comprised of two Hydrogen bond acceptor lipid and two Hydrophobic aromatic regions. And the active compounds hit rate (A%), identification index (N), and comprehensive evaluation index (CAI) are 100%, 3.852, and 3.852 respectively. Hypo1 was used to screen TCMD (version 2009) to identify potential inhibitors, which resulted in a hit list of 38 compounds with Lipinskis rule of five. In addition, docking was used to refine pharmacophore-based screening results by using ezetimibe as a reference. Then, 11 compounds with higher docking score than ezetimibe had been reserved. This paper provides a reliable utility for discovering natural NPC1L1 receptor inhibitors from traditional Chinese herbs.

Virtual screening of cytochrome P450 2A6 inhibitors from traditional Chinese medicine using support vector machine and molecular docking

Cytochrome P450 2A6 (CYP2A6), which is a member of the cytochrome P450 (CYP450) mixed-function oxidase system and is highly expressed in liver, is involved in the metabolism of drugs in the body. The inhibition of it often reduces the metabolic rate of the corresponding metabolites and then may cause unwanted drug-drug interaction (DDI). In this study, discriminative models of CYP2A6 inhibitors were created by using the support vector machine (SVM) method. And the optimal model was selected based on three assessment criteria, including accuracy, sensitivity and specificity, which were all above 95%. Then, the optimal model was used to distinguish potential inhibitors of CYP2A6 from traditional Chinese medicine database (TCMD), which resulting in a hit list of 619 compounds. These compounds were further refined by using molecular docking and then 23 compounds with higher scores than the original ligand in the crystal structure of CYP2A6 enzyme were retained. Among them, Peucedanin, which has better prediction results, might exhibits inhibition effect on CYP2A6. This paper suggests the applicability of computational methods for obtaining potential inhibitors of CYP2A6 from Natural Products, and also provides guidance for the rational application of drugs in clinical.