Phytochemical databases of Chinese herbal constituents and bioactive plant compounds with known target specificities

Two databases have been constructed to facilitate applications of cheminformatics and molecular modeling to medicinal plants. The first contains data on known chemical constituents of 240 commonly used Chinese herbs, the other contains information on target specificities of bioactive plant compounds. Structures are available for all compounds. In the case of the Chinese herbal constituents database, further details include trivial and systematic names, compound class and skeletal type, botanical and Chinese (pinyin) names of associated herb(s), CAS registry number, chirality, pharmacological and toxicological information, and chemical references. For the bioactive plant compounds database, details of molecular target(s), IC50 and related measures, and associated botanical species are given. For Chinese herbs, approximately 7000 unique compounds are listed, though some are found in more than one herb, the total number for all herbs being 8264. For bioactive plant compounds, 2597 compounds active against 78 molecular targets are covered. Statistical relationships within and between the two databases are explored.     

Screening of new non-nucleoside reverse transcriptase inhibitors of HIV-1 based on traditional Chinese medicines database

HIV-1 RT is an important target for the treatment of AIDS.There are two major classes of antiviral agents that inhibit HIV-1 RT have been identified,nucleoside RT inhibitors(NRTIs) and non-nucleoside RT inhibitors(NNRTIs).In this report,a noval class of non-nucleoside compound with potential RT inhibitory activity were found from the traditional Chinese medicines database (TCMD) using a combination of virtual screening,docking,molecular dynamic simulations,where results were ranked by scoring function of...     

Virtual screening for anti-HIV-1 RT and anti-HIV-1 PR inhibitors from the Thai medicinal plants database: a combined docking with neural networks approach

The virtual screening approach for docking small molecules into a known protein structure is a powerful tool for drug design. In this work, a combined docking and neural network approach, using a self-organizing map, has been developed and applied to screen anti-HIV-1 inhibitors for two targets, HIV-1 RT and HIV-1 PR, from active compounds available in the Thai Medicinal Plants Database. Based on nevirapine and calanolide A as reference structures in the HIV-1 RT binding site and XK-263 in the HIV-1 PR binding site, 2,684 compounds in the database were docked into the target enzymes. Self-organizing maps were then generated with respect to three types of pharmacophoric groups. The map of the reference structures were then superimposed on the feature maps of all screened compounds. Only the structures having similar features to the reference compounds were accepted. By using the SOMs, the number of candidates for HIV-1 RT was reduced to six and nine compounds consistent with nevirapine and calanolide A, respectively, as references. For the HIV-1 PR target, there are 135 screened compounds showed good agreement with the XK-263 feature map. These screened compounds will be further tested for their HIV-1 inhibitory affinities. The obtained results indicate that this combined method is clearly helpful to perform the successive screening and to reduce the analyzing step from AutoDock and scoring procedure.     

靶向亲和-液相色谱-质谱联用技术快速筛选黄藤总生物碱中乙酰胆碱酯酶抑制剂

采用靶向亲和-液相色谱-质谱联用技术(Target molecule affinity-LC-ESI-MSn)快速筛选黄藤总生物碱中能够抑制乙酰胆碱酯酶活性的成分,共筛选出12种具有潜在抑制乙酰胆碱酯酶的活性成分,并鉴定了6种成分,分别为黄藤素(Palmatine)、小檗碱(Berberine)、药根碱(Jatrorrhizine)、巴马汀红碱(Palmatrubine)、7,8-二氢-8-羟基小檗碱(7,8-Dihydro-8-hydroxyberberine)、Groenlandicine,结合体外酶学实验对这6种化合物进行了活性验证实验.结果表明,黄藤素抑制活性最强,其抑制作用强于阳性对照药盐酸多奈哌齐,说明黄藤素具有开发成抗阿尔茨海默症药物的潜力.本方法简单、快速、准确地从复杂的中药提取物中筛选出具有抑制乙酰胆碱酯酶活性的成分,适用于复杂体系中的高通量筛选.     

Consideration of molecular weight during compound selection in virtual target-based database screening

Virtual database screening allows for millions of chemical compounds to be computationally selected based on structural complimentary to known inhibitors or to a target binding site on a biological macromolecule. Compound selection in virtual database screening when targeting a biological macromolecule is typically based on the interaction energy between the chemical compound and the target macromolecule. In the present study it is shown that this approach is biased toward the selection of high molecular weight compounds due to the contribution of the compound size to the energy score. To account for molecular weight during energy based screening, we propose normalization strategies based on the total number of heavy atoms in the chemical compounds being screened. This approach is computationally efficient and produces molecular weight distributions of selected compounds that can be selected to be (1) lower than that of the original database used in the virtual screening, which may be desirable for selection of leadlike compounds or (2) similar to that of the original database, which may be desirable for the selection of drug-like compounds. By eliminating the bias in target-based database screening toward higher molecular weight compounds it is anticipated that the proposed procedure will enhance the success rate of computer-aided drug design.     

Pharmacophore modeling and parallel screening for PPAR ligands

We describe the generation and validation of pharmacophore models for PPARs, as well as a large scale validation of the parallel screening approach by screening PPAR ligands against a large database of structure-based models. A large test set of 357 PPAR ligands was screened against 48 PPAR models to determine the best models for agonists of PPAR-alpha, PPAR-delta, and PPAR-gamma. Afterwards, a parallel screen was performed using the 357 PPAR ligands and 47 structure-based models for PPARs, which were integrated into a 1537 models comprising in-house pharmacophore database, to assess the enrichment of PPAR ligands within the PPAR hypotheses. For these purposes, we categorized the 1537 database models into 181 protein targets and developed a score that ranks the retrieved targets for each ligand. Thus, we tried to find out if the concept of parallel screening is able to predict the correct pharmacological target for a set of compounds. The PPAR target was ranked first more often than any other target. This confirms the ability of parallel screening to forecast the pharmacological active target for a set of compounds.     

Virtual Screening of Human O-GlcNAc Transferase Inhibitors

O-GlcNAc transferase (OGT) is one of essential mammalian enzymes, which catalyze the transfer of N-acetylglucosamine from UDP-N-acetylglucosamine (UDP-GlcNAc) to hydroxyl groups of serines and threonines (Ser/Thr) in proteins. Dysregulations of cellular O-GlcNAc have been implicated in diabetes, neurodegenerative disease, and cancer, which brings great interest in developing potent and speci c small-molecular OGT inhibitors. In this work, we performed virtual screening on OGT catalytic site to identify potential inhibitors. 7134792 drug-like compounds from ZINC (a free database of commercially available compounds for virtual screening) and 4287550 compounds generated by FOG (fragment optimized growth program) were screened and the top 116 compounds ranked by docking score were analyzed. By comparing the screening results, we found FOG program can generate more compounds with better docking scores than ZINC. The top ZINC compounds ranked by docking score were grouped into two classes, which held the binding positions of UDP and GlcNAc of UDPGlcNAc. Combined with individual fragments in binding pocket, de novo compounds were designed and proved to have better docking score. The screened and designed compounds may become a starting point for developing new drugs.     

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.     

Mining the ChEMBL database: an efficient chemoinformatics workflow for assembling an ion channel-focused screening library

The ChEMBL database was mined to efficiently assemble an ion channel-focused screening library. The compiled library consists of 3241 compounds representing 123 templates across nine ion channel categories. Compounds in the screening library are annotated with their respective ion channel category to facilitate back-tracing of prospective molecular targets from phenotypic screening results. The established workflow is adaptable to the construction of focused screening libraries for other therapeutic target classes with diverse recognition motifs.     

Exploration of the mechanism of Zisheng Shenqi decoction against gout arthritis using network pharmacology

BackgroundIn this study, the network pharmacological methods were used to predict the target of effective components of compounds in Zisheng Shenqi Decoction (ZSD, or Nourishing Kidney Qi Decoction) in the treatment of gouty arthritis (GA).MethodThe main effective components and corresponding key targets of herbs in the ZSD were discerned through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis (TCMSP), Bioinformatics Analysis Tool for Molecular mechanism of Traditional Chinese Medicine (BATMAN-TCM) database. UniProt database and Swiss Target Prediction (STP) database was used to rectify and unify the target names and supply the target information. The targets related to GA were obtained by using GeneCards database. After we discovered the potential common targets between ZSD and GA, the interaction network diagram of “ZSD-component-GA-target” was constructed by Cytoscape software (Version 3.7.1). Subsequently, the Protein-protein interaction (PPI) network of ZSD effective components-targets and GA-related targets was constructed by Search Tool for the Retrieval of Interacting Genes Database (STRING). Bioconductor package “org.Hs.eg.db” and “cluster profiler” package were installed in R software (Version 3.6.0) which used for Gene Ontology analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis.Results146 components and 613 targets of 11 herbal medicines in the ZSD were got from TCMSP database and BATMAN-TCM database. 987 targets of GA were obtained from GeneCards database. After intersected and removed duplications, 132 common targets between ZSD and GA were screened out by Cytoscape software (Version 3.7.1). These common targets derived from 81 effective components of 146 components, such as quercetin, stigmasterol and kaempferol. They were closely related to anti-inflammatory, analgesic and anti oxidative stress and the principal targets comprised of Purinergic receptor P2X, ligand-gated ion channel 7 (P2x7R), Nod-like receptor protein 3 (NLRP3) and IL-1β. GO enrichment analysis and KEGG pathway enrichment analysis by R software (Version 3.6.0) showed that the key target genes had close relationship with oxidative stress, reactive oxygen species (ROS) metabolic process and leukocyte migration in aspects of biological process, cell components and molecular function. It also indicated that ZSD could decrease inflammatory reaction, alleviate ROS accumulation and attenuate pain by regulating P2 × 7R and NOD like receptor signaling pathway of inflammatory reaction.ConclusionA total of 81 effective components and 132 common target genes between ZSD and GA were screened by network pharmacology. The PPI network, GO enrichment analysis and KEGG pathway enrichment analysis suggested that ZSD can exerte anti-inflammatory and analgesic effects on the treatment of GA by reducing decreasing inflammatory reaction, alleviating ROS accumulation, and attenuating pain. The possible molecular mechanism of it mainly involved multiple components, multiple targets and multiple signaling pathways, which provided a comprehensive understanding for further study. In general, the network pharmacological method applied in this study provides an alternative strategy for the mechanism of ZSD in the treatment of GA.     

Receptor-based virtual ligand screening for the identification of novel CDC25 phosphatase inhibitors

CDC25 phosphatases play critical roles in cell cycle regulation and are attractive targets for anticancer therapies. Several small non-peptide molecules are known to inhibit CDC25, but many of them appear to form a covalent bond with the enzyme or act through oxidation of the thiolate group of the catalytic cysteine. Structure-based virtual ligand screening computations were performed with FRED, Surflex, and LigandFit, a compound collection of over 310,000 druglike molecules and the crystal structure of CDC25B in order to identify novel classes of ligands. In vitro experiments carried out on a selected list of 1500 molecules led to the discovery of 99 compounds able to inhibit CDC25B activity at 100 microM. Further docking computations were applied, allowing us to propose a binding mode for the most potent molecule (IC50 = 13 microM). Our best compounds represent promising new classes of CDC25 inhibitors that also exhibit antiproliferative properties.     

A structural comparative approach to identifying novel antimalarial inhibitors

Malaria continues to affect millions of people annually. With the rise of drug resistant strains, the need for alternative treatments has become increasingly urgent. Recently, PfUCHL3 was identified as an essential deubiquitinating enzyme. The increasing number of drug target structures being solved has increased the feasibility of utilizing a structural comparative approach to identifying novel inhibitors. Using AutoDock Vina, we recently screened the NCI library of about 320,000 compounds against the crystal structure of PfUCHL3. The top hits were subsequently screened against its human ortholog UCHL3 as to identify compounds that could specifically target the PfUCHL3 over its human counterpart. This method was used to identify small molecule inhibitors that can preferentially inhibit the parasitic enzyme. Several compounds were identified that demonstrated significant binding affinity preference for the malaria target over the human enzyme. Two of these compounds demonstrated ng/mL activity.     

分子对接打分修正及在内皮脂肪酶选择性抑制剂筛选中的应用

内皮脂肪酶（EL）是脂代谢调控甘油三酯脂酶家族的新成员,其功能主要为水解富含磷脂的高密度脂蛋白（HDL）,对其进行选择性抑制而不影响其同源蛋白脂蛋白脂肪酶（LPL）能够提高血浆中HDLc的浓度水平,有利于预防及治疗动脉粥样硬化疾病。目前分子对接中的打分函数对大分子及大蛋白口袋具有偏向性,使得基于分子对接的虚拟筛选成功率普遍不高。本文中,我们将EL和LPL分别与Specs小分子库进行了分子对接,分析了对接打分与重原子数及接触面积的关系,发现对接打分与重原子数及接触面积之间有极高的相关性,即存在重原子数的叠加效应（重原子数越大,打分越好的趋势）。我们建立了基于重原子数和接触面积的EL、LPL对接打分标准曲线,利用此标准曲线进行对接打分的修正,并用已知抑制剂和生成的decoy分子作为验证集进行了验证。随后,我们应用此打分修正策略对传统中药库（TCMD）进行了基于分子对接的虚拟筛选,发现经过打分修正后的分子排名与重原子数之间的分布更为均衡,同时我们对EL打分较高、LPL打分较低且类药性较好的分子进行了结合模式的分析,为高活性高选择性EL抑制剂的发现奠定了基础。     

Identification of novel malarial cysteine protease inhibitors using structure-based virtual screening of a focused cysteine protease inhibitor library

Malaria, in particular that caused by Plasmodium falciparum , is prevalent across the tropics, and its medicinal control is limited by widespread drug resistance. Cysteine proteases of P. falciparum , falcipain-2 (FP-2) and falcipain-3 (FP-3), are major hemoglobinases, validated as potential antimalarial drug targets. Structure-based virtual screening of a focused cysteine protease inhibitor library built with soft rather than hard electrophiles was performed against an X-ray crystal structure of FP-2 using the Glide docking program. An enrichment study was performed to select a suitable scoring function and to retrieve potential candidates against FP-2 from a large chemical database. Biological evaluation of 50 selected compounds identified 21 diverse nonpeptidic inhibitors of FP-2 with a hit rate of 42%. Atomic Fukui indices were used to predict the most electrophilic center and its electrophilicity in the identified hits. Comparison of predicted electrophilicity of electrophiles in identified hits with those in known irreversible inhibitors suggested the soft-nature of electrophiles in the selected target compounds. The present study highlights the importance of focused libraries and enrichment studies in structure-based virtual screening. In addition, few compounds were screened against homologous human cysteine proteases for selectivity analysis. Further evaluation of structure-activity relationships around these nonpeptidic scaffolds could help in the development of selective leads for antimalarial chemotherapy.     

Exploring the mechanism of action Xianlingubao Prescription in the treatment of osteoporosis by network pharmacology

In this study, the network pharmacology analysis method was used to explore the bioactive components and targets of Xianlinggubao (XLGB) and further elucidate its potential biological mechanisms of action in the treatment of osteoporosis (OP). The bioactive compounds and predictive targets of XLGB were collected from the traditional Chinese medicine systems pharmacology databases and analysis platform(TCMSP), the Encyclopeida of traditional Chinese medicine (ETCM), traditional Chinese medicine Databse@Taiwan, ChEMBL, STITCH, and SymMap database. The targets corresponding to OP were obtained by using Online Mendelian Inheritance in Man® (OMIM), GeneCards, the National Center for Biotechnology Information-Gene database. The XLGB-OP targets were obtained by intersecting with the targets of XLGB and OP. Protien-Protien interaciton (PPI) network was constructed using STRING online database and analyzed using Cytoscape 3.7.0 software to screen out hub genes. Gene ontology (GO) and KEGG enrichment analysis of the target in the PPI network was conducted using the ClusterProfiler package in R with adjusted p-value<0.05. A total of 65 XLGB bioactive compounds were screened corresponding to 776 XLGB targets and 2556 OP targets. The GO analysis and KEGG enrichment analyses suggested XLGB played a therapeutic roles in OP treatment via the interleukin-17 signaling pathway, hypoxia-inducible factor-1 signaling pathway, insulin resistance, Th-17 signaling pathway, etc. Five hub genes (AKT1, MAPK1, MAPK8, TP53, and STAT3) were screened using the degree algorithm, and molecular docking stimulation results showed that most bioactive compounds of XLGB had strong binding efficiency with hub genes. Overall, this study laid the foundation for further in vivo and in vitro experimental research and expanded the clinical applications of XLGB.     

Design, synthesis, and evaluation of in vivo potency and selectivity of epoxysuccinyl-based inhibitors of papain-family cysteine proteases

The papain-family cathepsins are cysteine proteases that are emerging as promising therapeutic targets for a number of human disease conditions ranging from osteoporosis to cancer. Relatively few selective inhibitors for this family exist, and the in vivo selectivity of most existing compounds is unclear. We present here the synthesis of focused libraries of epoxysuccinyl-based inhibitors and their screening in crude tissue extracts. We identified a number of potent inhibitors that display selectivity for endogenous cathepsin targets both in vitro and in vivo. Importantly, the selectivity patterns observed in crude extracts were generally retained in vivo, as assessed by active-site labeling of tissues from treated animals. Overall, this study identifies several important compound classes and highlights the use of activity-based probes to assess pharmacodynamic properties of small-molecule inhibitors in vivo.     

Intracellular targets of cyclin-dependent kinase inhibitors: identification by affinity chromatography using immobilised inhibitors

BACKGROUND: Chemical inhibitors of cyclin-dependent kinases (CDKs) have great therapeutic potential against various proliferative and neurodegenerative disorders. Olomoucine, a 2,6,9-trisubstituted purine, has been optimized for activity against CDK1/cyclin B by combinatorial and medicinal chemistry efforts to yield the purvalanol inhibitors. Although many studies support the action of purvalanols against CDKs, the actual intracellular targets of 2,6, 9-trisubstituted purines remain unverified. RESULTS: To address this issue, purvalanol B (95. ) and an N6-methylated, CDK-inactive derivative (95M. ) were immobilized on an agarose matrix. Extracts from a diverse collection of cell types and organisms were screened for proteins binding purvalanol B. In addition to validating CDKs as intracellular targets, a variety of unexpected protein kinases were recovered from the 95. matrix. Casein kinase 1 (CK1) was identified as a principal 95. matrix binding protein in Plasmodium falciparum, Leishmania mexicana, Toxoplasma gondii and Trypanosoma cruzi. Purvalanol compounds also inhibit the proliferation of these parasites, suggesting that CK1 is a valuable target for further screening with 2,6,9-trisubstituted purine libraries. CONCLUSIONS: That a simple batchwise affinity chromatography approach using two purine derivatives facilitated isolation of a small set of highly purified kinases suggests that this could be a general method for identifying intracellular targets relevant to a particular class of ligands. This method allows a close correlation to be established between the pattern of proteins bound to a small family of related compounds and the pattern of cellular responses to these compounds.     

Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases

Target identification is a critical step following the discovery of small molecules that elicit a biological phenotype. The present work seeks to provide an in silico correlate of experimental target fishing technologies in order to rapidly fish out potential targets for compounds on the basis of chemical structure alone. A multiple-category Laplacian-modified na?ve Bayesian model was trained on extended-connectivity fingerprints of compounds from 964 target classes in the WOMBAT (World Of Molecular BioAcTivity) chemogenomics database. The model was employed to predict the top three most likely protein targets for all MDDR (MDL Drug Database Report) database compounds. On average, the correct target was found 77% of the time for compounds from 10 MDDR activity classes with known targets. For MDDR compounds annotated with only therapeutic or generic activities such as "antineoplastic", "kinase inhibitor", or "anti-inflammatory", the model was able to systematically deconvolute the generic activities to specific targets associated with the therapeutic effect. Examples of successful deconvolution are given, demonstrating the usefulness of the tool for improving knowledge in chemogenomics databases and for predicting new targets for orphan compounds.