[Discovery of cytochrome P450 enzymes-inhibiting components in traditional Chinese medicine].

With the widespread use of traditional Chinese medicine(TCM) and the integration of TCM and western medicine, drug-drug interaction(DDI) is considered as a major cause of therapeutic failures and side effects. Cytochrome P450 enzymes(CYPs) are responsible for large number of drug metabolism. CYP3 A4 and CYP2 D6, two important CYP isoforms, are responsible for about 80% drug metabolism of CYPs super family. The inhibition of CYPs is likely to be the most common factor leading to adverse DDI. Therefore, it is of great significance to predict potential CYP3 A4 and CYP2 D6 inhibitors to prevent the DDI. A fast and low-cost me-thod for calculating and predicting CYP inhibiting components was established in this paper, namely support vector machine(SVM) and molecular docking technology which are used to predict and screen drugs. Firstly, 12 qualitative models of two targets were established by using SVM, and the optimal model was selected to predict the compounds in traditional Chinese medicine database(TCMD). Then, molecular docking technology was used to establish docking model. By analyzing the key amino acids involved in drug-target interactions and combining with SVM model, potential inhibitors of CYP3 A4 and CYP2 D6 were found. From the computational results, astin D and epiberberine exhibited inhibition effect on CYP3 A4 and CYP2 D6, respectively. Astin D was only found in astins family from Aster tataricus, while epiberberine was considered to be the active constituent of Coptidis Rhizoma. Therefore, for the risk of DDI, extra attention should be paid to the source of these potential inhibitors, Asteris Radix et Rhizoma and Coptidis Rhizoma. This computational method provides technical support for discovering potential natural inhibitors of CYPs from Chinese herbs by using SVM and molecular docking model, and it is also helpful to recognize the CYPs-mediated DDI existing in TCM, providing research ideas for further pharmacovigilance of integrated therapy.

[Pharmacological mechanism analysis of oligopeptide from Pinctada fucata based on in silico proteolysis and protein interaction network].

Pinctada fucata oligopeptide is one of key pharmaceutical effective constituents of P. fucata. It is significant to analyze its pharmacological effect and mechanism. This study aims to discover the potential oligopeptides from P. fucata and analyze the mechanism of P. fucata oligopeptide based on in silico technologies and protein interaction network(PIN). First, main protein sequences of P. fucata were collected, and oligopeptides were obtained using in silico gastrointestinal tract proteolysis. Then, key potential targets of P. fucata oligopeptides were obtained through pharmacophore screening. The protein-protein interaction(PPI) of targets was achieved and implemented to construct PIN and analyze the mechanism of P. fucata oligopeptides. P. fucata oligopeptide database was constructed based on in silico technologies, including 458 oligopeptides. Twelve modules were identified from PIN by a graph theoretic clustering algorithm Molecular Complex Detection(MCODE) and analyzed by Gene ontology(GO) enrichment. The results indicated that P. fucata oligopeptides have an effect in treating neurological diseases, such as Alzheimer's disease. In silico proteolysis could be used to analyze the protein sequences of traditional Chinese medicine(TCM). According to the combination of in silico proteolysis and PIN, the biological activity of oligopeptides could be interpreted rapidly based on the known TCM protein sequence. The study provides the methodology basis for rapidly and efficiently implementing the mechanism analysis of TCM oligopeptides.

[Synergistic mechanism of traditional Chinese medicine based on target combination of PepT1 and PPARα].

Synergistic effect is main pharmacological mechanism of traditional Chinese medicine(TCM). The research method based on the key targets combination is an important method to explore the synergistic effect of TCM. Peptide transporter 1 (PepT1) is an essential target for drug uptake into the bloodstream, accounting for about 50% of the total transporter protein content from the small intestine. Peroxisome proliferator-activated receptor α(PPARα) is the lipid-lowering target of fibrates, which have a good hypolipidemic effect by activating PPARα. It has been reported that PPARα could activate the gene expression of PepT1s, and PPARα agonists can promote the uptake of PepT1 substrates, indicating their synergistic effect. In this paper, PepT1 substrates and PPARα agonists from TCM were discovered, and their synergistic mechanism was also been discussed based on the target combination of PepT1 and PPARα. The support vector machine(SVM) model of PepT1 substrates was first constructed and utilized to predict potential TCM components. Meanwhile, merged pharmacophore and docking model of PPARα agonists was used to screen the potential active ingredients from TCM. According to the analysis results of two groups, the TCM combination of Panax notoginseng and Ganoderma lucidum, as well as TCM combination of P. notoginseng and Salvia miltiorrhiza were identified to have the synergistic mechanism based on target combination of PepT1 and PPARα. In this study, synergistic mechanism of TCM was analyzed for absorption and hypolipidemic effect based on target combination, which provides a new way to explore the synergetic mechanism of TCM related to pharmacokinetics.

[Prediction of ETA oligopeptides antagonists from Glycine max based on in silico proteolysis].

Oligopeptides are one of the the key pharmaceutical effective constituents of traditional Chinese medicine(TCM). Systematic study on composition and efficacy of TCM oligopeptides is essential for the analysis of material basis and mechanism of TCM. In this study, the potential anti-hypertensive oligopeptides from Glycine max and their endothelin receptor A (ETA) antagonistic activity were discovered and predicted based on in silico technologies.Main protein sequences of G. max were collected and oligopeptides were obtained using in silico gastrointestinal tract proteolysis. Then, the pharmacophore of ETA antagonistic peptides was constructed and included one hydrophobic feature, one ionizable negative feature, one ring aromatic feature and five excluded volumes. Meanwhile, three-dimensional structure of ETA was developed by homology modeling methods for further docking studies. According to docking analysis and consensus score, the key amino acid of GLN165 was identified for ETA antagonistic activity. And 27 oligopeptides from G. max were predicted as the potential ETA antagonists by pharmacophore and docking studies.In silico proteolysis could be used to analyze the protein sequences from TCM. According to combination of in silico proteolysis and molecular simulation, the biological activities of oligopeptides could be predicted rapidly based on the known TCM protein sequence. It might provide the methodology basis for rapidly and efficiently implementing the mechanism analysis of TCM oligopeptides.

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.

[Discovery of potential LXRß agonists from Chinese herbs using molecular simulation methods].

Liver X receptor ß (LXRß) has been a new target in the treatment of hyperlipemia, which was related to the cholesterol homeostasis. In this study, the quantitative pharmacophores were constructed by 3D-QSAR pharmacophore (Hypogen) method based on the LXRß agonists. The optimal pharmacophore model containing one hydrogen bond acceptor, two hydrophobics and one ring aromatic was obtained based on five assessment indictors, including the correlation between predicted value and experimental value of the compounds in training set (correlation), Δcost of the models (Δcost), hit rate of active compounds (HRA), identification of effectiveness index (IEI) and comprehensive evaluation index (CAI). And the values of the five assessment indicators were 0.95, 128.65, 84.44%, 2.58 and 2.18 respectively. The best model as a query to screen the traditional Chinese medicine database (TCMD), a list of 309 compounds was obtained andwere then refined using Libdock program. Finally, based on the screening rules of the Libdock score of initial compound and the key interactions between initial compound and receptor, four compounds, demethoxycurcumin, isolicoflavonol, licochalcone E and silydianin, were selected as potential LXRß agonists. The molecular simulation methods were high-efficiency and time-saving to obtainthe potential LXRß agonists, which could provide assistance for further researchingnovel anti-hyperlipidemia drugs.

[Virtual screening for natural CETP inhibitors by structure-based pharmacophore].

Cholesterol ester transfer protein (CETP) is a key regulator of high density lipoprotein (HDL). Owing to its important role in the reverse of cholesterol transport, CETP has become a hotspot target in modulating lipid drug design. In this paper, structure based pharmacophore (SBP) models for CETP inhibitors were built based on the protein structure 4F2A from Protein Database (PDB). The best pharmacophore contained six hydrophobic features, one hydrogen bond acceptor feature and nine excluded volume features, with the N and CAI value was 3.33 and 2.31 respectively. Then the model was used to search the traditional Chinese medicine database (TCMD) and 629 compounds originated from 315 TCM herbs were obtained. Molecular docking was also used to validate SBP by analyzing the critical amino acid residue and the interaction between potential active compounds and receptor. In this study, several TCM herbs, like Lycii Frutus and Schisandrae chinensis fructus, which contained more optimal SBP based screening results, have been reported hypolipidemic effect, and need to be studied deeply in a more focused research on herbal active constituents. Therefore, this study could provide reliable fundamental data for exploring the action mechanisms of TCM, and be applicable to identify lead candidates, which can be utilized as starting scaffolds for natural CETP inhibitors.

[Application of CADD on multi-target drug R&D in natural products].

Multi-target drugs can simultaneously adjust multiple links of the disease network. Despite the higher efficacy and lower toxicity caused by single targets, multi-target drugs become ideal drugs for treating complicated diseases as well the main direction of drug R & D. By virtue of their structural diversity, higher multi-target activity and lower toxicity, natural products become an important source for developing multi-target drugs. Computer-aided drug design (CADD) is a commonly used multi-target drug R&D method, which mainly includes virtual screening and pharmacophore design. In this paper, the authors made a systematical analysis and discussed the prospects and advantages of various methods for multi-target drug R&D with natural products.

[Study on structure-activity relationship of flavonoids' multidrug resistance-associated protein inhibitory activity].

To study the quantitative structure-activity relationship (QSAR) between the stuctures of 29 flavonoids and the inhibitory activity of their multidrug resistance-associated protein (MRP) 1 and 2 by using the comparative molecular similarity index analysis (CoMSIA). By studying the impact of the combination of different molecular force fields, researchers obtained the molecular force fields that played an important role in inhibiting the activity of MRP1 and MRP2, built the optimized QSAR model, and discussed the structural modification method for flavonoids' multidrug resistance-associated protein inhibitor. The results of the study could not only provide the guidance for new drug R&D, but also help partially discuss the synergy mechanism between MRP1 and MRP2 receptors and traditional Chinese medicines containing flavonoids.

[Study on lipid-lowering traditional Chinese medicines based on pharmacophore technology and patent retrieval].

The combined application of statins that inhibit HMG-CoA reductase and fibrates that activate PPAR-α can produce a better lipid-lowering effect than the simple application, but with stronger adverse reactions at the same time. In the treatment of hyperlipidemia, the combined administration of TCMs and HMG-CoA reductase inhibitor in treating hyperlipidemia shows stable efficacy and less adverse reactions, and provides a new option for the combined application of drugs. In this article, the pharmacophore technology was used to search chemical components of TCMs, trace their source herbs, and determine the potential common TCMs that could activate PPAR-α. Because there is no hyperlipidemia-related medication reference in modern TCM classics, to ensure the high safety and efficacy of all selected TCMs, we selected TCMs that are proved to be combined with statins in the World Traditional/Natural Medicine Patent Database, analyzed corresponding drugs in pharmacophore results based on that, and finally obtained common TCMs that can be applied in PPAR-α and combined with statins. Specifically, the pharmacophore model was based on eight receptor-ligand complexes of PPAR-α. The Receptor-Ligand Pharmacophore Generation module in the DS program was used to build the model, optimize with the Screen Library module, and get the best sub-pharmacophore, which consisted of two hydrogen bond acceptor, three hydrophobic groups and 19 excluded volumes, with the identification effectiveness index value N of 2. 82 and the comprehensive evaluation index CAI value of 1. 84. The model was used to screen the TCMD database, hit 5,235 kinds of chemical components and 1 193 natural animals and plants, and finally determine 62 TCMs. Through patent retrieval, we found 38 TCMs; After comparing with the virtual screening results, we finally got seven TCMs.

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 Lipinski's 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.