Traditional Chinese Medicine Database Research Articles

Computer-Assisted Drug Virtual Screening Based on the Natural Product Databases.

Computer-assisted drug virtual screening models the process of drug screening through computer simulation technology, by docking small molecules in some of the databases to a certain protein target. There are many kinds of small molecules databases available for drug screening, including natural product databases. Plants have been used as a source of medication for millennia. About 80% of drugs were either natural products or related analogues by 1990, and many natural products are biologically active and have favorable absorption, distribution, metabolization, excretion, and toxicology. In this paper, we review the natural product databases' contributions to drug discovery based on virtual screening, focusing particularly on the introductions of plant natural products, microorganism natural product, Traditional Chinese medicine databases, as well as natural product toxicity prediction databases. We highlight the applications of these databases in many fields of virtual screening, and attempt to forecast the importance of the natural product database in next-generation drug discovery.

Identification of flower herbs in Chinese pharmacopoeia based on DNA barcoding

ObjectiveFlower herbs are an important category of traditional Chinese medicinal materials, some of which are used as healthcare tea in folk. However, the increasing adulteration of medicinal herbs is threatening consumer safety. The adulteration of flower herbs and their healthcare tea products in the market were investigated. MethodsA total of 33 flower herb samples from several retail pharmacies in China were randomly collected and 27 flower healthcare tea samples were purchased online. They were identified using ITS2-based Traditional Chinese Medicine Database (TCMD). Additionally, Lonicerae Japonicae Flos and the adulterants were compared in the ITS2 secondary structures. ResultsThere were one adulterant (Inulae Flos) in flower herb materials and eight adulterants in healthcare tea samples. Inula linariifolia was an adulterate species of Inulae Flos, Robinia pseudoacacia was of Sophorae Flos, and Lonicera macranthoides was of Lonicerae Japonicae Flos. Sophorae Flos and Lonicerae Japonicae Flos were two healthcare tea products with high adulteration rates. ConclusionThe TCMD is powerful tool to identify flower herbs and the adulterants that frequently occurred in the flower herb market, especially online shops.

Evaluation of potential inhibitors of squalene synthase based on virtual screening and in vitro studies

Squalene synthase (SQS) is a potential target for hyperlipidemia treatment. To identify novel chemical scaffolds of SQS inhibitors, we generated 3D-QSAR pharmacophore models using HypoGen. The best quantitative pharmacophore model, Hypo 1, was selected for virtual screening using two chemical databases, Specs and Traditional Chinese Medicine database (TCM). The best-mapped hit compounds were then subjected to filtering by Lipinski’s rule of five and docking studies to refine the hits. Finally, five compounds were selected from the top-ranked hit compounds for SQS inhibitory assay in vitro. Three of these compounds could inhibit SQS in vitro, and should be further evaluated pre-clinically as a treatment for hyperlipidemia.

Lipid regulation mechanism and dosage form selection of Xixian Tongshuan Preparation based on molecular simulation methods

Atherosclerosis is the main cause of stroke, and dyslipidemia is the most important risk factor for atherosclerosis. In this paper, pharmacophore and molecular docking models of eight key lipid-lowering targets, namely NPC1 L1, HMG-CoA reductase, SQS, MTP, CETP, PPARα, LXRα and LXRβ, were used to screen out the small molecular database of traditional Chinese medicine(TCM), which was made up of ingredients of thirteen Chinese herbal medicines contained in Xixian Tongshuan Preparation. The screening results indicated that the preparation could showed an effect in regulating lipid on target NPC1 L1, HMG-CoA reductase, LXRβ and SQS through four groups of potential active compounds, namely prupersin A in peach kernel and suffruticoside A in gastrodiaelata, limocitrin-β-D-glucoside in Ligusticum chuanxiong, 2'-(2,3-dihydroxybenzoyl)-sweroside in Pinellia ternate and quercitrin in Panax notoginseng, 4-tert-butyl-2-[(5-tert-butyl-2-hydroxy-phenyl)methoxy-methyl]-6-(hydroxymethyl)phenol in Gastrodia elata. Moreover, the properties and extraction process of the most potentialactive compounds were consistent with the preparation process of Xixian Tongshuan Capsules, which indicated that the capsule had more advantages than the pill in the existing two dosage forms of Xixian Tongshuan Preparation. This study analyzed the pharmacodynamic basis and mechanism of Xixian Tongshuan Capsules in regulating lipid for treating stroke, and provided evidence for its further research and clinical application.

Deep Learning and Random Forest Approach for Finding the Optimal Traditional Chinese Medicine Formula for Treatment of Alzheimer's Disease.

It has demonstrated that glycogen synthase kinase 3β (GSK3β) is related to Alzheimer's disease (AD). On the basis of the world largest traditional Chinese medicine (TCM) database, a network-pharmacology-based approach was utilized to investigate TCM candidates that can dock well with multiple targets. Support vector machine (SVM) and multiple linear regression (MLR) methods were utilized to obtain predicted models. In particular, the deep learning method and the random forest (RF) algorithm were adopted. We achieved R2 values of 0.927 on the training set and 0.862 on the test set with deep learning and 0.869 on the training set and 0.890 on the test set with RF. Besides, comparative molecular similarity indices analysis (CoMSIA) was performed to get a predicted model. All of the training models achieved good results on the test set. The stability of GSK3β protein-ligand complexes was evaluated using 100 ns of MD simulation. Methyl 3- O-feruloylquinate and cynanogenin A induced both more compactness to the GSK3β complex and stable conditions at all simulation times, and the GSK3β complex also had no substantial fluctuations after a simulation time of 5 ns. For TCM molecules, we used the trained models to calculate predicted bioactivity values, and the optimum TCM candidates were obtained by ranking the predicted values. The results showed that methyl 3- O-feruloylquinate contained in Phellodendron amurense and cynanogenin A contained in Cynanchum atratum are capable of forming stable interactions with GSK3β.

Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment.

The research field of systems biology has greatly advanced and, as a result, the concept of network pharmacology has been developed. This advancement, in turn, has shifted the paradigm from a “one-target, one-drug” mode to a “network-target, multiple-component-therapeutics” mode. Network pharmacology is more effective for establishing a “compound-protein/gene-disease” network and revealing the regulation principles of small molecules in a high-throughput manner. This approach makes it very powerful for the analysis of drug combinations, especially Traditional Chinese Medicine (TCM) preparations. In this work, we first summarized the databases and tools currently used for TCM research. Second, we focused on several representative applications of network pharmacology for TCM research, including studies on TCM compatibility, TCM target prediction, and TCM network toxicology research. Third, we compared the general statistics of several current TCM databases and evaluated and compared the search results of these databases based on 10 famous herbs. In summary, network pharmacology is a rational approach for TCM studies, and with the development of TCM research, powerful and comprehensive TCM databases have emerged but need further improvements. Additionally, given that several diseases could be treated by TCMs, with the mediation of gut microbiota, future studies should focus on both the microbiome and TCMs to better understand and treat microbiome-related diseases.

Identification and Evaluation of Inhibitors of Lipase from Malassezia restricta using Virtual High-Throughput Screening and Molecular Dynamics Studies

Recent studies revealed the role of lipase in the pathogenicity of Malassezia restricta in dandruff and seborrheic dermatitis (D/SD). The lipase from M. restricta (Mrlip1) is considered a potential target for dandruff therapy. In this work, we performed structure-based virtual screening in Zinc database to find the natural bioactive inhibitors of Mrlip1. We identified three compounds bearing superior affinity and specificity from the Traditional Chinese Medicine database (~60,000 compounds), and their binding patterns with Mrlip1 were analyzed in detail. Additionally, we performed three sets of 100 ns MD simulations of each complex in order to understand the interaction mechanism of Mrlip1 with known inhibitor RHC80267 and the newly identified compounds such as ZINC85530919, ZINC95914464 and ZINC85530320, respectively. These compounds bind to the active site cavity and cause conformational changes in Mrlip1. The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) studies suggested that the average binding energy was stronger in the case of Mrlip1-ZINC85530919 and Mrlip1-ZINC95914464. The selected natural inhibitors might act as promising lead drugs against Mrlip1. Further, the present study will contribute to various steps involved in developing and creating potent drugs for several skin diseases including dandruff.

Nature-effect relationship research of Curcumae Longae Rhizoma,Curcumae Radix,and Curcumae Rhizoma based on nature combination

Curcumae Longae Rhizoma,Curcumae Radix and Curcumae Rhizoma are different medicinal parts of the same plant.Nevertheless,they are different in medicinal effects due to the different Chinese herbal nature. In this study,traditional Chinese medicines database( TCMD2009),traditional Chinese medicine system( TCMSP),and Ch EMBL database were retrieved to screen the active components and targets,and construct the target PPI network. By a graph theoretic clustering algorithm identifying protein complex algorithm( IPCA),the protein modules were identified and analyzed by gene ontology( GO) enrichment. A comparative study of Curcumae Longae Rhizoma and Curcumae Radix illustrate that Curcumae Longae Rhizoma regulates blood coagulation through P2 RY12,GNG2 and other genes to exert the analgesic effect. Curcumae Radix regulates lipid metabolism,plasma lipoprotein particle levels,platelet activation,response to oxidative stress,apoptotic process through LDLR,APOB,PRKCA,SOD1,TP53 and other genes to perform a function in clearing the heart and cooling the blood. A comparative study of Curcumae Radix and Curcumae Rhizoma demonstrate that Curcumae Rhizoma on regulates the nervous system by GRIA2,GRIA4 and other genes to exert blood-breaking effect; Curcumae Radix regulates lipid metabolism,plasma lipoprotein particle levels,platelet activation,response to oxidative stress,apoptotic process by genes such as CALM1,LPL,APOB,SOD1 and TP53 to play the role of clearing heart and cooling blood. Cluster analysis of the protein interaction network of the nature combination is helpful to explain the intrinsic link between the nature combination and efficacy.

Discovery of Novel N-Glycoside and Non-Glycoside hSGLT2 Inhibitors for the Treatment of Type 2 Diabetes Mellitus

Human sodium-glucose cotransporter 2 (hSGLT2) is a membrane protein responsible for glucose reabsorption from the glomerular filtrate in the proximal tubule. Inhibition of hSGLT2 has been regarded as a brand new therapeutic approach for the treatment of type 2 diabetes mellitus (T2DM) due to its non-insulin related characteristics with less side effects. Current commercially available hSGLT2 inhibitors are all C-glycoside inhibitors. Previous studies have reported that N-glycoside inhibitors have better potential to serve as new drugs due to their good metabolic stability. In addition, non-glycoside inhibitors have been shown to exhibit the capability to overcome the existing problems of current glycoside inhibitors, including low tissue permeability, poor stability and short serum half-time. Here, we aimed to discover novel N-glycoside and non-glycoside hSGLT2 inhibitors by a combination of several computational approaches. A ligand-based pharmacophore model was generated, well validated and subsequently utilized as a 3D query to identify novel hSGLT2 inhibitors from National Cancer Institute (NCI) and Traditional Chinese Medicine (TCM) databases. Finally, one N-glycoside (NSC679207) and one non-glycoside (TCM_Piperenol_A) hSGLT2 inhibitors were successfully identified, which were proven to exhibit excellent binding affinities, pharmacokinetic properties and less toxicity than the commercially available hSGLT2 inhibitor, canagliflozin, via molecular docking, ADMET prediction, molecular dynamics (MD) simulations and binding free energy calculations. All together, our results strongly suggest that these two compounds have great potential to serve as novel hSGLT2 inhibitors for the treatment of T2DM and their efficacies may be further examined by a series of in vitro and/or in vivo bioassays.

Identification of new promising plant-based lead compounds for inhibition of prokaryotic replicative DNA polymerases: combination of in silico and in vitro studies

Emerging widespread bacterial resistance to current antibiotics with traditional targets is one of the major global concerns. Therefore, so many investigations are exploring the potential of other druggable macromolecules of bacteria such as replication machinery components that are not addressed by previous antibiotics. DNA polymerase is the major part of this machine. However, a few studies have been done on it so far. In this respect, we report the discovery of four new plant-based leads against DNA polymerase (pol) IIIC (three leads) and pol IIIE (one lead) of Gram-positive and negative bacteria by combining a sequentially constrained high-throughput virtual screenings on Traditional Chinese Medicine Database with in vitro assays. The compounds displayed relatively good levels of inhibitory effect. They were active against their designated targets at micromolar concentrations. The IC50 values for them are ranged from 25 to 111 μM. In addition, they showed minimum inhibitory concentrations in the range of 8–128 μg/mL against five representatives of pathogenic bacteria species. However, they were inactive against Pseudomonas aeruginosa. Given these results, these leads hold promise for future modification and optimization to be more effective in lower concentrations and also against most of the important bacterial species.Communicated by Ramaswamy H. Sarma

SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping.

Recently, the pharmaceutical industry has heavily emphasized phenotypic drug discovery (PDD), which relies primarily on knowledge about phenotype changes associated with diseases. Traditional Chinese medicine (TCM) provides a massive amount of information on natural products and the clinical symptoms they are used to treat, which are the observable disease phenotypes that are crucial for clinical diagnosis and treatment. Curating knowledge of TCM symptoms and their relationships to herbs and diseases will provide both candidate leads and screening directions for evidence-based PDD programs. Therefore, we present SymMap, an integrative database of traditional Chinese medicine enhanced by symptom mapping. We manually curated 1717 TCM symptoms and related them to 499 herbs and 961 symptoms used in modern medicine based on a committee of 17 leading experts practicing TCM. Next, we collected 5235 diseases associated with these symptoms, 19 595 herbal constituents (ingredients) and 4302 target genes, and built a large heterogeneous network containing all of these components. Thus, SymMap integrates TCM with modern medicine in common aspects at both the phenotypic and molecular levels. Furthermore, we inferred all pairwise relationships among SymMap components using statistical tests to give pharmaceutical scientists the ability to rank and filter promising results to guide drug discovery. The SymMap database can be accessed at http://www.symmap.org/ and https://www.bioinfo.org/symmap.

Identification of natural product compounds as quorum sensing inhibitors in Pseudomonas fluorescens P07 through virtual screening

Pseudomonas fluorescens, a Gram-negative psychrotrophic bacteria, is the main microorganism causing spoilage of chilled raw milk and aquatic products. Quorum sensing (QS) widely exists in bacteria to monitor their population densities and regulate numerous physiological activities, such as the secretion of siderophores, swarming motility and biofilm formation. Thus, searching for quorum sensing inhibitors (QSIs) may be another promising way to control the deterioration of food caused by P. fluorescens. Here, we screened a traditional Chinese medicine (TCM) database to discover potential QSIs with lesser toxicity. The gene sequences of LuxI- and LuxR-type proteins of P. fluorescens P07 were obtained through whole-genome sequencing. In addition, the protein structures built by homology modelling were used as targets to screen for QSIs. Twenty-one compounds with a dock score greater than 6 were purchased and tested by biosensor strains (Chromobacterium violaceum CV026 and Agrobacterium tumefaciens A136). The results showed that 10 of the compounds were determined as hits (hit rate: 66.67%). Benzyl alcohol, rhodinyl formate and houttuynine were effective QSIs. The impact of the most active compound (benzyl alcohol) on the phenotypes of P. fluorescens P07, including swimming and swarming motility, production of extracellular enzymes and siderophores, N-acylhomoserine lactone (AHLs) content and biofilm formation were determined. The inhibitory mechanism of benzyl alcohol on the QS system of P. fluorescens P07 is further discussed. This study reveals the feasibility of searching for novel QSIs through virtual screening.

Application of Ferulic Acid for Alzheimer's Disease: Combination of Text Mining and Experimental Validation.

Alzheimer’s disease (AD) is an increasing concern in human health. Despite significant research, highly effective drugs to treat AD are lacking. The present study describes the text mining process to identify drug candidates from a traditional Chinese medicine (TCM) database, along with associated protein target mechanisms. We carried out text mining to identify literatures that referenced both AD and TCM and focused on identifying compounds and protein targets of interest. After targeting one potential TCM candidate, corresponding protein-protein interaction (PPI) networks were assembled in STRING to decipher the most possible mechanism of action. This was followed by validation using Western blot and co-immunoprecipitation in an AD cell model. The text mining strategy using a vast amount of AD-related literature and the TCM database identified curcumin, whose major component was ferulic acid (FA). This was used as a key candidate compound for further study. Using the top calculated interaction score in STRING, BACE1 and MMP2 were implicated in the activity of FA in AD. Exposure of SHSY5Y-APP cells to FA resulted in the decrease in expression levels of BACE-1 and APP, while the expression of MMP-2 and MMP-9 increased in a dose-dependent manner. This suggests that FA induced BACE1 and MMP2 pathways maybe novel potential mechanisms involved in AD. The text mining of literature and TCM database related to AD suggested FA as a promising TCM ingredient for the treatment of AD. Potential mechanisms interconnected and integrated with Aβ aggregation inhibition and extracellular matrix remodeling underlying the activity of FA were identified using in vitro studies.

Bioinformatics Based Therapeutic Effects of Sinomenium Acutum.

To decipher the possible mechanisms of Sinomenium Acutum (SA) in treating diseases by a bioinformatics method. SA ingredients were searched according to Chinese Pharmacopoeia, Chinese Medicine Dictionary and Traditional Chinese Medicines Database (TCMD). Active compounds and target proteins of SA were acquired through the Pubchem platform. Pathway, network and function analyses of SA were performed with ingenuity pathway analysis (IPA), a bioinformatics analysis platform. Disease, biofunction-target networks were established with Cytoscape. Eighteen ingredients from SA were obtained. Seven active ingredients with 31 active target proteins were acquired according to PubChem Bioassay test. By IPA analysis, 277 canonical pathways belonging to 17 function categories were collected, 23 kinds of diseases, 21 categories bio-functions were obtained. Based on P value, calculated by IPA, the top 5 significant pathway of SA targets include phosphatidylinositol 3 kinase/Akt (PI3K/Akt) signaling, prostate cancer signaling, macrophage migration inhibitory factor (MIF) regulation of innate immunity, Guanosine-binding protein coupled receptor (GPCR) signaling, and ataxia telangiectasia mutated protein (ATM) signaling. Disease and bio-function network analysis indicated that mitogen activated protein kinase 1 (MAPK1), MAPK3, p65 nuclear factor κB (RELA), nuclear factor of κB inhibitor alpha (NFκBIA), interleukin 1β(IL-1β), prostaglandin G/H synthase 2 (PTGS2) and tumor protein 53 (TP53) were the critical targets in various diseases treated by SA. In the different view of target, pathway, disease and bio-function, inflammation was found to be a central theme in many chronic conditions. SA could be used not only as an anti-inflammatory agent, but also for the treatment of cancers, neurological diseases, psychological disorders and metabolic diseases.

Isolates from Alpinia officinarum Hance attenuate LPS‐induced inflammation in HepG2: Evidence from in silico and in vitro studies

In an attempt to connect the legacy of centuries of invaluable knowledge from traditional medicine and the current understanding to the molecular mechanism of diseases, we took the advantage of the emergence of in silico screening as a promising tool for identification of potential leads from libraries of natural products. Traditional Chinese Medicine database was subjected to structure based virtual screening for identification of anti-inflammatory compounds using the 3D crystal structure of p38 alpha mitogen activated protein kinase. The molecular docking studies revealed the potential activity of several classes of compounds known to be the constituents of the rhizomes of Alpinia officinarum Hance (Lesser galangal). Five compounds, galangin, kaempferide, isorhamnetin, and two diarylheptanoids, were isolated from the rhizomes of the plant using vacuum liquid chromatography and flash chromatography techniques. The anti-inflammatory activity of these compounds was investigated on HepG2 cells stimulated by lipopolysaccharide. The latter induced the gene expression of proinflammatory cytokines; interleukin-1β, interleukin-6, tumor necrosis factor alpha. Addition of the 5 isolated compounds downregulated this increased gene expression in a dose dependent manner. Thus, these results indicate that the isolated compounds from A.officinarum could be used as a beneficial source for preventing and treating inflammatory diseases.

In Silico Analysis of the Association Relationship between Neuroprotection and Flavors of Traditional Chinese Medicine Based on the mGluRs.

The metabotropic glutamate receptors (mGluRs) are known as both synaptic receptors and taste receptors. This feature is highly similar to the Property and Flavor theory of Traditional Chinese medicine (TCM), which has the pharmacological effect and flavor. In this study, six ligand based pharmacophore (LBP) models, seven homology modeling models, and fourteen molecular docking models of mGluRs were built based on orthosteric and allosteric sites to screening potential compounds from Traditional Chinese Medicine Database (TCMD). Based on the Pharmacopoeia of the People’s Republic of China, TCMs of compounds and their flavors were traced and listed. According to the tracing result, we found that the TCMs of the compounds which bound to orthosteric sites of mGluRs are highly correlated to a sweet flavor, while the allosteric site corresponds to a bitter flavor. Meanwhile, the pharmacological effects of TCMs with highly frequent flavors were further analyzed. We found that those TCMs play a neuroprotective role through the efficiencies of detumescence, promoting blood circulation, analgesic effect, and so on. This study provides a guide for developing new neuroprotective drugs from TCMs which target mGluRs. Moreover, it is the first study to present a novel approach to discuss the association relationship between flavor and the neuroprotective mechanism of TCM based on mGluRs.

A Component Formula of Chinese Medicine for Hypercholesterolemia Based on Virtual Screening and Biology Network.

Hypercholesterolemia is a risk factor to atherosclerosis and coronary heart disease II. The abnormal rise of cholesterol in plasma is the main symptom. Cholesterol synthesis pathway is an important pathway of the origin of cholesterol, which is an essential pathway for the therapy of hypercholesterolemia. The 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMG-CoA reductase), squalene synthase (SQS), and sterol regulatory element binding protein-2 (SREBP-2) are closely connected with the synthesis of cholesterol. The inhibition of these targets can reduce the cholesterol in plasma. This study aimed to build a component formula including three Traditional Chinese Medicines (TCM) components with the inhibition activity of these targets by using virtual screening and biological network. Structure-based pharmacophore models of HMG-CoA reductase and SQS and ligand-based pharmacophore model of SREBP-2 were constructed to screen the Traditional Chinese Medicine Database (TCMD). Molecular docking was used for further screening of components of HMG-CoA reductase and SQS. Then, metabolic network was constructed to elucidate the comprehensive interaction of three targets for lipid metabolism. Finally, three potential active compounds were obtained, which are poncimarin, hexahydrocurcumin, and forsythoside C. The source plants of the compounds were also taken into account, which should have known action of lowering hyperlipidemia. The lipid-lowering effect of hexahydrocurcumin was verified by experiment in vitro. The components that originated from TCMs with lipid-lowering efficacy made up a formula with a synergistic effect through the computer aid drug design methods. The research provides a fast and efficient method to build TCM component formula and it may inspire the study of the explanation of TCM formula mechanism.

Co-chaperon p23 inhibitors: Identification of anticancer compounds from traditional Chinese medicine database

The heat shock protein 90 (Hsp90) is a molecular chaperon, which plays a vital action to favor tumor growth and metastasis among different types of cancers due to its significant role in stimulating the over-expression of anti-apoptotic proteins Hsp70 and Hsp27, which assist in the sustainable development of cancer cells. For the activation of the chaperoning activity, Hsp90 requires a small protein called co-chaperon p23. Recently, it has been reported that the binding of gedunin, a natural compound, with co-chaperon p23, inhibits p23 co-chaperoning activity through blocking its molecular interaction with Hsp90 and leads to cancer cell death by apoptosis. Hence, the above facts support the strong position of co-chaperon p23 as a potential therapeutic target for cancer treatment. In this study, we employed the virtual screening technique to explore the potent inhibitors of co-chaperon p23 from Traditional Chinese Medicine (TCM) database. Total 200 inhibitors from TCM against co-chaperon p23 were screened and only four TCM compounds like dihydro-n-caffeoyltyramine, lithospermic acid, oleuropein and salvianolic acid were selected for further analysis, which showed binding energies, −6.0, −6.6, −6.2 and −6.7kcal/mol, respectively. The findings of this study suggest that these TCM compounds can be considered as potent inhibitors of co-chaperon p23 for further in vitro and in vivo validation for designing new potential drugs for cancer treatment.

Pharmacophore-based drug design for potential AChE inhibitors from Traditional Chinese Medicine Database

Alzheimer's disease (AD) is a neurodegenerative disorder. Substrate-specific Acetylcholinesterase (AChE) plays a vital role in the AD treatment. Flavonoids with AChE inhibitory activities and low toxicity are used to developing new anti-AD agents. In this study, the best 3D QSAR pharmacophore model Hypo1 was generated by HypoGen program in Discovery Studio2016 based on the training set of flavonoids. We performed a virtual screening from Traditional Chinese Medicine (TCM), Druglike and MiniMaybridge databases using Hypo1. From docking analyses, we got the top 10 AChE inhibitors which were further evaluated by 8 different scoring functions. De Novo Evolution designed the top 10 derivatives, and three potential AChE inhibitor candidates were obtained eventually.

Predicting tyrosinase inhibition by 3D QSAR pharmacophore models and designing potential tyrosinase inhibitors from Traditional Chinese medicine database

BackgroundTyrosinase plays a key role in the formation of skin melanin. The excessive accumulation of skin melanin will cause the serious aesthetic problems for human beings. Hypothesis/purposeTo find the potent tyrosinase inhibitors using computational simulation from TCM Database@Taiwan. Study designInhibitors of tyrosinase have been thought as potential drugs for the decrease of melanin synthesis in the process of pigmentation. To develop new tyrosinase inhibitors, we performed a virtual screening from Traditional Chinese medicine (TCM) and Druglike Databases using the best 3D QSAR pharmacophore model as a 3D search query. MethodsA total of 109 compounds were obtained after filtering by Lipinski's rule of five. Finally, 148 compounds (22 from training set, 17 from test set, 109 from TCM and Druglike databases) were selected for further docking studies. De Novo Evolution designed the top 10 candidates from the docking results. ResultsHypo1 was selected as the best quantitative pharmacophore model, because Hypo1 has characters of the highest cost difference (353.773), the lowest RMS (1.985), the lowest Error (121.440), and the best correlation coefficient (0.933). By the analysis of interaction amino acids in the top 10 hits including two controls, HIS42, HIS60, HIS204, HIS208, ARG209 and VAL218 are identified as the key binding site residues, ARG209 and VAL218 are the critical residues for the inhibitory activity of tyrosinase. This finding is consistent with the results from literatures. ConclusionDe Novo Evolution study suggested Tyrosinase_1*_Evo_4, Tyrosinase_23*_Evo_7, magnolone.cdx_15_Evo_4, compound_2.cdx_2_Evo_2, Compound_B_Evo_5, Compound_C_Evo_9, Compound_D_Evo_6 and malabaricone_C.cdx_3_Evo_10 as the potential tyrosinase inhibitor candidates. De Novo Evolution study also suggested compound_2.cdx_2_Evo_2 as the most potential tyrosinase inhibitor candidate. A total of ten novel leading compounds were identified to have the favorable interaction with tyrosinase by the docking analyses.