中药网络药理学研究中的生物信息学方法

为了更有效地治疗癌症、心血管疾病、免疫系统疾病等复杂疾病,基于分子网络的多靶点药物发现理念逐渐成为一种新的趋势,而中药整体、辨证、协同的用药观再一次引起了药物发现领域的极大兴趣。中药在治疗复杂慢性疾病方面有确切的疗效和较小的毒副作用。中药网络药理学从分子网络调控的水平上阐明中药的作用机制,为多靶点药物发现提供有益的启示和借鉴,并有可能从临床有效的中药反向开发现代多组分、多靶点新药。针对基于生物分子网络的中药药理学研究路线中的4 个步骤,介绍近年来中药网络药理学研究中相关的生物信息学方法。     

网络药理学和分子对接技术研究桑黄类真菌对疾病的潜在作用机制

桑黄类真菌是一类极具研究价值的药用真菌。近年来,对于桑黄类真菌的研究,多集中于对某一个物种的成分及药理活性的研究,系统比较桑黄类真菌中成分及药理活性的研究较少。本研究利用网络药理学和分子对接技术从理论上初步探讨了5种桑黄类真菌中化合物与疾病之间的分子作用机制。研究结果表明5种桑黄类真菌(栎木桑黄Sanghuangporus quercicola、鲍姆桑黄Sanghuangporus baumii、粗毛纤孔菌Inonotus hispidus、裂蹄木层孔菌Tropicorus linteus、黑盖木层孔菌Phellinus nigrians)中的39种有效成分,对应潜在靶点588个。KEGG通路富集筛选得到165条通路,分析结果发现这39种化合物的靶点主要分布在与炎症、糖尿病、肝癌、阿尔茨海默病和衰老相关的信号通路上。筛选出桑黄类真菌中抗病的潜在靶点共486个,构建抗病靶点的蛋白互作(PPI)网络,并筛选出LCK、STAT3、PTPN11、STAT1、STAT5B、MAPK1、JAK1、MAPK3、JAK3和JAK2作为关键靶点,构建5种桑黄类真菌-化合物-关键靶点-5种疾病的网络互作图,并进行分子对接验证。筛选出的桑黄类真菌中的12个有效成分均可与这些关键靶点产生相互作用,其中酚类化合物居多,此外二萜类化合物异海松酸与MAPK1结合能力最强。因此,5种桑黄类真菌可以通过多种化合物、多种靶点和多种途径起到抗病的作用,本研究为探索桑黄类真菌治疗和预防疾病潜在机制提供了理论基础。     

Evaluating the Pharmacological Mechanism of Chinese Medicine Si-Wu-Tang through Multi-Level Data Integration

Si-Wu-Tang (SWT) is a Traditional Chinese Medicine (TCM) formula widely used for the treatments of gynecological diseases. To explore the pharmacological mechanism of SWT, we incorporated microarray data of SWT with our herbal target database TCMID to analyze the potential activity mechanism of SWT''s herbal ingredients and targets. We detected 2,405 differentially expressed genes in the microarray data, 20 of 102 proteins targeted by SWT were encoded by these DEGs and can be targeted by 2 FDA-approved drugs and 39 experimental drugs. The results of pathway enrichment analysis of the 20 predicted targets were consistent with that of 2,405 differentially expressed genes, elaborating the potential pharmacological mechanisms of SWT. Further study from a perspective of protein-protein interaction (PPI) network showed that the predicted targets of SWT function cooperatively to perform their multi-target effects. We also constructed a network to combine herbs, ingredients, targets and drugs together which bridges the gap between SWT and conventional medicine, and used it to infer the potential mechanisms of herbal ingredients. Moreover, based on the hypothesis that the same or similar effects between different TCM formulae may result from targeting the same proteins, we analyzed 27 other TCM formulae which can also treat the gynecological diseases, the subsequent result provides additional insight to understand the potential mechanisms of SWT in treating amenorrhea. Our bioinformatics approach to detect the pharmacology of SWT may shed light on drug discovery for gynecological diseases and could be utilized to investigate other TCM formulae as well.     

Computational methods and applications for quantitative systems pharmacology

Background: Quantitative systems pharmacology (QSP) is an emerging discipline that integrates diverse data to quantitatively explore the interactions between drugs and multi-scale systems including small compounds, nucleic acids, proteins, pathways, cells, organs and disease processes. Results: Various computational methods such as ADME/T evaluation, molecular modeling, logical modeling, network modeling, pathway analysis, multi-scale systems pharmacology platforms and virtual patient for QSP have been developed. We reviewed the major progresses and broad applications in medical guidance, drug discovery and exploration of pharmacodynamic material basis and mechanism of traditional Chinese medicine. Conclusion: QSP has significant achievements in recent years and is a promising approach for quantitative evaluation of drug efficacy and systematic exploration of mechanisms of action of drugs.     

Pathway as a Pharmacological Target for Herbal Medicines: An Investigation from Reduning Injection

As a rich natural resource for drug discovery, Traditional Chinese Medicine (TCM) plays an important role in complementary and alternative medical systems. TCM shows a daunting complexity of compounds featuring multi-components and multi-targets to cure diseases, which thus always makes it extremely difficult to systematically explain the molecular mechanisms adequately using routine methods. In the present work, to reveal the systematic mechanism of herbal formulae, we developed a pathway-based strategy by combining the pathways integrating, target selection, reverse drug targeting and network analysis together, and then exemplified it by Reduning injection (RDN), a clinically widely used herbal medicine injection, in combating inflammation. The anti-inflammatory effects exerted by the major ingredients of RDN at signaling pathways level were systematically investigated. More importantly, our predicted results were also experimentally validated. Our strategy provides a deep understanding of the pharmacological functions of herbal formulae from molecular to systematic level, which may lead to more successful applications of systems pharmacology for drug discovery and development.     

A system-level investigation into the mechanisms of chinese traditional medicine: compound danshen formula for cardiovascular disease treatment

Compound Danshen Formula (CDF) is a widely used Traditional Chinese Medicine (TCM) which has been extensively applied in clinical treatment of cardiovascular diseases (CVDs). However, the underlying mechanism of clinical administrating CDF on CVDs is not clear. In this study, the pharmacological effect of CDF on CVDs was analyzed at a systemic point of view. A systems-pharmacological model based on chemical, chemogenomics and pharmacological data is developed via network reconstruction approach. By using this model, we performed a high-throughput in silico screen and obtained a group of compounds from CDF which possess desirable pharmacodynamical and pharmacological characteristics. These compounds and the corresponding protein targets are further used to search against biological databases, such as the compound-target associations, compound-pathway connections and disease-target interactions for reconstructing the biologically meaningful networks for a TCM formula. This study not only made a contribution to a better understanding of the mechanisms of CDF, but also proposed a strategy to develop novel TCM candidates at a network pharmacology level.     

Insights into the antineoplastic mechanism of Chelidonium majus via systems pharmacology approach

Background: The antineoplastic activity of Chelidonium majus has been reported, but its mechanism of action (MoA) is unsuspected. The emerging theory of systems pharmacology may be a useful approach to analyze the complicated MoA of this multi-ingredient traditional Chinese medicine (TCM). Methods: We collected the ingredients and related compound-target interactions of C. majus from several databases. The bSDTNBI (balanced substructure-drug-target network-based inference) method was applied to predict each ingredient’s targets. Pathway enrichment analysis was subsequently conducted to illustrate the potential MoA, and prognostic genes were identified to predict the certain types of cancers that C. majus might be beneficial in treatment. Bioassays and literature survey were used to validate the in silico results. Results: Systems pharmacology analysis demonstrated that C. majus exerted experimental or putative interactions with 18 cancer-associated pathways, and might specifically act on 13 types of cancers. Chelidonine, sanguinarine, chelerythrine, berberine, and coptisine, which are the predominant components of C. majus, may suppress the cancer genes by regulating cell cycle, inducing cell apoptosis and inhibiting proliferation. Conclusions: The antineoplastic MoA of C. majus was investigated by systems pharmacology approach. C. majus exhibited promising pharmacological effect against cancer, and may consequently be useful material in further drug development. The alkaloids are the key components in C. majus that exhibit anticancer activity.     

Assessing drug target association using semantic linked data

The rapidly increasing amount of public data in chemistry and biology provides new opportunities for large-scale data mining for drug discovery. Systematic integration of these heterogeneous sets and provision of algorithms to data mine the integrated sets would permit investigation of complex mechanisms of action of drugs. In this work we integrated and annotated data from public datasets relating to drugs, chemical compounds, protein targets, diseases, side effects and pathways, building a semantic linked network consisting of over 290,000 nodes and 720,000 edges. We developed a statistical model to assess the association of drug target pairs based on their relation with other linked objects. Validation experiments demonstrate the model can correctly identify known direct drug target pairs with high precision. Indirect drug target pairs (for example drugs which change gene expression level) are also identified but not as strongly as direct pairs. We further calculated the association scores for 157 drugs from 10 disease areas against 1683 human targets, and measured their similarity using a [Formula: see text] score matrix. The similarity network indicates that drugs from the same disease area tend to cluster together in ways that are not captured by structural similarity, with several potential new drug pairings being identified. This work thus provides a novel, validated alternative to existing drug target prediction algorithms. The web service is freely available at: http://chem2bio2rdf.org/slap.     

Pharmacodynamics simulation of HOEC by a computational model of arachidonic acid metabolic network

Background: Arachidonic acid (AA) metabolic network is activated in the most inflammatory related diseases, and small-molecular drugs targeting AA network are increasingly available. However, side effects of above mentioned drugs have always been the biggest obstacle. (+)-2-(1-hydroxyl-4-oxocyclohexyl) ethyl caffeate (HOEC), a natural product acted as an inhibitor of 5-lipoxygenase (5-LOX) and 15-LOX in vitro, exhibited weaker therapeutic effect in high dose than that in low dose to collagen induced arthritis (CIA) rats. In this study, we tried to elucidate the potential regulatory mechanism by using quantitative pharmacology. Methods: First, we generated an experimental data set by monitoring the dynamics of AA metabolites’ concentration in A23187 stimulated and different doses of HOEC co-incubated RAW264.7. Then we constructed a dynamic model of A23187-stimulated AA metabolic model to evaluate how a model-based simulation of AA metabolic data assists to find the most suitable treatment dose by predicting the pharmacodynamics of HOEC. Results: Compared to the experimental data, the model could simulate the inhibitory effect of HOEC on 5-LOX and 15-LOX, and reproduced the increase of the metabolic flux in the cyclooxygenase (COX) pathway. However, a concomitant, early-stage of stimulation-related decrease of prostaglandins (PGs) production in HOEC incubated RAW264.7 cells was not simulated in the model. Conclusion: Using the model, we predict that higher dose of HOEC disrupts the flux balance in COX and LOX of the AA network, and increased COX flux can interfere the curative effects of LOX inhibitor on resolution of inflammation which is crucial for the efficient and safe drug design.     

蛋白质组学技术在网络药理学中的应用

蛋白质组学发展至今已日趋成熟,在生物医药相关领域研究中的应用显著增加,与之相关的样品制备技术、蛋白定量方法及先进的质谱仪器也得到了快速发展。网络药理学是近年来提出的新药发现新策略,是药理学的新兴分支学科,它从整体的角度探索药物与疾病的关联性,发现药物靶标,指导新药研发。将蛋白质组学技术应用于网络药理学研究,能使研究人员系统地预测和解释药物的作用,加速药物靶点的确认,从而设计多靶点药物或药物组合。综述了蛋白质组学技术的新近研究进展,并简单概述了其在网络药理学中的应用。     

An integrated approach of network-based systems biology,molecular docking,and molecular dynamics approach to unravel the role of existing antiviral molecules against AIDS-associated cancer

A serious challenge in cancer treatment is to reposition the activity of various already known drug candidates against cancer. There is a need to rewrite and systematically analyze the detailed mechanistic aspect of cellular networks to gain insight into the novel role played by various molecules. Most Human Immunodeficiency Virus infection-associated cancers are caused by oncogenic viruses like Human Papilloma Viruses and Epstein–Bar Virus. As the onset of AIDS-associated cancers marks the severity of AIDS, there might be possible interconnections between the targets and mechanism of both the diseases. We have explored the possibility of certain antiviral compounds to act against major AIDS-associated cancers: Kaposi’s Sarcoma, Non-Hodgkin Lymphoma, and Cervical Cancer with the help of systems pharmacology approach that includes screening for targets and molecules through the construction of a series of drug–target and drug–target–diseases network. Two molecules (Calanolide A and Chaetochromin B) and the target “HRAS” were finally screened with the help of molecular docking and molecular dynamics simulation. The results provide novel antiviral molecules against HRAS target to treat AIDS defining cancers and an insight for understanding the pharmacological, therapeutic aspects of similar unexplored molecules against various cancers.     

河蟹螺原体非编码RNA及其毒力靶标的多组学系统挖掘*

我国是世界最大水产养殖国,每年甲壳动物因病害造成的经济损失约为70亿元。其中,螺原体(Spiroplasma)是甲壳动物重要的致病菌之一,可造成虾蟹大面积死亡,已列入农业农村部三类疫病。非编码RNA(ncRNA)广泛存在于细菌中,其主要通过碱基配对识别靶标mRNA在转录后水平调节基因的表达,部分ncRNAs通过与蛋白质相互作用而影响蛋白质功能。近年研究表明,细菌ncRNAs在毒力调控中扮演极为重要的角色。为了研究河蟹螺原体ncRNAs在甲壳动物致病中的分子调控作用,需系统筛选鉴定螺原体感染相关的ncRNAs和毒力靶标。通过比较基因组、差异转录组、定量蛋白质组、系统生物学和分子相互作用联合研究得到:整合基因组和转录组挖掘得到河蟹螺原体ncRNAs 共54个;在体内感染和体外培养的不同时期,利用数字基因表达谱分析分别得到11个和28个差异显著ncRNAs;利用4款生物软件预测ncRNAs靶标,取交集得到423个;利用定量蛋白质组检测,鉴定出68个差异毒力蛋白,这些差异毒力蛋白与ncRNAs的30个毒力靶标中的21个相同;利用网络生物学分析得到主要的节点Hub-ncRNA共有6个;利用RNA pull-down、原核链特异性测序和LC-MS/MS综合分析,得到重要节点ncRNA SR05的互作RNA 53个、互作蛋白质120个。相关研究成果,可为诠释河蟹螺原体致病机制及其与宿主相互作用机制奠定基础,为虾蟹该疾病的综合防治提供科学依据。     

Network Pharmacology and Molecular Docking Combined to Analyze the Molecular and Pharmacological Mechanism of Pinellia ternata in the Treatment of Hypertension

Hypertension is a cardiovascular disease that causes great harm to health and life, affecting the function of important organs and accompanied by a variety of secondary diseases, which need to be treated with drugs for a long time. P. ternata alone or combination with western medicine has played an important role in traditional Chinese medicine. Although P. ternata is used clinically to treat hypertension, its functional molecular mechanism and pharmacological mechanism have not been elucidated. Therefore, in this study, the potentially effective components, and targets of P. ternata in the treatment of hypertension were screened by the method of network pharmacology, and the mechanism of P. ternata in the treatment of hypertension was analyzed by constructing a component-target relationship network, PPI interaction network, targets’ function analysis, and molecular docking. In the study, 12 potentially effective components and 88 targets were screened, and 3 potential protein modules were found and analyzed after constructing a PPI network using targets. In addition, 10 targets were selected as core targets of the PPI network. After that, the targets were analyzed by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, the molecular docking method is used to study the interaction between the targets and the active components. The above evidence shows that the mechanism of P. ternata in the treatment of hypertension is complicated, as it acts in many ways, mainly by affecting nerve signal transmission, cell proliferation, and apoptosis, calcium channels, and so on. The binding between targets and active components mainly depends on Pi bonds and hydrogen bonds. Using the method of network pharmacology and molecular docking to analyze the mechanism of P. ternata in the treatment of hypertension will help to provide a better scientific basis for the combined use of traditional Chinese medicine and western medicine, and will better help to improve the quality of P. ternata and point out its direction.     

Metabolic network modeling and simulation for drug targeting and discovery

Systems biology has greatly contributed toward the analysis and understanding of biological systems under various genotypic and environmental conditions on a much larger scale than ever before. One of the applications of systems biology can be seen in unraveling and understanding complicated human diseases where the primary causes for a disease are often not clear. The in silico genome-scale metabolic network models can be employed for the analysis of diseases and for the discovery of novel drug targets suitable for treating the disease. Also, new antimicrobial targets can be discovered by analyzing, at the systems level, the genome-scale metabolic network of pathogenic microorganisms. Such applications are possible as these genome-scale metabolic network models contain extensive stoichiometric relationships among the metabolites constituting the organism's metabolism and information on the associated biophysical constraints. In this review, we highlight applications of genome-scale metabolic network modeling and simulations in predicting drug targets and designing potential strategies in combating pathogenic infection. Also, the use of metabolic network models in the systematic analysis of several human diseases is examined. Other computational and experimental approaches are discussed to complement the use of metabolic network models in the analysis of biological systems and to facilitate the drug discovery pipeline.     

Network pharmacology and drug discovery

The pharmaceutical industry has historically relied on particular families of 'druggable' proteins against which to develop compounds with desired actions. But proteins rarely function in isolation in and outside the cell; rather, proteins operate as part of highly interconnected cellular networks. Network pharmacology is an emerging area of pharmacology which utilizes network analysis of drug action. By considering drug actions in the context of the cellular networks, network analysis promises to greatly increase our knowledge of the mechanisms underlying the multiple actions of drugs. Network pharmacology can provide new approaches for drug discovery for complex diseases. This review introduced the recent progress of network pharmacology and its importance to understand the mechanism of drug actions and drug discovery.     

Regulation by competition: a hidden layer of gene regulatory network

Background: Molecular competition brings about trade-offs of shared limited resources among the cellular components, and thus introduces a hidden layer of regulatory mechanism by connecting components even without direct physical interactions. Several molecular competition scenarios have been observed recently, but there is still a lack of systematic quantitative understanding to reveal the essence of molecular competition. Methods: Here, by abstracting the analogous competition mechanism behind diverse molecular systems, we built a unified coarse-grained competition motif model to systematically integrate experimental evidences in these processes and analyzed general properties shared behind them from steady-state behavior to dynamic responses. Results: We could predict in what molecular environments competition would reveal threshold behavior or display a negative linear dependence. We quantified how competition can shape regulator-target dose-response curve, modulate dynamic response speed, control target expression noise, and introduce correlated fluctuations between targets. Conclusions: This work uncovered the complexity and generality of molecular competition effect as a hidden layer of gene regulatory network, and therefore provided a unified insight and a theoretical framework to understand and employ competition in both natural and synthetic systems.     

天然活性成分作用靶点识别及确证方法的研究进展

药物靶标的发现和验证是新药研发的关键环节,对新药创制具有源头创新意义。天然产物是新药创制的重要来源,识别其作用靶点不仅为临床预防治疗提供可能新策略,也为进一步阐释中草药及其复方的作用特点及分子机制提供参考依据。随着生命科学和信息学的发展,药物靶点的识别及确证方法不断涌现,生物信息学、网络药理学、蛋白质组学、亲和色谱、药物亲和稳定性、芯片技术、基因敲除技术、RNA干扰等技术的广泛应用,越来越多的天然活性成分的靶点得以识别和验证。因此,本文对近五年来天然活性成分作用靶点识别及确证方法做一简要综述,以供参考。     

meta-PPISP: a meta web server for protein-protein interaction site prediction

A number of complementary methods have been developed for predicting protein-protein interaction sites. We sought to increase prediction robustness and accuracy by combining results from different predictors, and report here a meta web server, meta-PPISP, that is built on three individual web servers: cons-PPISP (http://pipe.scs.fsu.edu/ppisp.html), Promate (http://bioportal.weizmann.ac.il/promate), and PINUP (http://sparks.informatics.iupui.edu/PINUP/). A linear regression method, using the raw scores of the three servers as input, was trained on a set of 35 nonhomologous proteins. Cross validation showed that meta-PPISP outperforms all the three individual servers. At coverages identical to those of the individual methods, the accuracy of meta-PPISP is higher by 4.8 to 18.2 percentage points. Similar improvements in accuracy are also seen on CAPRI and other targets. AVAILABILITY: meta-PPISP can be accessed at http://pipe.scs.fsu.edu/meta-ppisp.html