基于SVM 的药物靶点预测方法及其应用

目的:基于已知药物靶点和潜在药物靶点蛋白的一级结构相似性,结合SVM技术研究新的有效的药物靶点预测方法。方法:构造训练样本集,提取蛋白质序列的一级结构特征,进行数据预处理,选择最优核函数,优化参数并进行特征选择,训练最优预测模型,检验模型的预测效果。以G蛋白偶联受体家族的蛋白质为预测集,应用建立的最优分类模型对其进行潜在药物靶点挖掘。结果:基于SVM所建立的最优分类模型预测的平均准确率为81.03%。应用最优分类器对构造的G蛋白预测集进行预测,结果发现预测排位在前20的蛋白质中有多个与疾病相关。特别的,其中有两个G蛋白在治疗靶点数据库(TTD)中显示已作为临床试验的药物靶点。结论:基于SVM和蛋白质序列特征的药物靶点预测方法是有效的,应用该方法预测出的潜在药物靶点能够为发现新的药靶提供参考。     

Effects of anti-microtubule agents on microtubule organization in cells lacking the kinesin-13 MCAK

Dynamic microtubules are necessary for proper mitotic spindle assembly and chromosome segregation during mitosis. Members of the kinesin superfamily of molecular motor proteins are important to spindle function. Of particular interest is the Kinesin-13 family member MCAK, which acts to regulate microtubule dynamics during spindle assembly and to ensure proper attachments of chromosomes to spindle microtubules. The unique ability of MCAK to regulate microtubule dynamics makes it a potential target for development of new drugs that alter spindle function. Here, we knocked down MCAK via RNAi in normal and malignant cell lines and found that the two tested malignant cell lines were acutely sensitive to MCAK knockdown, while the tested normal cells were less sensitive. In addition, we looked at the effect of combining MCAK knockdown and drug treatment with paclitaxel or vinblastine to identify spindle assembly defects. We found that MCAK knockdown increased the morphological defects of the microtubule cytoskeleton in HeLa cells caused by anti-microtubule drugs. Our studies support the idea that MCAK would be a good target for new chemotherapeutic development and may be particular useful in combination therapies with currently available anti-microtubule agents.     

A quantitative basis for antiretroviral therapy for HIV-1 infection

Highly active antiretroviral therapy (HAART) has dramatically decreased mortality from HIV-1 infection and is a major achievement of modern medicine. However, there is no fundamental theory of HAART. Elegant models describe the dynamics of viral replication, but a metric for the antiviral activity of drug combinations relative to a target value needed for control of replication is lacking. Treatment guidelines are based on empirical results of clinical trials in which other factors such as regimen tolerability also affect outcome. Why only certain drug combinations control viral replication remains unclear. Here we quantify the intrinsic antiviral activity of antiretroviral drug combinations. We show that most single antiretroviral drugs show previously unappreciated complex nonlinear pharmacodynamics that determine their inhibitory potential at clinical concentrations. We demonstrate that neither of the major theories for drug combinations accurately predicts the combined effects of multiple antiretrovirals. However, the combined effects can be understood with a new approach that considers the degree of independence of drug effects. This analysis allows a direct comparison of the inhibitory potential of different drug combinations under clinical concentrations, reconciles the results of clinical trials, defines a target level of inhibition associated with treatment success and provides a rational basis for treatment simplification and optimization.     

Probing the interaction of the diarylquinoline TMC207 with its target mycobacterial ATP synthase

Infections with Mycobacterium tuberculosis are substantially increasing on a worldwide scale and new antibiotics are urgently needed to combat concomitantly emerging drug-resistant mycobacterial strains. The diarylquinoline TMC207 is a highly promising drug candidate for treatment of tuberculosis. This compound kills M. tuberculosis by binding to a new target, mycobacterial ATP synthase. In this study we used biochemical assays and binding studies to characterize the interaction between TMC207 and ATP synthase. We show that TMC207 acts independent of the proton motive force and does not compete with protons for a common binding site. The drug is active on mycobacterial ATP synthesis at neutral and acidic pH with no significant change in affinity between pH 5.25 and pH 7.5, indicating that the protonated form of TMC207 is the active drug entity. The interaction of TMC207 with ATP synthase can be explained by a one-site binding mechanism, the drug molecule thus binds to a defined binding site on ATP synthase. TMC207 affinity for its target decreases with increasing ionic strength, suggesting that electrostatic forces play a significant role in drug binding. Our results are consistent with previous docking studies and provide experimental support for a predicted function of TMC207 in mimicking key residues in the proton transfer chain and blocking rotary movement of subunit c during catalysis. Furthermore, the high affinity of TMC207 at low proton motive force and low pH values may in part explain the exceptional ability of this compound to efficiently kill mycobacteria in different microenvironments.     

新型冠状病毒基因组G-四链体结构的初步研究

21世纪以来,冠状病毒频频引起危害人类健康的重要传染病,其中包括2003年严重急性呼吸综合征冠状病毒(SARS-CoV)、2012年中东呼吸综合征冠状病毒(MERS-CoV)和新型冠状病毒(SARS-CoV-2),目前对这些病毒引发的疾病并无特效的治疗药物。G-四链体(G-quadruplex,G4)是在DNA或RNA的鸟嘌呤富集区形成的非典型二级结构,可存在于人类和病毒基因组中,G-四链体的不同位置对病毒复制和感染等过程发挥重要调控作用。本研究针对七种与人类疾病相关的冠状病毒以及与SARS-CoV-2同源性较高的三种蝙蝠相关病毒,通过全基因组序列分析潜在四链体形成序列(Potential quadruplex-forming sequences,PQS),结果发现,十种病毒中均存在一定数量的PQS基序,同时对SARS-CoV-2 G-四链体存在位置及形成潜力进行评估,并分析了不同变异株间G-四链体基序的保守性。本研究对SARS-CoV-2基因组中G-四链体进行初步预测与探讨,旨在为COVID-19治疗提供一种新的药物靶点,使其更好地应用于临床研究。     

Natural product–drug conjugates for modulation of TRPV1-expressing tumors

We report the design, synthesis and biological evaluation of natural product–drug conjugates for treatment of prostate cancers over-expressing the transient receptor potential vanilloid 1 (TRPV1) channel. We validate the relevance of TRPV1 as a target in prostate cancer patients by using a bioinformatics approach and provide proof-of-concept for the drug delivery strategy through bioorthogonal chemistry and stability assays under simulated physiological conditions. In cell-based assays, the constructs displayed modest activity. Moreover, we serendipitously discover that a stoichiometric combination of a TRPV1 agonist with a small, positively charged cytotoxic may provide new research avenues in personalized medicines for prostate cancer.     

Arrested oocyst maturation in Plasmodium parasites lacking type II NADH:ubiquinone dehydrogenase

The Plasmodium mitochondrial electron transport chain has received considerable attention as a potential target for new antimalarial drugs. Atovaquone, a potent inhibitor of Plasmodium cytochrome bc(1), in combination with proguanil is recommended for chemoprophylaxis and treatment of malaria. The type II NADH:ubiquinone oxidoreductase (NDH2) is considered an attractive drug target, as its inhibition is thought to lead to the arrest of the mitochondrial electron transport chain and, as a consequence, pyrimidine biosynthesis, an essential pathway for the parasite. Using the rodent malaria parasite Plasmodium berghei as an in vivo infection model, we studied the role of NDH2 during Plasmodium life cycle progression. NDH2 can be deleted by targeted gene disruption and, thus, is dispensable for the pathogenic asexual blood stages, disproving the candidacy for an anti-malarial drug target. After transmission to the insect vector, NDH2-deficient ookinetes display an intact mitochondrial membrane potential. However, ndh2(-) parasites fail to develop into mature oocysts in the mosquito midgut. We propose that Plasmodium blood stage parasites rely on glycolysis as the main ATP generating process, whereas in the invertebrate vector, a glucose-deprived environment, the malaria parasite is dependent on an intact mitochondrial respiratory chain.     

Zafirlukast inhibits the growth of lung adenocarcinoma via inhibiting TMEM16A channel activity

Lung cancer has the highest mortality among cancers worldwide due to its high incidence and lack of the effective cures. We have previously demonstrated that the membrane ion channel TMEM16A is a potential drug target for the treatment of lung adenocarcinoma and have identified a pocket of inhibitor binding that provides the basis for screening promising new inhibitors. However, conventional drug discovery strategies are lengthy and costly, and the unpredictable side effects lead to a high failure rate in drug development. Therefore, finding new therapeutic directions for already marketed drugs may be a feasible strategy to obtain safe and effective therapeutic drugs. Here, we screened a library of over 1400 Food and Drug Administration–approved drugs through virtual screening and activity testing. We identified a drug candidate, Zafirlukast (ZAF), clinically approved for the treatment of asthma, that could inhibit the TMEM16A channel in a concentration-dependent manner. Molecular dynamics simulations and site-directed mutagenesis experiments showed that ZAF can bind to S387/N533/R535 in the nonselective inhibitor binding pocket, thereby blocking the channel pore. Furthermore, we demonstrate ZAF can target TMEM16A channel to inhibit the proliferation and migration of lung adenocarcinoma LA795 cells. In vivo experiments showed that ZAF can significantly inhibit lung adenocarcinoma tumor growth in mice. Taken together, we identified ZAF as a novel TMEM16A channel inhibitor with excellent anticancer activity, and as such, it represents a promising candidate for future preclinical and clinical studies.     

Molecular design of new aggrecanases-2 inhibitors

Aggrecanases-2 is a very important potential drug target for the treatment of osteoarthritis. In this study, a series of known aggrecanases-2 inhibitors was analyzed by the technologies of three-dimensional quantitative structure–activity relationships (3D-QSAR) and molecular docking. Two 3D-QSAR models, which based on comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods, were established. Molecular docking was employed to explore the details of the interaction between inhibitors and aggrecanases-2 protein. According to the analyses for these models, several new potential inhibitors with higher activity predicted were designed, and were supported by the simulation of molecular docking. This work propose the fast and effective approach to design and prediction for new potential inhibitors, and the study of the interaction mechanism provide a better understanding for the inhibitors binding into the target protein, which will be useful for the structure-based drug design and modifications.     

A novel genomics approach for the identification of drug targets in pathogens, with special reference to Pseudomonas aeruginosa

Complete genome sequences of several pathogenic bacteria have been determined, and many more such projects are currently under way. While these data potentially contain all the determinants of host-pathogen interactions and possible drug targets, computational tools for selecting suitable candidates for further experimental analyses are currently limited. Detection of bacterial genes that are non-homologous to human genes, and are essential for the survival of the pathogen represents a promising means of identifying novel drug targets. We have used three-way genome comparisons to identify essential genes from Pseudomonas aeruginosa. Our approach identified 306 essential genes that may be considered as potential drug targets. The resultant analyses are in good agreement with the results of systematic gene deletion experiments. This approach enables rapid potential drug target identification, thereby greatly facilitating the search for new antibiotics. These results underscore the utility of large genomic databases for in silico systematic drug target identification in the post-genomic era.     

Proteomic methods for drug target discovery

The field of drug target discovery is currently very popular with a great potential for advancing biomedical research and chemical genomics. Innovative strategies have been developed to aid the process of target identification, either by elucidating the primary mechanism-of-action of a drug, by understanding side effects involving unanticipated 'off-target' interactions, or by finding new potential therapeutic value for an established drug. Several promising proteomic methods have been introduced for directly isolating and identifying the protein targets of interest that are bound by active small molecules or for visualizing enzyme activities affected by drug treatment. Significant progress has been made in this rapidly advancing field, speeding the clinical validation of drug candidates and the discovery of the novel targets for lead compounds developed using cell-based phenotypic screens. Using these proteomic methods, further insight into drug activity and toxicity can be ascertained.     

核纤层蛋白B1的功能及其在神经系统疾病和肿瘤中的研究新进展

核纤层蛋白B1 (Lamin B1)是核纤层蛋白家族重要成员之一,其主要功能在于维持细胞核骨架完整性,并通过影响染色体分布、基因表达及DNA损伤修复等参与细胞的增殖和衰老。其表达异常与多种疾病有关,如神经系统疾病(神经管畸形,ADLD)及肿瘤(胰腺癌)等,是潜在的药物靶点和肿瘤标志物。对Lamin B1功能的深入研究,将有助于对相关神经系统疾病和肿瘤发生发展的分子机制的了解并为治疗靶点研究提供新方向。     

Parameters determining the relative efficacy of hydroxy-naphthoquinone inhibitors of the cytochrome bc1 complex

Hydroxy-naphthoquinones are competitive inhibitors of the cytochrome bc(1) complex that bind to the ubiquinol oxidation site between cytochrome b and the iron-sulfur protein and presumably mimic a transition state in the ubiquinol oxidation reaction catalyzed by the enzyme. The parameters that affect efficacy of binding of these inhibitors to the bc(1) complex are not well understood. Atovaquone, a hydroxy-naphthoquinone, has been used therapeutically to treat Pneumocystis carinii and Plasmodium infections. As the pathogens have developed resistance to this drug, it is important to understand the molecular basis of the drug resistance and to develop new drugs that can circumvent the drug resistance. We previously developed the yeast and bovine bc(1) complexes as surrogates to model the interaction of atovaquone with the bc(1) complexes of the target pathogens and human host. As a first step to identify new cytochrome bc(1) complex inhibitors with therapeutic potential and to better understand the determinants of inhibitor binding, we have screened a library of 2-hydroxy-naphthoquinones with aromatic, cyclic, and non-cyclic alkyl side-chain substitutions at carbon-3 on the hydroxy-quinone ring. We found a group of compounds with alkyl side-chains that effectively inhibit the yeast bc(1) complex. Molecular modeling of these into the crystal structure of the yeast cytochrome bc(1) complex provides structural and quantitative explanations for their binding efficacy to the target enzyme. In addition we also identified a 2-hydroxy-naphthoquinone with a branched side-chain that has potential for development as an anti-fungal and anti-parasitic therapeutic.     

Use of antisense oligonucleotides in functional genomics and target validation

With the completion of sequencing of the human genome, a great deal of interest has been shifted toward functional genomics-based research for identification of novel drug targets for treatment of various diseases. The major challenge facing the pharmaceutical industry is to identify disease-causing genes and elucidate additional roles for genes of known functions. Gene functionalization and target validation are probably the most important steps involved in identifying novel potential drug targets. This review focuses on recent advances in antisense technology and its use for rapid identification and validation of new drug targets. The significance and applicability of this technology as a beginning of the drug discovery process are underscored by relevant cell culture-based assays and positive correlation in specific animal disease models. Some of the antisense inhibitors used to validate gene targets are themselves being developed as drugs. The current clinical trials based on such leads that were identified in a very short time further substantiate the importance of antisense technology-based functional genomics as an integral part of target validation and drug target identification.     

Computable features required to evaluate the efficacy of drugs and a universal algorithm to find optimally effective drug in a drug complex

Background

The H1N1 pandemic in 2009 and the H5N1 pandemic in 2005 demonstrated that the drugs approved to treat influenza A viruses have low efficacy. This provided a stimulus for new studies of influenza A viruses in the context of the methods used in drug design developed over the past 100 years. Finding new universal drugs is the ultimate goal but its long time horizon is incompatible with emergency situations created by reoccurring influenza outbreaks. Therefore, we propose a computer-aided method for finding efficacious drugs and drug complexes based on the use of the DrugBank database.

Methods

(1) We start by assembling a panel of target proteins. (2) We then assemble a panel of drugs. (3) This is followed by a selection of benchmark binding pockets based on the panel of target proteins and the panel of drugs. (4) We generate a set of computational features, which measure the efficacy of a drug. (5) We propose a universal program to search for drugs and drug complexes. (6) A case study we report here illustrates how to use this universal program for finding an optimal drug and a drug complex for a given target. (7) Validation of the Azirchromycin and Aspirin complex is provided mathematically. (8) Finally, we propose a simple strategy to validate our computational prediction that the Azirchromycin and Aspirin complex should prove clinically effective.

Result

A set of computable features are mined and then based on these features, a universal program for finding the potential drug &drug complexes is proposed. Using this universal program, the Azirchromycin and Aspirin complex is selected and its efficacy is predicted mathematically. For clinical validation of this finding, future work is still required.     

Improving protein function prediction methods with integrated literature data

Background  

Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problem's complexity and scale. Identifying a protein's function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity.     

The chemotherapeutic drug 5-fluorouracil promotes PKR-mediated apoptosis in a p53-independent manner in colon and breast cancer cells

The chemotherapeutic drug 5-FU is widely used in the treatment of a range of cancers, but resistance to the drug remains a major clinical problem. Since defects in the mediators of apoptosis may account for chemo-resistance, the identification of new targets involved in 5-FU-induced apoptosis is of main clinical interest. We have identified the ds-RNA-dependent protein kinase (PKR) as a key molecular target of 5-FU involved in apoptosis induction in human colon and breast cancer cell lines. PKR distribution and activation, apoptosis induction and cytotoxic effects were analyzed during 5-FU and 5-FU/IFNα treatment in several colon and breast cancer cell lines with different p53 status. PKR protein was activated by 5-FU treatment in a p53-independent manner, inducing phosphorylation of the protein synthesis translation initiation factor eIF-2α and cell death by apoptosis. Furthermore, PKR interference promoted a decreased response to 5-FU treatment and those cells were not affected by the synergistic antitumor activity of 5-FU/IFNα combination. These results, taken together, provide evidence that PKR is a key molecular target of 5-FU with potential relevance in the clinical use of this drug.