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Sample records for applications predicting drug-target

  1. Toward more realistic drug-target interaction predictions.

    PubMed

    Pahikkala, Tapio; Airola, Antti; Pietilä, Sami; Shakyawar, Sushil; Szwajda, Agnieszka; Tang, Jing; Aittokallio, Tero

    2015-03-01

    A number of supervised machine learning models have recently been introduced for the prediction of drug-target interactions based on chemical structure and genomic sequence information. Although these models could offer improved means for many network pharmacology applications, such as repositioning of drugs for new therapeutic uses, the prediction models are often being constructed and evaluated under overly simplified settings that do not reflect the real-life problem in practical applications. Using quantitative drug-target bioactivity assays for kinase inhibitors, as well as a popular benchmarking data set of binary drug-target interactions for enzyme, ion channel, nuclear receptor and G protein-coupled receptor targets, we illustrate here the effects of four factors that may lead to dramatic differences in the prediction results: (i) problem formulation (standard binary classification or more realistic regression formulation), (ii) evaluation data set (drug and target families in the application use case), (iii) evaluation procedure (simple or nested cross-validation) and (iv) experimental setting (whether training and test sets share common drugs and targets, only drugs or targets or neither). Each of these factors should be taken into consideration to avoid reporting overoptimistic drug-target interaction prediction results. We also suggest guidelines on how to make the supervised drug-target interaction prediction studies more realistic in terms of such model formulations and evaluation setups that better address the inherent complexity of the prediction task in the practical applications, as well as novel benchmarking data sets that capture the continuous nature of the drug-target interactions for kinase inhibitors. © The Author 2014. Published by Oxford University Press.

  2. A Review of Computational Methods for Predicting Drug Targets.

    PubMed

    Huang, Guohua; Yan, Fengxia; Tan, Duoduo

    2016-11-14

    Drug discovery and development is not only a time-consuming and labor-intensive process but also full of risk. Identifying targets of small molecules helps evaluate safety of drugs and find new therapeutic applications. The biotechnology measures a wide variety of properties related to drug and targets from different perspectives, thus generating a large body of data. This undoubtedly provides a solid foundation to explore relationships between drugs and targets. A large number of computational techniques have recently been developed for drug target prediction. In this paper, we summarize these computational methods and classify them into structure-based, molecular activity-based, side-effect-based and multi-omics-based predictions according to the used data for inference. The multi-omics-based methods are further grouped into two types: classifier-based and network-based predictions. Furthermore,the advantages and limitations of each type of methods are discussed. Finally, we point out the future directions of computational predictions for drug targets.

  3. An eigenvalue transformation technique for predicting drug-target interaction.

    PubMed

    Kuang, Qifan; Xu, Xin; Li, Rong; Dong, Yongcheng; Li, Yan; Huang, Ziyan; Li, Yizhou; Li, Menglong

    2015-09-09

    The prediction of drug-target interactions is a key step in the drug discovery process, which serves to identify new drugs or novel targets for existing drugs. However, experimental methods for predicting drug-target interactions are expensive and time-consuming. Therefore, the in silico prediction of drug-target interactions has recently attracted increasing attention. In this study, we propose an eigenvalue transformation technique and apply this technique to two representative algorithms, the Regularized Least Squares classifier (RLS) and the semi-supervised link prediction classifier (SLP), that have been used to predict drug-target interaction. The results of computational experiments with these techniques show that algorithms including eigenvalue transformation achieved better performance on drug-target interaction prediction than did the original algorithms. These findings show that eigenvalue transformation is an efficient technique for improving the performance of methods for predicting drug-target interactions. We further show that, in theory, eigenvalue transformation can be viewed as a feature transformation on the kernel matrix. Accordingly, although we only apply this technique to two algorithms in the current study, eigenvalue transformation also has the potential to be applied to other algorithms based on kernels.

  4. Predicting Drug-Target Interactions Using Drug-Drug Interactions

    PubMed Central

    Kim, Shinhyuk; Jin, Daeyong; Lee, Hyunju

    2013-01-01

    Computational methods for predicting drug-target interactions have become important in drug research because they can help to reduce the time, cost, and failure rates for developing new drugs. Recently, with the accumulation of drug-related data sets related to drug side effects and pharmacological data, it has became possible to predict potential drug-target interactions. In this study, we focus on drug-drug interactions (DDI), their adverse effects () and pharmacological information (), and investigate the relationship among chemical structures, side effects, and DDIs from several data sources. In this study, data from the STITCH database, from drugs.com, and drug-target pairs from ChEMBL and SIDER were first collected. Then, by applying two machine learning approaches, a support vector machine (SVM) and a kernel-based L1-norm regularized logistic regression (KL1LR), we showed that DDI is a promising feature in predicting drug-target interactions. Next, the accuracies of predicting drug-target interactions using DDI were compared to those obtained using the chemical structure and side effects based on the SVM and KL1LR approaches, showing that DDI was the data source contributing the most for predicting drug-target interactions. PMID:24278248

  5. Drug-target interaction prediction from PSSM based evolutionary information.

    PubMed

    Mousavian, Zaynab; Khakabimamaghani, Sahand; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-01-01

    The labor-intensive and expensive experimental process of drug-target interaction prediction has motivated many researchers to focus on in silico prediction, which leads to the helpful information in supporting the experimental interaction data. Therefore, they have proposed several computational approaches for discovering new drug-target interactions. Several learning-based methods have been increasingly developed which can be categorized into two main groups: similarity-based and feature-based. In this paper, we firstly use the bi-gram features extracted from the Position Specific Scoring Matrix (PSSM) of proteins in predicting drug-target interactions. Our results demonstrate the high-confidence prediction ability of the Bigram-PSSM model in terms of several performance indicators specifically for enzymes and ion channels. Moreover, we investigate the impact of negative selection strategy on the performance of the prediction, which is not widely taken into account in the other relevant studies. This is important, as the number of non-interacting drug-target pairs are usually extremely large in comparison with the number of interacting ones in existing drug-target interaction data. An interesting observation is that different levels of performance reduction have been attained for four datasets when we change the sampling method from the random sampling to the balanced sampling.

  6. DRUG TARGET PREDICTIONS BASED ON HETEROGENEOUS GRAPH INFERENCE

    PubMed Central

    Wang, Wenhui; Yang, Sen; Li, JING

    2013-01-01

    A key issue in drug development is to understand the hidden relationships among drugs and targets. Computational methods for novel drug target predictions can greatly reduce time and costs compared with experimental methods. In this paper, we propose a network based computational approach for novel drug and target association predictions. More specifically, a heterogeneous drug-target graph, which incorporates known drug-target interactions as well as drug-drug and target-target similarities, is first constructed. Based on this graph, a novel graph-based inference method is introduced. Compared with two state-of-the-art methods, large-scale cross-validation results indicate that the proposed method can greatly improve novel target predictions. PMID:23424111

  7. Gaussian interaction profile kernels for predicting drug-target interaction.

    PubMed

    van Laarhoven, Twan; Nabuurs, Sander B; Marchiori, Elena

    2011-11-01

    The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug-target pairs in current datasets are experimentally validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. We show that a simple machine learning method that uses the drug-target network as the only source of information is capable of predicting true interaction pairs with high accuracy. Specifically, we introduce interaction profiles of drugs (and of targets) in a network, which are binary vectors specifying the presence or absence of interaction with every target (drug) in that network. We define a kernel on these profiles, called the Gaussian Interaction Profile (GIP) kernel, and use a simple classifier, (kernel) Regularized Least Squares (RLS), for prediction drug-target interactions. We test comparatively the effectiveness of RLS with the GIP kernel on four drug-target interaction networks used in previous studies. The proposed algorithm achieves area under the precision-recall curve (AUPR) up to 92.7, significantly improving over results of state-of-the-art methods. Moreover, we show that using also kernels based on chemical and genomic information further increases accuracy, with a neat improvement on small datasets. These results substantiate the relevance of the network topology (in the form of interaction profiles) as source of information for predicting drug-target interactions. Software and Supplementary Material are available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2011/. tvanlaarhoven@cs.ru.nl; elenam@cs.ru.nl. Supplementary data are available at Bioinformatics online.

  8. Predicting drug-target interactions using restricted Boltzmann machines

    PubMed Central

    Wang, Yuhao; Zeng, Jianyang

    2013-01-01

    Motivation: In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. Results: We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Availability: Software and datasets are available

  9. Deep-Learning-Based Drug-Target Interaction Prediction.

    PubMed

    Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

    2017-04-07

    Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

  10. Predicting drug-target interactions using probabilistic matrix factorization.

    PubMed

    Cobanoglu, Murat Can; Liu, Chang; Hu, Feizhuo; Oltvai, Zoltán N; Bahar, Ivet

    2013-12-23

    Quantitative analysis of known drug-target interactions emerged in recent years as a useful approach for drug repurposing and assessing side effects. In the present study, we present a method that uses probabilistic matrix factorization (PMF) for this purpose, which is particularly useful for analyzing large interaction networks. DrugBank drugs clustered based on PMF latent variables show phenotypic similarity even in the absence of 3D shape similarity. Benchmarking computations show that the method outperforms those recently introduced provided that the input data set of known interactions is sufficiently large--which is the case for enzymes and ion channels, but not for G-protein coupled receptors (GPCRs) and nuclear receptors. Runs performed on DrugBank after hiding 70% of known interactions show that, on average, 88 of the top 100 predictions hit the hidden interactions. De novo predictions permit us to identify new potential interactions. Drug-target pairs implicated in neurobiological disorders are overrepresented among de novo predictions.

  11. Drug target prediction using adverse event report systems: a pharmacogenomic approach.

    PubMed

    Takarabe, Masataka; Kotera, Masaaki; Nishimura, Yosuke; Goto, Susumu; Yamanishi, Yoshihiro

    2012-09-15

    Unexpected drug activities derived from off-targets are usually undesired and harmful; however, they can occasionally be beneficial for different therapeutic indications. There are many uncharacterized drugs whose target proteins (including the primary target and off-targets) remain unknown. The identification of all potential drug targets has become an important issue in drug repositioning to reuse known drugs for new therapeutic indications. We defined pharmacological similarity for all possible drugs using the US Food and Drug Administration's (FDA's) adverse event reporting system (AERS) and developed a new method to predict unknown drug-target interactions on a large scale from the integration of pharmacological similarity of drugs and genomic sequence similarity of target proteins in the framework of a pharmacogenomic approach. The proposed method was applicable to a large number of drugs and it was useful especially for predicting unknown drug-target interactions that could not be expected from drug chemical structures. We made a comprehensive prediction for potential off-targets of 1874 drugs with known targets and potential target profiles of 2519 drugs without known targets, which suggests many potential drug-target interactions that were not predicted by previous chemogenomic or pharmacogenomic approaches. Softwares are available upon request. yamanishi@bioreg.kyushu-u.ac.jp Datasets and all results are available at http://cbio.ensmp.fr/~yyamanishi/aers/.

  12. DT-Web: a web-based application for drug-target interaction and drug combination prediction through domain-tuned network-based inference

    PubMed Central

    2015-01-01

    Background The identification of drug-target interactions (DTI) is a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Algorithms may aim to design new therapies based on a single approved drug or a combination of them. Recently, recommendation methods relying on network-based inference in connection with knowledge coming from the specific domain have been proposed. Description Here we propose a web-based interface to the DT-Hybrid algorithm, which applies a recommendation technique based on bipartite network projection implementing resources transfer within the network. This technique combined with domain-specific knowledge expressing drugs and targets similarity is used to compute recommendations for each drug. Our web interface allows the users: (i) to browse all the predictions inferred by the algorithm; (ii) to upload their custom data on which they wish to obtain a prediction through a DT-Hybrid based pipeline; (iii) to help in the early stages of drug combinations, repositioning, substitution, or resistance studies by finding drugs that can act simultaneously on multiple targets in a multi-pathway environment. Our system is periodically synchronized with DrugBank and updated accordingly. The website is free, open to all users, and available at http://alpha.dmi.unict.it/dtweb/. Conclusions Our web interface allows users to search and visualize information on drugs and targets eventually providing their own data to compute a list of predictions. The user can visualize information about the characteristics of each drug, a list of predicted and validated targets, associated enzymes and transporters. A table containing key information and GO classification allows the users to perform their own analysis on our data. A special interface for data submission allows the execution of a pipeline, based on DT-Hybrid, predicting new targets with the corresponding p

  13. DT-Web: a web-based application for drug-target interaction and drug combination prediction through domain-tuned network-based inference.

    PubMed

    Alaimo, Salvatore; Bonnici, Vincenzo; Cancemi, Damiano; Ferro, Alfredo; Giugno, Rosalba; Pulvirenti, Alfredo

    2015-01-01

    The identification of drug-target interactions (DTI) is a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Algorithms may aim to design new therapies based on a single approved drug or a combination of them. Recently, recommendation methods relying on network-based inference in connection with knowledge coming from the specific domain have been proposed. Here we propose a web-based interface to the DT-Hybrid algorithm, which applies a recommendation technique based on bipartite network projection implementing resources transfer within the network. This technique combined with domain-specific knowledge expressing drugs and targets similarity is used to compute recommendations for each drug. Our web interface allows the users: (i) to browse all the predictions inferred by the algorithm; (ii) to upload their custom data on which they wish to obtain a prediction through a DT-Hybrid based pipeline; (iii) to help in the early stages of drug combinations, repositioning, substitution, or resistance studies by finding drugs that can act simultaneously on multiple targets in a multi-pathway environment. Our system is periodically synchronized with DrugBank and updated accordingly. The website is free, open to all users, and available at http://alpha.dmi.unict.it/dtweb/. Our web interface allows users to search and visualize information on drugs and targets eventually providing their own data to compute a list of predictions. The user can visualize information about the characteristics of each drug, a list of predicted and validated targets, associated enzymes and transporters. A table containing key information and GO classification allows the users to perform their own analysis on our data. A special interface for data submission allows the execution of a pipeline, based on DT-Hybrid, predicting new targets with the corresponding p-values expressing the reliability of

  14. Weighted feature value based Drug Target Protein prediction.

    PubMed

    Hyun, Bo-ra; Jung, Hwiesung; Jang, Woo-Hyuk; Jung, Suk Hoon; Han, Dong-Soo

    2008-01-01

    Drug discovery is a long process in which only a few successful new therapeutic discoveries are made and identification of drug target candidate proteins requires considerable time and efforts. However, the accumulation of information on drugs has made it possible to devise new computational methods for classifying drug target candidates. In this paper, we devise a Drug Target Protein (DT-P) classification method by the summation of weighted features which is extracted from known DT-P. The method is validated using Bayesian decision theory and SVM, and it was revealed to achieve high specificity of 89.5% with 88% accuracy.

  15. Drug-targeting methodologies with applications: A review

    PubMed Central

    Kleinstreuer, Clement; Feng, Yu; Childress, Emily

    2014-01-01

    Targeted drug delivery to solid tumors is a very active research area, focusing mainly on improved drug formulation and associated best delivery methods/devices. Drug-targeting has the potential to greatly improve drug-delivery efficacy, reduce side effects, and lower the treatment costs. However, the vast majority of drug-targeting studies assume that the drug-particles are already at the target site or at least in its direct vicinity. In this review, drug-delivery methodologies, drug types and drug-delivery devices are discussed with examples in two major application areas: (1) inhaled drug-aerosol delivery into human lung-airways; and (2) intravascular drug-delivery for solid tumor targeting. The major problem addressed is how to deliver efficiently the drug-particles from the entry/infusion point to the target site. So far, most experimental results are based on animal studies. Concerning pulmonary drug delivery, the focus is on the pros and cons of three inhaler types, i.e., pressurized metered dose inhaler, dry powder inhaler and nebulizer, in addition to drug-aerosol formulations. Computational fluid-particle dynamics techniques and the underlying methodology for a smart inhaler system are discussed as well. Concerning intravascular drug-delivery for solid tumor targeting, passive and active targeting are reviewed as well as direct drug-targeting, using optimal delivery of radioactive microspheres to liver tumors as an example. The review concludes with suggestions for future work, considereing both pulmonary drug targeting and direct drug delivery to solid tumors in the vascular system. PMID:25516850

  16. Drug-target interaction prediction: databases, web servers and computational models.

    PubMed

    Chen, Xing; Yan, Chenggang Clarence; Zhang, Xiaotian; Zhang, Xu; Dai, Feng; Yin, Jian; Zhang, Yongdong

    2016-07-01

    Identification of drug-target interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drug-target interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug-target associations on a large scale. In this review, databases and web servers involved in drug-target identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drug-target interactions prediction, including network-based method, machine learning-based method and so on. Specially, for the machine learning-based method, much attention was paid to supervised and semi-supervised models, which have essential difference in the adoption of negative samples. Although significant improvements for drug-target interaction prediction have been obtained by many effective computational models, both network-based and machine learning-based methods have their disadvantages, respectively. Furthermore, we discuss the future directions of the network-based drug discovery and network approach for personalized drug discovery based on personalized medicine, genome sequencing, tumor clone-based network and cancer hallmark-based network. Finally, we discussed the new evaluation validation framework and the formulation of drug-target interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  17. Optimized shapes of magnetic arrays for drug targeting applications

    NASA Astrophysics Data System (ADS)

    Barnsley, Lester C.; Carugo, Dario; Stride, Eleanor

    2016-06-01

    Arrays of permanent magnet elements have been utilized as light-weight, inexpensive sources for applying external magnetic fields in magnetic drug targeting applications, but they are extremely limited in the range of depths over which they can apply useful magnetic forces. In this paper, designs for optimized magnet arrays are presented, which were generated using an optimization routine to maximize the magnetic force available from an arbitrary arrangement of magnetized elements, depending on a set of design parameters including the depth of targeting (up to 50 mm from the magnet) and direction of force required. A method for assembling arrays in practice is considered, quantifying the difficulty of assembly and suggesting a means for easing this difficulty without a significant compromise to the applied field or force. Finite element simulations of in vitro magnetic retention experiments were run to demonstrate the capability of a subset of arrays to retain magnetic microparticles against flow. The results suggest that, depending on the choice of array, a useful proportion of particles (more than 10% ) could be retained at flow velocities up to 100 mm s-1 or to depths as far as 50 mm from the magnet. Finally, the optimization routine was used to generate a design for a Halbach array optimized to deliver magnetic force to a depth of 50 mm inside the brain.

  18. A two-step similarity-based method for prediction of drug's target group.

    PubMed

    Chen, Lei; Zeng, Wei-Ming

    2013-03-01

    Determination of drug's target protein is very important for studying drug-target interaction network, while drug-target interaction network is a key area in the drug discovery pipeline. Thus correct prediction of drug's target protein is very helpful to promote the development of drug discovery. In this study, we developed a two-step similarity-based method to predict drug's target group. In each step, a similarity score (obtained by graph representation in the first step, and chemical functional group representation in the second step) was employed to make prediction. Since some drugs can target proteins distributing in more than one group of proteins, the method provided a series of candidate target groups for each drug. As a result, the first-order prediction accuracy on training set and test set were 79.01% and 76.43%, respectively, which were much higher than the success rate of a random guess. The results show that using graph representation to encode drug is a good choice in this area. We expect that this contribution will provide some help to understand drug-target interaction network.

  19. Comparative modeling: the state of the art and protein drug target structure prediction.

    PubMed

    Liu, Tianyun; Tang, Grace W; Capriotti, Emidio

    2011-07-01

    The goal of computational protein structure prediction is to provide three-dimensional (3D) structures with resolution comparable to experimental results. Comparative modeling, which predicts the 3D structure of a protein based on its sequence similarity to homologous structures, is the most accurate computational method for structure prediction. In the last two decades, significant progress has been made on comparative modeling methods. Using the large number of protein structures deposited in the Protein Data Bank (~65,000), automatic prediction pipelines are generating a tremendous number of models (~1.9 million) for sequences whose structures have not been experimentally determined. Accurate models are suitable for a wide range of applications, such as prediction of protein binding sites, prediction of the effect of protein mutations, and structure-guided virtual screening. In particular, comparative modeling has enabled structure-based drug design against protein targets with unknown structures. In this review, we describe the theoretical basis of comparative modeling, the available automatic methods and databases, and the algorithms to evaluate the accuracy of predicted structures. Finally, we discuss relevant applications in the prediction of important drug target proteins, focusing on the G protein-coupled receptor (GPCR) and protein kinase families.

  20. Genome evolution predicts genetic interactions in protein complexes and reveals cancer drug targets

    PubMed Central

    Lu, Xiaowen; Kensche, Philip R.; Huynen, Martijn A.; Notebaart, Richard A.

    2013-01-01

    Genetic interactions reveal insights into cellular function and can be used to identify drug targets. Here we construct a new model to predict negative genetic interactions in protein complexes by exploiting the evolutionary history of genes in parallel converging pathways in metabolism. We evaluate our model with protein complexes of Saccharomyces cerevisiae and show that the predicted protein pairs more frequently have a negative genetic interaction than random proteins from the same complex. Furthermore, we apply our model to human protein complexes to predict novel cancer drug targets, and identify 20 candidate targets with empirical support and 10 novel targets amenable to further experimental validation. Our study illustrates that negative genetic interactions can be predicted by systematically exploring genome evolution, and that this is useful to identify novel anti-cancer drug targets. PMID:23851603

  1. Some Remarks on Prediction of Drug-Target Interaction with Network Models.

    PubMed

    Zhang, Shao-Wu; Yan, Xiao-Ying

    2017-01-01

    System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Thus, it is highly desired to develop computational methods for efficiently and effectively analyzing and detecting new drug-target interaction pairs. With the explosive growth of different types of omics data, such as genome, pharmacology, phenotypic, and other kinds of molecular networks, numerous computational approaches have been developed to predict Drug-Target Interactions (DTI). In this review, we make a survey on the recent advances in predicting drug-target interaction with network-based models from the following aspects: i) Available public data sources and benchmark datasets; ii) Drug/target similarity metrics; iii) Network construction; iv) Common network algorithms; v) Performance comparison of existing network-based DTI predictors. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  2. Application of RNAi to Genomic Drug Target Validation in Schistosomes

    PubMed Central

    Guidi, Alessandra; Mansour, Nuha R.; Paveley, Ross A.; Carruthers, Ian M.; Besnard, Jérémy; Hopkins, Andrew L.; Gilbert, Ian H.; Bickle, Quentin D.

    2015-01-01

    Concerns over the possibility of resistance developing to praziquantel (PZQ), has stimulated efforts to develop new drugs for schistosomiasis. In addition to the development of improved whole organism screens, the success of RNA interference (RNAi) in schistosomes offers great promise for the identification of potential drug targets to initiate drug discovery. In this study we set out to contribute to RNAi based validation of putative drug targets. Initially a list of 24 target candidates was compiled based on the identification of putative essential genes in schistosomes orthologous of C. elegans essential genes. Knockdown of Calmodulin (Smp_026560.2) (Sm-Calm), that topped this list, produced a phenotype characterised by waves of contraction in adult worms but no phenotype in schistosomula. Knockdown of the atypical Protein Kinase C (Smp_096310) (Sm-aPKC) resulted in loss of viability in both schistosomula and adults and led us to focus our attention on other kinase genes that were identified in the above list and through whole organism screening of known kinase inhibitor sets followed by chemogenomic evaluation. RNAi knockdown of these kinase genes failed to affect adult worm viability but, like Sm-aPKC, knockdown of Polo-like kinase 1, Sm-PLK1 (Smp_009600) and p38-MAPK, Sm-MAPK p38 (Smp_133020) resulted in an increased mortality of schistosomula after 2-3 weeks, an effect more marked in the presence of human red blood cells (hRBC). For Sm-PLK-1 the same effects were seen with the specific inhibitor, BI2536, which also affected viable egg production in adult worms. For Sm-PLK-1 and Sm-aPKC the in vitro effects were reflected in lower recoveries in vivo. We conclude that the use of RNAi combined with culture with hRBC is a reliable method for evaluating genes important for larval development. However, in view of the slow manifestation of the effects of Sm-aPKC knockdown in adults and the lack of effects of Sm-PLK-1 and Sm-MAPK p38 on adult viability, these

  3. Drug target prediction using adverse event report systems: a pharmacogenomic approach

    PubMed Central

    Takarabe, Masataka; Kotera, Masaaki; Nishimura, Yosuke; Goto, Susumu; Yamanishi, Yoshihiro

    2012-01-01

    Motivation: Unexpected drug activities derived from off-targets are usually undesired and harmful; however, they can occasionally be beneficial for different therapeutic indications. There are many uncharacterized drugs whose target proteins (including the primary target and off-targets) remain unknown. The identification of all potential drug targets has become an important issue in drug repositioning to reuse known drugs for new therapeutic indications. Results: We defined pharmacological similarity for all possible drugs using the US Food and Drug Administration's (FDA's) adverse event reporting system (AERS) and developed a new method to predict unknown drug–target interactions on a large scale from the integration of pharmacological similarity of drugs and genomic sequence similarity of target proteins in the framework of a pharmacogenomic approach. The proposed method was applicable to a large number of drugs and it was useful especially for predicting unknown drug–target interactions that could not be expected from drug chemical structures. We made a comprehensive prediction for potential off-targets of 1874 drugs with known targets and potential target profiles of 2519 drugs without known targets, which suggests many potential drug–target interactions that were not predicted by previous chemogenomic or pharmacogenomic approaches. Availability: Softwares are available upon request. Contact: yamanishi@bioreg.kyushu-u.ac.jp Supplementary Information: Datasets and all results are available at http://cbio.ensmp.fr/~yyamanishi/aers/. PMID:22962489

  4. Predicting drug-target interactions through integrative analysis of chemogenetic assays in yeast.

    PubMed

    Heiskanen, Marja A; Aittokallio, Tero

    2013-04-05

    Chemical-genomic and genetic interaction profiling approaches are widely used to study mechanisms of drug action and resistance. However, there exist a number of scoring algorithms customized to different experimental assays, the relative performance of which remains poorly understood, especially with respect to different types of chemogenetic assays. Using yeast Saccharomyces cerevisiae as a test bed, we carried out a systematic evaluation among the main drug target analysis approaches in terms of predicting global drug-target interaction networks. We found drastic differences in their performance across different chemical-genomic assay types, such as those based on heterozygous and homozygous diploid or haploid deletion mutant libraries. Moreover, a relatively small overlap in the predicted targets was observed between those approaches that use either chemical-genomic screening alone or combined with genetic interaction profiling. A rank-based integration of the complementary scoring approaches led to improved overall performance, demonstrating that genetic interaction profiling provides added information on drug target prediction. Optimal performance was achieved when focusing specifically on the negative tail of the genetic interactions, suggesting that combining synthetic lethal interactions with chemical-genetic interactions provides highest information on drug-target interactions. A network view of rapamycin-interacting genes, pathways and complexes was used as an example to demonstrate the benefits of such integrated and optimized analysis of chemogenetic assays in yeast.

  5. Using compound similarity and functional domain composition for prediction of drug-target interaction networks.

    PubMed

    Chen, Lei; He, Zhi-Song; Huang, Tao; Cai, Yu-Dong

    2010-11-01

    Study of interactions between drugs and target proteins is an essential step in genomic drug discovery. It is very hard to determine the compound-protein interactions or drug-target interactions by experiment alone. As supplementary, effective prediction model using machine learning or data mining methods can provide much help. In this study, a prediction method based on Nearest Neighbor Algorithm and a novel metric, which was obtained by combining compound similarity and functional domain composition, was proposed. The target proteins were divided into the following groups: enzymes, ion channels, G protein-coupled receptors, and nuclear receptors. As a result, four predictors with the optimal parameters were established. The overall prediction accuracies, evaluated by jackknife cross-validation test, for four groups of target proteins are 90.23%, 94.74%, 97.80%, and 97.51%, respectively, indicating that compound similarity and functional domain composition are very effective to predict drug-target interaction networks.

  6. Predicting drug-target interactions by dual-network integrated logistic matrix factorization

    PubMed Central

    Hao, Ming; Bryant, Stephen H.; Wang, Yanli

    2017-01-01

    In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research. PMID:28079135

  7. Predicting drug-target interactions by dual-network integrated logistic matrix factorization

    NASA Astrophysics Data System (ADS)

    Hao, Ming; Bryant, Stephen H.; Wang, Yanli

    2017-01-01

    In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

  8. Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction

    PubMed Central

    2016-01-01

    De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function. Dedicated computational models for protein simulation and DTI prediction are crucial for speed and to reduce the costs associated with DTI identification. In this paper we present a computational pipeline that enables the discovery of putative leads for drug repositioning that can be applied to any microbial proteome, as long as the interactome of interest is at least partially known. Network metrics calculated for the interactome of the bacterial organism of interest were used to identify putative drug-targets. Then, a random forest classification model for DTI prediction was constructed using known DTI data from publicly available databases, resulting in an area under the ROC curve of 0.91 for classification of out-of-sampling data. A drug-target network was created by combining 3,081 unique ligands and the expected ten best drug targets. This network was used to predict new DTIs and to calculate the probability of the positive class, allowing the scoring of the predicted instances. Molecular docking experiments were performed on the best scoring DTI pairs and the results were compared with those of the same ligands with their original targets. The results obtained suggest that the proposed pipeline can be used in the identification of new leads for drug repositioning. The proposed classification model is available at http://bioinformatics.ua.pt/software/dtipred/. PMID:27893735

  9. A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data

    PubMed Central

    Xu, Xue; Li, Yan; Zhao, Huihui; Fang, Yupeng; Li, Xiuxiu; Zhou, Wei; Wang, Wei; Wang, Yonghua

    2012-01-01

    In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes. PMID:22666371

  10. A systematic prediction of drug-target interactions using molecular fingerprints and protein sequences.

    PubMed

    Huang, Yu-An; You, Zhu-Hong; Chen, Xing

    2016-11-21

    Drug-Target Interactions (DTI) play a crucial role in discovering new drug candidates and finding new proteins to target for drug development. Although the number of detected DTI obtained by high-throughput techniques has been increasing, the number of known DTI is still limited. On the other hand, the experimental methods for detecting the interactions among drugs and proteins are costly and inefficient. Therefore, computational approaches for predicting DTI are drawing increasing attention in recent years. In this paper, we report a novel computational model for predicting the DTI using extremely randomized trees model and protein amino acids information. More specifically, the protein sequence is represented as a Pseudo Substitution Matrix Representation (Pseudo-SMR) descriptor in which the influence of biological evolutionary information is retained. For the representation of drug molecules, a novel fingerprint feature vector is utilized to describe its substructure information. Then the DTI pair is characterized by concatenating the two vector spaces of protein sequence and drug substructure. Finally, the proposed method is explored for predicting the DTI on four benchmark datasets: Enzyme, Ion Channel, GPCRs and Nuclear Receptor. The experimental results demonstrate that this method achieves promising prediction accuracies of 89.85%, 87.87%, 82.99% and 81.67%, respectively. For further evaluation, we compared the performance of Extremely Randomized Trees model with that of the state-of-the-art Support Vector Machine classifier. And we also compared the proposed model with existing computational models, and confirmed 15 potential drug-target interactions by looking for existing databases. The experiment results show that the proposed method is feasible and promising for predicting drug-target interactions for new drug candidate screening based on sizeable features.

  11. The application of antitumor drug-targeting models on liver cancer.

    PubMed

    Yan, Yan; Chen, Ningbo; Wang, Yunbing; Wang, Ke

    2016-06-01

    Hepatocarcinoma animal models, such as the induced tumor model, transplanted tumor model, gene animal model, are significant experimental tools for the evaluation of targeting drug delivery system as well as the pre-clinical studies of liver cancer. The application of antitumor drug-targeting models not only furnishes similar biological characteristics to human liver cancer but also offers guarantee of pharmacokinetic indicators of the liver-targeting preparations. In this article, we have reviewed some kinds of antitumor drug-targeting models of hepatoma and speculated that the research on this field would be capable of attaining a deeper level and expecting a superior achievement in the future.

  12. Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features

    PubMed Central

    Shi, Xiao-He; Hu, Le-Le; Kong, Xiangyin; Cai, Yu-Dong; Chou, Kuo-Chen

    2010-01-01

    Background Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner. Methods/Principal Findings To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively. Conclusion/Significance Our results indicate that the network prediction system thus established is quite promising and encouraging. PMID:20300175

  13. SELF-BLM: Prediction of drug-target interactions via self-training SVM

    PubMed Central

    Keum, Jongsoo; Nam, Hojung

    2017-01-01

    Predicting drug-target interactions is important for the development of novel drugs and the repositioning of drugs. To predict such interactions, there are a number of methods based on drug and target protein similarity. Although these methods, such as the bipartite local model (BLM), show promise, they often categorize unknown interactions as negative interaction. Therefore, these methods are not ideal for finding potential drug-target interactions that have not yet been validated as positive interactions. Thus, here we propose a method that integrates machine learning techniques, such as self-training support vector machine (SVM) and BLM, to develop a self-training bipartite local model (SELF-BLM) that facilitates the identification of potential interactions. The method first categorizes unlabeled interactions and negative interactions among unknown interactions using a clustering method. Then, using the BLM method and self-training SVM, the unlabeled interactions are self-trained and final local classification models are constructed. When applied to four classes of proteins that include enzymes, G-protein coupled receptors (GPCRs), ion channels, and nuclear receptors, SELF-BLM showed the best performance for predicting not only known interactions but also potential interactions in three protein classes compare to other related studies. The implemented software and supporting data are available at https://github.com/GIST-CSBL/SELF-BLM. PMID:28192537

  14. Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection

    PubMed Central

    2010-01-01

    Background Leishmaniasis is a virulent parasitic infection that causes a worldwide disease burden. Most treatments have toxic side-effects and efficacy has decreased due to the emergence of resistant strains. The outlook is worsened by the absence of promising drug targets for this disease. We have taken a computational approach to the detection of new drug targets, which may become an effective strategy for the discovery of new drugs for this tropical disease. Results We have predicted the protein interaction network of Leishmania major by using three validated methods: PSIMAP, PEIMAP, and iPfam. Combining the results from these methods, we calculated a high confidence network (confidence score > 0.70) with 1,366 nodes and 33,861 interactions. We were able to predict the biological process for 263 interacting proteins by doing enrichment analysis of the clusters detected. Analyzing the topology of the network with metrics such as connectivity and betweenness centrality, we detected 142 potential drug targets after homology filtering with the human proteome. Further experiments can be done to validate these targets. Conclusion We have constructed the first protein interaction network of the Leishmania major parasite by using a computational approach. The topological analysis of the protein network enabled us to identify a set of candidate proteins that may be both (1) essential for parasite survival and (2) without human orthologs. These potential targets are promising for further experimental validation. This strategy, if validated, may augment established drug discovery methodologies, for this and possibly other tropical diseases, with a relatively low additional investment of time and resources. PMID:20875130

  15. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique.

    PubMed

    Hao, Ming; Wang, Yanli; Bryant, Stephen H

    2016-02-25

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision-recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets.

  16. Prediction of drug-target interaction by label propagation with mutual interaction information derived from heterogeneous network.

    PubMed

    Yan, Xiao-Ying; Zhang, Shao-Wu; Zhang, Song-Yao

    2016-02-01

    The identification of potential drug-target interaction pairs is very important, which is useful not only for providing greater understanding of protein function, but also for enhancing drug research, especially for drug function repositioning. Recently, numerous machine learning-based algorithms (e.g. kernel-based, matrix factorization-based and network-based inference methods) have been developed for predicting drug-target interactions. All these methods implicitly utilize the assumption that similar drugs tend to target similar proteins and yield better results for predicting interactions between drugs and target proteins. To further improve the accuracy of prediction, a new method of network-based label propagation with mutual interaction information derived from heterogeneous networks, namely LPMIHN, is proposed to infer the potential drug-target interactions. LPMIHN separately performs label propagation on drug and target similarity networks, but the initial label information of the target (or drug) network comes from the drug (or target) label network and the known drug-target interaction bipartite network. The independent label propagation on each similarity network explores the cluster structure in its network, and the label information from the other network is used to capture mutual interactions (bicluster structures) between the nodes in each pair of the similarity networks. As compared to other recent state-of-the-art methods on the four popular benchmark datasets of binary drug-target interactions and two quantitative kinase bioactivity datasets, LPMIHN achieves the best results in terms of AUC and AUPR. In addition, many of the promising drug-target pairs predicted from LPMIHN are also confirmed on the latest publicly available drug-target databases such as ChEMBL, KEGG, SuperTarget and Drugbank. These results demonstrate the effectiveness of our LPMIHN method, indicating that LPMIHN has a great potential for predicting drug-target interactions.

  17. Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity

    PubMed Central

    Wang, Kejian; Sun, Jiazhi; Zhou, Shufeng; Wan, Chunling; Qin, Shengying; Li, Can; He, Lin; Yang, Lun

    2013-01-01

    Small drug molecules usually bind to multiple protein targets or even unintended off-targets. Such drug promiscuity has often led to unwanted or unexplained drug reactions, resulting in side effects or drug repositioning opportunities. So it is always an important issue in pharmacology to identify potential drug-target interactions (DTI). However, DTI discovery by experiment remains a challenging task, due to high expense of time and resources. Many computational methods are therefore developed to predict DTI with high throughput biological and clinical data. Here, we initiatively demonstrate that the on-target and off-target effects could be characterized by drug-induced in vitro genomic expression changes, e.g. the data in Connectivity Map (CMap). Thus, unknown ligands of a certain target can be found from the compounds showing high gene-expression similarity to the known ligands. Then to clarify the detailed practice of CMap based DTI prediction, we objectively evaluate how well each target is characterized by CMap. The results suggest that (1) some targets are better characterized than others, so the prediction models specific to these well characterized targets would be more accurate and reliable; (2) in some cases, a family of ligands for the same target tend to interact with common off-targets, which may help increase the efficiency of DTI discovery and explain the mechanisms of complicated drug actions. In the present study, CMap expression similarity is proposed as a novel indicator of drug-target interactions. The detailed strategies of improving data quality by decreasing the batch effect and building prediction models are also effectively established. We believe the success in CMap can be further translated into other public and commercial data of genomic expression, thus increasing research productivity towards valid drug repositioning and minimal side effects. PMID:24244130

  18. Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets

    PubMed Central

    2012-01-01

    of proteins encoded by the 10 highest-confidence target genes, and by 15 genes with differential regulation in normal and cancer conditions, reveals 75% to be potential drug targets. Conclusions Our study represents a concrete application of gene regulatory network inference to ovarian cancer, demonstrating the complete cycle of computational systems biology research, from genome-scale data analysis via network inference, evaluation of methods, to the generation of novel testable hypotheses, their prioritization for experimental validation, and discovery of potential drug targets. PMID:22548828

  19. Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis

    PubMed Central

    2010-01-01

    Background Despite enormous efforts to combat malaria the disease still afflicts up to half a billion people each year of which more than one million die. Currently no approved vaccine is available and resistances to antimalarials are widely spread. Hence, new antimalarial drugs are urgently needed. Results Here, we present a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. We assembled a compartmentalized metabolic model and predicted life cycle stage specific metabolism with the help of a flux balance approach that integrates gene expression data. Predicted metabolite exchanges between parasite and host were found to be in good accordance with experimental findings when the parasite's metabolic network was embedded into that of its host (erythrocyte). Knock-out simulations identified 307 indispensable metabolic reactions within the parasite. 35 out of 57 experimentally demonstrated essential enzymes were recovered and another 16 enzymes, if additionally the assumption was made that nutrient uptake from the host cell is limited and all reactions catalyzed by the inhibited enzyme are blocked. This predicted set of putative drug targets, shown to be enriched with true targets by a factor of at least 2.75, was further analyzed with respect to homology to human enzymes, functional similarity to therapeutic targets in other organisms and their predicted potency for prophylaxis and disease treatment. Conclusions The results suggest that the set of essential enzymes predicted by our flux balance approach represents a promising starting point for further drug development. PMID:20807400

  20. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

    PubMed

    Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

  1. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

    NASA Astrophysics Data System (ADS)

    Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

  2. RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions using Drug Structure and Protein Sequence Information.

    PubMed

    Wang, Lei; You, Zhu-Hong; Chen, Xing; Yan, Xin; Liu, Gang; Zhang, Wei

    2016-11-14

    Identification of interaction between drugs and target proteins plays an important role in discovering new drug candidates. However, through the experimental method to identify the drug-target interactions remain to be extremely time-consuming, expensive and challenging even nowadays. Therefore, it is urgent to develop new computational methods to predict potential drug-target interactions (DTI). In this article, a novel computational model is developed for predicting potential drug-target interactions under the theory that each drug-target interaction pair can be represented by the structural properties from drugs and evolutionary information derived from proteins. Specifically, the protein sequences are encoded as Position-Specific Scoring Matrix (PSSM) descriptor which contains information of biological evolutionary and the drug molecules are encoded as fingerprint feature vector which represents the existence of certain functional groups or fragments. Four benchmark datasets involving enzymes, ion channels, GPCRs and nuclear receptors, are independently used for establishing predictive models with Rotation Forest (RF) model. The proposed method achieved the prediction accuracy of 91.3%, 89.1%, 84.1% and 71.1% for four datasets respectively. In order to make our method more persuasive, we compared our classifier with the state-of-the-art Support Vector Machine (SVM) classifier. We also compared the proposed method with other excellent methods. Experimental results demonstrate that the proposed method is effective in the prediction of DTI, and can provide assistance for new drug research and development.

  3. Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space.

    PubMed

    Peón, Antonio; Naulaerts, Stefan; Ballester, Pedro J

    2017-06-19

    Many computational methods to predict the macromolecular targets of small organic molecules have been presented to date. Despite progress, target prediction methods still have important limitations. For example, the most accurate methods implicitly restrict their predictions to a relatively small number of targets, are not systematically validated on drugs (whose targets are harder to predict than those of non-drug molecules) and often lack a reliability score associated with each predicted target. Here we present a systematic validation of ligand-centric target prediction methods on a set of clinical drugs. These methods exploit a knowledge-base covering 887,435 known ligand-target associations between 504,755 molecules and 4,167 targets. Based on this dataset, we provide a new estimate of the polypharmacology of drugs, which on average have 11.5 targets below IC50 10 µM. The average performance achieved across clinical drugs is remarkable (0.348 precision and 0.423 recall, with large drug-dependent variability), especially given the unusually large coverage of the target space. Furthermore, we show how a sparse ligand-target bioactivity matrix to retrospectively validate target prediction methods could underestimate prospective performance. Lastly, we present and validate a first-in-kind score capable of accurately predicting the reliability of target predictions.

  4. Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway

    NASA Astrophysics Data System (ADS)

    Huang, Lu; Jiang, Yuyang; Chen, Yuzong

    2017-01-01

    Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems.

  5. Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway

    PubMed Central

    Huang, Lu; Jiang, Yuyang; Chen, Yuzong

    2017-01-01

    Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems. PMID:28102344

  6. Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway.

    PubMed

    Huang, Lu; Jiang, Yuyang; Chen, Yuzong

    2017-01-19

    Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems.

  7. Magnetic microgels for drug targeting applications: Physical-chemical properties and cytotoxicity evaluation

    NASA Astrophysics Data System (ADS)

    Turcu, Rodica; Craciunescu, Izabell; Garamus, Vasil M.; Janko, Christina; Lyer, Stefan; Tietze, Rainer; Alexiou, Christoph; Vekas, Ladislau

    2015-04-01

    Magnetoresponsive microgels with high saturation magnetization values have been obtained by a strategy based on the miniemulsion method using high colloidal stability organic carrier ferrofluid as primary material. Hydrophobic nanoparticles Fe3O4/oleic acid are densely packed into well-defined spherical nanoparticle clusters coated with polymers with sizes in the range 40-350 nm. Physical-chemical characteristics of magnetic microgels were investigated by TEM, SAXS, XPS and VSM measurements with the focus on the structure-properties relationship. The impact of magnetic microgels loaded with anticancer drug mitoxantrone (MTO) on the non-adherent human T cell leukemia line Jurkat was investigated in multiparameter flow cytometry. We showed that both MTO and microgel-loaded MTO penetrate into cells and both induce apoptosis and later secondary necrosis in a time- and dose dependent manner. In contrast, microgels without MTO are not cytotoxic in the corresponding concentrations. Our results show that MTO-loaded microgels are promising structures for application in magnetic drug targeting.

  8. Halbach arrays consisting of cubic elements optimised for high field gradients in magnetic drug targeting applications.

    PubMed

    Barnsley, Lester C; Carugo, Dario; Owen, Joshua; Stride, Eleanor

    2015-11-07

    A key challenge in the development of magnetic drug targeting (MDT) as a clinically relevant technique is designing systems that can apply sufficient magnetic force to actuate magnetic drug carriers at useful tissue depths. In this study an optimisation routine was developed to generate designs of Halbach arrays consisting of multiple layers of high grade, cubic, permanent magnet elements, configured to deliver the maximum pull or push force at a position of interest between 5 and 50 mm from the array, resulting in arrays capable of delivering useful magnetic forces to depths past 20 mm. The optimisation routine utilises a numerical model of the magnetic field and force generated by an arbitrary configuration of magnetic elements. Simulated field and force profiles of optimised arrays were evaluated, also taking into account the forces required for assembling the array in practice. The resultant selection for the array, consisting of two layers, was then constructed and characterised to verify the simulations. Finally the array was utilised in a set of in vitro experiments to demonstrate its capacity to separate and retain microbubbles loaded with magnetic nanoparticles against a constant flow. The optimised designs are presented as light-weight, inexpensive options for applying high-gradient, external magnetic fields in MDT applications.

  9. In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences.

    PubMed

    Li, Zhengwei; Han, Pengyong; You, Zhu-Hong; Li, Xiao; Zhang, Yusen; Yu, Haiquan; Nie, Ru; Chen, Xing

    2017-09-11

    Analysis of drug-target interactions (DTIs) is of great importance in developing new drug candidates for known protein targets or discovering new targets for old drugs. However, the experimental approaches for identifying DTIs are expensive, laborious and challenging. In this study, we report a novel computational method for predicting DTIs using the highly discriminative information of drug-target interactions and our newly developed discriminative vector machine (DVM) classifier. More specifically, each target protein sequence is transformed as the position-specific scoring matrix (PSSM), in which the evolutionary information is retained; then the local binary pattern (LBP) operator is used to calculate the LBP histogram descriptor. For a drug molecule, a novel fingerprint representation is utilized to describe its chemical structure information representing existence of certain functional groups or fragments. When applying the proposed method to the four datasets (Enzyme, GPCR, Ion Channel and Nuclear Receptor) for predicting DTIs, we obtained good average accuracies of 93.16%, 89.37%, 91.73% and 92.22%, respectively. Furthermore, we compared the performance of the proposed model with that of the state-of-the-art SVM model and other previous methods. The achieved results demonstrate that our method is effective and robust and can be taken as a useful tool for predicting DTIs.

  10. Defining the Schistosoma haematobium kinome enables the prediction of essential kinases as anti-schistosome drug targets

    PubMed Central

    Stroehlein, Andreas J.; Young, Neil D.; Jex, Aaron R.; Sternberg, Paul W.; Tan, Patrick; Boag, Peter R.; Hofmann, Andreas; Gasser, Robin B.

    2015-01-01

    The blood fluke Schistosoma haematobium causes urogenital schistosomiasis, a neglected tropical disease (NTD) that affects more than 110 million people. Treating this disease by targeted or mass administration with a single chemical, praziquantel, carries the risk that drug resistance will develop in this pathogen. Therefore, there is an imperative to search for new drug targets in S. haematobium and other schistosomes. In this regard, protein kinases have potential, given their essential roles in biological processes and as targets for drugs already approved by the US Food and Drug Administration (FDA) for use in humans. In this context, we defined here the kinome of S. haematobium using a refined bioinformatic pipeline. We classified, curated and annotated predicted kinases, and assessed the developmental transcription profiles of kinase genes. Then, we prioritised a panel of kinases as potential drug targets and inferred chemicals that bind to them using an integrated bioinformatic pipeline. Most kinases of S. haematobium are very similar to those of its congener, S. mansoni, offering the prospect of designing chemicals that kill both species. Overall, this study provides a global insight into the kinome of S. haematobium and should assist the repurposing or discovery of drugs against schistosomiasis. PMID:26635209

  11. Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interaction of Natural Products to Enable of new Targeted Cancer Therapy.

    PubMed

    Fang, Jiansong; Wu, Zengrui; Cai, Chuipu; Wang, Qi; Tang, Yun; Cheng, Feixiong

    2017-09-28

    Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The new predicted targets of natural products (e.g., resveratrol, genistein and kaempherol) with high scores were validated by various literatures. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with the known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram and metformin) with new mechanism-of-action were validated by various published experimental evidences. In summary, this study offers powerful computational systems pharmacology approaches and tools for development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.

  12. Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework

    PubMed Central

    Yamanishi, Yoshihiro; Kotera, Masaaki; Kanehisa, Minoru; Goto, Susumu

    2010-01-01

    Motivation: In silico prediction of drug–target interactions from heterogeneous biological data is critical in the search for drugs and therapeutic targets for known diseases such as cancers. There is therefore a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently. Results: In this article, we investigate the relationship between the chemical space, the pharmacological space and the topology of drug–target interaction networks, and show that drug–target interactions are more correlated with pharmacological effect similarity than with chemical structure similarity. We then develop a new method to predict unknown drug–target interactions from chemical, genomic and pharmacological data on a large scale. The proposed method consists of two steps: (i) prediction of pharmacological effects from chemical structures of given compounds and (ii) inference of unknown drug–target interactions based on the pharmacological effect similarity in the framework of supervised bipartite graph inference. The originality of the proposed method lies in the prediction of potential pharmacological similarity for any drug candidate compounds and in the integration of chemical, genomic and pharmacological data in a unified framework. In the results, we make predictions for four classes of important drug–target interactions involving enzymes, ion channels, GPCRs and nuclear receptors. Our comprehensively predicted drug–target interaction networks enable us to suggest many potential drug–target interactions and to increase research productivity toward genomic drug discovery. Supplementary information: Datasets and all prediction results are available at http://cbio.ensmp.fr/~yyamanishi/pharmaco/. Availability: Softwares are available upon request. Contact: yoshihiro.yamanishi@ensmp.fr PMID:20529913

  13. Secretome Prediction of Two M. tuberculosis Clinical Isolates Reveals Their High Antigenic Density and Potential Drug Targets

    PubMed Central

    Cornejo-Granados, Fernanda; Zatarain-Barrón, Zyanya L.; Cantu-Robles, Vito A.; Mendoza-Vargas, Alfredo; Molina-Romero, Camilo; Sánchez, Filiberto; Del Pozo-Yauner, Luis; Hernández-Pando, Rogelio; Ochoa-Leyva, Adrián

    2017-01-01

    druggability analysis of the secretomes, we found potential drug targets such as cytochrome P450, thiol peroxidase, the Ag85C, and Ribonucleoside Reductase in the secreted proteins that could be used as drug targets for novel treatments against Tuberculosis. PMID:28223967

  14. Secretome Prediction of Two M. tuberculosis Clinical Isolates Reveals Their High Antigenic Density and Potential Drug Targets.

    PubMed

    Cornejo-Granados, Fernanda; Zatarain-Barrón, Zyanya L; Cantu-Robles, Vito A; Mendoza-Vargas, Alfredo; Molina-Romero, Camilo; Sánchez, Filiberto; Del Pozo-Yauner, Luis; Hernández-Pando, Rogelio; Ochoa-Leyva, Adrián

    2017-01-01

    analysis of the secretomes, we found potential drug targets such as cytochrome P450, thiol peroxidase, the Ag85C, and Ribonucleoside Reductase in the secreted proteins that could be used as drug targets for novel treatments against Tuberculosis.

  15. Multiple pathway assessment to predict anti-atherogenic efficacy of drugs targeting macrophages in atherosclerotic plaques.

    PubMed

    Alaarg, Amr; Zheng, Kang He; van der Valk, Fleur M; da Silva, Acarilia Eduardo; Versloot, Miranda; van Ufford, Linda C Quarles; Schulte, Dominik M; Storm, Gert; Metselaar, Josbert M; Stroes, Erik S G; Hamers, Anouk A J

    2016-07-01

    Macrophages play a central role in atherosclerosis development and progression, hence, targeting macrophage activity is considered an attractive therapeutic. Recently, we documented nanomedicinal delivery of the anti-inflammatory compound prednisolone to atherosclerotic plaque macrophages in patients, which did however not translate into therapeutic efficacy. This unanticipated finding calls for in-depth screening of drugs intended for targeting plaque macrophages. We evaluated the effect of several candidate drugs on macrophage activity, rating overall performance with respect to changes in cytokine release, oxidative stress, lipid handling, endoplasmic reticulum (ER) stress, and proliferation of macrophages. Using this in vitro approach, we observed that the anti-inflammatory effect of prednisolone was counterbalanced by multiple adverse effects on other key pathways. Conversely, pterostilbene, T0901317 and simvastatin had an overall anti-atherogenic effect on multiple pathways, suggesting their potential for liposomal delivery. This dedicated assay setup provides a framework for high-throughput assessment. Further in vivo studies are warranted to determine the predictive value of this macrophage-based screening approach and its potential value in nanomedicinal drug development for cardiovascular patients. Copyright © 2016 Elsevier Inc. All rights reserved.

  16. Towards New Drug Targets? Function Prediction of Putative Proteins of Neisseria meningitidis MC58 and Their Virulence Characterization

    PubMed Central

    Shahbaaz, Mohd.; Bisetty, Krishna; Ahmad, Faizan

    2015-01-01

    Abstract Neisseria meningitidis is a Gram-negative aerobic diplococcus, responsible for a variety of meningococcal diseases. The genome of N. meningitidis MC58 is comprised of 2114 genes that are translated into 1953 proteins. The 698 genes (∼35%) encode hypothetical proteins (HPs), because no experimental evidence of their biological functions are available. Analyses of these proteins are important to understand their functions in the metabolic networks and may lead to the discovery of novel drug targets against the infections caused by N. meningitidis. This study aimed at the identification and categorization of each HP present in the genome of N. meningitidis MC58 using computational tools. Functions of 363 proteins were predicted with high accuracy among the annotated set of HPs investigated. The reliably predicted 363 HPs were further grouped into 41 different classes of proteins, based on their possible roles in cellular processes such as metabolism, transport, and replication. Our studies revealed that 22 HPs may be involved in the pathogenesis caused by this microorganism. The top two HPs with highest virulence scores were subjected to molecular dynamics (MD) simulations to better understand their conformational behavior in a water environment. We also compared the MD simulation results with other virulent proteins present in N. meningitidis. This study broadens our understanding of the mechanistic pathways of pathogenesis, drug resistance, tolerance, and adaptability for host immune responses to N. meningitidis. PMID:26076386

  17. Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering.

    PubMed

    Shi, Jian-Yu; Yiu, Siu-Ming; Li, Yiming; Leung, Henry C M; Chin, Francis Y L

    2015-07-15

    Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are "missing" in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a "super-target" to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. Our approach is available at http://web.hku.hk/∼liym1018/projects/drug/drug.html or http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html.

  18. UniDrug-Target: A Computational Tool to Identify Unique Drug Targets in Pathogenic Bacteria

    PubMed Central

    Chanumolu, Sree Krishna; Rout, Chittaranjan; Chauhan, Rajinder S.

    2012-01-01

    Background Targeting conserved proteins of bacteria through antibacterial medications has resulted in both the development of resistant strains and changes to human health by destroying beneficial microbes which eventually become breeding grounds for the evolution of resistances. Despite the availability of more than 800 genomes sequences, 430 pathways, 4743 enzymes, 9257 metabolic reactions and protein (three-dimensional) 3D structures in bacteria, no pathogen-specific computational drug target identification tool has been developed. Methods A web server, UniDrug-Target, which combines bacterial biological information and computational methods to stringently identify pathogen-specific proteins as drug targets, has been designed. Besides predicting pathogen-specific proteins essentiality, chokepoint property, etc., three new algorithms were developed and implemented by using protein sequences, domains, structures, and metabolic reactions for construction of partial metabolic networks (PMNs), determination of conservation in critical residues, and variation analysis of residues forming similar cavities in proteins sequences. First, PMNs are constructed to determine the extent of disturbances in metabolite production by targeting a protein as drug target. Conservation of pathogen-specific protein's critical residues involved in cavity formation and biological function determined at domain-level with low-matching sequences. Last, variation analysis of residues forming similar cavities in proteins sequences from pathogenic versus non-pathogenic bacteria and humans is performed. Results The server is capable of predicting drug targets for any sequenced pathogenic bacteria having fasta sequences and annotated information. The utility of UniDrug-Target server was demonstrated for Mycobacterium tuberculosis (H37Rv). The UniDrug-Target identified 265 mycobacteria pathogen-specific proteins, including 17 essential proteins which can be potential drug targets. Conclusions

  19. GWAS and drug targets

    PubMed Central

    2014-01-01

    Background Genome wide association studies (GWAS) have revealed a large number of links between genome variation and complex disease. Among other benefits, it is expected that these insights will lead to new therapeutic strategies, particularly the identification of new drug targets. In this paper, we evaluate the power of GWAS studies to find drug targets by examining how many existing drug targets have been directly 'rediscovered' by this technique, and the extent to which GWAS results may be leveraged by network information to discover known and new drug targets. Results We find that only a very small fraction of drug targets are directly detected in the relevant GWAS studies. We investigate two possible explanations for this observation. First, we find evidence of negative selection acting on drug target genes as a consequence of strong coupling with the disease phenotype, so reducing the incidence of SNPs linked to the disease. Second, we find that GWAS genes are substantially longer on average than drug targets and than all genes, suggesting there is a length related bias in GWAS results. In spite of the low direct relationship between drug targets and GWAS reported genes, we found these two sets of genes are closely coupled in the human protein network. As a consequence, machine-learning methods are able to recover known drug targets based on network context and the set of GWAS reported genes for the same disease. We show the approach is potentially useful for identifying drug repurposing opportunities. Conclusions Although GWA studies do not directly identify most existing drug targets, there are several reasons to expect that new targets will nevertheless be discovered using these data. Initial results on drug repurposing studies using network analysis are encouraging and suggest directions for future development. PMID:25057111

  20. Review of QSAR models for enzyme classes of drug targets: Theoretical background and applications in parasites, hosts, and other organisms.

    PubMed

    Concu, Riccardo; Podda, Gianni; Ubeira, Florencio M; González-Díaz, Humberto

    2010-01-01

    The number of protein 3D structures without function annotation in Protein Data Bank (PDB) has been steadily increased. Many of these proteins are relevant for Pharmaceutical Design because they may be enzymes of different classes that could become drug targets. This fact has led in turn to an increment of demand for theoretical models to give a quick characterization of these proteins. In this work, we present a review and discussion of Alignment-Free Methods (AFMs) for fast prediction of the Enzyme Classification (EC) number from structural patterns. We referred to both methods based on linear techniques such as Linear Discriminant Analysis (LDA) and/or non-linear models like Artificial Neural Networks (ANN) or Support Vector Machine (SVM) in order to compare linear vs. non-linear classifiers. We also detected which of these models have been implemented as Web Servers free to the public and compiled a list of some of these web sites. For instance, we reviewed the servers implemented at portal Bio-AIMS (http://miaja.tic.udc.es/Bio-AIMS/EnzClassPred.php) and the server EzyPred (http://www.csbio.sjtu.edu.cn/bioinf/EzyPred/).

  1. The application of tetracyclineregulated gene expression systems in the validation of novel drug targets in Mycobacterium tuberculosis

    PubMed Central

    Evans, Joanna C.; Mizrahi, Valerie

    2015-01-01

    Although efforts to identify novel therapies for the treatment of tuberculosis have led to the identification of several promising drug candidates, the identification of high-quality hits from conventional whole-cell screens remains disappointingly low. The elucidation of the genome sequence of Mycobacterium tuberculosis (Mtb) facilitated a shift to target-based approaches to drug design but these efforts have proven largely unsuccessful. More recently, regulated gene expression systems that enable dose-dependent modulation of gene expression have been applied in target validation to evaluate the requirement of individual genes for the growth of Mtb both in vitro and in vivo. Notably, these systems can also provide a measure of the extent to which putative targets must be depleted in order to manifest a growth inhibitory phenotype. Additionally, the successful implementation of Mtb strains engineered to under-express specific molecular targets in whole-cell screens has enabled the simultaneous identification of cell-permeant inhibitors with defined mechanisms of action. Here, we review the application of tetracycline-regulated gene expression systems in the validation of novel drug targets in Mtb, highlighting both the strengths and limitations associated with this approach to target validation. PMID:26300875

  2. Ligand efficiency-based support vector regression models for predicting bioactivities of ligands to drug target proteins.

    PubMed

    Sugaya, Nobuyoshi

    2014-10-27

    The concept of ligand efficiency (LE) indices is widely accepted throughout the drug design community and is frequently used in a retrospective manner in the process of drug development. For example, LE indices are used to investigate LE optimization processes of already-approved drugs and to re-evaluate hit compounds obtained from structure-based virtual screening methods and/or high-throughput experimental assays. However, LE indices could also be applied in a prospective manner to explore drug candidates. Here, we describe the construction of machine learning-based regression models in which LE indices are adopted as an end point and show that LE-based regression models can outperform regression models based on pIC50 values. In addition to pIC50 values traditionally used in machine learning studies based on chemogenomics data, three representative LE indices (ligand lipophilicity efficiency (LLE), binding efficiency index (BEI), and surface efficiency index (SEI)) were adopted, then used to create four types of training data. We constructed regression models by applying a support vector regression (SVR) method to the training data. In cross-validation tests of the SVR models, the LE-based SVR models showed higher correlations between the observed and predicted values than the pIC50-based models. Application tests to new data displayed that, generally, the predictive performance of SVR models follows the order SEI > BEI > LLE > pIC50. Close examination of the distributions of the activity values (pIC50, LLE, BEI, and SEI) in the training and validation data implied that the performance order of the SVR models may be ascribed to the much higher diversity of the LE-based training and validation data. In the application tests, the LE-based SVR models can offer better predictive performance of compound-protein pairs with a wider range of ligand potencies than the pIC50-based models. This finding strongly suggests that LE-based SVR models are better than pIC50-based

  3. Training based on ligand efficiency improves prediction of bioactivities of ligands and drug target proteins in a machine learning approach.

    PubMed

    Sugaya, Nobuyoshi

    2013-10-28

    Machine learning methods based on ligand-protein interaction data in bioactivity databases are one of the current strategies for efficiently finding novel lead compounds as the first step in the drug discovery process. Although previous machine learning studies have succeeded in predicting novel ligand-protein interactions with high performance, all of the previous studies to date have been heavily dependent on the simple use of raw bioactivity data of ligand potencies measured by IC50, EC50, K(i), and K(d) deposited in databases. ChEMBL provides us with a unique opportunity to investigate whether a machine-learning-based classifier created by reflecting ligand efficiency other than the IC50, EC50, K(i), and Kd values can also offer high predictive performance. Here we report that classifiers created from training data based on ligand efficiency show higher performance than those from data based on IC50 or K(i) values. Utilizing GPCRSARfari and KinaseSARfari databases in ChEMBL, we created IC50- or K(i)-based training data and binding efficiency index (BEI) based training data then constructed classifiers using support vector machines (SVMs). The SVM classifiers from the BEI-based training data showed slightly higher area under curve (AUC), accuracy, sensitivity, and specificity in the cross-validation tests. Application of the classifiers to the validation data demonstrated that the AUCs and specificities of the BEI-based classifiers dramatically increased in comparison with the IC50- or K(i)-based classifiers. The improvement of the predictive power by the BEI-based classifiers can be attributed to (i) the more separated distributions of positives and negatives, (ii) the higher diversity of negatives in the BEI-based training data in a feature space of SVMs, and (iii) a more balanced number of positives and negatives in the BEI-based training data. These results strongly suggest that training data based on ligand efficiency as well as data based on classical IC50

  4. Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory.

    PubMed

    Durán, Claudio; Daminelli, Simone; Thomas, Josephine M; Haupt, V Joachim; Schroeder, Michael; Cannistraci, Carlo Vittorio

    2017-04-26

    The bipartite network representation of the drug-target interactions (DTIs) in a biosystem enhances understanding of the drugs' multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared-using standard and innovative validation frameworks-with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory-initially detected in brain-network topological self-organization and afterwards generalized to any complex network-is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug-target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering. © The Author 2017. Published by Oxford University Press.

  5. Predictive systems biology approach to broad-spectrum, host-directed drug target discovery in infectious diseases.

    PubMed

    Felciano, Ramon M; Bavari, Sina; Richards, Daniel R; Billaud, Jean-Noel; Warren, Travis; Panchal, Rekha; Krämer, Andreas

    2013-01-01

    Knowledge of immune system and host-pathogen pathways can inform development of targeted therapies and molecular diagnostics based on a mechanistic understanding of disease pathogenesis and the host response. We investigated the feasibility of rapid target discovery for novel broad-spectrum molecular therapeutics through comprehensive systems biology modeling and analysis of pathogen and host-response pathways and mechanisms. We developed a system to identify and prioritize candidate host targets based on strength of mechanistic evidence characterizing the role of the target in pathogenesis and tractability desiderata that include optimal delivery of new indications through potential repurposing of existing compounds or therapeutics. Empirical validation of predicted targets in cellular and mouse model systems documented an effective target prediction rate of 34%, suggesting that such computational discovery approaches should be part of target discovery efforts in operational clinical or biodefense research initiatives. We describe our target discovery methodology, technical implementation, and experimental results. Our work demonstrates the potential for in silico pathway models to enable rapid, systematic identification and prioritization of novel targets against existing or emerging biological threats, thus accelerating drug discovery and medical countermeasures research.

  6. In Silico Identification of Proteins Associated with Drug-induced Liver Injury Based on the Prediction of Drug-target Interactions.

    PubMed

    Ivanov, Sergey; Semin, Maxim; Lagunin, Alexey; Filimonov, Dmitry; Poroikov, Vladimir

    2017-07-01

    Drug-induced liver injury (DILI) is the leading cause of acute liver failure as well as one of the major reasons for drug withdrawal from clinical trials and the market. Elucidation of molecular interactions associated with DILI may help to detect potentially hazardous pharmacological agents at the early stages of drug development. The purpose of our study is to investigate which interactions with specific human protein targets may cause DILI. Prediction of interactions with 1534 human proteins was performed for the dataset with information about 699 drugs, which were divided into three categories of DILI: severe (178 drugs), moderate (310 drugs) and without DILI (211 drugs). Based on the comparison of drug-target interactions predicted for different drugs' categories and interpretation of those results using clustering, Gene Ontology, pathway and gene expression analysis, we identified 61 protein targets associated with DILI. Most of the revealed proteins were linked with hepatocytes' death caused by disruption of vital cellular processes, as well as the emergence of inflammation in the liver. It was found that interaction of a drug with the identified targets is the essential molecular mechanism of the severe DILI for the most of the considered pharmaceuticals. Thus, pharmaceutical agents interacting with many of the identified targets may be considered as candidates for filtering out at the early stages of drug research. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. Chloride channels as drug targets

    PubMed Central

    Verkman, Alan S.; Galietta, Luis J. V.

    2013-01-01

    Chloride channels represent a relatively under-explored target class for drug discovery as elucidation of their identity and physiological roles has lagged behind that of many other drug targets. Chloride channels are involved in a wide range of biological functions, including epithelial fluid secretion, cell-volume regulation, neuroexcitation, smooth-muscle contraction and acidification of intracellular organelles. Mutations in several chloride channels cause human diseases, including cystic fibrosis, macular degeneration, myotonia, kidney stones, renal salt wasting and hyperekplexia. Chloride-channel modulators have potential applications in the treatment of some of these disorders, as well as in secretory diarrhoeas, polycystic kidney disease, osteoporosis and hypertension. Modulators of GABAA (γ-aminobutyric acid A) receptor chloride channels are in clinical use and several small-molecule chloride-channel modulators are in preclinical development and clinical trials. Here, we discuss the broad opportunities that remain in chloride-channel-based drug discovery. PMID:19153558

  8. Properties of Protein Drug Target Classes

    PubMed Central

    Bull, Simon C.; Doig, Andrew J.

    2015-01-01

    Accurate identification of drug targets is a crucial part of any drug development program. We mined the human proteome to discover properties of proteins that may be important in determining their suitability for pharmaceutical modulation. Data was gathered concerning each protein’s sequence, post-translational modifications, secondary structure, germline variants, expression profile and drug target status. The data was then analysed to determine features for which the target and non-target proteins had significantly different values. This analysis was repeated for subsets of the proteome consisting of all G-protein coupled receptors, ion channels, kinases and proteases, as well as proteins that are implicated in cancer. Machine learning was used to quantify the proteins in each dataset in terms of their potential to serve as a drug target. This was accomplished by first inducing a random forest that could distinguish between its targets and non-targets, and then using the random forest to quantify the drug target likeness of the non-targets. The properties that can best differentiate targets from non-targets were primarily those that are directly related to a protein’s sequence (e.g. secondary structure). Germline variants, expression levels and interactions between proteins had minimal discriminative power. Overall, the best indicators of drug target likeness were found to be the proteins’ hydrophobicities, in vivo half-lives, propensity for being membrane bound and the fraction of non-polar amino acids in their sequences. In terms of predicting potential targets, datasets of proteases, ion channels and cancer proteins were able to induce random forests that were highly capable of distinguishing between targets and non-targets. The non-target proteins predicted to be targets by these random forests comprise the set of the most suitable potential future drug targets, and should therefore be prioritised when building a drug development programme. PMID

  9. Drug Target Protein-Protein Interaction Networks: A Systematic Perspective.

    PubMed

    Feng, Yanghe; Wang, Qi; Wang, Tengjiao

    2017-01-01

    The identification and validation of drug targets are crucial in biomedical research and many studies have been conducted on analyzing drug target features for getting a better understanding on principles of their mechanisms. But most of them are based on either strong biological hypotheses or the chemical and physical properties of those targets separately. In this paper, we investigated three main ways to understand the functional biomolecules based on the topological features of drug targets. There are no significant differences between targets and common proteins in the protein-protein interactions network, indicating the drug targets are neither hub proteins which are dominant nor the bridge proteins. According to some special topological structures of the drug targets, there are significant differences between known targets and other proteins. Furthermore, the drug targets mainly belong to three typical communities based on their modularity. These topological features are helpful to understand how the drug targets work in the PPI network. Particularly, it is an alternative way to predict potential targets or extract nontargets to test a new drug target efficiently and economically. By this way, a drug target's homologue set containing 102 potential target proteins is predicted in the paper.

  10. iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach.

    PubMed

    Xiao, Xuan; Min, Jian-Liang; Lin, Wei-Zhong; Liu, Zi; Cheng, Xiang; Chou, Kuo-Chen

    2015-01-01

    Information about the interactions of drug compounds with proteins in cellular networking is very important for drug development. Unfortunately, all the existing predictors for identifying drug-protein interactions were trained by a skewed benchmark data-set where the number of non-interactive drug-protein pairs is overwhelmingly larger than that of the interactive ones. Using this kind of highly unbalanced benchmark data-set to train predictors would lead to the outcome that many interactive drug-protein pairs might be mispredicted as non-interactive. Since the minority interactive pairs often contain the most important information for drug design, it is necessary to minimize this kind of misprediction. In this study, we adopted the neighborhood cleaning rule and synthetic minority over-sampling technique to treat the skewed benchmark datasets and balance the positive and negative subsets. The new benchmark datasets thus obtained are called the optimized benchmark datasets, based on which a new predictor called iDrug-Target was developed that contains four sub-predictors: iDrug-GPCR, iDrug-Chl, iDrug-Ezy, and iDrug-NR, specialized for identifying the interactions of drug compounds with GPCRs (G-protein-coupled receptors), ion channels, enzymes, and NR (nuclear receptors), respectively. Rigorous cross-validations on a set of experiment-confirmed datasets have indicated that these new predictors remarkably outperformed the existing ones for the same purpose. To maximize users' convenience, a public accessible Web server for iDrug-Target has been established at http://www.jci-bioinfo.cn/iDrug-Target/ , by which users can easily get their desired results. It has not escaped our notice that the aforementioned strategy can be widely used in many other areas as well.

  11. Identification of human drug targets using machine-learning algorithms.

    PubMed

    Kumari, Priyanka; Nath, Abhigyan; Chaube, Radha

    2015-01-01

    Identification of potential drug targets is a crucial task in the drug-discovery pipeline. Successful identification of candidate drug targets in entire genomes is very useful, and computational prediction methods can speed up this process. In the current work we have developed a sequence-based prediction method for the successful identification and discrimination of human drug target proteins, from human non-drug target proteins. The training features include sequence-based features, such as amino acid composition, amino acid property group composition, and dipeptide composition for generating predictive models. The classification of human drug target proteins presents a classic example of class imbalance. We have addressed this issue by using SMOTE (Synthetic Minority Over-sampling Technique) as a preprocessing step, for balancing the training data with a ratio of 1:1 between drug targets (minority samples) and non-drug targets (majority samples). Using ensemble classification learning method-Rotation Forest and ReliefF feature-selection technique for selecting the optimal subset of salient features, the best model with selected features can achieve 87.1% sensitivity, 83.6% specificity, and 85.3% accuracy, with 0.71 Matthews correlation coefficient (mcc) on a tenfold stratified cross-validation test. The subset of identified optimal features may help in assessing the compositional patterns in human drug targets. For further validation, using a rigorous leave-one-out cross-validation test, the model achieved 88.1% sensitivity, 83.0% specificity, 85.5% accuracy, and 0.712 mcc. The proposed method was tested on a second dataset, for which the current pipeline gave promising results. We suggest that the present approach can be applied successfully as a complementary tool to existing methods for novel drug target prediction. Copyright © 2014 Elsevier Ltd. All rights reserved.

  12. Drug-Target Kinetics in Drug Discovery.

    PubMed

    Tonge, Peter J

    2017-07-14

    The development of therapies for the treatment of neurological cancer faces a number of major challenges including the synthesis of small molecule agents that can penetrate the blood-brain barrier (BBB). Given the likelihood that in many cases drug exposure will be lower in the CNS than in systemic circulation, it follows that strategies should be employed that can sustain target engagement at low drug concentration. Time dependent target occupancy is a function of both the drug and target concentration as well as the thermodynamic and kinetic parameters that describe the binding reaction coordinate, and sustained target occupancy can be achieved through structural modifications that increase target (re)binding and/or that decrease the rate of drug dissociation. The discovery and deployment of compounds with optimized kinetic effects requires information on the structure-kinetic relationships that modulate the kinetics of binding, and the molecular factors that control the translation of drug-target kinetics to time-dependent drug activity in the disease state. This Review first introduces the potential benefits of drug-target kinetics, such as the ability to delineate both thermodynamic and kinetic selectivity, and then describes factors, such as target vulnerability, that impact the utility of kinetic selectivity. The Review concludes with a description of a mechanistic PK/PD model that integrates drug-target kinetics into predictions of drug activity.

  13. Chemical proteomics: terra incognita for novel drug target profiling

    PubMed Central

    Huang, Fuqiang; Zhang, Boya; Zhou, Shengtao; Zhao, Xia; Bian, Ce; Wei, Yuquan

    2012-01-01

    The growing demand for new therapeutic strategies in the medical and pharmaceutic fields has resulted in a pressing need for novel druggable targets. Paradoxically, however, the targets of certain drugs that are already widely used in clinical practice have largely not been annotated. Because the pharmacologic effects of a drug can only be appreciated when its interactions with cellular components are clearly delineated, an integrated deconvolution of drug-target interactions for each drug is necessary. The emerging field of chemical proteomics represents a powerful mass spectrometry (MS)-based affinity chromatography approach for identifying proteome-wide small molecule-protein interactions and mapping these interactions to signaling and metabolic pathways. This technique could comprehensively characterize drug targets, profile the toxicity of known drugs, and identify possible off-target activities. With the use of this technique, candidate drug molecules could be optimized, and predictable side effects might consequently be avoided. Herein, we provide a holistic overview of the major chemical proteomic approaches and highlight recent advances in this area as well as its potential applications in drug discovery. PMID:22640626

  14. Fluid mechanics aspects of magnetic drug targeting.

    PubMed

    Odenbach, Stefan

    2015-10-01

    Experiments and numerical simulations using a flow phantom for magnetic drug targeting have been undertaken. The flow phantom is a half y-branched tube configuration where the main tube represents an artery from which a tumour-supplying artery, which is simulated by the side branch of the flow phantom, branches off. In the experiments a quantification of the amount of magnetic particles targeted towards the branch by a magnetic field applied via a permanent magnet is achieved by impedance measurement using sensor coils. Measuring the targeting efficiency, i.e. the relative amount of particles targeted to the side branch, for different field configurations one obtains targeting maps which combine the targeting efficiency with the magnetic force densities in characteristic points in the flow phantom. It could be shown that targeting efficiency depends strongly on the magnetic field configuration. A corresponding numerical model has been set up, which allows the simulation of targeting efficiency for variable field configuration. With this simulation good agreement of targeting efficiency with experimental data has been found. Thus, the basis has been laid for future calculations of optimal field configurations in clinical applications of magnetic drug targeting. Moreover, the numerical model allows the variation of additional parameters of the drug targeting process and thus an estimation of the influence, e.g. of the fluid properties on the targeting efficiency. Corresponding calculations have shown that the non-Newtonian behaviour of the fluid will significantly influence the targeting process, an aspect which has to be taken into account, especially recalling the fact that the viscosity of magnetic suspensions depends strongly on the magnetic field strength and the mechanical load.

  15. Explicit Drug Re-positioning: Predicting Novel Drug-Target Interactions of the Shelved Molecules with QM/MM Based Approaches.

    PubMed

    Omer, Ankur; Suryanarayanan, Venkatesan; Selvaraj, Chandrabose; Singh, Sanjeev Kumar; Singh, Poonam

    2015-01-01

    With the demand to enhance the speed of the drug discovery process there has been an increased usage of computational approaches in drug discovery studies. However because of their probabilistic outcomes, the challenge is to exactly mimic the natural environment which can provide the exact charge polarization effect while estimating the binding energy between protein and ligand. There has been a large number of scoring functions from simple one to the complex one available for estimating binding energy. The quantum mechanics/molecular mechanics (QM/MM) hybrid approach has been the preferred choice of interest since last decade for modeling reactions in biomolecular systems. The application of QM/MM approach has been expanded right from rescoring the already known complexes and depicting the correct position of some novel molecule to ranking a large number of molecules. It is expected that the application of QM/MM-based scoring will grow in all areas of drug discovery. However, the most promising area will be its application in repositioning, that is, assigning novel functions or targets to the already existing drugs, as this would stop the rising attrition rates as well as reduce the overall time and cost of drug discovery procedure. © 2015 Elsevier Inc. All rights reserved.

  16. Homodimeric enzymes as drug targets.

    PubMed

    Cardinale, D; Salo-Ahen, O M H; Ferrari, S; Ponterini, G; Cruciani, G; Carosati, E; Tochowicz, A M; Mangani, S; Wade, R C; Costi, M P

    2010-01-01

    Many enzymes and proteins are regulated by their quaternary structure and/or by their association in homo- and/or hetero-oligomer complexes. Thus, these protein-protein interactions can be good targets for blocking or modulating protein function therapeutically. The large number of oligomeric structures in the Protein Data Bank (http://www.rcsb.org/) reflects growing interest in proteins that function as multimeric complexes. In this review, we consider the particular case of homodimeric enzymes as drug targets. There is intense interest in drugs that inhibit dimerization of a functionally obligate homodimeric enzyme. Because amino acid conservation within enzyme interfaces is often low compared to conservation in active sites, it may be easier to achieve drugs that target protein interfaces selectively and specifically. Two main types of dimerization inhibitors have been developed: peptides or peptidomimetics based on sequences involved in protein-protein interactions, and small molecules that act at hot spots in protein-protein interfaces. Examples include inhibitors of HIV protease and HIV integrase. Studying the mechanisms of action and locating the binding sites of such inhibitors requires different techniques for different proteins. For some enzymes, ligand binding is only detectable in vivo or after unfolding of the complexes. Here, we review the structural features of dimeric enzymes and give examples of inhibition through interference in dimer stability. Several techniques for studying these complex phenomena will be presented.

  17. Target-based drug discovery for [Formula: see text]-globin disorders: drug target prediction using quantitative modeling with hybrid functional Petri nets.

    PubMed

    Mehraei, Mani; Bashirov, Rza; Tüzmen, Şükrü

    2016-10-01

    Recent molecular studies provide important clues into treatment of [Formula: see text]-thalassemia, sickle-cell anaemia and other [Formula: see text]-globin disorders revealing that increased production of fetal hemoglobin, that is normally suppressed in adulthood, can ameliorate the severity of these diseases. In this paper, we present a novel approach for drug prediction for [Formula: see text]-globin disorders. Our approach is centered upon quantitative modeling of interactions in human fetal-to-adult hemoglobin switch network using hybrid functional Petri nets. In accordance with the reverse pharmacology approach, we pose a hypothesis regarding modulation of specific protein targets that induce [Formula: see text]-globin and consequently fetal hemoglobin. Comparison of simulation results for the proposed strategy with the ones obtained for already existing drugs shows that our strategy is the optimal as it leads to highest level of [Formula: see text]-globin induction and thereby has potential beneficial therapeutic effects on [Formula: see text]-globin disorders. Simulation results enable verification of model coherence demonstrating that it is consistent with qPCR data available for known strategies and/or drugs.

  18. Uncovering pharmacological mechanisms of Wu-tou decoction acting on rheumatoid arthritis through systems approaches: drug-target prediction, network analysis and experimental validation

    PubMed Central

    Zhang, Yanqiong; Bai, Ming; Zhang, Bo; Liu, Chunfang; Guo, Qiuyan; Sun, Yanqun; Wang, Danhua; Wang, Chao; Jiang, Yini; Lin, Na; Li, Shao

    2015-01-01

    Wu-tou decoction (WTD) has been extensively used for the treatment of rheumatoid arthritis (RA). Due to lack of appropriate methods, pharmacological mechanisms of WTD acting on RA have not been fully elucidated. In this study, a list of putative targets for compositive compounds containing in WTD were predicted by drugCIPHER-CS. Then, the interaction network of the putative targets of WTD and known RA-related targets was constructed and hub nodes were identified. After constructing the interaction network of hubs, four topological features of each hub, including degree, node betweenness, closeness and k-coreness, were calculated and 79 major hubs were identified as candidate targets of WTD, which were implicated into the imbalance of the nervous, endocrine and immune (NEI) systems, leading to the main pathological changes during the RA progression. Further experimental validation also demonstrated the preventive effects of WTD on inflammation and joint destruction in collagen-induced arthritis (CIA) rats and its regulatory effects on candidate targets both in vitro and in vivo systems. In conclusion, we performed an integrative analysis to offer the convincing evidence that WTD may attenuate RA partially by restoring the balance of NEI system and subsequently reversing the pathological events during RA progression. PMID:25820382

  19. Uncovering pharmacological mechanisms of Wu-tou decoction acting on rheumatoid arthritis through systems approaches: drug-target prediction, network analysis and experimental validation.

    PubMed

    Zhang, Yanqiong; Bai, Ming; Zhang, Bo; Liu, Chunfang; Guo, Qiuyan; Sun, Yanqun; Wang, Danhua; Wang, Chao; Jiang, Yini; Lin, Na; Li, Shao

    2015-03-30

    Wu-tou decoction (WTD) has been extensively used for the treatment of rheumatoid arthritis (RA). Due to lack of appropriate methods, pharmacological mechanisms of WTD acting on RA have not been fully elucidated. In this study, a list of putative targets for compositive compounds containing in WTD were predicted by drugCIPHER-CS. Then, the interaction network of the putative targets of WTD and known RA-related targets was constructed and hub nodes were identified. After constructing the interaction network of hubs, four topological features of each hub, including degree, node betweenness, closeness and k-coreness, were calculated and 79 major hubs were identified as candidate targets of WTD, which were implicated into the imbalance of the nervous, endocrine and immune (NEI) systems, leading to the main pathological changes during the RA progression. Further experimental validation also demonstrated the preventive effects of WTD on inflammation and joint destruction in collagen-induced arthritis (CIA) rats and its regulatory effects on candidate targets both in vitro and in vivo systems. In conclusion, we performed an integrative analysis to offer the convincing evidence that WTD may attenuate RA partially by restoring the balance of NEI system and subsequently reversing the pathological events during RA progression.

  20. Application of the Subtractive Genomics and Molecular Docking Analysis for the Identification of Novel Putative Drug Targets against Salmonella enterica subsp. enterica serovar Poona.

    PubMed

    Hossain, Tanvir; Kamruzzaman, Mohammad; Choudhury, Talita Zahin; Mahmood, Hamida Nooreen; Nabi, A H M Nurun; Hosen, Md Ismail

    2017-01-01

    The emergence of novel pathogenic strains with increased antibacterial resistance patterns poses a significant threat to the management of infectious diseases. In this study, we aimed at utilizing the subtractive genomic approach to identify novel drug targets against Salmonella enterica subsp. enterica serovar Poona strain ATCC BAA-1673. We employed in silico bioinformatics tools to subtract the strain-specific paralogous and host-specific homologous sequences from the bacterial proteome. The sorted proteome was further refined to identify the essential genes in the pathogenic bacterium using the database of essential genes (DEG). We carried out metabolic pathway and subcellular location analysis of the essential proteins of the pathogen to elucidate the involvement of these proteins in important cellular processes. We found 52 unique essential proteins in the target proteome that could be utilized as novel targets to design newer drugs. Further, we investigated these proteins in the DrugBank databases and 11 of the unique essential proteins showed druggability according to the FDA approved drug bank databases with diverse broad-spectrum property. Molecular docking analyses of the novel druggable targets with the drugs were carried out by AutoDock Vina option based on scoring functions. The results showed promising candidates for novel drugs against Salmonella infections.

  1. Drug targeting using solid lipid nanoparticles.

    PubMed

    Rostami, Elham; Kashanian, Soheila; Azandaryani, Abbas H; Faramarzi, Hossain; Dolatabadi, Jafar Ezzati Nazhad; Omidfar, Kobra

    2014-07-01

    The present review aims to show the features of solid lipid nanoparticles (SLNs) which are at the forefront of the rapidly developing field of nanotechnology with several potential applications in drug delivery and research. Because of some unique features of SLNs such as their unique size dependent properties it offers possibility to develop new therapeutics. A common denominator of all these SLN-based platforms is to deliver drugs into specific tissues or cells in a pathological setting with minimal adverse effects on bystander cells. SLNs are capable to incorporate drugs into nanocarriers which lead to a new prototype in drug delivery which maybe used for drug targeting. Hence solid lipid nanoparticles hold great promise for reaching the goal of controlled and site specific drug delivery and hence attracted wide attention of researchers. This review presents a broad treatment of targeted solid lipid nanoparticles discussing their types such as antibody SLN, magnetic SLN, pH sensitive SLN and cationic SLN.

  2. Multidrug transporters as drug targets.

    PubMed

    Liang, X-J; Aszalos, A

    2006-08-01

    Transport molecules can significantly affect the pharmacodynamics and pharmacokinetics of drugs. An important transport molecule, the 170 kDa P-glycoprotein (Pgp), is constitutively expressed at several organ sites in the human body. Pgp is expressed at the blood-brain barrier, in the kidneys, liver, intestines and in certain T cells. Other transporters such as the multidrug resistance protein 1 (MRP1) and MRP2 also contribute to drug distribution in the human body, although to a lesser extent than Pgp. These three transporters, and especially Pgp, are often targets of drugs. Pgp can be an intentional or unintentional target. It is directly targeted when one wants to block its function by a modifier drug so that another drug, also a substrate of Pgp, can penetrate the cell membrane, which would otherwise be impermeable. Unintentional targeting occurs when several drugs are administered to a patient and as a consequence, the physiological function of Pgp is blocked at different organ sites. Like Pgp, MRP1 also has the capacity to mediate transport of many drugs and other compounds. MRP1 has a protective role in preventing accumulation of toxic compounds and drugs in epithelial tissue covering the choroid plexus/cerebrospinal fluid compartment, oral epithelium, sertoli cells, intesticular tubules and urinary collecting duct cells. MRP2 primarily transports weakly basic drugs and bilirubin from the liver to bile. Most compounds that efficiently block Pgp have only low affinity for MRP1 and MRP2. There are only a few effective and specific MRP inhibitors available. Drug targeting of these transporters may play a role in cancer chemotherapy and in the pharmacokinetics of substrate drugs.

  3. Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach

    PubMed Central

    Cao, Dong-Sheng; Liang, Yi-Zeng; Deng, Zhe; Hu, Qian-Nan; He, Min; Xu, Qing-Song; Zhou, Guang-Hua; Zhang, Liu-Xia; Deng, Zi-xin; Liu, Shao

    2013-01-01

    The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-Ki was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-Ki server is freely available via: http://sdd.whu.edu.cn/dpiki. PMID:23577055

  4. PBIT: pipeline builder for identification of drug targets for infectious diseases.

    PubMed

    Shende, Gauri; Haldankar, Harshala; Barai, Ram Shankar; Bharmal, Mohammed Husain; Shetty, Vinit; Idicula-Thomas, Susan

    2016-12-30

    PBIT (Pipeline Builder for Identification of drug Targets) is an online webserver that has been developed for screening of microbial proteomes for critical features of human drug targets such as being non-homologous to human proteome as well as the human gut microbiota, essential for the pathogen's survival, participation in pathogen-specific pathways etc. The tool has been validated by analyzing 57 putative targets of Candida albicans documented in literature. PBIT integrates various in silico approaches known for drug target identification and will facilitate high-throughput prediction of drug targets for infectious diseases, including multi-pathogenic infections.

  5. Genome-Wide Identification of Potential Drug Target in Enterobacteriaceae Family: A Homology-Based Method.

    PubMed

    Hadizadeh, Morteza; Tabatabaiepour, Seyyede Nasim; Tabatabaiepour, Seyyede Zahra; Hosseini Nave, Hossein; Mohammadi, Mohsen; Sohrabi, Seyyed Mohsen

    2017-05-18

    The Enterobacteriaceae is a large family of Gram-negative, facultative anaerobic, non-spore forming rod-shaped bacteria that includes harmless and pathogenic organisms. The emergence and development of drug resistance in Enterobacteriaceae is complicating the treatment of serious infections. The aim of this study is to predict and characterize putative drug targets in Enterobacteriaceae family employing a homology-based computational method. The final putative drug targets were qualitatively characterized via cellular function prediction, subcellular localization prediction, broad-spectrum, and druggability analyses. Of 6,327 analyzed proteins, 35 proteins were selected as final putative drug targets in Enterobacteriaceae family. These putative drug targets were involved in different vital pathways like metabolism, biosynthesis of macromolecule, and cell division. Predicted drug targets were also localized in the cytoplasm and cytoplasmic membrane of the pathogen that acts as antimicrobial or vaccine targets. Of 35 drug targets, 5 targets were druggable and 30 targets were not druggable and were predicted as novel drug targets, which should be further evaluated to develop new antimicrobial. Thirteen drug targets were considered as broad-spectrum targets. It is expected that results of our study could facilitate the production of novel antibacterial for efficient treatment of infections caused by Enterobacteriaceae pathogens.

  6. Assessing drug target association using semantic linked data.

    PubMed

    Chen, Bin; Ding, Ying; Wild, David J

    2012-01-01

    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.

  7. Mouse model phenotypes provide information about human drug targets

    PubMed Central

    Hoehndorf, Robert; Hiebert, Tanya; Hardy, Nigel W.; Schofield, Paul N.; Gkoutos, Georgios V.; Dumontier, Michel

    2014-01-01

    Motivation: Methods for computational drug target identification use information from diverse information sources to predict or prioritize drug targets for known drugs. One set of resources that has been relatively neglected for drug repurposing is animal model phenotype. Results: We investigate the use of mouse model phenotypes for drug target identification. To achieve this goal, we first integrate mouse model phenotypes and drug effects, and then systematically compare the phenotypic similarity between mouse models and drug effect profiles. We find a high similarity between phenotypes resulting from loss-of-function mutations and drug effects resulting from the inhibition of a protein through a drug action, and demonstrate how this approach can be used to suggest candidate drug targets. Availability and implementation: Analysis code and supplementary data files are available on the project Web site at https://drugeffects.googlecode.com. Contact: leechuck@leechuck.de or roh25@aber.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24158600

  8. Can We Rely on Computational Predictions To Correctly Identify Ligand Binding Sites on Novel Protein Drug Targets? Assessment of Binding Site Prediction Methods and a Protocol for Validation of Predicted Binding Sites.

    PubMed

    Broomhead, Neal K; Soliman, Mahmoud E

    2017-03-01

    In the field of medicinal chemistry there is increasing focus on identifying key proteins whose biochemical functions can firmly be linked to serious diseases. Such proteins become targets for drug or inhibitor molecules that could treat or halt the disease through therapeutic action or by blocking the protein function respectively. The protein must be targeted at the relevant biologically active site for drug or inhibitor binding to be effective. As insufficient experimental data is available to confirm the biologically active binding site for novel protein targets, researchers often rely on computational prediction methods to identify binding sites. Presented herein is a short review on structure-based computational methods that (i) predict putative binding sites and (ii) assess the druggability of predicted binding sites on protein targets. This review briefly covers the principles upon which these methods are based, where they can be accessed and their reliability in identifying the correct binding site on a protein target. Based on this review, we believe that these methods are useful in predicting putative binding sites, but as they do not account for the dynamic nature of protein-ligand binding interactions, they cannot definitively identify the correct site from a ranked list of putative sites. To overcome this shortcoming, we strongly recommend using molecular docking to predict the most likely protein-ligand binding site(s) and mode(s), followed by molecular dynamics simulations and binding thermodynamics calculations to validate the docking results. This protocol provides a valuable platform for experimental and computational efforts to design novel drugs and inhibitors that target disease-related proteins.

  9. Drug target identification in protozoan parasites

    PubMed Central

    Müller, Joachim; Hemphill, Andrew

    2016-01-01

    Introduction Despite the fact that diseases caused by protozoan parasites represent serious challenges for public health, animal production and welfare, only a limited panel of drugs has been marketed for clinical applications. Areas covered Herein, the authors investigate two strategies, namely whole organism screening and target-based drug design. The present pharmacopoeia has resulted from whole organism screening, and the mode of action and targets of selected drugs are discussed. However, the more recent extensive genome sequencing efforts and the development of dry and wet lab genomics and proteomics that allow high-throughput screening of interactions between micromolecules and recombinant proteins has resulted in target-based drug design as the predominant focus in anti-parasitic drug development. Selected examples of target-based drug design studies are presented, and calcium-dependent protein kinases, important drug targets in apicomplexan parasites, are discussed in more detail. Expert opinion Despite the enormous efforts in target-based drug development, this approach has not yet generated market-ready antiprotozoal drugs. However, whole-organism screening approaches, comprising of both in vitro and in vivo investigations, should not be disregarded. The repurposing of already approved and marketed drugs could be a suitable strategy to avoid fastidious approval procedures, especially in the case of neglected or veterinary parasitoses. PMID:27238605

  10. Drug target identification in protozoan parasites.

    PubMed

    Müller, Joachim; Hemphill, Andrew

    2016-08-01

    Despite the fact that diseases caused by protozoan parasites represent serious challenges for public health, animal production and welfare, only a limited panel of drugs has been marketed for clinical applications. Herein, the authors investigate two strategies, namely whole organism screening and target-based drug design. The present pharmacopoeia has resulted from whole organism screening, and the mode of action and targets of selected drugs are discussed. However, the more recent extensive genome sequencing efforts and the development of dry and wet lab genomics and proteomics that allow high-throughput screening of interactions between micromolecules and recombinant proteins has resulted in target-based drug design as the predominant focus in anti-parasitic drug development. Selected examples of target-based drug design studies are presented, and calcium-dependent protein kinases, important drug targets in apicomplexan parasites, are discussed in more detail. Despite the enormous efforts in target-based drug development, this approach has not yet generated market-ready antiprotozoal drugs. However, whole-organism screening approaches, comprising of both in vitro and in vivo investigations, should not be disregarded. The repurposing of already approved and marketed drugs could be a suitable strategy to avoid fastidious approval procedures, especially in the case of neglected or veterinary parasitoses.

  11. Detecting drug targets with minimum side effects in metabolic networks.

    PubMed

    Li, Z; Wang, R-S; Zhang, X-S; Chen, L

    2009-11-01

    High-throughput techniques produce massive data on a genome-wide scale which facilitate pharmaceutical research. Drug target discovery is a crucial step in the drug discovery process and also plays a vital role in therapeutics. In this study, the problem of detecting drug targets was addressed, which finds a set of enzymes whose inhibition stops the production of a given set of target compounds and meanwhile minimally eliminates non-target compounds in the context of metabolic networks. The model aims to make the side effects of drugs as small as possible and thus has practical significance of potential pharmaceutical applications. Specifically, by exploiting special features of metabolic systems, a novel approach was proposed to exactly formulate this drug target detection problem as an integer linear programming model, which ensures that optimal solutions can be found efficiently without any heuristic manipulations. To verify the effectiveness of our approach, computational experiments on both Escherichia coli and Homo sapiens metabolic pathways were conducted. The results show that our approach can identify the optimal drug targets in an exact and efficient manner. In particular, it can be applied to large-scale networks including the whole metabolic networks from most organisms.

  12. Biocomputational strategies for microbial drug target identification.

    PubMed

    Sakharkar, Kishore R; Sakharkar, Meena K; Chow, Vincent T K

    2008-01-01

    The complete genome sequences of about 300 bacteria (mostly pathogenic) have been determined, and many more such projects are currently in progress. The detection of bacterial genes that are non-homologous to human genes and are essential for the survival of the pathogen represent a promising means of identifying novel drug targets. We present a subtractive genomics approach for the identification of putative drug targets in microbial genomes and demonstrate its execution using Pseudomonas aeruginosa as an example. The resultant analyses are in good agreement with the results of systematic gene deletion experiments. This strategy enables rapid potential drug target identification, thereby greatly facilitating the search for new antibiotics. It should be recognized that there are limitations to this computational approach for drug target identification. Distant gene relationships may be missed since the alignment scores are likely to have low statistical significance. In conclusion, the results of such a strategy underscore the utility of large genomic databases for in silico systematic drug target identification in the post-genomic era.

  13. Hyperthermia-induced drug targeting.

    PubMed

    May, Jonathan P; Li, Shyh-Dar

    2013-04-01

    Specific delivery of a drug to a target site is a major goal of drug delivery research. Using temperature-sensitive liposomes (TSLs) is one way to achieve this; the liposome acts as a protective carrier, allowing increased drug to flow through the bloodstream by minimizing clearance and non-specific uptake. On reaching microvessels within a heated tumor, the drug is released and quickly penetrates. A major advance in the field is ThermoDox® (Celsion), demonstrating significant improvements to the drug release rates and drug uptake in heated tumors (∼ 41°C). Most recently, magnetic resonance-guided focused ultrasound (MRgFUS) has been combined with TSL drug delivery to provide localized chemotherapy with simultaneous quantification of drug release within the tumor. In this article the field of hyperthermia-induced drug delivery is discussed, with an emphasis on the development of TSLs and their combination with hyperthermia (both mild and ablative) in cancer therapy. State-of-the-art image-guided heating technologies used with this combination strategy will also be presented, with examples of real-time monitoring of drug delivery and prediction of efficacy. The specific delivery of drugs by combining hyperthermia with TSLs is showing great promise in the clinic and its potential will be even greater as the use of image-guided focused ultrasound becomes more widespread - a technique capable of penetrating deep within the body to heat a specific area with improved control. In conjunction with this, it is anticipated that multifunctional TSLs will be a major topic of study in this field.

  14. Recent discoveries of influenza A drug target sites to combat virus replication.

    PubMed

    Patel, Hershna; Kukol, Andreas

    2016-06-15

    Sequence variations in the binding sites of influenza A proteins are known to limit the effectiveness of current antiviral drugs. Clinically, this leads to increased rates of virus transmission and pathogenicity. Potential influenza A inhibitors are continually being discovered as a result of high-throughput cell based screening studies, whereas the application of computational tools to aid drug discovery has further increased the number of predicted inhibitors reported. This review brings together the aspects that relate to the identification of influenza A drug target sites and the findings from recent antiviral drug discovery strategies.

  15. Automated High Throughput Drug Target Crystallography

    SciTech Connect

    Rupp, B

    2005-02-18

    The molecular structures of drug target proteins and receptors form the basis for 'rational' or structure guided drug design. The majority of target structures are experimentally determined by protein X-ray crystallography, which as evolved into a highly automated, high throughput drug discovery and screening tool. Process automation has accelerated tasks from parallel protein expression, fully automated crystallization, and rapid data collection to highly efficient structure determination methods. A thoroughly designed automation technology platform supported by a powerful informatics infrastructure forms the basis for optimal workflow implementation and the data mining and analysis tools to generate new leads from experimental protein drug target structures.

  16. In silico re-identification of properties of drug target proteins.

    PubMed

    Kim, Baeksoo; Jo, Jihoon; Han, Jonghyun; Park, Chungoo; Lee, Hyunju

    2017-05-31

    Computational approaches in the identification of drug targets are expected to reduce time and effort in drug development. Advances in genomics and proteomics provide the opportunity to uncover properties of druggable genomes. Although several studies have been conducted for distinguishing drug targets from non-drug targets, they mainly focus on the sequences and functional roles of proteins. Many other properties of proteins have not been fully investigated. Using the DrugBank (version 3.0) database containing nearly 6,816 drug entries including 760 FDA-approved drugs and 1822 of their targets and human UniProt/Swiss-Prot databases, we defined 1578 non-redundant drug target and 17,575 non-drug target proteins. To select these non-redundant protein datasets, we built four datasets (A, B, C, and D) by considering clustering of paralogous proteins. We first reassessed the widely used properties of drug target proteins. We confirmed and extended that drug target proteins (1) are likely to have more hydrophobic, less polar, less PEST sequences, and more signal peptide sequences higher and (2) are more involved in enzyme catalysis, oxidation and reduction in cellular respiration, and operational genes. In this study, we proposed new properties (essentiality, expression pattern, PTMs, and solvent accessibility) for effectively identifying drug target proteins. We found that (1) drug targetability and protein essentiality are decoupled, (2) druggability of proteins has high expression level and tissue specificity, and (3) functional post-translational modification residues are enriched in drug target proteins. In addition, to predict the drug targetability of proteins, we exploited two machine learning methods (Support Vector Machine and Random Forest). When we predicted drug targets by combining previously known protein properties and proposed new properties, an F-score of 0.8307 was obtained. When the newly proposed properties are integrated, the prediction performance

  17. ING Proteins as Potential Anticancer Drug Targets

    PubMed Central

    Unoki, M.; Kumamoto, K.; Harris, C.C.

    2009-01-01

    Recent emerging evidence suggests that ING family proteins play roles in carcinogenesis both as oncogenes and tumor suppressor genes depending on the family members and on cell status. Previous results from non-physiologic overexpression experiments showed that all five family members induce apoptosis or cell cycle arrest, thus it had been thought until very recently that all of the family members function as tumor suppressor genes. Therefore restoration of ING family proteins in cancer cells has been proposed as a treatment for cancers. However, ING2 knockdown experiments showed unexpected results: ING2 knockdown led to senescence in normal human fibroblast cells and suppressed cancer cell growth. ING2 is also overexpressed in colorectal cancer, and promotes cancer cell invasion through an MMP13 dependent pathway. Additionally, it was reported that ING2 has two isoforms, ING2a and ING2b. Although expression of ING2a predominates compared with ING2b, both isoforms confer resistance against cell cycle arrest or apoptosis to cancer cells, thus knockdown of both isoforms is critical to remove this resistance. Taken together, these results suggest that ING2 can function as an oncogene in some specific types of cancer cells, indicating restoration of this gene in cancer cells could cause cancer progression. Because knockdown of ING2 suppresses cancer cell invasion and induces apoptosis or cell cycle arrest, ING2 may be an anticancer drug target. In this brief review, we discuss possible clinical applications of ING2 with the latest knowledge of molecular targeted therapies. PMID:19442116

  18. Exploring Drug Targets in Isoprenoid Biosynthetic Pathway for Plasmodium falciparum.

    PubMed

    Qidwai, Tabish; Jamal, Farrukh; Khan, Mohd Y; Sharma, Bechan

    2014-01-01

    Emergence of rapid drug resistance to existing antimalarial drugs in Plasmodium falciparum has created the need for prediction of novel targets as well as leads derived from original molecules with improved activity against a validated drug target. The malaria parasite has a plant plastid-like apicoplast. To overcome the problem of falciparum malaria, the metabolic pathways in parasite apicoplast have been used as antimalarial drug targets. Among several pathways in apicoplast, isoprenoid biosynthesis is one of the important pathways for parasite as its multiplication in human erythrocytes requires isoprenoids. Therefore targeting this pathway and exploring leads with improved activity is a highly attractive approach. This report has explored progress towards the study of proteins and inhibitors of isoprenoid biosynthesis pathway. For more comprehensive analysis, antimalarial drug-protein interaction has been covered.

  19. A comparative study of disease genes and drug targets in the human protein interactome

    PubMed Central

    2015-01-01

    Background Disease genes cause or contribute genetically to the development of the most complex diseases. Drugs are the major approaches to treat the complex disease through interacting with their targets. Thus, drug targets are critical for treatment efficacy. However, the interrelationship between the disease genes and drug targets is not clear. Results In this study, we comprehensively compared the network properties of disease genes and drug targets for five major disease categories (cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease). We first collected disease genes from genome-wide association studies (GWAS) for five disease categories and collected their corresponding drugs based on drugs' Anatomical Therapeutic Chemical (ATC) classification. Then, we obtained the drug targets for these five different disease categories. We found that, though the intersections between disease genes and drug targets were small, disease genes were significantly enriched in targets compared to their enrichment in human protein-coding genes. We further compared network properties of the proteins encoded by disease genes and drug targets in human protein-protein interaction networks (interactome). The results showed that the drug targets tended to have higher degree, higher betweenness, and lower clustering coefficient in cancer Furthermore, we observed a clear fraction increase of disease proteins or drug targets in the near neighborhood compared with the randomized genes. Conclusions The study presents the first comprehensive comparison of the disease genes and drug targets in the context of interactome. The results provide some foundational network characteristics for further designing computational strategies to predict novel drug targets and drug repurposing. PMID:25861037

  20. A comparative study of disease genes and drug targets in the human protein interactome.

    PubMed

    Sun, Jingchun; Zhu, Kevin; Zheng, W; Xu, Hua

    2015-01-01

    Disease genes cause or contribute genetically to the development of the most complex diseases. Drugs are the major approaches to treat the complex disease through interacting with their targets. Thus, drug targets are critical for treatment efficacy. However, the interrelationship between the disease genes and drug targets is not clear. In this study, we comprehensively compared the network properties of disease genes and drug targets for five major disease categories (cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease). We first collected disease genes from genome-wide association studies (GWAS) for five disease categories and collected their corresponding drugs based on drugs' Anatomical Therapeutic Chemical (ATC) classification. Then, we obtained the drug targets for these five different disease categories. We found that, though the intersections between disease genes and drug targets were small, disease genes were significantly enriched in targets compared to their enrichment in human protein-coding genes. We further compared network properties of the proteins encoded by disease genes and drug targets in human protein-protein interaction networks (interactome). The results showed that the drug targets tended to have higher degree, higher betweenness, and lower clustering coefficient in cancer Furthermore, we observed a clear fraction increase of disease proteins or drug targets in the near neighborhood compared with the randomized genes. The study presents the first comprehensive comparison of the disease genes and drug targets in the context of interactome. The results provide some foundational network characteristics for further designing computational strategies to predict novel drug targets and drug repurposing.

  1. Two-stage flux balance analysis of metabolic networks for drug target identification

    PubMed Central

    2011-01-01

    Background Efficient identification of drug targets is one of major challenges for drug discovery and drug development. Traditional approaches to drug target identification include literature search-based target prioritization and in vitro binding assays which are both time-consuming and labor intensive. Computational integration of different knowledge sources is a more effective alternative. Wealth of omics data generated from genomic, proteomic and metabolomic techniques changes the way researchers view drug targets and provides unprecedent opportunities for drug target identification. Results In this paper, we develop a method based on flux balance analysis (FBA) of metabolic networks to identify potential drug targets. This method consists of two linear programming (LP) models, which first finds the steady optimal fluxes of reactions and the mass flows of metabolites in the pathologic state and then determines the fluxes and mass flows in the medication state with the minimal side effect caused by the medication. Drug targets are identified by comparing the fluxes of reactions in both states and examining the change of reaction fluxes. We give an illustrative example to show that the drug target identification problem can be solved effectively by our method, then apply it to a hyperuricemia-related purine metabolic pathway. Known drug targets for hyperuricemia are correctly identified by our two-stage FBA method, and the side effects of these targets are also taken into account. A number of other promising drug targets are found to be both effective and safe. Conclusions Our method is an efficient procedure for drug target identification through flux balance analysis of large-scale metabolic networks. It can generate testable predictions, provide insights into drug action mechanisms and guide experimental design of drug discovery. PMID:21689470

  2. Mining nematode genome data for novel drug targets.

    PubMed

    Foster, Jeremy M; Zhang, Yinhua; Kumar, Sanjay; Carlow, Clotilde K S

    2005-03-01

    Expressed sequence tag projects have currently produced over 400 000 partial gene sequences from more than 30 nematode species and the full genomic sequences of selected nematodes are being determined. In addition, functional analyses in the model nematode Caenorhabditis elegans have addressed the role of almost all genes predicted by the genome sequence. This recent explosion in the amount of available nematode DNA sequences, coupled with new gene function data, provides an unprecedented opportunity to identify pre-validated drug targets through efficient mining of nematode genomic databases. This article describes the various information sources available and strategies that can expedite this process.

  3. PhID: an open-access integrated pharmacology interactions database for drugs, targets, diseases, genes, side-effects and pathways.

    PubMed

    Deng, Zhe; Tu, Weizhong; Deng, Zixin; Hu, Qian-Nan

    2017-09-14

    The current network pharmacology study encountered a bottleneck with a lot of public data scattered in different databases. There is the lack of open-access and consolidated platform that integrates this information for systemic research. To address this issue, we have developed PhID, an integrated pharmacology database which integrates >400,000 pharmacology elements (drug, target, disease, gene, side-effect, and pathway) and >200,000 element interactions in branches of public databases. The PhID has three major applications: (1) assists scientists searching through the overwhelming amount of pharmacology elements interaction data by names, public IDs, molecule structures, or molecular sub-structures; (2) helps visualizing pharmacology elements and their interactions with a web-based network graph; (3) provides prediction of drug-target interactions through two modules: PreDPI-ki and FIM, by which users can predict drug-target interactions of the PhID entities or some drug-target pairs they interest. To get a systems-level understanding of drug action and disease complexity, PhID as a network pharmacology tool was established from the perspective of data layer, visualization layer and prediction model layer to present information untapped by current databases. Database URL: http://phid.ditad.org/.

  4. Malaria heat shock proteins: drug targets that chaperone other drug targets.

    PubMed

    Pesce, E-R; Cockburn, I L; Goble, J L; Stephens, L L; Blatch, G L

    2010-06-01

    Ongoing research into the chaperone systems of malaria parasites, and particularly of Plasmodium falciparum, suggests that heat shock proteins (Hsps) could potentially be an excellent class of drug targets. The P. falciparum genome encodes a vast range and large number of chaperones, including 43 Hsp40, six Hsp70, and three Hsp90 proteins (PfHsp40s, PfHsp70s and PfHsp90s), which are involved in a number of fundamental cellular processes including protein folding and assembly, protein translocation, signal transduction and the cellular stress response. Despite the fact that Hsps are relatively conserved across different species, PfHsps do exhibit a considerable number of unique structural and functional features. One PfHsp90 is thought to be sufficiently different to human Hsp90 to allow for selective targeting. PfHsp70s could potentially be used as drug targets in two ways: either by the specific inhibition of Hsp70s by small molecule modulators, as well as disruption of the interactions between Hsp70s and co-chaperones such as the Hsp70/Hsp90 organising protein (Hop) and Hsp40s. Of the many PfHsp40s present on the parasite, there are certain unique or essential members which are considered to have good potential as drug targets. This review critically evaluates the potential of Hsps as malaria drug targets, as well as the use of chaperones as aids in the heterologous expression of other potential malarial drug targets.

  5. Open Challenges in Magnetic Drug Targeting

    PubMed Central

    Kulkarni, Sandip; Nacev, Aleksander; Muro, Silvia; Stepanov, Pavel Y.; Weinberg, Irving N.

    2014-01-01

    The principle of magnetic drug targeting, wherein therapy is attached to magnetically responsive carriers and magnetic fields are used to direct that therapy to disease locations, has been around for nearly two decades. Yet our ability to safely and effectively direct therapy to where it needs to go, for instance to deep tissue targets, remains limited. To date, magnetic targeting methods have not yet passed regulatory approval or reached clinical use. Below we outline key challenges to magnetic targeting, which include designing and selecting magnetic carriers for specific clinical indications, safely and effectively reaching targets behind tissue and anatomical barriers, real-time carrier imaging, and magnet design and control for deep and precise targeting. Addressing these challenges will require interactions across disciplines. Nanofabricators and chemists should work with biologists, mathematicians and engineers to better understand how carriers move through live tissues and how to optimize carrier and magnet designs to better direct therapy to disease targets. Clinicians should be involved early on and throughout the whole process to ensure the methods that are being developed meet a compelling clinical need and will be practical in a clinical setting. Our hope is that highlighting these challenges will help researchers translate magnetic drug targeting from a novel concept to a clinically-available treatment that can put therapy where it needs to go in human patients. PMID:25377422

  6. Amphotericin B formulations and drug targeting.

    PubMed

    Torrado, J J; Espada, R; Ballesteros, M P; Torrado-Santiago, S

    2008-07-01

    Amphotericin B is a low-soluble polyene antibiotic which is able to self-aggregate. The aggregation state can modify its activity and pharmacokinetical characteristics. In spite of its high toxicity it is still widely employed for the treatment of systemic fungal infections and parasitic disease and different formulations are marketed. Some of these formulations, such as liposomal formulations, can be considered as classical examples of drug targeting. The pharmacokinetics, toxicity and activity are clearly dependent on the type of amphotericin B formulation. New drug delivery systems such as liposomes, nanospheres and microspheres can result in higher concentrations of AMB in the liver and spleen, but lower concentrations in kidney and lungs, so decreasing its toxicity. Moreover, the administration of these drug delivery systems can enhance the drug accessibility to organs and tissues (e.g., bone marrow) otherwise inaccessible to the free drug. During the last few years, new AMB formulations (AmBisome, Abelcet, and Amphotec) with an improved efficacy/toxicity ratio have been marketed. This review compares the different formulations of amphotericin B in terms of pharmacokinetics, toxicity and activity and discusses the possible drug targeting effect of some of these new formulations.

  7. Screening drug-target interactions with positive-unlabeled learning.

    PubMed

    Peng, Lihong; Zhu, Wen; Liao, Bo; Duan, Yu; Chen, Min; Chen, Yi; Yang, Jialiang

    2017-08-14

    Identifying drug-target interaction (DTI) candidates is crucial for drug repositioning. However, usually only positive DTIs are deposited in known databases, which challenges computational methods to predict novel DTIs due to the lack of negative samples. To overcome this dilemma, researchers usually randomly select negative samples from unlabeled drug-target pairs, which introduces a lot of false-positives. In this study, a negative sample extraction method named NDTISE is first developed to screen strong negative DTI examples based on positive-unlabeled learning. A novel DTI screening framework, PUDTI, is then designed to infer new drug repositioning candidates by integrating NDTISE, probabilities that remaining ambiguous samples belong to the positive and negative classes, and an SVM-based optimization model. We investigated the effectiveness of NDTISE on a DTI data provided by NCPIS. NDTISE is much better than random selection and slightly outperforms NCPIS. We then compared PUDTI with 6 state-of-the-art methods on 4 classes of DTI datasets from human enzymes, ion channels, GPCRs and nuclear receptors. PUDTI achieved the highest AUC among the 7 methods on all 4 datasets. Finally, we validated a few top predicted DTIs through mining independent drug databases and literatures. In conclusion, PUDTI provides an effective pre-filtering method for new drug design.

  8. A weighted and integrated drug-target interactome: drug repurposing for schizophrenia as a use case

    PubMed Central

    2015-01-01

    Background Computational pharmacology can uniquely address some issues in the process of drug development by providing a macroscopic view and a deeper understanding of drug action. Specifically, network-assisted approach is promising for the inference of drug repurposing. However, the drug-target associations coming from different sources and various assays have much noise, leading to an inflation of the inference errors. To reduce the inference errors, it is necessary and critical to create a comprehensive and weighted data set of drug-target associations. Results In this study, we created a weighted and integrated drug-target interactome (WinDTome) to provide a comprehensive resource of drug-target associations for computational pharmacology. We first collected drug-target interactions from six commonly used drug-target centered data sources including DrugBank, KEGG, TTD, MATADOR, PDSP Ki Database, and BindingDB. Then, we employed the record linkage method to normalize drugs and targets to the unique identifiers by utilizing the public data sources including PubChem, Entrez Gene, and UniProt. To assess the reliability of the drug-target associations, we assigned two scores (Score_S and Score_R) to each drug-target association based on their data sources and publication references. Consequently, the WinDTome contains 546,196 drug-target associations among 303,018 compounds and 4,113 genes. To assess the application of the WinDTome, we designed a network-based approach for drug repurposing using mental disorder schizophrenia (SCZ) as a case. Starting from 41 known SCZ drugs and their targets, we inferred a total of 264 potential SCZ drugs through the associations of drug-target with Score_S higher than two in WinDTome and human protein-protein interactions. Among the 264 SCZ-related drugs, 39 drugs have been investigated in clinical trials for SCZ treatment and 74 drugs for the treatment of other mental disorders, respectively. Compared with the results using other

  9. Evaluation of drug-targetable genes by defining modes of abnormality in gene expression.

    PubMed

    Park, Junseong; Lee, Jungsul; Choi, Chulhee

    2015-09-04

    In the post-genomic era, many researchers have taken a systematic approach to identifying abnormal genes associated with various diseases. However, the gold standard has not been established, and most of these abnormalities are difficult to be rehabilitated in real clinical settings. In addition to identifying abnormal genes, for a practical purpose, it is necessary to investigate abnormality diversity. In this context, this study is aimed to demonstrate simply restorable genes as useful drug targets. We devised the concept of "drug targetability" to evaluate several different modes of abnormal genes by predicting events after drug treatment. As a representative example, we applied our method to breast cancer. Computationally, PTPRF, PRKAR2B, MAP4K3, and RICTOR were calculated as highly drug-targetable genes for breast cancer. After knockdown of these top-ranked genes (i.e., high drug targetability) using siRNA, our predictions were validated by cell death and migration assays. Moreover, inhibition of RICTOR or PTPRF was expected to prolong lifespan of breast cancer patients according to patient information annotated in microarray data. We anticipate that our method can be widely applied to elaborate selection of novel drug targets, and, ultimately, to improve the efficacy of disease treatment.

  10. Leveraging human genetics to guide drug target discovery.

    PubMed

    Stitziel, Nathan O; Kathiresan, Sekar

    2017-07-01

    Identifying appropriate molecular targets is a critical step in drug development. Despite many advantages, the traditional tools of observational epidemiology and cellular or animal models of disease can be misleading in identifying causal pathways likely to lead to successful therapeutics. Here, we review some favorable aspects of human genetics studies that have the potential to accelerate drug target discovery. These include using genetic studies to identify pathways relevant to human disease, leveraging human genetics to discern causal relationships between biomarkers and disease, and studying genetic variation in humans to predict the potential efficacy and safety of inhibitory compounds aimed at molecular targets. We present some examples taken from studies of plasma lipids and coronary artery disease to highlight how human genetics can accelerate therapeutics development. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Cognitive 'Omics': Pattern-Based Validation of Potential Drug Targets.

    PubMed

    Gyertyán, István

    2017-02-01

    Despite the abundance of cognitive enhancer mechanisms identified in basic research, drugs approved for cognitive disorders are scarce and of limited efficacy. Although the so-called 'gold-standard' animal assays are well suited to the study of fundamental learning processes, they fail to predict clinical efficacy against complex and robust cognitive defects. Preclinical validation of potential drug targets requires new approaches with higher translational value. Here I propose a rodent cognitive test system that encompasses several learning paradigms each modeling a certain human cognitive domain. Cognitive deficits are brought about by several impairing methods and a particular mechanism of action is tested on each defective cognitive function. The outcome is a cognitive efficacy pattern that should then be matched to the cognitive deficit patterns of the clinical disorders. The best fit will highlight the clinical indication with the greatest chance for success.

  12. P2X Receptors as Drug Targets

    PubMed Central

    Jarvis, Michael F.

    2013-01-01

    The study of P2X receptors has long been handicapped by a poverty of small-molecule tools that serve as selective agonists and antagonists. There has been progress, particularly in the past 10 years, as cell-based high-throughput screening methods were applied, together with large chemical libraries. This has delivered some drug-like molecules in several chemical classes that selectively target P2X1, P2X3, or P2X7 receptors. Some of these are, or have been, in clinical trials for rheumatoid arthritis, pain, and cough. Current preclinical research programs are studying P2X receptor involvement in pain, inflammation, osteoporosis, multiple sclerosis, spinal cord injury, and bladder dysfunction. The determination of the atomic structure of P2X receptors in closed and open (ATP-bound) states by X-ray crystallography is now allowing new approaches by molecular modeling. This is supported by a large body of previous work using mutagenesis and functional expression, and is now being supplemented by molecular dynamic simulations and in silico ligand docking. These approaches should lead to P2X receptors soon taking their place alongside other ion channel proteins as therapeutically important drug targets. PMID:23253448

  13. Zika Virus Protease: An Antiviral Drug Target.

    PubMed

    Kang, CongBao; Keller, Thomas H; Luo, Dahai

    2017-10-01

    The recent outbreak of Zika virus (ZIKV) infection has caused global concern due to its link to severe damage to the brain development of foetuses and neuronal complications in adult patients. A worldwide research effort has been undertaken to identify effective and safe treatment and vaccination options. Among the proposed viral and host components, the viral NS2B-NS3 protease represents an attractive drug target due to its essential role in the virus life cycle. Here, we outline recent progress in studies on the Zika protease. Biochemical, biophysical, and structural studies on different protease constructs provide new insight into the structure and activity of the protease. The unlinked construct displays higher enzymatic activity and better mimics the native state of the enzyme and therefore is better suited for drug discovery. Furthermore, the structure of the free enzyme adopts a closed conformation and a preformed active site. The availability of a lead fragment hit and peptide inhibitors, as well as the attainability of soakable crystals, suggest that the unlinked construct is a promising tool for drug discovery. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Magnetic Drug Targeting in Arterial Flows

    NASA Astrophysics Data System (ADS)

    Williams, Alicia; Puri, Ishwar; Vlachos, Pavlos

    2006-11-01

    Magnetic Drug Targeting (MDT) is a promising technique to effectively deliver medicinal drugs via functionalized magnetic particles to target sites during the treatment of diseases. In this paper we investigate the interaction of coronary and pulsatile flows laden with superparamagnetic microparticles in a vessel under the influence of a magnetic field induced by a 1 Tesla permanent magnet. Coronary and peripheral pulsatile flows were examined across a range of conditions that are representative of those found within the cardiovascular system. The flow in the model was measured using TRDPIV (Time Resolved Digital Particle Image Velocimetry) and data was acquired with sampling up to 1 kHz. The data obtained from the experiment indicates that for the range of flows studied, the behavior of the ferrofluid mass is physically abundant. The ferrofluid mass deforms in response to the pulsatility of the flow, generating wavy structures that ultimately shed portions of the ferrofluid downstream in a fashion similar to a Kelvin-Helmholtz shear layer. This experiment is the first to address the fluid dynamics of the interactions between the flow and the ferrofluid mass over the range of biological conditions.

  15. Hsp70 Protein Complexes as Drug Targets

    PubMed Central

    Assimon, Victoria A.; Gillies, Anne T.; Rauch, Jennifer N.; Gestwicki, Jason E.

    2013-01-01

    Heat shock protein 70 (Hsp70) plays critical roles in proteostasis and is an emerging target for multiple diseases. However, competitive inhibition of the enzymatic activity of Hsp70 has proven challenging and, in some cases, may not be the most productive way to redirect Hsp70 function. Another approach is to inhibit Hsp70’s interactions with important co-chaperones, such as J proteins, nucleotide exchange factors (NEFs) and tetratricopeptide repeat (TPR) domain-containing proteins. These co-chaperones normally bind Hsp70 and guide its many diverse cellular activities. Complexes between Hsp70 and co-chaperones have been shown to have specific functions, such as pro-folding, pro-degradation and pro-trafficking. Thus, a promising strategy may be to block protein-protein interactions between Hsp70 and its co-chaperones or to target allosteric sites that disrupt these contacts. Such an approach might shift the balance of Hsp70 complexes and re-shape the proteome and it has the potential to restore healthy proteostasis. In this review, we discuss specific challenges and opportunities related to those goals. By pursuing Hsp70 complexes as drug targets, we might not only develop new leads for therapeutic development, but also discover new chemical probes for use in understanding Hsp70 biology. PMID:22920901

  16. Hsp70 protein complexes as drug targets.

    PubMed

    Assimon, Victoria A; Gillies, Anne T; Rauch, Jennifer N; Gestwicki, Jason E

    2013-01-01

    Heat shock protein 70 (Hsp70) plays critical roles in proteostasis and is an emerging target for multiple diseases. However, competitive inhibition of the enzymatic activity of Hsp70 has proven challenging and, in some cases, may not be the most productive way to redirect Hsp70 function. Another approach is to inhibit Hsp70's interactions with important co-chaperones, such as J proteins, nucleotide exchange factors (NEFs) and tetratricopeptide repeat (TPR) domain-containing proteins. These co-chaperones normally bind Hsp70 and guide its many diverse cellular activities. Complexes between Hsp70 and co-chaperones have been shown to have specific functions, including roles in pro-folding, pro-degradation and pro-trafficking pathways. Thus, a promising strategy may be to block protein- protein interactions between Hsp70 and its co-chaperones or to target allosteric sites that disrupt these contacts. Such an approach might shift the balance of Hsp70 complexes and re-shape the proteome and it has the potential to restore healthy proteostasis. In this review, we discuss specific challenges and opportunities related to these goals. By pursuing Hsp70 complexes as drug targets, we might not only develop new leads for therapeutic development, but also discover new chemical probes for use in understanding Hsp70 biology.

  17. Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection

    PubMed Central

    Ndieyira, Joseph W.; Watari, Moyu; McKendry, Rachel A.

    2013-01-01

    The cantilever sensor, which acts as a transducer of reactions between model bacterial cell wall matrix immobilized on its surface and antibiotic drugs in solution, has shown considerable potential in biochemical sensing applications with unprecedented sensitivity and specificity1-5. The drug-target interactions generate surface stress, causing the cantilever to bend, and the signal can be analyzed optically when it is illuminated by a laser. The change in surface stress measured with nano-scale precision allows disruptions of the biomechanics of model bacterial cell wall targets to be tracked in real time. Despite offering considerable advantages, multiple cantilever sensor arrays have never been applied in quantifying drug-target binding interactions. Here, we report on the use of silicon multiple cantilever arrays coated with alkanethiol self-assembled monolayers mimicking bacterial cell wall matrix to quantitatively study antibiotic binding interactions. To understand the impact of vancomycin on the mechanics of bacterial cell wall structures1,6,7. We developed a new model1 which proposes that cantilever bending can be described by two independent factors; i) namely a chemical factor, which is given by a classical Langmuir adsorption isotherm, from which we calculate the thermodynamic equilibrium dissociation constant (Kd) and ii) a geometrical factor, essentially a measure of how bacterial peptide receptors are distributed on the cantilever surface. The surface distribution of peptide receptors (p) is used to investigate the dependence of geometry and ligand loading. It is shown that a threshold value of p ~10% is critical to sensing applications. Below which there is no detectable bending signal while above this value, the bending signal increases almost linearly, revealing that stress is a product of a local chemical binding factor and a geometrical factor combined by the mechanical connectivity of reacted regions and provides a new paradigm for design of

  18. Nanomechanics of drug-target interactions and antibacterial resistance detection.

    PubMed

    Ndieyira, Joseph W; Watari, Moyu; McKendry, Rachel A

    2013-10-25

    The cantilever sensor, which acts as a transducer of reactions between model bacterial cell wall matrix immobilized on its surface and antibiotic drugs in solution, has shown considerable potential in biochemical sensing applications with unprecedented sensitivity and specificity. The drug-target interactions generate surface stress, causing the cantilever to bend, and the signal can be analyzed optically when it is illuminated by a laser. The change in surface stress measured with nano-scale precision allows disruptions of the biomechanics of model bacterial cell wall targets to be tracked in real time. Despite offering considerable advantages, multiple cantilever sensor arrays have never been applied in quantifying drug-target binding interactions. Here, we report on the use of silicon multiple cantilever arrays coated with alkanethiol self-assembled monolayers mimicking bacterial cell wall matrix to quantitatively study antibiotic binding interactions. To understand the impact of vancomycin on the mechanics of bacterial cell wall structures. We developed a new model(1) which proposes that cantilever bending can be described by two independent factors; i) namely a chemical factor, which is given by a classical Langmuir adsorption isotherm, from which we calculate the thermodynamic equilibrium dissociation constant (Kd) and ii) a geometrical factor, essentially a measure of how bacterial peptide receptors are distributed on the cantilever surface. The surface distribution of peptide receptors (p) is used to investigate the dependence of geometry and ligand loading. It is shown that a threshold value of p ~10% is critical to sensing applications. Below which there is no detectable bending signal while above this value, the bending signal increases almost linearly, revealing that stress is a product of a local chemical binding factor and a geometrical factor combined by the mechanical connectivity of reacted regions and provides a new paradigm for design of powerful

  19. Drug Target Mining and Analysis of the Chinese Tree Shrew for Pharmacological Testing

    PubMed Central

    Liu, Jie; Lee, Wen-hui; Zhang, Yun

    2014-01-01

    The discovery of new drugs requires the development of improved animal models for drug testing. The Chinese tree shrew is considered to be a realistic candidate model. To assess the potential of the Chinese tree shrew for pharmacological testing, we performed drug target prediction and analysis on genomic and transcriptomic scales. Using our pipeline, 3,482 proteins were predicted to be drug targets. Of these predicted targets, 446 and 1,049 proteins with the highest rank and total scores, respectively, included homologs of targets for cancer chemotherapy, depression, age-related decline and cardiovascular disease. Based on comparative analyses, more than half of drug target proteins identified from the tree shrew genome were shown to be higher similarity to human targets than in the mouse. Target validation also demonstrated that the constitutive expression of the proteinase-activated receptors of tree shrew platelets is similar to that of human platelets but differs from that of mouse platelets. We developed an effective pipeline and search strategy for drug target prediction and the evaluation of model-based target identification for drug testing. This work provides useful information for future studies of the Chinese tree shrew as a source of novel targets for drug discovery research. PMID:25105297

  20. Is hippocampal atrophy a future drug target?

    PubMed

    Dhikav, Vikas; Anand, Kuljeet Singh

    2007-01-01

    atrophy would be clinically useful in affecting disease, viz slowing its progression, reducing morbidity, complications or positively affecting the outcome of one or more of its clinically important aspects. If the answer to this is yes, we would have to know at what stage of the disease we use the drugs, dose, duration, follow-up and efficacy. The use of these drugs in the above mentioned conditions can not only test the potential of atrophy as a future drug target, but could also help in learning more about the hippocampus in both health and diseases.

  1. Using Click Chemistry to Identify Potential Drug Targets in Plasmodium

    DTIC Science & Technology

    2016-06-01

    AWARD NUMBER: W81XWH-13-1-0429 TITLE: Using "Click Chemistry " to Identify Potential Drug Targets in Plasmodium PRINCIPAL INVESTIGATOR...29Mar2016 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER W81XWH-13-1-0429 Using click chemistry to identify potential drug targets in Plasmodium 5b...Al-Tsp derivatives begins. Two classes of Tsp derivatives (Al-Tsp) are appropriate for click chemistry (Fig. 1). Class I derivatives carry a

  2. The exploration of network motifs as potential drug targets from post-translational regulatory networks.

    PubMed

    Zhang, Xiao-Dong; Song, Jiangning; Bork, Peer; Zhao, Xing-Ming

    2016-02-08

    Phosphorylation and proteolysis are among the most common post-translational modifications (PTMs), and play critical roles in various biological processes. More recent discoveries imply that the crosstalks between these two PTMs are involved in many diseases. In this work, we construct a post-translational regulatory network (PTRN) consists of phosphorylation and proteolysis processes, which enables us to investigate the regulatory interplays between these two PTMs. With the PTRN, we identify some functional network motifs that are significantly enriched with drug targets, some of which are further found to contain multiple proteins targeted by combinatorial drugs. These findings imply that the network motifs may be used to predict targets when designing new drugs. Inspired by this, we propose a novel computational approach called NetTar for predicting drug targets using the identified network motifs. Benchmarking results on real data indicate that our approach can be used for accurate prediction of novel proteins targeted by known drugs.

  3. Novel Insight from Computational Virtual Screening Depict the Binding Potential of Selected Phytotherapeutics Against Probable Drug Targets of Clostridium difficile.

    PubMed

    Kamath, Suman; Skariyachan, Sinosh

    2017-02-20

    This study explores computational screening and molecular docking approaches to screen novel herbal therapeutics against probable drug targets of Clostridium difficile. The essential genes were predicted by comparative genome analysis of C. difficile and best homologous organisms using BLAST search at database of essential genes (DEG). The functions of these genes in various metabolic pathways were predicted and some of these genes were considered as potential targets. Three major proteins were selected as putative targets, namely permease IIC component, ABC transporter and histidine kinase. The three-dimensional structures of these targets were predicted by molecular modelling. The herbal bioactive compounds were screened by computer-aided virtual screening and binding potentials against the drug targets were predicted by molecular docking. Quercetin present in Psidium guajava (binding energy of -9.1 kcal/mol), Ellagic acid found in Punica granatum and Psidium guajava (binding energy -9.0 kcal/mol) and Curcumin, present in Curcuma longa (binding energy -7.8 kcal/mol) demonstrated minimum binding energy and more number of interacting residues with the drug targets. Further, comparative study revealed that phytoligands demonstrated better binding affinities to the drug targets in comparison with usual ligands. Thus, this investigation explores the therapeutic probabilities of selected phytoligands against the putative drug targets of C. difficile.

  4. Rational optimization of drug-target residence time: Insights from inhibitor binding to the S. aureus FabI enzyme-product complex

    PubMed Central

    Chang, Andrew; Schiebel, Johannes; Yu, Weixuan; Bommineni, Gopal R.; Pan, Pan; Baxter, Michael V.; Khanna, Avinash; Sotriffer, Christoph A.; Kisker, Caroline; Tonge, Peter J.

    2013-01-01

    Drug-target kinetics has recently emerged as an especially important facet of the drug discovery process. In particular, prolonged drug-target residence times may confer enhanced efficacy and selectivity in the open in vivo system. However, the lack of accurate kinetic and structural data for series of congeneric compounds hinders the rational design of inhibitors with decreased off-rates. Therefore, we chose the Staphylococcus aureus enoyl-ACP reductase (saFabI) - an important target for the development of new anti-staphylococcal drugs - as a model system to rationalize and optimize the drug-target residence time on a structural basis. Using our new, efficient and widely applicable mechanistically informed kinetic approach, we obtained a full characterization of saFabI inhibition by a series of 20 diphenyl ethers complemented by a collection of 9 saFabI-inhibitor crystal structures. We identified a strong correlation between the affinities of the investigated saFabI diphenyl ether inhibitors and their corresponding residence times, which can be rationalized on a structural basis. Due to its favorable interactions with the enzyme, the residence time of our most potent compound exceeds 10 hours. In addition, we found that affinity and residence time in this system can be significantly enhanced by modifications predictable by a careful consideration of catalysis. Our study provides a blueprint for investigating and prolonging drug-target kinetics and may aid in the rational design of long-residence-time inhibitors targeting the essential saFabI enzyme. PMID:23697754

  5. A Drug-Target Network-Based Approach to Evaluate the Efficacy of Medicinal Plants for Type II Diabetes Mellitus

    PubMed Central

    Gu, Jiangyong; Chen, Lirong; Yuan, Gu; Xu, Xiaojie

    2013-01-01

    The use of plants as natural medicines in the treatment of type II diabetes mellitus (T2DM) has long been of special interest. In this work, we developed a docking score-weighted prediction model based on drug-target network to evaluate the efficacy of medicinal plants for T2DM. High throughput virtual screening from chemical library of natural products was adopted to calculate the binding affinity between natural products contained in medicinal plants and 33 T2DM-related proteins. The drug-target network was constructed according to the strength of the binding affinity if the molecular docking score satisfied the threshold. By linking the medicinal plant with T2DM through drug-target network, the model can predict the efficacy of natural products and medicinal plant for T2DM. Eighteen thousand nine hundred ninety-nine natural products and 1669 medicinal plants were predicted to be potentially bioactive. PMID:24223610

  6. Stapled peptides for intracellular drug targets.

    PubMed

    Verdine, Gregory L; Hilinski, Gerard J

    2012-01-01

    Proteins that engage in intracellular interactions with other proteins are widely considered among the most biologically appealing yet chemically intractable targets for drug discovery. The critical interaction surfaces of these proteins typically lack the deep hydrophobic involutions that enable potent, selective targeting by small organic molecules, and their localization within the cell puts them beyond the reach of protein therapeutics. Considerable interest has therefore arisen in next-generation targeting molecules that combine the broad target recognition capabilities of protein therapeutics with the robust cell-penetrating ability of small molecules. One type that has shown promise in early-stage studies is hydrocarbon-stapled α-helical peptides, a novel class of synthetic miniproteins locked into their bioactive α-helical fold through the site-specific introduction of a chemical brace, an all-hydrocarbon staple. Stapling can greatly improve the pharmacologic performance of peptides, increasing their target affinity, proteolytic resistance, and serum half-life while conferring on them high levels of cell penetration through endocytic vesicle trafficking. Here, we discuss considerations crucial to the successful design and evaluation of potent stapled peptide interactions, our intention being to facilitate the broad application of this technology to intractable targets of both basic biologic interest and potential therapeutic value.

  7. Is the Mitochondrion a Good Malaria Drug Target?

    PubMed

    Goodman, Christopher D; Buchanan, Hayley D; McFadden, Geoffrey I

    2017-03-01

    Rapid emergence of resistance to atovaquone, which targets electron transport in the malaria parasite mitochondrion, relegated its use to prophylaxis and even cast a shadow over the development of drugs targeting other parasite mitochondrial pathways. Here we argue for a renewed focus on the mitochondrion as a drug target, focusing particularly on the issues of resistance. We posit a hypothesis for why atovaquone resistance emerges so quickly, and we explain how facile acquisition of resistance is apparently offset by an inability of parasites to spread this resistance. We also explore the utility and resistance issues for emerging new drugs targeting parasite mitochondria, concluding that the mitochondrion is indeed an excellent target. Copyright © 2016. Published by Elsevier Ltd.

  8. Drug-Target Binding Investigated by Quantum Mechanical/Molecular Mechanical (QM/MM) Methods

    NASA Astrophysics Data System (ADS)

    Rothlisberger, U.; Carloni, P.

    Many important drugs, also used in the clinics, exert their function by binding covalently to their targets. Understanding their action requires quantum mechanical simulations. Here, after briefly reviewing few basic concepts of thermodynamics and kinetics of drug-target binding, we summarize principles and applications of Car-Parrinello quantum mechanics/molecular mechanics (QM/MM) simulations. From this discussion, this approach emerges as a computational methodology particularly well suited to investigate covalent binding in systems of pharmacological relevance.

  9. Novel antibacterial compounds and their drug targets - successes and challenges.

    PubMed

    Kaczor, Agnieszka A; Polski, Andrzej; Sobótka-Polska, Karolina; Pachuta-Stec, Anna; Makarska-Bialokoz, Magdalena; Pitucha, Monika

    2016-12-12

    molecular basis of drug resistance, drug targets for novel antibacterial drugs, and new compounds (since year 2010) from different chemical classes with antibacterial activity, focusing on structure-activity relationships.

  10. An immuno-chemo-proteomics method for drug target deconvolution.

    PubMed

    Saxena, Chaitanya; Zhen, Eugene; Higgs, Richard E; Hale, John E

    2008-08-01

    Chemical proteomics is an emerging technique for drug target deconvolution and profiling the toxicity of known drugs. With the use of this technique, the specificity of a small molecule inhibitor toward its potential targets can be characterized and information thus obtained can be used in optimizing lead compounds. Most commonly, small molecules are immobilized on solid supports and used as affinity chromatography resins to bind targets. However, it is difficult to evaluate the effect of immobilization on the affinity of the compounds to their targets. Here, we describe the development and application of a soluble probe where a small molecule was coupled with a peptide epitope which was used to affinity isolate binding proteins from cell lysate. The soluble probe allowed direct verification that the compound after coupling with peptide epitope retained its binding characteristics. The PKC-alpha inhibitor Bisindolylmaleimide-III was coupled with a peptide containing the FLAG epitope. Following incubation with cellular lysates, the compound and associated proteins were affinity isolated using anti-FLAG antibody beads. Using this approach, we identified the known Bisindolylmaleimide-III targets, PKC-alpha, GSK3-beta, CaMKII, adenosine kinase, CDK2, and quinine reductase type 2, as well as previously unidentified targets PKAC-alpha, prohibitin, VDAC and heme binding proteins. This method was directly compared to the solid-phase method (small molecule was immobilized to a solid support) providing an orthogonal strategy to aid in target deconvolution and help to eliminate false positives originating from nonspecific binding of the proteins to the matrix.

  11. Comparative genomics study for identification of putative drug targets in Salmonella typhi Ty2.

    PubMed

    Batool, Nisha; Waqar, Maleeha; Batool, Sidra

    2016-01-15

    Typhoid presents a major health concern in developing countries with an estimated annual infection rate of 21 million. The disease is caused by Salmonella typhi, a pathogenic bacterium acquiring multiple drug resistance. We aim to identify proteins that could prove to be putative drug targets in the genome of S. typhi str. Ty2. We employed comparative and subtractive genomics to identify targets that are absent in humans and are essential to S. typhi Ty2. We concluded that 46 proteins essential to pathogen are absent in the host genome. Filtration on the basis of drug target prioritization singled out 20 potentially therapeutic targets. Their absence in the host and specificity to S. typhi Ty2 makes them ideal targets for treating typhoid in Homo sapiens. 3D structures of two of the final target enzymes, MurA and MurB have been predicted via homology modeling which are then used for a docking study.

  12. Sirtuins as potential drug targets for metablic diseases

    USDA-ARS?s Scientific Manuscript database

    Recent studies of the sirtuin family of proteins, which possess NAD+/-dependent deacetylase and ADP ribosyltransferase activities, indicate that they regulate many biological functions, such as longevity and metabolism. These findings also suggest that sirtuins might serve as valuable drug targets f...

  13. Drug target prioritization by perturbed gene expression and network information

    PubMed Central

    Isik, Zerrin; Baldow, Christoph; Cannistraci, Carlo Vittorio; Schroeder, Michael

    2015-01-01

    Drugs bind to their target proteins, which interact with downstream effectors and ultimately perturb the transcriptome of a cancer cell. These perturbations reveal information about their source, i.e., drugs’ targets. Here, we investigate whether these perturbations and protein interaction networks can uncover drug targets and key pathways. We performed the first systematic analysis of over 500 drugs from the Connectivity Map. First, we show that the gene expression of drug targets is usually not significantly affected by the drug perturbation. Hence, expression changes after drug treatment on their own are not sufficient to identify drug targets. However, ranking of candidate drug targets by network topological measures prioritizes the targets. We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information. The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time. Local radiality identifies more diverse targets with fewer neighbors and possibly less side effects. PMID:26615774

  14. Pharmacological Validation of Trypanosoma brucei Phosphodiesterases as Novel Drug Targets

    PubMed Central

    de Koning, Harry P.; Gould, Matthew K.; Sterk, Geert Jan; Tenor, Hermann; Kunz, Stefan; Luginbuehl, Edith; Seebeck, Thomas

    2012-01-01

    The development of drugs for neglected infectious diseases often uses parasite-specific enzymes as targets. We here demonstrate that parasite enzymes with highly conserved human homologs may represent a promising reservoir of new potential drug targets. The cyclic nucleotide-specific phosphodiesterases (PDEs) of Trypanosoma brucei, causative agent of the fatal human sleeping sickness, are essential for the parasite. The highly conserved human homologs are well-established drug targets. We here describe what is to our knowledge the first pharmacological validation of trypanosomal PDEs as drug targets. High-throughput screening of a proprietary compound library identified a number of potent hits. One compound, the tetrahydrophthalazinone compound A (Cpd A), was further characterized. It causes a dramatic increase of intracellular cyclic adenosine monophosphate (cAMP). Short-term cell viability is not affected, but cell proliferation is inhibited immediately, and cell death occurs within 3 days. Cpd A prevents cytokinesis, resulting in multinucleated, multiflagellated cells that eventually lyse. These observations pharmacologically validate the highly conserved trypanosomal PDEs as potential drug targets. PMID:22291195

  15. Discovery of cancer drug targets by CRISPR-Cas9 screening of protein domains.

    PubMed

    Shi, Junwei; Wang, Eric; Milazzo, Joseph P; Wang, Zihua; Kinney, Justin B; Vakoc, Christopher R

    2015-06-01

    CRISPR-Cas9 genome editing technology holds great promise for discovering therapeutic targets in cancer and other diseases. Current screening strategies target CRISPR-Cas9-induced mutations to the 5' exons of candidate genes, but this approach often produces in-frame variants that retain functionality, which can obscure even strong genetic dependencies. Here we overcome this limitation by targeting CRISPR-Cas9 mutagenesis to exons encoding functional protein domains. This generates a higher proportion of null mutations and substantially increases the potency of negative selection. We also show that the magnitude of negative selection can be used to infer the functional importance of individual protein domains of interest. A screen of 192 chromatin regulatory domains in murine acute myeloid leukemia cells identifies six known drug targets and 19 additional dependencies. A broader application of this approach may allow comprehensive identification of protein domains that sustain cancer cells and are suitable for drug targeting.

  16. In vivo imaging of specific drug target binding at subcellular resolution

    PubMed Central

    Dubach, J.M.; Vinegoni, C.; Mazitschek, R.; Fumene Feruglio, P.; Cameron, L.A.; Weissleder, R.

    2015-01-01

    The possibility to measure binding of small molecule drugs to desired targets in live cells could provide a better understanding of drug action. However, current approaches mostly yield static data, require lysis or rely on indirect assays and thus often provide an incomplete understanding of drug action. Here, we present a multiphoton fluorescence anisotropy microscopy live cell imaging technique to measure and map drug-target interaction in real time at subcellular resolution. This approach is generally applicable using any fluorescently labeled drug and enables high resolution spatial and temporal mapping of bound and unbound drug distribution. To illustrate our approach we measure intracellular target engagement of the chemotherapeutic Olaparib, a poly(ADP-ribose) polymerase inhibitor, in live cells and within a tumor in vivo. These results are the first generalizable approach to directly measure drug-target binding in vivo and present a promising tool to enhance understanding of drug activity. PMID:24867710

  17. In vivo imaging of specific drug-target binding at subcellular resolution

    NASA Astrophysics Data System (ADS)

    Dubach, J. M.; Vinegoni, C.; Mazitschek, R.; Fumene Feruglio, P.; Cameron, L. A.; Weissleder, R.

    2014-05-01

    The possibility of measuring binding of small-molecule drugs to desired targets in live cells could provide a better understanding of drug action. However, current approaches mostly yield static data, require lysis or rely on indirect assays and thus often provide an incomplete understanding of drug action. Here, we present a multiphoton fluorescence anisotropy microscopy live cell imaging technique to measure and map drug-target interaction in real time at subcellular resolution. This approach is generally applicable using any fluorescently labelled drug and enables high-resolution spatial and temporal mapping of bound and unbound drug distribution. To illustrate our approach we measure intracellular target engagement of the chemotherapeutic Olaparib, a poly(ADP-ribose) polymerase inhibitor, in live cells and within a tumour in vivo. These results are the first generalizable approach to directly measure drug-target binding in vivo and present a promising tool to enhance understanding of drug activity.

  18. Quantification of pancreatic cancer proteome and phosphorylome: indicates molecular events likely contributing to cancer and activity of drug targets.

    PubMed

    Britton, David; Zen, Yoh; Quaglia, Alberto; Selzer, Stefan; Mitra, Vikram; Löβner, Christopher; Jung, Stephan; Böhm, Gitte; Schmid, Peter; Prefot, Petra; Hoehle, Claudia; Koncarevic, Sasa; Gee, Julia; Nicholson, Robert; Ward, Malcolm; Castellano, Leandro; Stebbing, Justin; Zucht, Hans Dieter; Sarker, Debashis; Heaton, Nigel; Pike, Ian

    2014-01-01

    LC-MS/MS phospho-proteomics is an essential technology to help unravel the complex molecular events that lead to and propagate cancer. We have developed a global phospho-proteomic workflow to determine activity of signaling pathways and drug targets in pancreatic cancer tissue for clinical application. Peptides resulting from tryptic digestion of proteins extracted from frozen tissue of pancreatic ductal adenocarcinoma and background pancreas (n = 12), were labelled with tandem mass tags (TMT 8-plex), separated by strong cation exchange chromatography, then were analysed by LC-MS/MS directly or first enriched for phosphopeptides using IMAC and TiO2, prior to analysis. In-house, commercial and freeware bioinformatic platforms were used to identify relevant biological events from the complex dataset. Of 2,101 proteins identified, 152 demonstrated significant difference in abundance between tumor and non-tumor tissue. They included proteins that are known to be up-regulated in pancreatic cancer (e.g. Mucin-1), but the majority were new candidate markers such as HIPK1 & MLCK. Of the 6,543 unique phosphopeptides identified (6,284 unique phosphorylation sites), 635 showed significant regulation, particularly those from proteins involved in cell migration (Rho guanine nucleotide exchange factors & MRCKα) and formation of focal adhesions. Activator phosphorylation sites on FYN, AKT1, ERK2, HDAC1 and other drug targets were found to be highly modulated (≥2 fold) in different cases highlighting their predictive power. Here we provided critical information enabling us to identify the common and unique molecular events likely contributing to cancer in each case. Such information may be used to help predict more bespoke therapy suitable for an individual case.

  19. Quantification of Pancreatic Cancer Proteome and Phosphorylome: Indicates Molecular Events Likely Contributing to Cancer and Activity of Drug Targets

    PubMed Central

    Britton, David; Zen, Yoh; Quaglia, Alberto; Selzer, Stefan; Mitra, Vikram; Lößner, Christopher; Jung, Stephan; Böhm, Gitte; Schmid, Peter; Prefot, Petra; Hoehle, Claudia; Koncarevic, Sasa; Gee, Julia; Nicholson, Robert; Ward, Malcolm; Castellano, Leandro; Stebbing, Justin; Zucht, Hans Dieter; Sarker, Debashis; Heaton, Nigel; Pike, Ian

    2014-01-01

    Objective LC-MS/MS phospho-proteomics is an essential technology to help unravel the complex molecular events that lead to and propagate cancer. We have developed a global phospho-proteomic workflow to determine activity of signaling pathways and drug targets in pancreatic cancer tissue for clinical application. Methods Peptides resulting from tryptic digestion of proteins extracted from frozen tissue of pancreatic ductal adenocarcinoma and background pancreas (n = 12), were labelled with tandem mass tags (TMT 8-plex), separated by strong cation exchange chromatography, then were analysed by LC-MS/MS directly or first enriched for phosphopeptides using IMAC and TiO2, prior to analysis. In-house, commercial and freeware bioinformatic platforms were used to identify relevant biological events from the complex dataset. Results Of 2,101 proteins identified, 152 demonstrated significant difference in abundance between tumor and non-tumor tissue. They included proteins that are known to be up-regulated in pancreatic cancer (e.g. Mucin-1), but the majority were new candidate markers such as HIPK1 & MLCK. Of the 6,543 unique phosphopeptides identified (6,284 unique phosphorylation sites), 635 showed significant regulation, particularly those from proteins involved in cell migration (Rho guanine nucleotide exchange factors & MRCKα) and formation of focal adhesions. Activator phosphorylation sites on FYN, AKT1, ERK2, HDAC1 and other drug targets were found to be highly modulated (≥2 fold) in different cases highlighting their predictive power. Conclusion Here we provided critical information enabling us to identify the common and unique molecular events likely contributing to cancer in each case. Such information may be used to help predict more bespoke therapy suitable for an individual case. PMID:24670416

  20. Sexually transmitted diseases putative drug target database: a comprehensive database of putative drug targets of pathogens identified by comparative genomics.

    PubMed

    Malipatil, Vijayakumari; Madagi, Shivkumar; Bhattacharjee, Biplab

    2013-01-01

    Sexually transmitted diseases (STD) are the serious public health problems and also impose a financial burden on the economy. Sexually transmitted infections are cured with single or multiple antibiotics. However, in many cases the organism showed persistence even after treatment. In the current study, the set of druggable targets in STD pathogens have been identified by comparative genomics. The subtractive genomics scheme exploits the properties of non-homology, essentiality, membrane localization and metabolic pathway uniqueness in identifying the drug targets. To achieve the effective use of data and to understand properties of drug target under single canopy, an integrated knowledge database of drug targets in STD bacteria was created. Data for each drug targets include biochemical pathway, function, cellular localization, essentiality score and structural details. The proteome of STD pathogens yielded 44 membrane associated proteins possessing unique metabolic pathways when subjected to the algorithm. The database can be accessed at http://biomedresearchasia.org/index.html. Diverse data merged in the common framework of this database is expected to be valuable not only for basic studies in clinical bioinformatics, but also for basic studies in immunological, biotechnological and clinical fields.

  1. Crowd Sourcing a New Paradigm for Interactome Driven Drug Target Identification in Mycobacterium tuberculosis

    PubMed Central

    Rohira, Harsha; Bhat, Ashwini G.; Passi, Anurag; Mukherjee, Keya; Choudhary, Kumari Sonal; Kumar, Vikas; Arora, Anshula; Munusamy, Prabhakaran; Subramanian, Ahalyaa; Venkatachalam, Aparna; S, Gayathri; Raj, Sweety; Chitra, Vijaya; Verma, Kaveri; Zaheer, Salman; J, Balaganesh; Gurusamy, Malarvizhi; Razeeth, Mohammed; Raja, Ilamathi; Thandapani, Madhumohan; Mevada, Vishal; Soni, Raviraj; Rana, Shruti; Ramanna, Girish Muthagadhalli; Raghavan, Swetha; Subramanya, Sunil N.; Kholia, Trupti; Patel, Rajesh; Bhavnani, Varsha; Chiranjeevi, Lakavath; Sengupta, Soumi; Singh, Pankaj Kumar; Atray, Naresh; Gandhi, Swati; Avasthi, Tiruvayipati Suma; Nisthar, Shefin; Anurag, Meenakshi; Sharma, Pratibha; Hasija, Yasha; Dash, Debasis; Sharma, Arun; Scaria, Vinod; Thomas, Zakir; Chandra, Nagasuma; Brahmachari, Samir K.; Bhardwaj, Anshu

    2012-01-01

    A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative ‘Connect to Decode’ (C2D) to generate the first and largest manually curated interactome of Mtb termed ‘interactome pathway’ (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach. PMID:22808064

  2. Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis.

    PubMed

    Vashisht, Rohit; Mondal, Anupam Kumar; Jain, Akanksha; Shah, Anup; Vishnoi, Priti; Priyadarshini, Priyanka; Bhattacharyya, Kausik; Rohira, Harsha; Bhat, Ashwini G; Passi, Anurag; Mukherjee, Keya; Choudhary, Kumari Sonal; Kumar, Vikas; Arora, Anshula; Munusamy, Prabhakaran; Subramanian, Ahalyaa; Venkatachalam, Aparna; Gayathri, S; Raj, Sweety; Chitra, Vijaya; Verma, Kaveri; Zaheer, Salman; Balaganesh, J; Gurusamy, Malarvizhi; Razeeth, Mohammed; Raja, Ilamathi; Thandapani, Madhumohan; Mevada, Vishal; Soni, Raviraj; Rana, Shruti; Ramanna, Girish Muthagadhalli; Raghavan, Swetha; Subramanya, Sunil N; Kholia, Trupti; Patel, Rajesh; Bhavnani, Varsha; Chiranjeevi, Lakavath; Sengupta, Soumi; Singh, Pankaj Kumar; Atray, Naresh; Gandhi, Swati; Avasthi, Tiruvayipati Suma; Nisthar, Shefin; Anurag, Meenakshi; Sharma, Pratibha; Hasija, Yasha; Dash, Debasis; Sharma, Arun; Scaria, Vinod; Thomas, Zakir; Chandra, Nagasuma; Brahmachari, Samir K; Bhardwaj, Anshu

    2012-01-01

    A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

  3. The Gastric H,K ATPase as a Drug Target

    PubMed Central

    Sachs, George; Shin, Jai Moo; Vagin, Olga; Lambrecht, Nils; Yakubov, Iskandar; Munson, Keith

    2010-01-01

    The recent progress in therapy if acid disease has relied heavily on the performance of drugs targeted against the H,K ATPase of the stomach and the H2 receptor antagonists. It has become apparent in the last decade that the proton pump is the target that has the likelihood of being the most sustainable area of therapeutic application in the regulation of acid suppression. The process of activation of acid secretion requires a change in location of the ATPase from cytoplasmic tubules into the microvilli of the secretory canaliculus of the parietal cell. Stimulation of the resting parietal cell, with involvement of F-actin and ezrin does not use significant numbers of SNARE proteins, because their message is depleted in the pure parietal cell transcriptome. The cell morphology and gene expression suggest a tubule fusion-eversion event. As the active H,K ATPase requires efflux of KCl for activity we have, using the transcriptome derived from 99% pure parietal cells and immunocytochemistry, provided evidence that the KCl pathway is mediated by a KCQ1/KCNE2 complex for supplying K+ and CLIC6 for supplying the accompanying Cl−. The pump has been modeled on the basis of the structures of different conformations of the sr Ca ATPase related to the catalytic cycle. These models use the effects of site directed mutations and identification of the binding domain of the K competitive acid pump antagonists or the defined site of binding for the covalent class of proton pump inhibitors. The pump undergoes conformational changes associated with phosphorylation to allow the ion binding site to change exposure from cytoplasmic to luminal exposure. We have been able to postulate that the very low gastric pH is achieved by lysine 791 motion extruding the hydronium ion bound to carboxylates in the middle of the membrane domain. These models also allow description of the K+ entry to form the K+ liganded form of the enzyme and the reformation of the ion site inward conformation thus

  4. Members of FOX family could be drug targets of cancers.

    PubMed

    Wang, Jinhua; Li, Wan; Zhao, Ying; Kang, De; Fu, Weiqi; Zheng, Xiangjin; Pang, Xiaocong; Du, Guanhua

    2017-08-19

    FOX families play important roles in biological processes, including metabolism, development, differentiation, proliferation, apoptosis, migration, invasion and longevity. Here we are focusing on roles of FOX members in cancers, FOX members and drug resistance, FOX members and stem cells. Finally, FOX members as drug targets of cancer treatment were discussed. Future perspectives of FOXC1 research were described in the end. Copyright © 2017. Published by Elsevier Inc.

  5. Progress in functional genomics approaches to antifungal drug target discovery.

    PubMed

    De Backer, Marianne D; Van Dijck, Patrick

    2003-10-01

    Antifungal drug discovery is starting to benefit from the enormous advances in the genomics field, which have occurred in the past decade. As traditional drug screening on existing targets is not delivering the long-awaited potent antifungals, efforts to use novel genetics and genomics-based strategies to aid in the discovery of novel drug targets are gaining increased importance. The current paradigm in antifungal drug target discovery focuses on basically two main classes of targets to evaluate: genes essential for viability and virulence or pathogenicity factors. Here we report on recent advances in genetics and genomics-based technologies that will allow us not only to identify and validate novel fungal drug targets, but hopefully in the longer run also to discover potent novel therapeutic agents. Fungal pathogens have typically presented significant obstacles when subjected to genetics, but the creativity of scientists in the anti-infectives field and the cross-talk with scientists in other areas is now yielding exciting new tools and technologies to tackle the problem of finding potent, specific and non-toxic antifungal therapeutics.

  6. Identification of putative drug targets in Vancomycin-resistant Staphylococcus aureus (VRSA) using computer aided protein data analysis.

    PubMed

    Hasan, Md Anayet; Khan, Md Arif; Sharmin, Tahmina; Hasan Mazumder, Md Habibul; Chowdhury, Afrin Sultana

    2016-01-01

    Vancomycin-resistant Staphylococcus aureus (VRSA) is a Gram-positive, facultative aerobic bacterium which is evolved from the extensive exposure of Vancomycin to Methicillin resistant S. aureus (MRSA) that had become the most common cause of hospital and community-acquired infections. Due to the emergence of different antibiotic resistance strains, there is an exigency to develop novel drug targets to address the provocation of multidrug-resistant bacteria. In this study, in-silico genome subtraction methodology was used to design potential and pathogen specific drug targets against VRSA. Our study divulged 1987 proteins from the proteome of 34,549 proteins, which have no homologues in human genome after sequential analysis through CD-HIT and BLASTp. The high stringency analysis of the remaining proteins against database of essential genes (DEG) resulted in 169 proteins which are essential for S. aureus. Metabolic pathway analysis of human host and pathogen by KAAS at the KEGG server sorted out 19 proteins involved in unique metabolic pathways. 26 human non-homologous membrane-bound essential proteins including 4 which were also involved in unique metabolic pathway were deduced through PSORTb, CELLO v.2.5, ngLOC. Functional classification of uncharacterized proteins through SVMprot derived 7 human non-homologous membrane-bound hypothetical essential proteins. Study of potential drug target against Drug Bank revealed pbpA-penicillin-binding protein 1 and hypothetical protein MQW_01796 as the best drug target candidate. 2D structure was predicted by PRED-TMBB, 3D structure and functional analysis was also performed. Protein-protein interaction network of potential drug target proteins was analyzed by using STRING. The identified drug targets are expected to have great potential for designing novel drugs against VRSA infections and further screening of the compounds against these new targets may result in the discovery of novel therapeutic compounds that can be

  7. DINIES: drug-target interaction network inference engine based on supervised analysis.

    PubMed

    Yamanishi, Yoshihiro; Kotera, Masaaki; Moriya, Yuki; Sawada, Ryusuke; Kanehisa, Minoru; Goto, Susumu

    2014-07-01

    DINIES (drug-target interaction network inference engine based on supervised analysis) is a web server for predicting unknown drug-target interaction networks from various types of biological data (e.g. chemical structures, drug side effects, amino acid sequences and protein domains) in the framework of supervised network inference. The originality of DINIES lies in prediction with state-of-the-art machine learning methods, in the integration of heterogeneous biological data and in compatibility with the KEGG database. The DINIES server accepts any 'profiles' or precalculated similarity matrices (or 'kernels') of drugs and target proteins in tab-delimited file format. When a training data set is submitted to learn a predictive model, users can select either known interaction information in the KEGG DRUG database or their own interaction data. The user can also select an algorithm for supervised network inference, select various parameters in the method and specify weights for heterogeneous data integration. The server can provide integrative analyses with useful components in KEGG, such as biological pathways, functional hierarchy and human diseases. DINIES (http://www.genome.jp/tools/dinies/) is publicly available as one of the genome analysis tools in GenomeNet.

  8. Novel drug target identification for the treatment of dementia using multi-relational association mining

    PubMed Central

    Nguyen, Thanh-Phuong; Priami, Corrado; Caberlotto, Laura

    2015-01-01

    Dementia is a neurodegenerative condition of the brain in which there is a progressive and permanent loss of cognitive and mental performance. Despite the fact that the number of people with dementia worldwide is steadily increasing and regardless of the advances in the molecular characterization of the disease, current medical treatments for dementia are purely symptomatic and hardly effective. We present a novel multi-relational association mining method that integrates the huge amount of scientific data accumulated in recent years to predict potential novel targets for innovative therapeutic treatment of dementia. Owing to the ability of processing large volumes of heterogeneous data, our method achieves a high performance and predicts numerous drug targets including several serine threonine kinase and a G-protein coupled receptor. The predicted drug targets are mainly functionally related to metabolism, cell surface receptor signaling pathways, immune response, apoptosis, and long-term memory. Among the highly represented kinase family and among the G-protein coupled receptors, DLG4 (PSD-95), and the bradikynin receptor 2 are highlighted also for their proposed role in memory and cognition, as described in previous studies. These novel putative targets hold promises for the development of novel therapeutic approaches for the treatment of dementia. PMID:26154857

  9. Deductive genomics: a functional approach to identify innovative drug targets in the post-genome era.

    PubMed

    Stumm, Gabriele; Russ, Andreas; Nehls, Michael

    2002-01-01

    The sequencing of the human genome has generated a drug discovery process that is based on sequence analysis and hypothesis-driven (inductive) prediction of gene function. This approach, which we term inductive genomics, is currently dominating the efforts of the pharmaceutical industry to identify new drug targets. According to recent studies, this sequence-driven discovery process is paradoxically increasing the average cost of drug development, thus falling short of the promise of the Human Genome Project to simplify the creation of much needed novel therapeutics. In the early stages of discovery, the flurry of new gene sequences makes it difficult to pick and prioritize the most promising product candidates for product development, as with existing technologies important decisions have to be based on circumstantial evidence that does not strongly predict therapeutic potential. This is because the physiological function of a potential target cannot be predicted by gene sequence analysis and in vitro technologies alone. In contrast, deductive genomics, or large-scale forward genetics, bridges the gap between sequence and function by providing a function-driven in vivo screen of a highly orthologous mammalian model genome for medically relevant physiological functions and drug targets. This approach allows drug discovery to move beyond the focus on sequence-driven identification of new members of classical drug-able protein families towards the biology-driven identification of innovative targets and biological pathways.

  10. DDTRP: Database of Drug Targets for Resistant Pathogens

    PubMed Central

    Sundaramurthi, Jagadish Chandrabose; Ramanandan, Prabhakaran; Brindha, Sridharan; Subhasree, Chelladurai Ramarathnam; Prasad, Abhimanyu; Kumaraswami, Vasanthapuram; Hanna, Luke Elizabeth

    2011-01-01

    Emergence of drug resistance is a major threat to public health. Many pathogens have developed resistance to most of the existing antibiotics, and multidrug-resistant and extensively drug resistant strains are extremely difficult to treat. This has resulted in an urgent need for novel drugs. We describe a database called ‘Database of Drug Targets for Resistant Pathogens’ (DDTRP). The database contains information on drugs with reported resistance, their respective targets, metabolic pathways involving these targets, and a list of potential alternate targets for seven pathogens. The database can be accessed freely at http://bmi.icmr.org.in/DDTRP. PMID:21938213

  11. Rho, ROCK and actomyosin contractility in metastasis as drug targets

    PubMed Central

    Bruce, Fanshawe; Sanz-Moreno, Victoria

    2016-01-01

    Metastasis is the spread of cancer cells around the body and the cause of the majority of cancer deaths. Metastasis is a very complex process in which cancer cells need to dramatically modify their cytoskeleton and cope with different environments to successfully colonize a secondary organ. In this review, we discuss recent findings pointing at Rho-ROCK or actomyosin force (or both) as major drivers of many of the steps required for metastatic success. We propose that these are important drug targets that need to be considered in the clinic to palliate metastatic disease. PMID:27158478

  12. Drug targeting systems for cancer therapy: nanotechnological approach.

    PubMed

    Tigli Aydin, R Seda

    2015-01-01

    Progress in cancer treatment remains challenging because of the great nature of tumor cells to be drug resistant. However, advances in the field of nanotechnology have enabled the delivery of drugs for cancer treatment by passively and actively targeting to tumor cells with nanoparticles. Dramatic improvements in nanotherapeutics, as applied to cancer, have rapidly accelerated clinical investigations. In this review, drug-targeting systems using nanotechnology and approved and clinically investigated nanoparticles for cancer therapy are discussed. In addition, the rationale for a nanotechnological approach to cancer therapy is emphasized because of its promising advances in the treatment of cancer patients.

  13. The drug target genes show higher evolutionary conservation than non-target genes.

    PubMed

    Lv, Wenhua; Xu, Yongdeng; Guo, Yiying; Yu, Ziqi; Feng, Guanglong; Liu, Panpan; Luan, Meiwei; Zhu, Hongjie; Liu, Guiyou; Zhang, Mingming; Lv, Hongchao; Duan, Lian; Shang, Zhenwei; Li, Jin; Jiang, Yongshuai; Zhang, Ruijie

    2016-01-26

    Although evidence indicates that drug target genes share some common evolutionary features, there have been few studies analyzing evolutionary features of drug targets from an overall level. Therefore, we conducted an analysis which aimed to investigate the evolutionary characteristics of drug target genes. We compared the evolutionary conservation between human drug target genes and non-target genes by combining both the evolutionary features and network topological properties in human protein-protein interaction network. The evolution rate, conservation score and the percentage of orthologous genes of 21 species were included in our study. Meanwhile, four topological features including the average shortest path length, betweenness centrality, clustering coefficient and degree were considered for comparison analysis. Then we got four results as following: compared with non-drug target genes, 1) drug target genes had lower evolutionary rates; 2) drug target genes had higher conservation scores; 3) drug target genes had higher percentages of orthologous genes and 4) drug target genes had a tighter network structure including higher degrees, betweenness centrality, clustering coefficients and lower average shortest path lengths. These results demonstrate that drug target genes are more evolutionarily conserved than non-drug target genes. We hope that our study will provide valuable information for other researchers who are interested in evolutionary conservation of drug targets.

  14. Parasite neuropeptide biology: Seeding rational drug target selection?

    PubMed Central

    McVeigh, Paul; Atkinson, Louise; Marks, Nikki J.; Mousley, Angela; Dalzell, Johnathan J.; Sluder, Ann; Hammerland, Lance; Maule, Aaron G.

    2011-01-01

    The rationale for identifying drug targets within helminth neuromuscular signalling systems is based on the premise that adequate nerve and muscle function is essential for many of the key behavioural determinants of helminth parasitism, including sensory perception/host location, invasion, locomotion/orientation, attachment, feeding and reproduction. This premise is validated by the tendency of current anthelmintics to act on classical neurotransmitter-gated ion channels present on helminth nerve and/or muscle, yielding therapeutic endpoints associated with paralysis and/or death. Supplementary to classical neurotransmitters, helminth nervous systems are peptide-rich and encompass associated biosynthetic and signal transduction components – putative drug targets that remain to be exploited by anthelmintic chemotherapy. At this time, no neuropeptide system-targeting lead compounds have been reported, and given that our basic knowledge of neuropeptide biology in parasitic helminths remains inadequate, the short-term prospects for such drugs remain poor. Here, we review current knowledge of neuropeptide signalling in Nematoda and Platyhelminthes, and highlight a suite of 19 protein families that yield deleterious phenotypes in helminth reverse genetics screens. We suggest that orthologues of some of these peptidergic signalling components represent appealing therapeutic targets in parasitic helminths. PMID:24533265

  15. The prokaryotic FAD synthetase family: a potential drug target.

    PubMed

    Serrano, Ana; Ferreira, Patricia; Martínez-Júlvez, Marta; Medina, Milagros

    2013-01-01

    Disruption of cellular production of the flavin cofactors, flavin adenine mononucleotide (FMN) and flavin adenine dinucleotide(FAD) will prevent the assembly of a large number of flavoproteins and flavoenzymes involved in key metabolic processes in all types of organisms. The enzymes responsible for FMN and FAD production in prokaryotes and eukaryotes exhibit various structural characteristics to catalyze the same chemistry, a fact that converts the prokaryotic FAD synthetase (FADS) in a potential drug target for the development of inhibitors endowed with anti-pathogenic activity. The first step before searching for selective inhibitors of FADS is to understand the structural and functional mechanisms for the riboflavin kinase and FMN adenylyltransferase activities of the prokaryotic enzyme, and particularly to identify their differential functional characteristics with regard to the enzymes performing similar functions in other organisms, particularly humans. In this paper, an overview of the current knowledge of the structure-function relationships in prokaryotic FADS has been presented, as well as of the state of the art in the use of these enzymes as drug targets.

  16. Dihydrofolate reductase: A potential drug target in trypanosomes and leishmania

    NASA Astrophysics Data System (ADS)

    Zuccotto, Fabio; Martin, Andrew C. R.; Laskowski, Roman A.; Thornton, Janet M.; Gilbert, Ian H.

    1998-05-01

    Dihydrofolate reductase has successfully been used as a drug target in the area of anti-cancer, anti-bacterial and anti-malarial chemotherapy. Little has been done to evaluate it as a drug target for treatment of the trypanosomiases and leishmaniasis. A crystal structure of Leishmania major dihydrofolate reductase has been published. In this paper, we describe the modelling of Trypanosoma cruzi and Trypanosoma brucei dihydrofolate reductases based on this crystal structure. These structures and models have been used in the comparison of protozoan, bacterial and human enzymes in order to highlight the different features that can be used in the design of selective anti-protozoan agents. Comparison has been made between residues present in the active site, the accessibility of these residues, charge distribution in the active site, and the shape and size of the active sites. Whilst there is a high degree of similarity between protozoan, human and bacterial dihydrofolate reductase active sites, there are differences that provide potential for selective drug design. In particular, we have identified a set of residues which may be important for selective drug design and identified a larger binding pocket in the protozoan than the human and bacterial enzymes.

  17. Drug targeting to myofibroblasts: Implications for fibrosis and cancer.

    PubMed

    Yazdani, Saleh; Bansal, Ruchi; Prakash, Jai

    2017-07-16

    Myofibroblasts are the key players in extracellular matrix remodeling, a core phenomenon in numerous devastating fibrotic diseases. Not only in organ fibrosis, but also the pivotal role of myofibroblasts in tumor progression, invasion and metastasis has recently been highlighted. Myofibroblast targeting has gained tremendous attention in order to inhibit the progression of incurable fibrotic diseases, or to limit the myofibroblast-induced tumor progression and metastasis. In this review, we outline the origin of myofibroblasts, their general characteristics and functions during fibrosis progression in three major organs: liver, kidneys and lungs as well as in cancer. We will then discuss the state-of-the art drug targeting technologies to myofibroblasts in context of the above-mentioned organs and tumor microenvironment. The overall objective of this review is therefore to advance our understanding in drug targeting to myofibroblasts, and concurrently identify opportunities and challenges for designing new strategies to develop novel diagnostics and therapeutics against fibrosis and cancer. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  18. Drug Target Validation Methods in Malaria - Protein Interference Assay (PIA) as a Tool for Highly Specific Drug Target Validation.

    PubMed

    Meissner, Kamila A; Lunev, Sergey; Wang, Yuan-Ze; Linzke, Marleen; de Assis Batista, Fernando; Wrenger, Carsten; Groves, Matthew R

    2017-01-01

    The validation of drug targets in malaria and other human diseases remains a highly difficult and laborious process. In the vast majority of cases, highly specific small molecule tools to inhibit a proteins function in vivo are simply not available. Additionally, the use of genetic tools in the analysis of malarial pathways is challenging. These issues result in difficulties in specifically modulating a hypothetical drug target's function in vivo. The current "toolbox" of various methods and techniques to identify a protein's function in vivo remains very limited and there is a pressing need for expansion. New approaches are urgently required to support target validation in the drug discovery process. Oligomerisation is the natural assembly of multiple copies of a single protein into one object and this self-assembly is present in more than half of all protein structures. Thus, oligomerisation plays a central role in the generation of functional biomolecules. A key feature of oligomerisation is that the oligomeric interfaces between the individual parts of the final assembly are highly specific. However, these interfaces have not yet been systematically explored or exploited to dissect biochemical pathways in vivo. This mini review will describe the current state of the antimalarial toolset as well as the potentially druggable malarial pathways. A specific focus is drawn to the initial efforts to exploit oligomerisation surfaces in drug target validation. As alternative to the conventional methods, Protein Interference Assay (PIA) can be used for specific distortion of the target protein function and pathway assessment in vivo. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  19. Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets.

    PubMed

    Rienksma, Rienk A; Suarez-Diez, Maria; Spina, Lucie; Schaap, Peter J; Martins dos Santos, Vitor A P

    2014-12-01

    Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metabolism. These represent potential drug targets that can be systematically probed with CB models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. These limitations can be circumvented by combining expression data from in vivo samples with a validated CB model, creating an accurate description of pathogen metabolism in the host. To this end, we present here a thoroughly curated and extended genome-scale CB metabolic model of Mtb quantitatively validated using 13C measurements. We describe some of the efforts made in integrating CB models and high-throughput data to generate condition specific models, and we will discuss challenges ahead. This knowledge and the framework herein presented will enable to identify potential new drug targets, and will foster the development of optimal therapeutic strategies.

  20. The Validation of Nematode-Specific Acetylcholine-Gated Chloride Channels as Potential Anthelmintic Drug Targets

    PubMed Central

    Wever, Claudia M.; Farrington, Danielle; Dent, Joseph A.

    2015-01-01

    New compounds are needed to treat parasitic nematode infections in humans, livestock and plants. Small molecule anthelmintics are the primary means of nematode parasite control in animals; however, widespread resistance to the currently available drug classes means control will be impossible without the introduction of new compounds. Adverse environmental effects associated with nematocides used to control plant parasitic species are also motivating the search for safer, more effective compounds. Discovery of new anthelmintic drugs in particular has been a serious challenge due to the difficulty of obtaining and culturing target parasites for high-throughput screens and the lack of functional genomic techniques to validate potential drug targets in these pathogens. We present here a novel strategy for target validation that employs the free-living nematode Caenorhabditis elegans to demonstrate the value of new ligand-gated ion channels as targets for anthelmintic discovery. Many successful anthelmintics, including ivermectin, levamisole and monepantel, are agonists of pentameric ligand-gated ion channels, suggesting that the unexploited pentameric ion channels encoded in parasite genomes may be suitable drug targets. We validated five members of the nematode-specific family of acetylcholine-gated chloride channels as targets of agonists with anthelmintic properties by ectopically expressing an ivermectin-gated chloride channel, AVR-15, in tissues that endogenously express the acetylcholine-gated chloride channels and using the effects of ivermectin to predict the effects of an acetylcholine-gated chloride channel agonist. In principle, our strategy can be applied to validate any ion channel as a putative anti-parasitic drug target. PMID:26393923

  1. Predicting application run times using historical information.

    SciTech Connect

    Foster, I.; Smith, W.; Taylor, V.

    1999-06-25

    The authors present a technique for deriving predictions for the run times of parallel applications from the run times of similar applications that have executed in the past. The novel aspect of the work is the use of search techniques to determine those application characteristics that yield the best definition of similarity for the purpose of making predictions. They use four workloads recorded from parallel computers at Argonne National Laboratory, the Cornell Theory Center, and the San Diego Supercomputer Center to evaluate the effectiveness of the approach.They show that on these workloads the techniques achieve predictions that are between 14 and 60% better than those achieved by other researchers; the approach achieves mean prediction errors that are between 41 and 65% of mean application run times.

  2. Neuronal and Cardiovascular Potassium Channels as Therapeutic Drug Targets

    PubMed Central

    Humphries, Edward S. A.

    2015-01-01

    Potassium (K+) channels, with their diversity, often tissue-defined distribution, and critical role in controlling cellular excitability, have long held promise of being important drug targets for the treatment of dysrhythmias in the heart and abnormal neuronal activity within the brain. With the exception of drugs that target one particular class, ATP-sensitive K+ (KATP) channels, very few selective K+ channel activators or inhibitors are currently licensed for clinical use in cardiovascular and neurological disease. Here we review what a range of human genetic disorders have told us about the role of specific K+ channel subunits, explore the potential of activators and inhibitors of specific channel populations as a therapeutic strategy, and discuss possible reasons for the difficulty in designing clinically relevant K+ channel modulators. PMID:26303307

  3. Drug targets for rational design against emerging coronaviruses.

    PubMed

    Zhao, Qi; Weber, Erin; Yang, Haitao

    2013-04-01

    The recent, fatal outbreak of the novel coronavirus strain in the Middle East highlights the real threat posed by this unique virus family. Neither pharmaceutical cures nor preventive vaccines are clinically available to fight against coronavirus associated syndromes, not to mention a lack of symptom soothing drugs. Development of treatment options is complicated by the unpredictable, recurring instances of cross-species viral transmission. The vastly distributing virus reservoir and the rapid rate of host-species exchange of coronavirus demands wide spectrum potency in an ideal therapeutic. Through summarizing the available information and progress in coronavirus research, this review provides a systematic assessment of the potential wide-spectrum features on the most popular drug targets including viral proteases, spike protein, RNA polymerases and editing enzymes as well as host-virus interaction pathways associated with coronaviruses.

  4. Revisiting AMPA receptors as an antiepileptic drug target.

    PubMed

    Rogawski, Michael A

    2011-03-01

    In the 1990s there was intense interest in ionotropic glutamate receptors as therapeutic targets for diverse neurological disorders, including epilepsy. NMDA receptors were thought to play a key role in the generation of seizures, leading to clinical studies of NMDA receptor blocking drugs in epilepsy. Disappointing results dampened enthusiasm for ionotropic glutamate receptors as a therapeutic target. Eventually it became appreciated that another type of ionotropic glutamate receptor, the AMPA receptor, is actually the predominant mediator of excitatory neurotransmission in the central nervous system and moreover that AMPA receptors are critical to the generation and spread of epileptic activity. As drugs became available that selectively target AMPA receptors, it was possible to demonstrate that AMPA receptor antagonists have powerful antiseizure activity in in vitro and in vivo models. A decade later, promising clinical studies with AMPA receptor antagonists, including the potent noncompetitive antagonist perampanel, are once again focusing attention on AMPA receptors as a drug target for epilepsy therapy.

  5. Inhibition of bacterial ribosome assembly: a suitable drug target?

    PubMed

    Maguire, Bruce A

    2009-03-01

    The assembly of bacterial ribosomes is viewed with increasing interest as a potential target for new antibiotics. The in vivo synthesis and assembly of ribosomes are briefly reviewed here, highlighting the many ways in which assembly can be perturbed. The process is compared with the model in vitro process from which much of our knowledge is derived. The coordinate synthesis of the ribosomal components is essential for their ordered and efficient assembly; antibiotics interfere with this coordination and therefore affect assembly. It has also been claimed that the binding of antibiotics to nascent ribosomes prevents their assembly. These two contrasting models of antibiotic action are compared and evaluated. Finally, the suitability and tractability of assembly as a drug target are assessed.

  6. Candidate Drug Targets for Prevention or Modification of Epilepsy

    PubMed Central

    Varvel, Nicholas H.; Jiang, Jianxiong; Dingledine, Raymond

    2015-01-01

    Epilepsy is a prevalent neurological disorder afflicting nearly 50 million people worldwide. The disorder is characterized clinically by recurrent spontaneous seizures attributed to abnormal synchrony of brain neurons. Despite advances in the treatment of epilepsy, nearly one-third of patients are resistant to current therapies, and the underlying mechanisms whereby a healthy brain becomes epileptic remain unresolved. Therefore, researchers have a major impetus to identify and exploit new drug targets. Here we distinguish between epileptic effectors, or proteins that set the seizure threshold, and epileptogenic mediators, which control the expression or functional state of the effector proteins. Under this framework, we then discuss attempts to regulate the mediators to control epilepsy. Further insights into the complex processes that render the brain susceptible to seizures and the identification of novel mediators of these processes will lead the way to the development of drugs to modify disease outcome and, potentially, to prevent epileptogenesis. PMID:25196047

  7. Voltage-gated Potassium Channels as Therapeutic Drug Targets

    PubMed Central

    Wulff, Heike; Castle, Neil A.; Pardo, Luis A.

    2009-01-01

    The human genome contains 40 voltage-gated potassium channels (KV) which are involved in diverse physiological processes ranging from repolarization of neuronal or cardiac action potentials, over regulating calcium signaling and cell volume, to driving cellular proliferation and migration. KV channels offer tremendous opportunities for the development of new drugs for cancer, autoimmune diseases and metabolic, neurological and cardiovascular disorders. This review first discusses pharmacological strategies for targeting KV channels with venom peptides, antibodies and small molecules and then highlights recent progress in the preclinical and clinical development of drugs targeting KV1.x, KV7.x (KCNQ), KV10.1 (EAG1) and KV11.1 (hERG) channels. PMID:19949402

  8. The path to oncology drug target validation: an industry perspective.

    PubMed

    Cortés-Cros, Marta; Schmelzle, Tobias; Stucke, Volker M; Hofmann, Francesco

    2013-01-01

    The advent of a variety of genomic, proteomic and other system-based scientific approaches has raised the expectations of identifying novel targets for oncology drug discovery. However, the complexity of human genome cancer alterations requires a careful analysis of the function of candidate targets identified by these efforts. The postulation and testing of a hypothesis that modulation of a protein or pathway will result in a therapeutic effect in a preclinical setting is crucial for target validation activities. In this chapter, we provide an overview on target identification and validation approaches to interrogate the functional and therapeutic relevance of a candidate cancer drug target as an essential step towards justifying the subsequent investment in drug discovery efforts.

  9. Toxoplasma histone acetylation remodelers as novel drug targets

    PubMed Central

    Vanagas, Laura; Jeffers, Victoria; Bogado, Silvina S; Dalmasso, Maria C; Sullivan, William J; Angel, Sergio O

    2013-01-01

    Toxoplasma gondii is a leading cause of neurological birth defects and a serious opportunistic pathogen. The authors and others have found that Toxoplasma uses a unique nucleosome composition supporting a fine gene regulation together with other factors. Post-translational modifications in histones facilitate the establishment of a global chromatin environment and orchestrate DNA-related biological processes. Histone acetylation is one of the most prominent post-translational modifications influencing gene expression. Histone acetyltransferases and histone deacetylases have been intensively studied as potential drug targets. In particular, histone deacetylase inhibitors have activity against apicomplexan parasites, underscoring their potential as a new class of antiparasitic compounds. In this review, we summarize what is known about Toxoplasma histone acetyltransferases and histone deacetylases, and discuss the inhibitors studied to date. Finally, the authors discuss the distinct possibility that the unique nucleosome composition of Toxoplasma, which harbors a nonconserved H2Bv variant histone, might be targeted in novel therapeutics directed against this parasite. PMID:23199404

  10. Increasing the Structural Coverage of Tuberculosis Drug Targets

    PubMed Central

    Baugh, Loren; Phan, Isabelle; Begley, Darren W.; Clifton, Matthew C.; Armour, Brianna; Dranow, David M.; Taylor, Brandy M.; Muruthi, Marvin M.; Abendroth, Jan; Fairman, James W.; Fox, David; Dieterich, Shellie H.; Staker, Bart L.; Gardberg, Anna S.; Choi, Ryan; Hewitt, Stephen N.; Napuli, Alberto J.; Myers, Janette; Barrett, Lynn K.; Zhang, Yang; Ferrell, Micah; Mundt, Elizabeth; Thompkins, Katie; Tran, Ngoc; Lyons-Abbott, Sally; Abramov, Ariel; Sekar, Aarthi; Serbzhinskiy, Dmitri; Lorimer, Don; Buchko, Garry W.; Stacy, Robin; Stewart, Lance J.; Edwards, Thomas E.; Van Voorhis, Wesley C.; Myler, Peter J.

    2015-01-01

    High-resolution three-dimensional structures of essential Mycobacterium tuberculosis (Mtb) proteins provide templates for TB drug design, but are available for only a small fraction of the Mtb proteome. Here we evaluate an intra-genus “homolog-rescue” strategy to increase the structural information available for TB drug discovery by using mycobacterial homologs with conserved active sites. Of 179 potential TB drug targets selected for x-ray structure determination, only 16 yielded a crystal structure. By adding 1675 homologs from nine other mycobacterial species to the pipeline, structures representing an additional 52 otherwise intractable targets were solved. To determine whether these homolog structures would be useful surrogates in TB drug design, we compared the active sites of 106 pairs of Mtb and non-TB mycobacterial (NTM) enzyme homologs with experimentally determined structures, using three metrics of active site similarity, including superposition of continuous pharmacophoric property distributions. Pair-wise structural comparisons revealed that 19/22 pairs with >55% overall sequence identity had active site Cα RMSD <1Å, >85% side chain identity, and ≥80% PSAPF (similarity based on pharmacophoric properties) indicating highly conserved active site shape and chemistry. Applying these results to the 52 NTM structures described above, 41 shared >55% sequence identity with the Mtb target, thus increasing the effective structural coverage of the 179 Mtb targets over three-fold (from 9% to 32%). The utility of these structures in TB drug design can be tested by designing inhibitors using the homolog structure and assaying the cognate Mtb enzyme; a promising test case, Mtb cytidylate kinase, is described. The homolog-rescue strategy evaluated here for TB is also generalizable to drug targets for other diseases. PMID:25613812

  11. Fatty acid biosynthesis as a drug target in apicomplexan parasites.

    PubMed

    Goodman, C D; McFadden, G I

    2007-01-01

    Apicomplexan parasitic diseases impose devastating impacts on much of the world's population. The increasing prevalence of drug resistant parasites and the growing number of immuno-compromised individuals are exacerbating the problem to the point that the need for novel, inexpensive drugs is greater now than ever. Discovery of a prokaryotic, Type II fatty acid synthesis (FAS) pathway associated with the plastid-like organelle (apicoplast) of Plasmodium and Toxoplasma has provided a wealth of novel drug targets. Since this pathway is both essential and fundamentally different from the cytosolic Type I pathway of the human host, apicoplast FAS has tremendous potential for the development of parasite-specific inhibitors. Many components of this pathway are already the target for existing antibiotics and herbicides, which should significantly reduce the time and cost of drug development. Continuing interest--both in the pharmaceutical and herbicide industries--in fatty acid synthesis inhibitors proffers an ongoing stream of potential new anti-parasitic compounds. It has now emerged that not all apicomplexan parasites have retained the Type II fatty acid biosynthesis pathway. No fatty acid biosynthesis enzymes are encoded in the genome of Theileria annulata or T. parva, suggesting that fatty acid synthesis is lacking in these parasites. The human intestinal parasite Cryptosporidium parvum appears to have lost the apicoplast entirely; instead relying on an unusual cytosolic Type I FAS. Nevertheless, newly developed anti-cancer and anti-obesity drugs targeting human Type I FAS may yet prove efficacious against Cryptosporidium and other apicomplexans that rely on this Type I FAS pathway.

  12. Increasing the structural coverage of tuberculosis drug targets

    DOE PAGES

    Baugh, Loren; Phan, Isabelle; Begley, Darren W.; ...

    2014-12-19

    High-resolution three-dimensional structures of essential Mycobacterium tuberculosis (Mtb) proteins provide templates for TB drug design, but are available for only a small fraction of the Mtb proteome. Here we evaluate an intra-genus “homolog-rescue” strategy to increase the structural information available for TB drug discovery by using mycobacterial homologs with conserved active sites. We found that of 179 potential TB drug targets selected for x-ray structure determination, only 16 yielded a crystal structure. By adding 1675 homologs from nine other mycobacterial species to the pipeline, structures representing an additional 52 otherwise intractable targets were solved. To determine whether these homolog structuresmore » would be useful surrogates in TB drug design, we compared the active sites of 106 pairs of Mtb and non-TB mycobacterial (NTM) enzyme homologs with experimentally determined structures, using three metrics of active site similarity, including superposition of continuous pharmacophoric property distributions. Pair-wise structural comparisons revealed that 19/22 pairs with >55% overall sequence identity had active site Cα RMSD <1 Å, >85% side chain identity, and ≥80% PSAPF (similarity based on pharmacophoric properties) indicating highly conserved active site shape and chemistry. Applying these results to the 52 NTM structures described above, 41 shared >55% sequence identity with the Mtb target, thus increasing the effective structural coverage of the 179 Mtb targets over three-fold (from 9% to 32%). The utility of these structures in TB drug design can be tested by designing inhibitors using the homolog structure and assaying the cognate Mtb enzyme; a promising test case, Mtb cytidylate kinase, is described. The homolog-rescue strategy evaluated here for TB is also generalizable to drug targets for other diseases.« less

  13. Increasing the structural coverage of tuberculosis drug targets

    SciTech Connect

    Baugh, Loren; Phan, Isabelle; Begley, Darren W.; Clifton, Matthew C.; Armour, Brianna; Dranow, David M.; Taylor, Brandy M.; Muruthi, Marvin M.; Abendroth, Jan; Fairman, James W.; Fox, David; Dieterich, Shellie H.; Staker, Bart L.; Gardberg, Anna S.; Choi, Ryan; Hewitt, Stephen N.; Napuli, Alberto J.; Myers, Janette; Barrett, Lynn K.; Zhang, Yang; Ferrell, Micah; Mundt, Elizabeth; Thompkins, Katie; Tran, Ngoc; Lyons-Abbott, Sally; Abramov, Ariel; Sekar, Aarthi; Serbzhinskiy, Dmitri; Lorimer, Don; Buchko, Garry W.; Stacy, Robin; Stewart, Lance J.; Edwards, Thomas E.; Van Voorhis, Wesley C.; Myler, Peter J.

    2014-12-19

    High-resolution three-dimensional structures of essential Mycobacterium tuberculosis (Mtb) proteins provide templates for TB drug design, but are available for only a small fraction of the Mtb proteome. Here we evaluate an intra-genus “homolog-rescue” strategy to increase the structural information available for TB drug discovery by using mycobacterial homologs with conserved active sites. We found that of 179 potential TB drug targets selected for x-ray structure determination, only 16 yielded a crystal structure. By adding 1675 homologs from nine other mycobacterial species to the pipeline, structures representing an additional 52 otherwise intractable targets were solved. To determine whether these homolog structures would be useful surrogates in TB drug design, we compared the active sites of 106 pairs of Mtb and non-TB mycobacterial (NTM) enzyme homologs with experimentally determined structures, using three metrics of active site similarity, including superposition of continuous pharmacophoric property distributions. Pair-wise structural comparisons revealed that 19/22 pairs with >55% overall sequence identity had active site Cα RMSD <1 Å, >85% side chain identity, and ≥80% PSAPF (similarity based on pharmacophoric properties) indicating highly conserved active site shape and chemistry. Applying these results to the 52 NTM structures described above, 41 shared >55% sequence identity with the Mtb target, thus increasing the effective structural coverage of the 179 Mtb targets over three-fold (from 9% to 32%). The utility of these structures in TB drug design can be tested by designing inhibitors using the homolog structure and assaying the cognate Mtb enzyme; a promising test case, Mtb cytidylate kinase, is described. The homolog-rescue strategy evaluated here for TB is also generalizable to drug targets for other diseases.

  14. In silico analysis and prioritization of drug targets in Fusarium solani.

    PubMed

    Sivashanmugam, Muthukumaran; Nagarajan, Hemavathy; Vetrivel, Umashankar; Ramasubban, Gayathri; Therese, Kulandai Lily; Narahari, Madhavan Hajib

    2015-02-01

    Mycotic keratitis has emerged as a major ophthalmic problem and a leading cause of blindness, since its recognition in 1879. Filamentous fungi are major causative of mycotic keratitis. In India, the main etiological organism responsible for mycotic keratitis is Aspergillus species followed by Fusarium species. In South India, Fusarium based keratitis scales up to 43%. Nearly one-third of mycotic keratitis treatment results in failure, as fungal infections are highly resistant to antibiotic therapies. Therefore, there is need to determine novel and specific targets to constrain Fusarium infections in human eye. In this study, we implemented subtractive proteomics coupled with in silico functional annotation to prioritize potential and specific drug targets which can be used to modulate the virulence of Fusarium solani subsp.pisi (Nectria haematococca MPVI). The results infer that Thiamine thiazole synthase (Thi4), an intracellular membrane bound protein as the potential target, which is a core protein in biological and metabolic process of this pathogen. Moreover, this protein occurs in the thiamine thiazole biosynthesis pathway which is unique to F.solani and devoid in human. Hence, we predicted a plausible structure for this protein and also performed ligand-binding cavity analysis which can be for a strong base for drug designing studies. This study will pave way in better understanding of potential drug targets in F.solani and also leading to therapeutic interventions of fungal keratitis.

  15. Extracting sets of chemical substructures and protein domains governing drug-target interactions.

    PubMed

    Yamanishi, Yoshihiro; Pauwels, Edouard; Saigo, Hiroto; Stoven, Véronique

    2011-05-23

    The identification of rules governing molecular recognition between drug chemical substructures and protein functional sites is a challenging issue at many stages of the drug development process. In this paper we develop a novel method to extract sets of drug chemical substructures and protein domains that govern drug-target interactions on a genome-wide scale. This is made possible using sparse canonical correspondence analysis (SCCA) for analyzing drug substructure profiles and protein domain profiles simultaneously. The method does not depend on the availability of protein 3D structures. From a data set of known drug-target interactions including enzymes, ion channels, G protein-coupled receptors, and nuclear receptors, we extract a set of chemical substructures shared by drugs able to bind to a set of protein domains. These two sets of extracted chemical substructures and protein domains form components that can be further exploited in a drug discovery process. This approach successfully clusters protein domains that may be evolutionary unrelated but that bind a common set of chemical substructures. As shown in several examples, it can also be very helpful for predicting new protein-ligand interactions and addressing the problem of ligand specificity. The proposed method constitutes a contribution to the recent field of chemogenomics that aims to connect the chemical space with the biological space.

  16. Discrete sequence prediction and its applications

    NASA Technical Reports Server (NTRS)

    Laird, Philip

    1992-01-01

    Learning from experience to predict sequences of discrete symbols is a fundamental problem in machine learning with many applications. We apply sequence prediction using a simple and practical sequence-prediction algorithm, called TDAG. The TDAG algorithm is first tested by comparing its performance with some common data compression algorithms. Then it is adapted to the detailed requirements of dynamic program optimization, with excellent results.

  17. Evaluating Prediction Markets for Internal Control Applications

    DTIC Science & Technology

    2016-05-01

    0 1. Introduction Since the beginning of prediction market research , various applications have been discussed . From the predictions of political...While the use 2 of corporate prediction markets for research purposes tends to lack transparency and reproducibility, the experi ment was...Econontic Research 1.60 Lecturer #3 Responsible Project Management 0.67 Lecturer #5 Macroeconomics 2. 14 Lecturer #6 Market and State 2.59

  18. Predictive Microbiology and Food Safety Applications

    USDA-ARS?s Scientific Manuscript database

    Mathematical modeling is the science of systematic study of recurrent events or phenomena. When models are properly developed, their applications may save costs and time. For microbial food safety research and applications, predictive microbiology models may be developed based on the fact that most ...

  19. Measurement of drug-target engagement in live cells by two-photon fluorescence anisotropy imaging.

    PubMed

    Vinegoni, Claudio; Fumene Feruglio, Paolo; Brand, Christian; Lee, Sungon; Nibbs, Antoinette E; Stapleton, Shawn; Shah, Sunil; Gryczynski, Ignacy; Reiner, Thomas; Mazitschek, Ralph; Weissleder, Ralph

    2017-07-01

    The ability to directly image and quantify drug-target engagement and drug distribution with subcellular resolution in live cells and whole organisms is a prerequisite to establishing accurate models of the kinetics and dynamics of drug action. Such methods would thus have far-reaching applications in drug development and molecular pharmacology. We recently presented one such technique based on fluorescence anisotropy, a spectroscopic method based on polarization light analysis and capable of measuring the binding interaction between molecules. Our technique allows the direct characterization of target engagement of fluorescently labeled drugs, using fluorophores with a fluorescence lifetime larger than the rotational correlation of the bound complex. Here we describe an optimized protocol for simultaneous dual-channel two-photon fluorescence anisotropy microscopy acquisition to perform drug-target measurements. We also provide the necessary software to implement stream processing to visualize images and to calculate quantitative parameters. The assembly and characterization part of the protocol can be implemented in 1 d. Sample preparation, characterization and imaging of drug binding can be completed in 2 d. Although currently adapted to an Olympus FV1000MPE microscope, the protocol can be extended to other commercial or custom-built microscopes.

  20. Polymeric micelles with stimuli-triggering systems for advanced cancer drug targeting.

    PubMed

    Nakayama, Masamichi; Akimoto, Jun; Okano, Teruo

    2014-08-01

    Since the 1990s, nanoscale drug carriers have played a pivotal role in cancer chemotherapy, acting through passive drug delivery mechanisms and subsequent pharmaceutical action at tumor tissues with reduction of adverse effects. Polymeric micelles, as supramolecular assemblies of amphiphilic polymers, have been considerably developed as promising drug carrier candidates, and a number of clinical studies of anticancer drug-loaded polymeric micelle carriers for cancer chemotherapy applications are now in progress. However, these systems still face several issues; at present, the simultaneous control of target-selective delivery and release of incorporated drugs remains difficult. To resolve these points, the introduction of stimuli-responsive mechanisms to drug carrier systems is believed to be a promising approach to provide better solutions for future tumor drug targeting strategies. As possible trigger signals, biological acidic pH, light, heating/cooling and ultrasound actively play significant roles in signal-triggering drug release and carrier interaction with target cells. This review article summarizes several molecular designs for stimuli-responsive polymeric micelles in response to variation of pH, light and temperature and discusses their potentials as next-generation tumor drug targeting systems.

  1. Protein painting reveals solvent-excluded drug targets hidden within native protein–protein interfaces

    PubMed Central

    Luchini, Alessandra; Espina, Virginia; Liotta, Lance A.

    2014-01-01

    Identifying the contact regions between a protein and its binding partners is essential for creating therapies that block the interaction. Unfortunately, such contact regions are extremely difficult to characterize because they are hidden inside the binding interface. Here we introduce protein painting as a new tool that employs small molecules as molecular paints to tightly coat the surface of protein–protein complexes. The molecular paints, which block trypsin cleavage sites, are excluded from the binding interface. Following mass spectrometry, only peptides hidden in the interface emerge as positive hits, revealing the functional contact regions that are drug targets. We use protein painting to discover contact regions between the three-way interaction of IL1β ligand, the receptor IL1RI and the accessory protein IL1RAcP. We then use this information to create peptides and monoclonal antibodies that block the interaction and abolish IL1β cell signalling. The technology is broadly applicable to discover protein interaction drug targets. PMID:25048602

  2. Heme Aggregation inhibitors: antimalarial drugs targeting an essential biomineralization process.

    PubMed

    Ziegler, J; Linck, R; Wright, D W

    2001-02-01

    Malaria, resulting from the parasites of the genus Plasmodium, places an untold burden on the global population. As recently as 40 years ago, only 10% of the world's population was at risk from malaria. Today, over 40% of the world's population is at risk. Due to increased parasite resistance to traditional drugs and vector resistance to insecticides, malaria is once again resurgent. An emergent theme from current strategies for the development of new antimalarials is that metal homeostasis within the parasite represents an important drug target. During the intra-erythrocytic phase of its life cycle, the malaria parasite can degrade up to 75% of an infected cell's hemoglobin. While hemoglobin proteolysis yields requisite amino acids, it also releases toxic free heme (Fe(III)PPIX). To balance the metabolic requirements for amino acids against the toxic effects of heme, malaria parasites have evolved a detoxification mechanism which involves the formation of a crystalline heme aggregate known as hemozoin. An overview of the biochemistry of the critical detoxification process will place it in the appropriate context with regards to drug targeting and design. Quinoline-ring antimalarial drugs are effective against the intraerythrocytic stages of pigment-producing parasites. Recent work on the mechanism of these compounds suggests that they prevent the formation of hemozoin. Evidence for such a mechanism is reviewed, especially in the context of the newly reported crystal structure of hemozoin. Additionally, novel drugs, such as the hydroxyxanthones, which have many of the characteristics of the quinolines are currently being investigated. Recent work has also highlighted two classes of inorganic complexes that have interesting antimalarial activity: (1) metal-N(4)O(2) Schiff base complexes and (2) porphyrins. The mechanism of action for these complexes is discussed. The use of these complexes as probes for the elucidation of structure-activity relationships in heme

  3. "Chameleonic" backbone hydrogen bonds in protein binding and as drug targets.

    PubMed

    Menéndez, C A; Accordino, S R; Gerbino, D C; Appignanesi, G A

    2015-10-01

    We carry out a time-averaged contact matrix study to reveal the existence of protein backbone hydrogen bonds (BHBs) whose net persistence in time differs markedly form their corresponding PDB-reported state. We term such interactions as "chameleonic" BHBs, CBHBs, precisely to account for their tendency to change the structural prescription of the PDB for the opposite bonding propensity in solution. We also find a significant enrichment of protein binding sites in CBHBs, relate them to local water exposure and analyze their behavior as ligand/drug targets. Thus, the dynamic analysis of hydrogen bond propensity might lay the foundations for new tools of interest in protein binding-site prediction and in lead optimization for drug design.

  4. Drug target identification in intracellular and extracellular protozoan parasites.

    PubMed

    Müller, Joachim; Hemphill, Andrew

    2011-01-01

    The increasing demand for novel anti-parasitic drugs due to resistance formation to well-established chemotherapeutically important compounds has increased the demands for a better understanding of the mechanism(s) of action of existing drugs and of drugs in development. While different approaches have been developed to identify the targets and thus mode of action of anti-parasitic compounds, it has become clear that many drugs act not only on one, but possibly several parasite molecules or even pathways. Ideally, these targets are not present in any cells of the host. In the case of apicomplexan parasites, the unique apicoplast, provides a suitable target for compounds binding to DNA or ribosomal RNA of prokaryotic origin. In the case of intracellular pathogens, a given drug might not only affect the pathogen by directly acting on parasite-associated targets, but also indirectly, by altering the host cell physiology. This in turn could affect the parasite development and lead to parasite death. In this review, we provide an overview of strategies for target identification, and present examples of selected drug targets, ranging from proteins to nucleic acids to intermediary metabolism.

  5. Computational Drug Target Screening through Protein Interaction Profiles

    PubMed Central

    Vilar, Santiago; Quezada, Elías; Uriarte, Eugenio; Costanzi, Stefano; Borges, Fernanda; Viña, Dolores; Hripcsak, George

    2016-01-01

    The development of computational methods to discover novel drug-target interactions on a large scale is of great interest. We propose a new method for virtual screening based on protein interaction profile similarity to discover new targets for molecules, including existing drugs. We calculated Target Interaction Profile Fingerprints (TIPFs) based on ChEMBL database to evaluate drug similarity and generated new putative compound-target candidates from the non-intersecting targets in each pair of compounds. A set of drugs was further studied in monoamine oxidase B (MAO-B) and cyclooxygenase-1 (COX-1) enzyme through molecular docking and experimental assays. The drug ethoxzolamide and the natural compound piperlongumine, present in Piper longum L, showed hMAO-B activity with IC50 values of 25 and 65 μM respectively. Five candidates, including lapatinib, SB-202190, RO-316233, GW786460X and indirubin-3′-monoxime were tested against human COX-1. Compounds SB-202190 and RO-316233 showed a IC50 in hCOX-1 of 24 and 25 μM respectively (similar range as potent inhibitors such as diclofenac and indomethacin in the same experimental conditions). Lapatinib and indirubin-3′-monoxime showed moderate hCOX-1 activity (19.5% and 28% of enzyme inhibition at 25 μM respectively). Our modeling constitutes a multi-target predictor for large scale virtual screening with potential in lead discovery, repositioning and drug safety. PMID:27845365

  6. Drug resistance mechanisms and novel drug targets for tuberculosis therapy.

    PubMed

    Islam, Md Mahmudul; Hameed, H M Adnan; Mugweru, Julius; Chhotaray, Chiranjibi; Wang, Changwei; Tan, Yaoju; Liu, Jianxiong; Li, Xinjie; Tan, Shouyong; Ojima, Iwao; Yew, Wing Wai; Nuermberger, Eric; Lamichhane, Gyanu; Zhang, Tianyu

    2017-01-20

    Drug-resistant tuberculosis (TB) poses a significant challenge to the successful treatment and control of TB worldwide. Resistance to anti-TB drugs has existed since the beginning of the chemotherapy era. New insights into the resistant mechanisms of anti-TB drugs have been provided. Better understanding of drug resistance mechanisms helps in the development of new tools for the rapid diagnosis of drug-resistant TB. There is also a pressing need in the development of new drugs with novel targets to improve the current treatment of TB and to prevent the emergence of drug resistance in Mycobacterium tuberculosis. This review summarizes the anti-TB drug resistance mechanisms, furnishes some possible novel drug targets in the development of new agents for TB therapy and discusses the usefulness using known targets to develop new anti-TB drugs. Whole genome sequencing is currently an advanced technology to uncover drug resistance mechanisms in M. tuberculosis. However, further research is required to unravel the significance of some newly discovered gene mutations in their contribution to drug resistance. Copyright © 2016 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China. Published by Elsevier Ltd. All rights reserved.

  7. The prospect of FKBP51 as a drug target.

    PubMed

    Schmidt, Mathias V; Paez-Pereda, Marcelo; Holsboer, Florian; Hausch, Felix

    2012-08-01

    The FK506 binding protein 51 (FKBP51) is best known as an Hsp90-associated co-chaperone that regulates the responsiveness of steroid hormone receptors. In human genetic association studies, FKBP51 has repeatedly been associated with emotion processing and numerous stress-related affective disorders. It has also been implicated in contributing to the glucocorticoid hyposensitivity observed in New World primates. More recently, several research groups have consistently shown a protective effect of FKBP51 knockout or knockdown on stress endocrinology and stress-coping behavior in animal models of depression and anxiety. The principal druggability of FKBP51 is exemplified by the prototypic FKBP ligands FK506 and rapamycin. Moreover, FKBP51 is highly suited for X-ray co-crystallography, which should facilitate the rational drug design of improved FKBP51 ligands. In summary, FKBP51 has emerged as a promising new drug target for stress-related disorders that should be amenable to drug discovery. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  8. TRPV1: A Potential Drug Target for Treating Various Diseases

    PubMed Central

    Brito, Rafael; Sheth, Sandeep; Mukherjea, Debashree; Rybak, Leonard P.; Ramkumar, Vickram

    2014-01-01

    Transient receptor potential vanilloid 1 (TRPV1) is an ion channel present on sensory neurons which is activated by heat, protons, capsaicin and a variety of endogenous lipids termed endovanilloids. As such, TRPV1 serves as a multimodal sensor of noxious stimuli which could trigger counteractive measures to avoid pain and injury. Activation of TRPV1 has been linked to chronic inflammatory pain conditions and peripheral neuropathy, as observed in diabetes. Expression of TRPV1 is also observed in non-neuronal sites such as the epithelium of bladder and lungs and in hair cells of the cochlea. At these sites, activation of TRPV1 has been implicated in the pathophysiology of diseases such as cystitis, asthma and hearing loss. Therefore, drugs which could modulate TRPV1 channel activity could be useful for the treatment of conditions ranging from chronic pain to hearing loss. This review describes the roles of TRPV1 in the normal physiology and pathophysiology of selected organs of the body and highlights how drugs targeting this channel could be important clinically. PMID:24861977

  9. Dominant drug targets suppress the emergence of antiviral resistance

    PubMed Central

    Tanner, Elizabeth J; Liu, Hong-mei; Oberste, M Steven; Pallansch, Mark; Collett, Marc S; Kirkegaard, Karla

    2014-01-01

    The emergence of drug resistance can defeat the successful treatment of pathogens that display high mutation rates, as exemplified by RNA viruses. Here we detail a new paradigm in which a single compound directed against a ‘dominant drug target’ suppresses the emergence of naturally occurring drug-resistant variants in mice and cultured cells. All new drug-resistant viruses arise during intracellular replication and initially express their phenotypes in the presence of drug-susceptible genomes. For the targets of most anti-viral compounds, the presence of these drug-susceptible viral genomes does not prevent the selection of drug resistance. Here we show that, for an inhibitor of the function of oligomeric capsid proteins of poliovirus, the expression of drug-susceptible genomes causes chimeric oligomers to form, thus rendering the drug-susceptible genomes dominant. The use of dominant drug targets should suppress drug resistance whenever multiple genomes arise in the same cell and express products in a common milieu. DOI: http://dx.doi.org/10.7554/eLife.03830.001 PMID:25365453

  10. Plasmodium Drug Targets Outside the Genetic Control of the Parasite

    PubMed Central

    Sullivan, David J.

    2014-01-01

    Drug development often seeks to find “magic bullets” which target microbiologic proteins while not affecting host proteins. Paul Ehrlich tested methylene blue as an antimalarial but this dye was not superior to quinine. Many successful antimalarial therapies are “magic shotguns” which target many Plasmodium pathways with little interference in host metabolism. Two malaria drug classes, the 8-aminoquinolines and the artemisinins interact with cytochrome P450s and host iron protoporphyrin IX or iron, respectively, to generate toxic metabolites and/or radicals, which kill the parasite by interference with many proteins. The non 8-amino antimalarial quinolines like quinine or piperaquine bind heme to inhibit the process of heme crystallization, which results in multiple enzyme inhibition and membrane dysfunction. The quinolines and artemisinins are rapidly parasiticidal in contrast to metal chelators, which have a slower parasite clearance rate with higher drug concentrations. Iron chelators interfere with the artemisinins but otherwise represent a strategy of targeting multiple enzymes containing iron. Interest has been revived in antineoplastic drugs that target DNA metabolism as antimalarials. Specific drug targeting or investigation of the innate immunity directed to the more permeable trophozoite or schizont infected erythrocyte membrane has been under explored. Novel drug classes in the antimalarial development pipeline which either target multiple proteins or unchangeable cellular targets will slow the pace of drug resistance acquisition. PMID:22973888

  11. Carbonic Anhydrase from Porphyromonas Gingivalis as a Drug Target.

    PubMed

    Supuran, Claudiu T; Capasso, Clemente

    2017-07-15

    Periodontitis originates from a microbial synergy causing the development of a mouth microbial imbalance (dysbiosis), consisting of a microbial community composed of anaerobic bacteria. Most studies concerning the treatment of periodontitis have primarily take into account the Gram-negative bacterium Porphyromonas gingivalis, because it is a prominent component of the oral microbiome and a successful colonizer of the oral epithelium. Here, we focus our attention on the study of the carbonic anhydrases (CAs, EC 4.2.1.1) encoded in the genome of this pathogen as a possible drug target. Carbonic anhydrases are a superfamily of metalloenzymes, which catalyze the simple but physiologically crucial reaction of carbon dioxide hydration to bicarbonate and protons. Bacterial CAs have attracted significant attention for affecting the survival, invasion, and pathogenicity of many microorganisms. The P. gingivalis genome encodes for two CAs belonging to β-CA (PgiCAβ) and γ-CA (PgiCAγ) families. These two enzymes were cloned, heterologously expressed in Escherichia coli, and purified to homogeneity. Moreover, they were subject to extensive inhibition studies using the classical CA inhibitors (sulfonamides and anions) with the aim of identifying selective inhibitors of PgiCAβ and PgiCAγ to be used as pharmacological tools for P. gingivalis eradication.

  12. GABAB receptors as drug targets to treat gastroesophageal reflux disease.

    PubMed

    Lehmann, Anders

    2009-06-01

    For many years, acid-suppressive therapy has been at the forefront of treating gastroesophageal reflux disease (GERD), yet despite the advent of the proton pump inhibitors (PPIs) some patients continue to experience persistent GERD symptoms. Therapeutic (non-surgical) options for such patients are currently limited. To tackle this clinical issue, research efforts have begun to focus on 'reflux inhibition' as a potential therapeutic target - i.e. inhibition of transient lower esophageal relaxations (TLESRs), the predominant mechanism of gastroesophageal reflux. Preclinical research has identified a number of drug targets through which TLESRs can be modulated, and the gamma-aminobutyric acid (GABA) type B (GABA(B)) receptor has emerged as one of the most promising. Studies with baclofen, a well-known agonist of this receptor, have demonstrated that reflux inhibition is a valid concept in the clinical setting in that reducing the incidence of TLESRs improves GERD symptoms. But baclofen is associated with significant central nervous system (CNS) side effects, rendering it undesirable for use as a treatment for GERD. Further development work has yielded a number of novel GABA(B) receptor agonists with reduced CNS side effect profiles, and clinical trials are currently being performed with several agents. Compounds that target TLESRs may therefore present a new add-on treatment for patients with persistent GERD symptoms despite PPI therapy.

  13. Affinity-based methods in drug-target discovery.

    PubMed

    Rylova, Gabriela; Ozdian, Tomas; Varanasi, Lakshman; Soural, Miroslav; Hlavac, Jan; Holub, Dusan; Dzubak, Petr; Hajduch, Marian

    2015-01-01

    Target discovery using the molecular approach, as opposed to the more traditional systems approach requires the study of the cellular or biological process underlying a condition or disease. The approaches that are employed by the "bench" scientist may be genetic, genomic or proteomic and each has its rightful place in the drug-target discovery process. Affinity-based proteomic techniques currently used in drug-discovery draw upon several disciplines, synthetic chemistry, cell-biology, biochemistry and mass spectrometry. An important component of such techniques is the probe that is specifically designed to pick out a protein or set of proteins from amongst the varied thousands in a cell lysate. A second component, that is just as important, is liquid-chromatography tandem massspectrometry (LC-MS/MS). LC-MS/MS and the supporting theoretical framework has come of age and is the tool of choice for protein identification and quantification. These proteomic tools are critical to maintaining the drug-candidate supply, in the larger context of drug discovery.

  14. Validating Aurora B as an anti-cancer drug target.

    PubMed

    Girdler, Fiona; Gascoigne, Karen E; Eyers, Patrick A; Hartmuth, Sonya; Crafter, Claire; Foote, Kevin M; Keen, Nicholas J; Taylor, Stephen S

    2006-09-01

    The Aurora kinases, a family of mitotic regulators, have received much attention as potential targets for novel anti-cancer therapeutics. Several Aurora kinase inhibitors have been described including ZM447439, which prevents chromosome alignment, spindle checkpoint function and cytokinesis. Subsequently, ZM447439-treated cells exit mitosis without dividing and lose viability. Because ZM447439 inhibits both Aurora A and B, we set out to determine which phenotypes are due to inhibition of which kinase. Using molecular genetic approaches, we show that inhibition of Aurora B kinase activity phenocopies ZM447439. Furthermore, a novel ZM compound, which is 100 times more selective for Aurora B over Aurora A in vitro, induces identical phenotypes. Importantly, inhibition of Aurora B kinase activity induces a penetrant anti-proliferative phenotype, indicating that Aurora B is an attractive anti-cancer drug target. Using molecular genetic and chemical-genetic approaches, we also probe the role of Aurora A kinase activity. We show that simultaneous repression of Aurora A plus induction of a catalytic mutant induces a monopolar phenotype. Consistently, another novel ZM-related inhibitor, which is 20 times as potent against Aurora A compared with ZM447439, induces a monopolar phenotype. Expression of a drug-resistant Aurora A mutant reverts this phenotype, demonstrating that Aurora A kinase activity is required for spindle bipolarity in human cells. Because small molecule-mediated inhibition of Aurora A and Aurora B yields distinct phenotypes, our observations indicate that the Auroras may present two avenues for anti-cancer drug discovery.

  15. Iontophoresis of minoxidil sulphate loaded microparticles, a strategy for follicular drug targeting?

    PubMed

    Gelfuso, Guilherme M; Barros, M Angélica de Oliveira; Delgado-Charro, M Begoña; Guy, Richard H; Lopez, Renata F V

    2015-10-01

    The feasibility of targeting drugs to hair follicles by a combination of microencapsulation and iontophoresis has been evaluated. Minoxidil sulphate (MXS), which is used in the treatment of alopecia, was selected as a relevant drug with respect to follicular penetration. The skin permeation and disposition of MXS encapsulated in chitosan microparticles (MXS-MP) was evaluated in vitro after passive and iontophoretic delivery. Uptake of MXS was quantified at different exposure times in the stratum corneum (SC) and hair follicles. Microencapsulation resulted in increased (6-fold) drug accumulation in the hair follicles relative to delivery from a simple MXS solution. Application of iontophoresis enhanced follicular delivery for both the solution and the microparticle formulations. It appears, therefore, that microencapsulation and iontophoresis can act synergistically to enhance topical drug targeting to hair follicles.

  16. Identification and Characterization of Skin Biomolecules for Drug Targeting and Monitoring by Vibrational Spectroscopy

    PubMed Central

    Eikje, Natalja Skrebova; Aizawa, Katsuo; Sota, Takayuki; Ozaki, Yukihiro; Arase, Seiji

    2008-01-01

    The article discusses the application of vibrational spectroscopy techniques for in vivo identification and characterization of glucose biomolecules monitored in the skin of healthy, prediabetes and diabetes subjects; for molecular characterization of water and proteins in in vivo monitored patch tested inflamed skin of the patients with contact dermatitis; for description of nucleic acids and proteins at the molecular level with progression to malignancy in skin cancerous lesions. The results of the studies show new possibilities to assess activity levels of glucose metabolism in the skin tissue of healthy, prediabetes and diabetes subjects; activity and severity of inflammation; activity of the processes of carcinogenesis with regard to benign, premalignant and malignant transformation. Based on our findings, we suggest that vibrational spectroscopy might be a rapid screening tool with sufficient sensitivity and specificity to identify and characterize skin biomolecules in described diseases for drug targeting and monitoring by the pharmacological community. PMID:19662142

  17. All-Atom Molecular Dynamics of Virus Capsids as Drug Targets

    PubMed Central

    2016-01-01

    Virus capsids are protein shells that package the viral genome. Although their morphology and biological functions can vary markedly, capsids often play critical roles in regulating viral infection pathways. A detailed knowledge of virus capsids, including their dynamic structure, interactions with cellular factors, and the specific roles that they play in the replication cycle, is imperative for the development of antiviral therapeutics. The following Perspective introduces an emerging area of computational biology that focuses on the dynamics of virus capsids and capsid–protein assemblies, with particular emphasis on the effects of small-molecule drug binding on capsid structure, stability, and allosteric pathways. When performed at chemical detail, molecular dynamics simulations can reveal subtle changes in virus capsids induced by drug molecules a fraction of their size. Here, the current challenges of performing all-atom capsid–drug simulations are discussed, along with an outlook on the applicability of virus capsid simulations to reveal novel drug targets. PMID:27128262

  18. [Advances in researches on β-carbonic anhydrases as anti-parasitic drug targets].

    PubMed

    Zhang, Cong-hui; Zhu, Huai-min

    2016-02-01

    β-carbonic anhydrases (β-CAs) are ubiquitous metalloenzymes which active site contains a zinc ion (Zn²⁺), and they could catalyze the hydration of carbon dioxide to bicarbonate and protons efficiently and are involved in many biological processes, such as respiration, pH and CO₂ homeostasis, biosynthetic reactions, virulence regulation and so on, and may play a critical role in the life activity of many organisms which contain these enzymes. β-CAs are widely distributed in fungi, bacteria, algae, plants and a small number of protozoan and metazoan except vertebrates. Therefore, as potential drug targets for designing and developing antibacterial and anti-parasitic drugs, β-CAs promise a broad application prospect. This paper focuses on the distribution, physiological function and the progress of researches on β-CAs in parasites and their vectors.

  19. Adenylating Enzymes in Mycobacterium tuberculosis as Drug Targets

    PubMed Central

    Duckworth, Benjamin P.; Nelson, Kathryn M.; Aldrich, Courtney C.

    2013-01-01

    Adenylation or adenylate-forming enzymes (AEs) are widely found in nature and are responsible for the activation of carboxylic acids to intermediate acyladenylates, which are mixed anhydrides of AMP. In a second reaction, AEs catalyze the transfer of the acyl group of the acyladenylate onto a nucleophilic amino, alcohol, or thiol group of an acceptor molecule leading to amide, ester, and thioester products, respectively. Mycobacterium tuberculosis encodes for more than 60 adenylating enzymes, many of which represent potential drug targets due to their confirmed essentiality or requirement for virulence. Several strategies have been used to develop potent and selective AE inhibitors including high-throughput screening, fragment-based screening, and the rationale design of bisubstrate inhibitors that mimic the acyladenylate. In this review, a comprehensive analysis of the mycobacterial adenylating enzymes will be presented with a focus on the identification of small molecule inhibitors. Specifically, this review will cover the aminoacyl tRNA-synthetases (aaRSs), MenE required for menaquinone synthesis, the FadD family of enzymes including the fatty acyl-AMP ligases (FAAL) and the fatty acyl-CoA ligases (FACLs) involved in lipid metabolism, and the nonribosomal peptide synthetase adenylation enzyme MbtA that is necessary for mycobactin synthesis. Additionally, the enzymes NadE, GuaA, PanC, and MshC involved in the respective synthesis of NAD, guanine, pantothenate, and mycothiol will be discussed as well as BirA that is responsible for biotinylation of the acyl CoA-carboxylases. PMID:22283817

  20. TRPV1 Channel: A Potential Drug Target for Treating Epilepsy

    PubMed Central

    Nazıroğlu, Mustafa

    2015-01-01

    Epilepsy has 2-3% incidence worldwide. However, present antiepileptic drugs provide only partial control of seizures. Calcium ion accumulation in hippocampal neurons has long been known as a major contributor to the etiology of epilepsy. TRPV1 is a calcium-permeable channel and mediator of epilepsy in the hippocampus. TRPV1 is expressed in epileptic brain areas such as CA1 area and dentate gyrus of the hippocampus. Here the author reviews the patent literature on novel molecules targeting TRPV1 that are currently being investigated in the laboratory and are candidates for future clinical evaluation in the management of epilepsy. A limited number of recent reports have implicated TRPV1 in the induction or treatment of epilepsy suggesting that this may be new area for potential drugs targeting this debilitating disease. Thus activation of TRPV1 by oxidative stress, resiniferatoxin, cannabinoid receptor (CB1) activators (i.e. anandamide) or capsaicin induced epileptic effects, and these effects could be reduced by appropriate inhibitors, including capsazepine (CPZ), 5'-iodoresiniferatoxin (IRTX), resolvins, and CB1 antagonists. It has been also reported that CPZ and IRTX reduced spontaneous excitatory synaptic transmission through modulation of glutaminergic systems and desensitization of TRPV1 channels in the hippocampus of rats. Immunocytochemical studies indicated that TRPV1 channel expression increased in the hippocampus of mice and patients with temporal lobe epilepsy Taken together, findings in the current literature support a role for calcium ion accumulation through TRPV1 channels in the etiology of epileptic seizures, indicating that inhibition of TRPV1 in the hippocampus may possibly be a novel target for prevention of epileptic seizures. PMID:26411767

  1. Legionella pneumophila Carbonic Anhydrases: Underexplored Antibacterial Drug Targets

    PubMed Central

    Supuran, Claudiu T.

    2016-01-01

    Carbonic anhydrases (CAs, EC 4.2.1.1) are metalloenzymes which catalyze the hydration of carbon dioxide to bicarbonate and protons. Many pathogenic bacteria encode such enzymes belonging to the α-, β-, and/or γ-CA families. In the last decade, enzymes from some of these pathogens, including Legionella pneumophila, have been cloned and characterized in detail. These enzymes were shown to be efficient catalysts for CO2 hydration, with kcat values in the range of (3.4–8.3) × 105 s−1 and kcat/KM values of (4.7–8.5) × 107 M−1·s−1. In vitro inhibition studies with various classes of inhibitors, such as anions, sulfonamides and sulfamates, were also reported for the two β-CAs from this pathogen, LpCA1 and LpCA2. Inorganic anions were millimolar inhibitors, whereas diethyldithiocarbamate, sulfamate, sulfamide, phenylboronic acid, and phenylarsonic acid were micromolar ones. The best LpCA1 inhibitors were aminobenzolamide and structurally similar sulfonylated aromatic sulfonamides, as well as acetazolamide and ethoxzolamide (KIs in the range of 40.3–90.5 nM). The best LpCA2 inhibitors belonged to the same class of sulfonylated sulfonamides, together with acetazolamide, methazolamide, and dichlorophenamide (KIs in the range of 25.2–88.5 nM). Considering such preliminary results, the two bacterial CAs from this pathogen represent promising yet underexplored targets for obtaining antibacterials devoid of the resistance problems common to most of the clinically used antibiotics, but further studies are needed to validate them in vivo as drug targets. PMID:27322334

  2. Predictive microbiology for food packaging applications

    USDA-ARS?s Scientific Manuscript database

    Mathematical modeling has been applied to describe the microbial growth and inactivation in foods for decades and is also known as ‘Predictive microbiology’. When models are developed and validated, their applications may save cost and time. The Pathogen Modeling Program (PMP), a collection of mode...

  3. An Approach for Identification of Novel Drug Targets in Streptococcus pyogenes SF370 Through Pathway Analysis.

    PubMed

    Singh, Satendra; Singh, Dev Bukhsh; Singh, Anamika; Gautam, Budhayash; Ram, Gurudayal; Dwivedi, Seema; Ramteke, Pramod W

    2016-12-01

    Streptococcus pyogenes is one of the most important pathogens as it is involved in various infections affecting upper respiratory tract and skin. Due to the emergence of multidrug resistance and cross-resistance, S. Pyogenes is becoming more pathogenic and dangerous. In the present study, an in silico comparative analysis of total 65 metabolic pathways of the host (Homo sapiens) and the pathogen was performed. Initially, 486 paralogous enzymes were identified so that they can be removed from possible drug target list. The 105 enzymes of the biochemical pathways of S. pyogenes from the KEGG metabolic pathway database were compared with the proteins from the Homo sapiens by performing a BLASTP search against the non-redundant database restricted to the Homo sapiens subset. Out of these, 83 enzymes were identified as non-human homologous while 30 enzymes of inadequate amino acid length were removed for further processing. Essential enzymes were finally mined from remaining 53 enzymes. Finally, 28 essential enzymes were identified in S. pyogenes SF370 (serotype M1). In subcellular localization study, 18 enzymes were predicted with cytoplasmic localization and ten enzymes with the membrane localization. These ten enzymes with putative membrane localization should be of particular interest. Acyl-carrier-protein S-malonyltransferase, DNA polymerase III subunit beta and dihydropteroate synthase are novel drug targets and thus can be used to design potential inhibitors against S. pyogenes infection. 3D structure of dihydropteroate synthase was modeled and validated that can be used for virtual screening and interaction study of potential inhibitors with the target enzyme.

  4. Progress in space weather predictions and applications

    NASA Astrophysics Data System (ADS)

    Lundstedt, H.

    The methods of today's predictions of space weather and effects are so much more advanced and yesterday's statistical methods are now replaced by integrated knowledge-based neuro-computing models and MHD methods. Within the ESA Space Weather Programme Study a real-time forecast service has been developed for space weather and effects. This prototype is now being implemented for specific users. Today's applications are not only so many more but also so much more advanced and user-oriented. A scientist needs real-time predictions of a global index as input for an MHD model calculating the radiation dose for EVAs. A power company system operator needs a prediction of the local value of a geomagnetically induced current. A science tourist needs to know whether or not aurora will occur. Soon we might even be able to predict the tropospheric climate changes and weather caused by the space weather.

  5. Proteomic and bioinformatic analysis of Trypanosoma cruzi chemotherapy and potential drug targets: new pieces for an old puzzle.

    PubMed

    Sadok Menna-Barreto, Rubem Figueiredo; Belloze, Kele Teixeira; Perales, Jonas; Silva-Jr, Floriano Paes

    2014-03-01

    Chagas disease is endemic in Latin America and is caused by the protozoan hemoflagellate parasite Trypanosoma cruzi. Nowadays, it has also been disseminated to non-endemic countries due to the ease of global mobility. The nitroheterocycle benznidazole is currently used to treat this neglected tropical disease, although this drug causes severe side effects and has limited efficacy during the chronic phase of the disease. Proteomics and bioinformatics have recently become powerful tools in the identification of new drug targets. In the last decade, proteomic profiles of different T. cruzi forms under distinct experimental conditions were assessed. These reports have pointed to many potential drug targets, with ergosterol biosynthesis-related proteins and redox system enzymes being the most promising candidates. Nevertheless, the majority of the compounds active against T. cruzi still have unclear mechanisms of action, and most proteomic efforts have studied epimastigotes (the non-clinically relevant insect form of the parasite). Additional analyses with the clinically relevant parasite forms should be performed to identify proteins that actually bind drugs active against T. cruzi. Nonetheless, due to the known technical hurdles in generating such experimental data, bioinformatic approaches that integrate currently available data to generate additional knowledge will also be useful. Here, we review T. cruzi proteomics and describe the main chemoproteomic methods and their application to the identification of trypanosomatid drug targets. Finally, we discuss the potential benefits of more extensively integrating all proteomic data with other molecular databases via bioinformatic analyses to develop novel, viable strategies for alternative treatments of Chagas disease.

  6. Halogen bond: its role beyond drug-target binding affinity for drug discovery and development.

    PubMed

    Xu, Zhijian; Yang, Zhuo; Liu, Yingtao; Lu, Yunxiang; Chen, Kaixian; Zhu, Weiliang

    2014-01-27

    Halogen bond has attracted a great deal of attention in the past years for hit-to-lead-to-candidate optimization aiming at improving drug-target binding affinity. In general, heavy organohalogens (i.e., organochlorines, organobromines, and organoiodines) are capable of forming halogen bonds while organofluorines are not. In order to explore the possible roles that halogen bonds could play beyond improving binding affinity, we performed a detailed database survey and quantum chemistry calculation with close attention paid to (1) the change of the ratio of heavy organohalogens to organofluorines along the drug discovery and development process and (2) the halogen bonds between organohalogens and nonbiopolymers or nontarget biopolymers. Our database survey revealed that (1) an obviously increasing trend of the ratio of heavy organohalogens to organofluorines was observed along the drug discovery and development process, illustrating that more organofluorines are worn and eliminated than heavy organohalogens during the process, suggesting that heavy halogens with the capability of forming halogen bonds should have priority for lead optimization; and (2) more than 16% of the halogen bonds in PDB are formed between organohalogens and water, and nearly 20% of the halogen bonds are formed with the proteins that are involved in the ADME/T process. Our QM/MM calculations validated the contribution of the halogen bond to the binding between organohalogens and plasma transport proteins. Thus, halogen bonds could play roles not only in improving drug-target binding affinity but also in tuning ADME/T property. Therefore, we suggest that albeit halogenation is a valuable approach for improving ligand bioactivity, more attention should be paid in the future to the application of the halogen bond for ligand ADME/T property optimization.

  7. New drugs targeting Th2 lymphocytes in asthma

    PubMed Central

    Caramori, Gaetano; Groneberg, David; Ito, Kazuhiro; Casolari, Paolo; Adcock, Ian M; Papi, Alberto

    2008-01-01

    Asthma represents a profound worldwide public health problem. The most effective anti-asthmatic drugs currently available include inhaled β2-agonists and glucocorticoids and control asthma in about 90-95% of patients. The current asthma therapies are not cures and symptoms return soon after treatment is stopped even after long term therapy. Although glucocorticoids are highly effective in controlling the inflammatory process in asthma, they appear to have little effect on the lower airway remodelling processes that appear to play a role in the pathophysiology of asthma at currently prescribed doses. The development of novel drugs may allow resolution of these changes. In addition, severe glucocorticoid-dependent and resistant asthma presents a great clinical burden and reducing the side-effects of glucocorticoids using novel steroid-sparing agents is needed. Furthermore, the mechanisms involved in the persistence of inflammation are poorly understood and the reasons why some patients have severe life threatening asthma and others have very mild disease are still unknown. Drug development for asthma has been directed at improving currently available drugs and findings new compounds that usually target the Th2-driven airway inflammatory response. Considering the apparently central role of T lymphocytes in the pathogenesis of asthma, drugs targeting disease-inducing Th2 cells are promising therapeutic strategies. However, although animal models of asthma suggest that this is feasible, the translation of these types of studies for the treatment of human asthma remains poor due to the limitations of the models currently used. The myriad of new compounds that are in development directed to modulate Th2 cells recruitment and/or activation will clarify in the near future the relative importance of these cells and their mediators in the complex interactions with the other pro-inflammatory/anti-inflammatory cells and mediators responsible of the different asthmatic

  8. CRIMALDDI: platform technologies and novel anti-malarial drug targets.

    PubMed

    Vial, Henri; Taramelli, Donatella; Boulton, Ian C; Ward, Steve A; Doerig, Christian; Chibale, Kelly

    2013-11-05

    The Coordination, Rationalization, and Integration of antiMALarial drug Discovery & Development Initiatives (CRIMALDDI) Consortium, funded by the EU Framework Seven Programme, has attempted, through a series of interactive and facilitated workshops, to develop priorities for research to expedite the discovery of new anti-malarials. This paper outlines the recommendations for the development of enabling technologies and the identification of novel targets.Screening systems must be robust, validated, reproducible, and represent human malaria. They also need to be cost-effective. While such systems exist to screen for activity against blood stage Plasmodium falciparum, they are lacking for other Plasmodium spp. and other stages of the parasite's life cycle. Priority needs to be given to developing high-throughput screens that can identify activity against the liver and sexual stages. This in turn requires other enabling technologies to be developed to allow the study of these stages and to allow for the culture of liver cells and the parasite at all stages of its life cycle.As these enabling technologies become available, they will allow novel drug targets to be studied. Currently anti-malarials are mostly targeting the asexual blood stage of the parasite's life cycle. There are many other attractive targets that need to be investigated. The liver stages and the sexual stages will become more important as malaria control moves towards malaria elimination. Sexual development is a process offering multiple targets, even though the mechanisms of differentiation are still not fully understood. However, designing a drug whose effect is not curative but would be used in asymptomatic patients is difficult given current safety thresholds. Compounds active against the liver schizont would have a prophylactic effect and Plasmodium vivax elimination requires effectors against the dormant liver hypnozoites. It may be that drugs to be used in elimination campaigns will also need

  9. Collaborative development of predictive toxicology applications.

    PubMed

    Hardy, Barry; Douglas, Nicki; Helma, Christoph; Rautenberg, Micha; Jeliazkova, Nina; Jeliazkov, Vedrin; Nikolova, Ivelina; Benigni, Romualdo; Tcheremenskaia, Olga; Kramer, Stefan; Girschick, Tobias; Buchwald, Fabian; Wicker, Joerg; Karwath, Andreas; Gütlein, Martin; Maunz, Andreas; Sarimveis, Haralambos; Melagraki, Georgia; Afantitis, Antreas; Sopasakis, Pantelis; Gallagher, David; Poroikov, Vladimir; Filimonov, Dmitry; Zakharov, Alexey; Lagunin, Alexey; Gloriozova, Tatyana; Novikov, Sergey; Skvortsova, Natalia; Druzhilovsky, Dmitry; Chawla, Sunil; Ghosh, Indira; Ray, Surajit; Patel, Hitesh; Escher, Sylvia

    2010-08-31

    OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH

  10. Collaborative development of predictive toxicology applications

    PubMed Central

    2010-01-01

    OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals. The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation. Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH

  11. Drug Target Exploitable Structural Features of Adenylyl Cyclase Activity in Schistosoma mansoni

    PubMed Central

    Mbah, Andreas N.; Kamga, Henri L.; Awofolu, Omotayo R.; Isokpehi, Raphael D.

    2012-01-01

    The draft genome sequence of the parasitic flatworm Schistosoma mansoni (S. mansoni), a cause of schistosomiasis, encodes a predicted guanosine triphosphate (GTP) binding protein tagged Smp_059340.1. Smp_059340.1 is predicted to be a member of the G protein alpha-s subunit responsible for regulating adenylyl cyclase activity in S. mansoni and a possible drug target against the parasite. Our structural bioinformatics analyses identified key amino acid residues (Ser53, Thr188, Asp207 and Gly210) in the two molecular switches responsible for cycling the protein between active (GTP bound) and inactive (GDP bound) states. Residue Thr188 is located on Switch I region while Gly210 is located on Switch II region with Switch II longer than Switch I. The Asp207 is located on the G3 box motif and Ser53 is the binding residue for magnesium ion. These findings offer new insights into the dynamic and functional determinants of the Smp_059340.1 protein in regulating the S. mansoni life cycle. The binding interfaces and their residues could be used as starting points for selective modulations of interactions within the pathway using small molecules, peptides or mutagenesis. PMID:23133313

  12. EphB1 as a Novel Drug Target to Combat Pain and Addiction

    DTIC Science & Technology

    2016-09-01

    Award Number: W81XWH-14-1-0220 Project Title: EphB1 as a Novel Drug Target to Combat Pain and Addiction Principal Investigator Name: Mark...REPORT DATE September 2016 2. REPORT TYPE Annual 3. DATES COVERED 1Sep2015 - 31Aug2016 4. TITLE AND SUBTITLE EphB1 as a Novel Drug Target to Combat...Pain and Addiction 5a. CONTRACT NUMBER EphB1 as a Novel Drug Target to Combat Pain and Addiction 5b. GRANT NUMBER W81XWH-14-1-0220 5c. PROGRAM

  13. Using epidemiology and archaeology to unearth new drug targets for rheumatoid arthritis therapy.

    PubMed

    Mobley, James L

    2006-01-01

    Epidemiological and archaeological evidence suggests that RA could be a consequence of enhanced immunity to Mycobacterium tuberculosis, and that by understanding this connection, new RA drug targets may be uncovered.

  14. Applications of predictive environmental strain models.

    PubMed

    Reardon, M J; Gonzalez, R R; Pandolf, K B

    1997-02-01

    Researchers at the U.S. Army Research Institute of Environmental Medicine have developed and validated numerical models capable of predicting the extent of physiologic strain and adverse terrain and weather-related medical consequences of military operations in harsh environments. A descriptive historical account is provided that details how physiologic models for hot and cold weather exposure have been integrated into portable field advisory devices, computer-based meteorologic planning software, and combat-oriented simulation systems. It is important that medical officers be aware of the existence of these types of decision support tools so that they can assure that outputs are interpreted in a balanced and medically realistic manner. Additionally, these modeling applications may facilitate timely preventive medicine planning and efficient dissemination of appropriate measures to prevent weather- and altitude-related illnesses and performance decrements. Such environmental response modeling applications may therefore be utilized to support deployment preventive medicine planning by field medical officers.

  15. TiD: Standalone software for mining putative drug targets from bacterial proteome.

    PubMed

    Gupta, Reena; Pradhan, Dibyabhaba; Jain, Arun Kumar; Rai, Chandra Shekhar

    2017-01-01

    TiD is a standalone application, which relies on basic assumption that a protein must be essential for pathogens survival and non-homologous with host to qualify as putative target. With an input bacterial proteome, TiD removes paralogous proteins, picks essential ones, and excludes proteins homologous with host organisms. The targets illustrate non-homology with at least 40 out of 84 gut microbes, considered safe for human. TiD classifies proposed targets as known, novel and virulent. Users can perform pathway analysis, choke point analysis, interactome analysis, subcellular localization and functional annotations through web servers cross-referenced with the application. Drug targets identified by TiD for Listeria monocytogenes, Bacillus anthracis and Pseudomonas aeruginosa have revealed significant overlaps with previous studies. TiD takes <2h to scan putative targets from a bacterial proteome with ~5000 proteins; hence, we propose it as a useful tool for rational drug design. TiD is available at http://bmicnip.in/TiD/. Copyright © 2016 Elsevier Inc. All rights reserved.

  16. Cardiological biopharmaceuticals in the conception of drug targeting delivery: practical results and research perspectives.

    PubMed

    Maksimenko, A V

    2012-07-01

    The results of the clinical use of thrombolytic and antithrombotic preparations developed on the basis of protein conjugates obtained within the framework of the conception of drug targeting delivery in the organism are considered. A decrease has been noted in the number of biomedical projects focused on these derivatives as a result of various factors: the significant depletion of financial and organizational funds, the saturation of the pharmaceutical market with preparations of this kind, and the appearance of original means for interventional procedures. Factors that actively facilitate the conspicuous potentiation of the efficacy of bioconjugates were revealed: the biomedical testing of protein domains and their selected combinations, the optimization of molecular sizes for the bioconjugates obtained, the density of target localization, the application of cell adhesion molecules as targets, and the application of connected enzyme activities. Enzyme antioxidants and the opportunity for further elaboration of the drug delivery conception via the elucidation and formation of therapeutic targets for effective drug reactions by means of pharmacological pre- and postconditioning of myocardium arouse significant interest.

  17. Mining significant substructure pairs for interpreting polypharmacology in drug-target network.

    PubMed

    Takigawa, Ichigaku; Tsuda, Koji; Mamitsuka, Hiroshi

    2011-02-23

    A current key feature in drug-target network is that drugs often bind to multiple targets, known as polypharmacology or drug promiscuity. Recent literature has indicated that relatively small fragments in both drugs and targets are crucial in forming polypharmacology. We hypothesize that principles behind polypharmacology are embedded in paired fragments in molecular graphs and amino acid sequences of drug-target interactions. We developed a fast, scalable algorithm for mining significantly co-occurring subgraph-subsequence pairs from drug-target interactions. A noteworthy feature of our approach is to capture significant paired patterns of subgraph-subsequence, while patterns of either drugs or targets only have been considered in the literature so far. Significant substructure pairs allow the grouping of drug-target interactions into clusters, covering approximately 75% of interactions containing approved drugs. These clusters were highly exclusive to each other, being statistically significant and logically implying that each cluster corresponds to a distinguished type of polypharmacology. These exclusive clusters cannot be easily obtained by using either drug or target information only but are naturally found by highlighting significant substructure pairs in drug-target interactions. These results confirm the effectiveness of our method for interpreting polypharmacology in drug-target network.

  18. NRDTD: a database for clinically or experimentally supported non-coding RNAs and drug targets associations

    PubMed Central

    Sun, Ya-Zhou; Zhang, De-Hong; Yan, Gui-Ying; An, Ji-Yong; You, Zhu-Hong

    2017-01-01

    Abstract In recent years, more and more non-coding RNAs (ncRNAs) have been identified and increasing evidences have shown that ncRNAs may affect gene expression and disease progression, making them a new class of targets for drug discovery. It thus becomes important to understand the relationship between ncRNAs and drug targets. For this purpose, an ncRNAs and drug targets association database would be extremely beneficial. Here, we developed ncRNA Drug Targets Database (NRDTD) that collected 165 entries of clinically or experimentally supported ncRNAs as drug targets, including 97 ncRNAs and 96 drugs. Moreover, we annotated ncRNA-drug target associations with drug information from KEGG, PubChem, DrugBank, CTD or Wikipedia, GenBank sequence links, OMIM disease ID, pathway and function annotation for ncRNAs, detailed description of associations between ncRNAs and diseases from HMDD or LncRNADisease and the publication PubMed ID. Additionally, we provided users a link to submit novel disease-ncRNA-drug associations and corresponding supporting evidences into the database. We hope NRDTD will be a useful resource for investigating the roles of ncRNAs in drug target identification, drug discovery and disease treatment. Database URL: http://chengroup.cumt.edu.cn/NRDTD

  19. In silico exploration of novel phytoligands against probable drug target of Clostridium tetani.

    PubMed

    Skariyachan, Sinosh; Prakash, Nisha; Bharadwaj, Navya

    2012-12-01

    Though tetanus is an old disease with well known medicines, its complications are still a serious issue worldwide. Tetanus is mainly due to a powerful neurotoxin, tetanolysin-O, produced by a Gram positive anaerobic bacterium, Clostridium tetani. The toxin has a thiol-activated cytolysin which causes lysis of human platelets, lysosomes and a variety of subcellular membranes. The existing therapy seems to have challenged as available vaccines are not so effective and the bacteria developed resistance to many drugs. Computer aided approach is a novel platform to screen drug targets and design potential inhibitors. The three dimensional structure of the toxin is essential for structure based drug design. But the structure of tetanolysin-O is not available in its native form. Moreover, the interaction and pharmacological activities of current drugs against tetanolysin-O is not clear. Hence, there is need for three dimensional model of the toxin. The model was generated by homology modeling using crystal structure of perfringolysin-O, chain-A (PDB ID: 1PFO) as the template. The modeled structure has 22.7% α helices, 27.51% β sheets and 41.75% random coils. A thiol-activated cytolysin was predicted in the region of 105 to 1579, which acts as a functional domain of the toxin. The hypothetical model showed the backbone root mean square deviation (RMSD) value of 0.6 Å and the model was validated by ProCheck. The Ramachandran plot of the model accounts for 92.3% residues in the most allowed region. The model was further refined by various tools and deposited to Protein Model Database (PMDB ID: PM0077550). The model was used as the drug target and the interaction of various lead molecules with protein was studied by molecular docking. We have selected phytoligands based on literatures and pharmacophoric studies. The efficiency of herbal compounds and chemical leads was compared. Our study concluded that herbal derivatives such as berberine (7, 8, 13, 13a-tetradehydro-9

  20. Inflammation and Immune Regulation as Potential Drug Targets in Antidepressant Treatment

    PubMed Central

    Schmidt, Frank M.; Kirkby, Kenneth C.; Lichtblau, Nicole

    2016-01-01

    Growing evidence supports a mutual relationship between inflammation and major depression. A variety of mechanisms are outlined, indicating how inflammation may be involved in the pathogenesis, course and treatment of major depression. In particular, this review addresses 1) inflammatory cytokines as markers of depression and potential predictors of treatment response, 2) findings that cytokines interact with antidepressants and non-pharmacological antidepressive therapies, such as electroconvulsive therapy, deep brain stimulation and physical activity, 3) the influence of cytokines on the cytochrome (CYP) p450-system and drug efflux transporters, and 4) how cascades of inflammation might serve as antidepressant drug targets. A number of clinical trials have focused on agents with immunmodulatory properties in the treatment of depression, of which this review covers nonsteroidal anti-inflammatory drugs (NSAIDs), cytokine inhibitors, ketamine, polyunsaturated fatty acids, statins and curcumin. A perspective is also provided on possible future immune targets for antidepressant therapy, such as toll-like receptor-inhibitors, glycogen synthase kinase-3 inhibitors, oleanolic acid analogs and minocycline. Concluding from the available data, markers of inflammation may become relevant factors for more personalised planning and prediction of response of antidepressant treatment strategies. Agents with anti-inflammatory properties have the potential to serve as clinically relevant antidepressants. Further studies are required to better define and identify subgroups of patients responsive to inflammatory agents as well as to define optimal time points for treatment onset and duration. PMID:26769225

  1. Rolling Bearing Life Prediction, Theory, and Application

    NASA Technical Reports Server (NTRS)

    Zaretsky, Erwin V.

    2013-01-01

    A tutorial is presented outlining the evolution, theory, and application of rolling-element bearing life prediction from that of A. Palmgren, 1924; W. Weibull, 1939; G. Lundberg and A. Palmgren, 1947 and 1952; E. Ioannides and T. Harris, 1985; and E. Zaretsky, 1987. Comparisons are made between these life models. The Ioannides-Harris model without a fatigue limit is identical to the Lundberg-Palmgren model. The Weibull model is similar to that of Zaretsky if the exponents are chosen to be identical. Both the load-life and Hertz stress-life relations of Weibull, Lundberg and Palmgren, and Ioannides and Harris reflect a strong dependence on the Weibull slope. The Zaretsky model decouples the dependence of the critical shear stress-life relation from the Weibull slope. This results in a nominal variation of the Hertz stress-life exponent. For 9th- and 8th-power Hertz stress-life exponents for ball and roller bearings, respectively, the Lundberg- Palmgren model best predicts life. However, for 12th- and 10th-power relations reflected by modern bearing steels, the Zaretsky model based on the Weibull equation is superior. Under the range of stresses examined, the use of a fatigue limit would suggest that (for most operating conditions under which a rolling-element bearing will operate) the bearing will not fail from classical rolling-element fatigue. Realistically, this is not the case. The use of a fatigue limit will significantly overpredict life over a range of normal operating Hertz stresses. Since the predicted lives of rolling-element bearings are high, the problem can become one of undersizing a bearing for a particular application.

  2. Drug targets in the cytokine universe for autoimmune disease.

    PubMed

    Liu, Xuebin; Fang, Lei; Guo, Taylor B; Mei, Hongkang; Zhang, Jingwu Z

    2013-03-01

    In autoimmune disease, a network of diverse cytokines is produced in association with disease susceptibility to constitute the 'cytokine milieu' that drives chronic inflammation. It remains elusive how cytokines interact in such a complex network to sustain inflammation in autoimmune disease. This has presented huge challenges for successful drug discovery because it has been difficult to predict how individual cytokine-targeted therapy would work. Here, we combine the principles of Chinese Taoism philosophy and modern bioinformatics tools to dissect multiple layers of arbitrary cytokine interactions into discernible interfaces and connectivity maps to predict movements in the cytokine network. The key principles presented here have important implications in our understanding of cytokine interactions and development of effective cytokine-targeted therapies for autoimmune disorders. Copyright © 2012 Elsevier Ltd. All rights reserved.

  3. A Synthetic Biology Project - Developing a single-molecule device for screening drug-target interactions.

    PubMed

    Firman, Keith; Evans, Luke; Youell, James

    2012-07-16

    This review describes a European-funded project in the area of Synthetic Biology. The project seeks to demonstrate the application of engineering techniques and methodologies to the design and construction of a biosensor for detecting drug-target interactions at the single-molecule level. Production of the proteins required for the system followed the principle of previously described "bioparts" concepts (a system where a database of biological parts - promoters, genes, terminators, linking tags and cleavage sequences - is used to construct novel gene assemblies) and cassette-type assembly of gene expression systems (the concept of linking different "bioparts" to produce functional "cassettes"), but problems were quickly identified with these approaches. DNA substrates for the device were also constructed using a cassette-system. Finally, micro-engineering was used to build a magnetoresistive Magnetic Tweezer device for detection of single molecule DNA modifying enzymes (motors), while the possibility of constructing a Hall Effect version of this device was explored. The device is currently being used to study helicases from Plasmodium as potential targets for anti-malarial drugs, but we also suggest other potential uses for the device. Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

  4. Retention of ferrofluid aggregates at the target site during magnetic drug targeting

    NASA Astrophysics Data System (ADS)

    Asfer, Mohammed; Saroj, Sunil Kumar; Panigrahi, Pradipta Kumar

    2017-08-01

    The present study reports the retention dynamics of a ferrofluid aggregate localized at the target site inside a glass capillary (500 × 500 μm2 square cross section) against a bulk flow of DI water (Re = 0.16 and 0.016) during the process of magnetic drug targeting (MDT). The dispersion dynamics of iron oxide nanoparticles (IONPs) into bulk flow for different initial size of aggregate at the target site is reported using the brightfield visualization technique. The flow field around the aggregate during the retention is evaluated using the μPIV technique. IONPs at the outer boundary experience a higher shear force as compared to the magnetic force, resulting in dispersion of IONPs into the bulk flow downstream to the aggregate. The blockage effect and the roughness of the outer boundary of the aggregate resulting from chain like clustering of IONPs contribute to the flow recirculation at the downstream region of the aggregate. The entrapment of seeding particles inside the chain like clusters of IONPs at the outer boundary of the aggregate reduces the degree of roughness resulting in a streamlined aggregate at the target site at later time. The effect of blockage, structure of the aggregate, and disturbed flow such as recirculation around the aggregate are the primary factors, which must be investigated for the effectiveness of the MDT process for in vivo applications.

  5. Targeted Tumor Therapy with "Magnetic Drug Targeting": Therapeutic Efficacy of Ferrofluid Bound Mitoxantrone

    NASA Astrophysics Data System (ADS)

    Alexiou, Ch.; Schmid, R.; Jurgons, R.; Bergemann, Ch.; Arnold, W.; Parak, F.G.

    The difference between success or failure of chemotherapy depends not only on the drug itself but also on how it is delivered to its target. Biocompatible ferrofluids (FF) are paramagnetic nanoparticles, that may be used as a delivery system for anticancer agents in locoregional tumor therapy, called "magnetic drug targeting". Bound to medical drugs, such magnetic nanoparticles can be enriched in a desired body compartment (tumor) using an external magnetic field, which is focused on the area of the tumor. Through this form of target directed drug application, one attempts to concentrate a pharmacological agent at its site of action in order to minimize unwanted side effects in the organism and to increase its locoregional effectiveness. Tumor bearing rabbits (VX2 squamous cell carcinoma) in the area of the hind limb, were treated by a single intra-arterial injection (A. femoralis) of mitoxantrone bound ferrofluids (FF-MTX), while focusing an external magnetic field (1.7 Tesla) onto the tumor for 60 minutes. Complete tumor remissions could be achieved in these animals in a dose related manner (20% and 50% of the systemic dose of mitoxantrone), without any negative side effects, like e.g. leucocytopenia, alopecia or gastrointestinal disorders. The strong and specific therapeutic efficacy in tumor treatment with mitoxantrone bound ferrofluids may indicate that this system could be used as a delivery system for anticancer agents, like radionuclids, cancer-specific antibodies, anti-angiogenetic factors, genes etc.

  6. All-atom molecular dynamics of virus capsids as drug targets

    DOE PAGES

    Perilla, Juan R.; Hadden, Jodi A.; Goh, Boon Chong; ...

    2016-04-29

    Virus capsids are protein shells that package the viral genome. Although their morphology and biological functions can vary markedly, capsids often play critical roles in regulating viral infection pathways. A detailed knowledge of virus capsids, including their dynamic structure, interactions with cellular factors, and the specific roles that they play in the replication cycle, is imperative for the development of antiviral therapeutics. The following Perspective introduces an emerging area of computational biology that focuses on the dynamics of virus capsids and capsid–protein assemblies, with particular emphasis on the effects of small-molecule drug binding on capsid structure, stability, and allosteric pathways.more » When performed at chemical detail, molecular dynamics simulations can reveal subtle changes in virus capsids induced by drug molecules a fraction of their size. Finally, the current challenges of performing all-atom capsid–drug simulations are discussed, along with an outlook on the applicability of virus capsid simulations to reveal novel drug targets.« less

  7. Rational polypharmacology: systematically identifying and engaging multiple drug targets to promote axon growth

    PubMed Central

    Al-Ali, Hassan; Lee, Do-Hun; Danzi, Matt C.; Nassif, Houssam; Gautam, Prson; Wennerberg, Krister; Zuercher, Bill; Drewry, David H.; Lee, Jae K.; Lemmon, Vance P.; Bixby, John L.

    2016-01-01

    Mammalian Central Nervous System (CNS) neurons regrow their axons poorly following injury, resulting in irreversible functional losses. Identifying therapeutics that encourage CNS axon repair has been difficult, in part because multiple etiologies underlie this regenerative failure. This suggests a particular need for drugs that engage multiple molecular targets. Although multi-target drugs are generally more effective than highly selective alternatives, we lack systematic methods for discovering such drugs. Target-based screening is an efficient technique for identifying potent modulators of individual targets. In contrast, phenotypic screening can identify drugs with multiple targets; however, these targets remain unknown. To address this gap, we combined the two drug discovery approaches using machine learning and information theory. We screened compounds in a phenotypic assay with primary CNS neurons and also in a panel of kinase enzyme assays. We used learning algorithms to relate the compounds’ kinase inhibition profiles to their influence on neurite outgrowth. This allowed us to identify kinases that may serve as targets for promoting neurite outgrowth, as well as others whose targeting should be avoided. We found that compounds that inhibit multiple targets (polypharmacology) promote robust neurite outgrowth in vitro. One compound with exemplary polypharmacology, was found to promote axon growth in a rodent spinal cord injury model. A more general applicability of our approach is suggested by its ability to deconvolve known targets for a breast cancer cell line, as well as targets recently shown to mediate drug resistance. PMID:26056718

  8. New strategies and paradigm for drug target discovery: a special focus on infectious diseases tuberculosis, malaria, leishmaniasis, trypanosomiasis and gastritis.

    PubMed

    Neelapu, Nageswara R R; Srimath-Tirumala-Peddinti, Ravi C P K; Nammi, Deepthi; Pasupuleti, Amita C M

    2013-10-01

    The discovery and exploitation of new drug targets is a key focus for both the pharmaceutical industry and academic research. To provide an insight into trends in the exploitation of new drug targets, we have analysed different methods during the past six decades and advances made in drug target discovery. A special focus remains on different methods used for drug target discovery on infectious diseases such as Tuberculosis, Gastritis, Malaria, Trypanosomiasis and Leishmaniasis. We herewith provide a paradigm that is can be used for drug target discovery in the near future.

  9. PACAP38: Emerging Drug Target in Migraine and Cluster Headache.

    PubMed

    Vollesen, Anne Luise Haulund; Ashina, Messoud

    2017-05-01

    Here, we review the role of pituitary adenylate cyclase-activating peptide-38 (PACAP38) in migraine and cluster headache (CH). Mounting evidence implicates signaling molecule PACAP38 in the pathophysiology of migraine. Human provocation studies show PACAP38 induces migraine attacks in migraine patients without aura and marked and sustained dilation of extracerebral arteries. PACAP38 selectively targets the PAC1 receptor making this receptor a promising candidate for targeted migraine therapy. Randomized clinical trials are warranted to pursue this possible treatment pathway. PACAP38 provocation studies in CH could elucidate possible involvement of PACAP38 in CH pathophysiology and predict efficacy of PACAP38 antagonists in this primary headache. © 2017 American Headache Society.

  10. Phage display selection and evaluation of cancer drug targets.

    PubMed

    Romanov, Victor I

    2003-04-01

    Techniques for the construction of phage display libraries of combinatorial proteins have dramatically improved. This has allowed researchers to expand the applications to the field of cancer biology. The most direct use of protein phage-displayed libraries is the selection of ligands for individual proteins. This includes identification of peptide ligands for receptor signaling molecules: integrins, cytokine and growth factor receptors. Selected peptides may be used as competitors for natural ligands and for the mapping of binding epitopes. This approach has been exploited for delineation of intracellular signal transduction pathways and for the selection of enzyme substrates and inhibitors. Recently, more complicated biological systems were used as targets for biopanning. This includes combination of soluble proteins, cellular surfaces and even the vasculature of whole organs. cDNA expression libraries in phage-based vectors have been recently introduced. The use of phage as a vector for targeted gene therapy is also considered. These and other applications of phage display for cancer research will be reviewed.

  11. Core Proteomic Analysis of Unique Metabolic Pathways of Salmonella enterica for the Identification of Potential Drug Targets

    PubMed Central

    2016-01-01

    Background Infections caused by Salmonella enterica, a Gram-negative facultative anaerobic bacteria belonging to the family of Enterobacteriaceae, are major threats to the health of humans and animals. The recent availability of complete genome data of pathogenic strains of the S. enterica gives new avenues for the identification of drug targets and drug candidates. We have used the genomic and metabolic pathway data to identify pathways and proteins essential to the pathogen and absent from the host. Methods We took the whole proteome sequence data of 42 strains of S. enterica and Homo sapiens along with KEGG-annotated metabolic pathway data, clustered proteins sequences using CD-HIT, identified essential genes using DEG database and discarded S. enterica homologs of human proteins in unique metabolic pathways (UMPs) and characterized hypothetical proteins with SVM-prot and InterProScan. Through this core proteomic analysis we have identified enzymes essential to the pathogen. Results The identification of 73 enzymes common in 42 strains of S. enterica is the real strength of the current study. We proposed all 73 unexplored enzymes as potential drug targets against the infections caused by the S. enterica. The study is comprehensive around S. enterica and simultaneously considered every possible pathogenic strain of S. enterica. This comprehensiveness turned the current study significant since, to the best of our knowledge it is the first subtractive core proteomic analysis of the unique metabolic pathways applied to any pathogen for the identification of drug targets. We applied extensive computational methods to shortlist few potential drug targets considering the druggability criteria e.g. Non-homologous to the human host, essential to the pathogen and playing significant role in essential metabolic pathways of the pathogen (i.e. S. enterica). In the current study, the subtractive proteomics through a novel approach was applied i.e. by considering only proteins

  12. Core Proteomic Analysis of Unique Metabolic Pathways of Salmonella enterica for the Identification of Potential Drug Targets.

    PubMed

    Uddin, Reaz; Sufian, Muhammad

    2016-01-01

    Infections caused by Salmonella enterica, a Gram-negative facultative anaerobic bacteria belonging to the family of Enterobacteriaceae, are major threats to the health of humans and animals. The recent availability of complete genome data of pathogenic strains of the S. enterica gives new avenues for the identification of drug targets and drug candidates. We have used the genomic and metabolic pathway data to identify pathways and proteins essential to the pathogen and absent from the host. We took the whole proteome sequence data of 42 strains of S. enterica and Homo sapiens along with KEGG-annotated metabolic pathway data, clustered proteins sequences using CD-HIT, identified essential genes using DEG database and discarded S. enterica homologs of human proteins in unique metabolic pathways (UMPs) and characterized hypothetical proteins with SVM-prot and InterProScan. Through this core proteomic analysis we have identified enzymes essential to the pathogen. The identification of 73 enzymes common in 42 strains of S. enterica is the real strength of the current study. We proposed all 73 unexplored enzymes as potential drug targets against the infections caused by the S. enterica. The study is comprehensive around S. enterica and simultaneously considered every possible pathogenic strain of S. enterica. This comprehensiveness turned the current study significant since, to the best of our knowledge it is the first subtractive core proteomic analysis of the unique metabolic pathways applied to any pathogen for the identification of drug targets. We applied extensive computational methods to shortlist few potential drug targets considering the druggability criteria e.g. Non-homologous to the human host, essential to the pathogen and playing significant role in essential metabolic pathways of the pathogen (i.e. S. enterica). In the current study, the subtractive proteomics through a novel approach was applied i.e. by considering only proteins of the unique metabolic

  13. Identifying New Drug Targets for Potent Phospholipase D Inhibitors: Combining Sequence Alignment, Molecular Docking, and Enzyme Activity/Binding Assays.

    PubMed

    Djakpa, Helene; Kulkarni, Aditya; Barrows-Murphy, Scheneque; Miller, Greg; Zhou, Weihong; Cho, Hyejin; Török, Béla; Stieglitz, Kimberly

    2016-05-01

    Phospholipase D enzymes cleave phospholipid substrates generating choline and phosphatidic acid. Phospholipase D from Streptomyces chromofuscus is a non-HKD (histidine, lysine, and aspartic acid) phospholipase D as the enzyme is more similar to members of the diverse family of metallo-phosphodiesterase/phosphatase enzymes than phospholipase D enzymes with active site HKD repeats. A highly efficient library of phospholipase D inhibitors based on 1,3-disubstituted-4-amino-pyrazolopyrimidine core structure was utilized to evaluate the inhibition of purified S. chromofuscus phospholipase D. The molecules exhibited inhibition of phospholipase D activity (IC50 ) in the nanomolar range with monomeric substrate diC4 PC and micromolar range with phospholipid micelles and vesicles. Binding studies with vesicle substrate and phospholipase D strongly indicate that these inhibitors directly block enzyme vesicle binding. Following these compelling results as a starting point, sequence searches and alignments with S. chromofuscus phospholipase D have identified potential new drug targets. Using AutoDock, inhibitors were docked into the enzymes selected from sequence searches and alignments (when 3D co-ordinates were available) and results analyzed to develop next-generation inhibitors for new targets. In vitro enzyme activity assays with several human phosphatases demonstrated that the predictive protocol was accurate. The strategy of combining sequence comparison, docking, and high-throughput screening assays has helped to identify new drug targets and provided some insight into how to make potential inhibitors more specific to desired targets.

  14. Genetic and proteomic approaches to identify cancer drug targets

    PubMed Central

    Roti, G; Stegmaier, K

    2012-01-01

    While target-based small-molecule discovery has taken centre-stage in the pharmaceutical industry, there are many cancer-promoting proteins not easily addressed with a traditional target-based screening approach. In order to address this problem, as well as to identify modulators of biological states in the absence of knowing the protein target of the state switch, alternative phenotypic screening approaches, such as gene expression-based and high-content imaging, have been developed. With this renewed interest in phenotypic screening, however, comes the challenge of identifying the binding protein target(s) of small-molecule hits. Emerging technologies have the potential to improve the process of target identification. In this review, we discuss the application of genomic (gene expression-based), genetic (short hairpin RNA and open reading frame screening), and proteomic approaches to protein target identification. PMID:22166799

  15. A comparison of machine learning techniques for detection of drug target articles.

    PubMed

    Danger, Roxana; Segura-Bedmar, Isabel; Martínez, Paloma; Rosso, Paolo

    2010-12-01

    Important progress in treating diseases has been possible thanks to the identification of drug targets. Drug targets are the molecular structures whose abnormal activity, associated to a disease, can be modified by drugs, improving the health of patients. Pharmaceutical industry needs to give priority to their identification and validation in order to reduce the long and costly drug development times. In the last two decades, our knowledge about drugs, their mechanisms of action and drug targets has rapidly increased. Nevertheless, most of this knowledge is hidden in millions of medical articles and textbooks. Extracting knowledge from this large amount of unstructured information is a laborious job, even for human experts. Drug target articles identification, a crucial first step toward the automatic extraction of information from texts, constitutes the aim of this paper. A comparison of several machine learning techniques has been performed in order to obtain a satisfactory classifier for detecting drug target articles using semantic information from biomedical resources such as the Unified Medical Language System. The best result has been achieved by a Fuzzy Lattice Reasoning classifier, which reaches 98% of ROC area measure.

  16. Inducible Mouse Models for Cancer Drug Target Validation

    PubMed Central

    Jeong, Joseph H.

    2016-01-01

    Genetically-engineered mouse (GEM) models have provided significant contributions to our understanding of cancer biology and developing anticancer therapeutic strategies. The development of GEM models that faithfully recapitulate histopathological and clinical features of human cancers is one of the most pressing needs to successfully conquer cancer. In particular, doxycycline-inducible transgenic mouse models allow us to regulate (induce or suppress) the expression of a specific gene of interest within a specific tissue in a temporal manner. Leveraging this mouse model system, we can determine whether the transgene expression is required for tumor maintenance, thereby validating the transgene product as a target for anticancer drug development (target validation study). In addition, there is always a risk of tumor recurrence with cancer therapy. By analyzing recurrent tumors derived from fully regressed tumors after turning off transgene expression in tumor-bearing mice, we can gain an insight into the molecular basis of how tumor cells escape from their dependence on the transgene (tumor recurrence study). Results from such studies will ultimately allow us to predict therapeutic responses in clinical settings and develop new therapeutic strategies against recurrent tumors. The aim of this review is to highlight the significance of doxycycline-inducible transgenic mouse models in studying target validation and tumor recurrence. PMID:28053958

  17. ADAM8 as a drug target in Pancreatic Cancer

    PubMed Central

    Schlomann, Uwe; Koller, Garrit; Conrad, Catharina; Ferdous, Taheera; Golfi, Panagiota; Garcia, Adolfo Molejon; Höfling, Sabrina; Parsons, Maddy; Costa, Patricia; Soper, Robin; Bossard, Maud; Hagemann, Thorsten; Roshani, Rozita; Sewald, Norbert; Ketchem, Randal R.; Moss, Marcia L.; Rasmussen, Fred H.; Miller, Miles A.; Lauffenburger, Douglas A.; Tuveson, David A.; Nimsky, Christopher; Bartsch, Jörg W.

    2016-01-01

    Pancreatic ductal adenocarcinoma (PDAC) has a grim prognosis with less than 5% survivors after 5 years. High expression levels of ADAM8, a metalloprotease-disintegrin, are correlated with poor clinical outcome. We show that ADAM8 expression is associated with increased migration and invasiveness of PDAC cells caused by activation of ERK 1/2 and higher MMP activities. For biological function, ADAM8 requires multimerisation and associates with β1-integrin on the cell surface. A peptidomimetic ADAM8 inhibitor, BK-1361, designed by structural modelling of the disintegrin domain, prevents ADAM8 multimerisation. In PDAC cells, BK-1361 affects ADAM8 function leading to reduced invasiveness, and less ERK 1/2 and MMP activation. BK-1361 application in mice decreased tumour burden and metastasis of implanted pancreatic tumour cells and provides improved metrics of clinical symptoms and survival in a KrasG12D-driven mouse model of PDAC. Thus, our data integrate ADAM8 in pancreatic cancer signalling and validate ADAM8 as a target for PDAC therapy. PMID:25629724

  18. Editorial: Current status and perspective on drug targets in tubercle bacilli and drug design of antituberculous agents based on structure-activity relationship.

    PubMed

    Tomioka, Haruaki

    2014-01-01

    present status of global research on novel drug targets related to the Toll-like receptor in the MTB pathogen, with special reference to mycobacterial virulence factors that cross-talk and interfere with signaling pathways of host macrophages [6]. The following four review articles deal with drug design of novel anti-TB agents employing QSAR techniques. Firstly, Drs. Nidhi and Mohammad Imran Siddiqi review 2D and 3D QSAR approaches and the recent trends of these methods integrated with virtual screening using the 3D pharmacophore and molecular docking approaches for the identification and design of novel antituberculous agents, by presenting a comprehensive overview of QSAR studies reported for newer antituberculous agents [7]. Secondly, Drs. Filomena Martins, Cristina Ventura, Susana Santos, and Miguel Viveiros review the current status of different QSAR-based strategies for the design of novel anti-TB drugs based upon the most active anti-TB agent, isoniazid, from the viewpoint of the development of promising derivatives that are active against isoniazid- resistant strains with katG mutations [8]. Thirdly, Drs. Sanchaita Rajkhowa and Ramesh C. Deka review current studies concerning 2D and 3D QSAR models that contain density-functional theory (DFT)-based descriptors as their parameters [9]. Notably, DFT-based descriptors such as atomic charges, molecular orbital energies, frontier orbital densities, and atom-atom polarizabilities are very useful in predicting the reactivity of atoms in molecules. Fourthly, Drs. Renata V. Bueno, Rodolpho C. Braga, Natanael D. Segretti, Elizabeth I. Ferreira, Gustavo H. G. Trossini, and Carolina H. Andrade review the current progress and applications of QSAR analysis for the discovery of innovative tuberculostatic agents as inhibitors of ribonucleotide reductase, DNA gyrase, ATP synthase, and thymidylate kinase enzymes, highlighting present challenges and new opportunities in TB drug design [10]. The aim of this issue is to address the

  19. A review of recent patents on the protozoan parasite HSP90 as a drug target.

    PubMed

    Angel, Sergio O; Matrajt, Mariana; Echeverria, Pablo C

    2013-04-01

    Diseases caused by protozoan parasites are still an important health problem. These parasites can cause a wide spectrum of diseases, some of which are severe and have high morbidity or mortality if untreated. Since they are still uncontrolled, it is important to find novel drug targets and develop new therapies to decrease their remarkable social and economic impact on human societies. In the past years, human HSP90 has become an interesting drug target that has led to a large number of investigations both at state organizations and pharmaceutical companies, followed by clinical trials. The finding that HSP90 has important biological roles in some protozoan parasites like Plasmodium spp, Toxoplasma gondii and trypanosomatids has allowed the expansion of the results obtained in human cancer to these infections. This review summarizes the latest important findings showing protozoan HSP90 as a drug target and presents three patents targeting T. gondii, P. falciparum and trypanosomatids HSP90.

  20. A Global Comparison of the Human and T. brucei Degradomes Gives Insights about Possible Parasite Drug Targets

    PubMed Central

    Mashiyama, Susan T.; Koupparis, Kyriacos; Caffrey, Conor R.; McKerrow, James H.; Babbitt, Patricia C.

    2012-01-01

    We performed a genome-level computational study of sequence and structure similarity, the latter using crystal structures and models, of the proteases of Homo sapiens and the human parasite Trypanosoma brucei. Using sequence and structure similarity networks to summarize the results, we constructed global views that show visually the relative abundance and variety of proteases in the degradome landscapes of these two species, and provide insights into evolutionary relationships between proteases. The results also indicate how broadly these sequence sets are covered by three-dimensional structures. These views facilitate cross-species comparisons and offer clues for drug design from knowledge about the sequences and structures of potential drug targets and their homologs. Two protease groups (“M32” and “C51”) that are very different in sequence from human proteases are examined in structural detail, illustrating the application of this global approach in mining new pathogen genomes for potential drug targets. Based on our analyses, a human ACE2 inhibitor was selected for experimental testing on one of these parasite proteases, TbM32, and was shown to inhibit it. These sequence and structure data, along with interactive versions of the protein similarity networks generated in this study, are available at http://babbittlab.ucsf.edu/resources.html. PMID:23236535

  1. A global comparison of the human and T. brucei degradomes gives insights about possible parasite drug targets.

    PubMed

    Mashiyama, Susan T; Koupparis, Kyriacos; Caffrey, Conor R; McKerrow, James H; Babbitt, Patricia C

    2012-01-01

    We performed a genome-level computational study of sequence and structure similarity, the latter using crystal structures and models, of the proteases of Homo sapiens and the human parasite Trypanosoma brucei. Using sequence and structure similarity networks to summarize the results, we constructed global views that show visually the relative abundance and variety of proteases in the degradome landscapes of these two species, and provide insights into evolutionary relationships between proteases. The results also indicate how broadly these sequence sets are covered by three-dimensional structures. These views facilitate cross-species comparisons and offer clues for drug design from knowledge about the sequences and structures of potential drug targets and their homologs. Two protease groups ("M32" and "C51") that are very different in sequence from human proteases are examined in structural detail, illustrating the application of this global approach in mining new pathogen genomes for potential drug targets. Based on our analyses, a human ACE2 inhibitor was selected for experimental testing on one of these parasite proteases, TbM32, and was shown to inhibit it. These sequence and structure data, along with interactive versions of the protein similarity networks generated in this study, are available at http://babbittlab.ucsf.edu/resources.html.

  2. Thiamin (Vitamin B1) Biosynthesis and Regulation: A Rich Source of Antimicrobial Drug Targets?

    PubMed Central

    Du, Qinglin; Wang, Honghai; Xie, Jianping

    2011-01-01

    Drug resistance of pathogens has necessitated the identification of novel targets for antibiotics. Thiamin (vitamin B1) is an essential cofactor for all organisms in its active form thiamin diphosphate (ThDP). Therefore, its metabolic pathways might be one largely untapped source of antibiotics targets. This review describes bacterial thiamin biosynthetic, salvage, and transport pathways. Essential thiamin synthetic enzymes such as Dxs and ThiE are proposed as promising drug targets. The regulation mechanism of thiamin biosynthesis by ThDP riboswitch is also discussed. As drug targets of existing antimicrobial compound pyrithiamin, the ThDP riboswitch might serves as alternative targets for more antibiotics. PMID:21234302

  3. Downburst prediction applications of meteorological geostationary satellites

    NASA Astrophysics Data System (ADS)

    Pryor, Kenneth L.

    2014-11-01

    A suite of products has been developed and evaluated to assess hazards presented by convective storm downbursts derived from the current generation of Geostationary Operational Environmental Satellite (GOES) (13-15). The existing suite of GOES downburst prediction products employs the GOES sounder to calculate risk based on conceptual models of favorable environmental profiles for convective downburst generation. A diagnostic nowcasting product, the Microburst Windspeed Potential Index (MWPI), is designed to infer attributes of a favorable downburst environment: 1) the presence of large convective available potential energy (CAPE), and 2) the presence of a surface-based or elevated mixed layer with a steep temperature lapse rate and vertical relative humidity gradient. These conditions foster intense convective downdrafts upon the interaction of sub-saturated air in the elevated or sub-cloud mixed layer with the storm precipitation core. This paper provides an updated assessment of the MWPI algorithm, presents recent case studies demonstrating effective operational use of the MWPI product over the Atlantic coastal region, and presents validation results for the United States Great Plains and Mid-Atlantic coastal region. In addition, an application of the brightness temperature difference (BTD) between GOES imager water vapor (6.5μm) and thermal infrared (11μm) channels that identifies regions where downbursts are likely to develop, due to mid-tropospheric dry air entrainment, will be outlined.

  4. Applications for predictive microbiology to food packaging

    USDA-ARS?s Scientific Manuscript database

    Predictive microbiology has been used for several years in the food industry to predict microbial growth, inactivation and survival. Predictive models provide a useful tool in risk assessment, HACCP set-up and GMP for the food industry to enhance microbial food safety. This report introduces the c...

  5. Molecular and Kinetic Characterization of Babesia microti Gray Strain Lactate Dehydrogenase as a Potential Drug Target

    PubMed Central

    Vudriko, Patrick; Masatani, Tatsunori; Cao, Shinuo; Terkawi, Mohamad Alla; Kamyingkird, Ketsarin; Mousa, Ahmed A; Adjou Moumouni, Paul F; Nishikawa, Yoshifumi; Xuan, Xuenan

    2014-01-01

    Babesia microti is an emerging zoonotic protozoan organism that causes “malaria-like” symptoms that can be fatal in immunocompromised people. Owing to lack of specific therapeutic regiment against the disease, we cloned and characterized B. microti lactate dehydrogenase (BmLDH) as a potential molecular drug receptor. The in vitro kinetic properties of BmLDH enzyme was evaluated using nicotinamide adenine dinucleotide (NAD+) as a co-factor and lactate as a substrate. Inhibitory assay was also done using gossypol as BmLDH inhibitor to determine the inhibitory concentration 50 (IC50). The result showed that the 0.99 kbp BmLDH gene codes for a barely soluble 36 kDa protein (332 amino acids) localized in both the cytoplasm and nucleus of the parasite. In vitro enzyme kinetic studies further revealed that BmLDH is an active enzyme with a high catalytic efficiency at optimal pH of 10.2. The Km values of NAD+ and lactate were 8.7 ± 0.57 mM and 99.9 ± 22.33 mM, respectively. The IC50 value for gossypol was 0.345 μM, while at 2.5 μM, gossypol caused 100% inhibition of BmLDH catalytic activity. These findings, therefore, provide initial evidence that BmLDH could be a potential drug target, although further in vivo studies are needed to validate the practical application of lactate dehydrogenase inhibitors against B. microti infection. PMID:25125971

  6. Identification of SRC as a potent drug target for asthma, using an integrative approach of protein interactome analysis and in silico drug discovery.

    PubMed

    Randhawa, Vinay; Bagler, Ganesh

    2012-10-01

    Network-biology inspired modeling of interactome data and computational chemistry have the potential to revolutionize drug discovery by complementing conventional methods. We consider asthma, a complex disease characterized by intricate molecular mechanisms, for our study. We aim to integrate prediction of potent drug targets using graph-theoretical methods and subsequent identification of small molecules capable of modulating activity of the best target. In this work, we construct the protein interactome underlying this disease: Asthma Protein Interactome (API). Using a strategy based on network analysis of the interactome, we identify a set of potential drug targets for asthma. Topologically and dynamically, v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (SRC) emerges as the most central target in API. SRC is known to play an important role in promoting airway smooth muscle cell growth and facilitating migration in airway remodeling. From interactome analysis, and with the reported role in respiratory mechanisms, SRC emerges as a promising drug target for asthma. Further, we proceed to identify leads for SRC from a public database of small molecules. We predict two potential leads for SRC using ligand-based virtual screening methodology.

  7. Chemogenomics profiling of drug targets of peptidoglycan biosynthesis pathway in Leptospira interrogans by virtual screening approaches.

    PubMed

    Bhattacharjee, Biplab; Simon, Rose Mary; Gangadharaiah, Chaithra; Karunakar, Prashantha

    2013-06-28

    Leptospirosis is a worldwide zoonosis of global concern caused by Leptospira interrogans. The availability of ligand libraries has facilitated the search for novel drug targets using chemogenomics approaches, compared with the traditional method of drug discovery, which is time consuming and yields few leads with little intracellular information for guiding target selection. Recent subtractive genomics studies have revealed the putative drug targets in peptidoglycan biosynthesis pathways in Leptospira interrogans. Aligand library for the murD ligase enzyme in the peptidoglycan pathway has also been identified. Our approach in this research involves screening of the pre-existing ligand library of murD with related protein family members in the putative drug target assembly in the peptidoglycan biosynthesis pathway. A chemogenomics approach has been implemented here, which involves screening of known ligands of a protein family having analogous domain architecture for identification of leads for existing druggable protein family members. By means of this approach, one murC and one murF inhibitor were identified, providing a platform for developing an antileptospirosis drug targeting the peptidoglycan biosynthesis pathway. Given that the peptidoglycan biosynthesis pathway is exclusive to bacteria, the in silico identified mur ligase inhibitors are expected to be broad-spectrum Gram-negative inhibitors if synthesized and tested in in vitro and in vivo assays.

  8. Drug Target Identification and Elucidation of Natural Inhibitors for Bordetella petrii: An In Silico Study

    PubMed Central

    Ray, Manisha; Pattnaik, Animesh; Pradhan, Sukanta Kumar

    2016-01-01

    Environmental microbes like Bordetella petrii has been established as a causative agent for various infectious diseases in human. Again, development of drug resistance in B. petrii challenged to combat against the infection. Identification of potential drug target and proposing a novel lead compound against the pathogen has a great aid and value. In this study, bioinformatics tools and technology have been applied to suggest a potential drug target by screening the proteome information of B. petrii DSM 12804 (accession No. PRJNA28135) from genome database of National Centre for Biotechnology information. In this regards, the inhibitory effect of nine natural compounds like ajoene (Allium sativum), allicin (A. sativum), cinnamaldehyde (Cinnamomum cassia), curcumin (Curcuma longa), gallotannin (active component of green tea and red wine), isoorientin (Anthopterus wardii), isovitexin (A. wardii), neral (Melissa officinalis), and vitexin (A. wardii) have been acknowledged with anti-bacterial properties and hence tested against identified drug target of B. petrii by implicating computational approach. The in silico studies revealed the hypothesis that lpxD could be a potential drug target and with recommendation of a strong inhibitory effect of selected natural compounds against infection caused due to B. petrii, would be further validated through in vitro experiments. PMID:28154518

  9. Drug-target residence time--a case for G protein-coupled receptors.

    PubMed

    Guo, Dong; Hillger, Julia M; IJzerman, Adriaan P; Heitman, Laura H

    2014-07-01

    A vast number of marketed drugs act on G protein-coupled receptors (GPCRs), the most successful category of drug targets to date. These drugs usually possess high target affinity and selectivity, and such combined features have been the driving force in the early phases of drug discovery. However, attrition has also been high. Many investigational new drugs eventually fail in clinical trials due to a demonstrated lack of efficacy. A retrospective assessment of successfully launched drugs revealed that their beneficial effects in patients may be attributed to their long drug-target residence times (RTs). Likewise, for some other GPCR drugs short RT could be beneficial to reduce the potential for on-target side effects. Hence, the compounds' kinetics behavior might in fact be the guiding principle to obtain a desired and durable effect in vivo. We therefore propose that drug-target RT should be taken into account as an additional parameter in the lead selection and optimization process. This should ultimately lead to an increased number of candidate drugs moving to the preclinical development phase and on to the market. This review contains examples of the kinetics behavior of GPCR ligands with improved in vivo efficacy and summarizes methods for assessing drug-target RT. © 2014 Wiley Periodicals, Inc.

  10. An in silico functional annotation and screening of potential drug targets derived from Leishmania spp. hypothetical proteins identified by immunoproteomics.

    PubMed

    Chávez-Fumagalli, Miguel A; Schneider, Mônica S; Lage, Daniela P; Machado-de-Ávila, Ricardo A; Coelho, Eduardo A F

    2017-05-01

    Leishmaniasis is a parasitic disease caused by the protozoan of the Leishmania genus. While no human vaccine is available, drugs such as pentavalent antimonials, pentamidine and amphotericin B are used for treat the patients. However, the high toxicity of these pharmaceutics, the emergence of parasite resistance and/or their high cost have showed to the urgent need of identify new targets to be employed in the improvement of the treatment against leishmaniasis. In a recent immunoproteomics approach performed in the Leishmania infantum species, 104 antigenic proteins were recognized by antibodies in sera of visceral leishmaniasis (VL) dogs. Some of them were later showed to be effective diagnostic markers and/or vaccine candidates against the disease. Between these proteins, 24 considered as hypothetical were identified in the promastigote and amastigote-like extracts of the parasites. The present study aimed to use bioinformatics tools to select new drug targets between these hypothetical proteins. Their cellular localization was predicted to be seven membrane proteins, as well as eight cytoplasmic, three nuclear, one mitochondrial and five proteins remained unclassified. Their functions were predicted as being two transport proteins, as well as five with metabolic activity, three as cell signaling and fourteen proteins remained unclassified. Ten hypothetical proteins were well-annotated and compared to their homology regarding to human proteins. Two proteins, a calpain-like and clavaminate synthase-like proteins were selected by using Docking analysis as being possible drug targets. In this sense, the present study showed the employ of new strategies to select possible drug candidates, according their localization and biological function in Leishmania parasites, aiming to treat against VL. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Analysis of adverse drug reactions using drug and drug target interactions and graph-based methods.

    PubMed

    Lin, Shih-Fang; Xiao, Ke-Ting; Huang, Yu-Ting; Chiu, Chung-Cheng; Soo, Von-Wun

    2010-01-01

    The purpose of this study was to integrate knowledge about drugs, drug targets, and topological methods. The goals were to build a system facilitating the study of adverse drug events, to make it easier to find possible explanations, and to group similar drug-drug interaction cases in the adverse drug reaction reports from the US Food and Drug Administration (FDA). We developed a system that analyses adverse drug reaction (ADR) cases reported by the FDA. The system contains four modules. First, we integrate drug and drug target databases that provide information related to adverse drug reactions. Second, we classify drug and drug targets according to anatomical therapeutic chemical classification (ATC) and drug target ontology (DTO). Third, we build drug target networks based on drug and drug target databases. Finally, we apply topological analysis to reveal drug interaction complexity for each ADR case reported by the FDA. We picked 1952 ADR cases from the years 2005-2006. Our dataset consisted of 1952 cases, of which 1471 cases involved ADR targets, 845 cases involved absorption, distribution, metabolism, and excretion (ADME) targets, and 507 cases involved some drugs acting on the same targets, namely, common targets (CTs). We then investigated the cases involving ADR targets, ADME targets, and CTs using the ATC system and DTO. In the cases that led to death, the average number of common targets (NCTs) was 0.879 and the average of average clustering coefficient (ACC) was 0.067. In cases that did not lead to death, the average NCTs was 0.551, and the average of ACC was 0.039. We implemented a system that can find possible explanations and cluster similar ADR cases reported by the FDA. We found that the average of ACC and the average NCTs in cases leading to death are higher than in cases not leading to death, suggesting that the interactions in cases leading to death are generally more complicated than in cases not leading to death. This indicates that our system

  12. Identification of potential drug targets by subtractive genome analysis of Escherichia coli O157:H7: an in silico approach

    PubMed Central

    Mondal, Shakhinur Islam; Ferdous, Sabiha; Jewel, Nurnabi Azad; Akter, Arzuba; Mahmud, Zabed; Islam, Md Muzahidul; Afrin, Tanzila; Karim, Nurul

    2015-01-01

    Bacterial enteric infections resulting in diarrhea, dysentery, or enteric fever constitute a huge public health problem, with more than a billion episodes of disease annually in developing and developed countries. In this study, the deadly agent of hemorrhagic diarrhea and hemolytic uremic syndrome, Escherichia coli O157:H7 was investigated with extensive computational approaches aimed at identifying novel and broad-spectrum antibiotic targets. A systematic in silico workflow consisting of comparative genomics, metabolic pathways analysis, and additional drug prioritizing parameters was used to identify novel drug targets that were essential for the pathogen’s survival but absent in its human host. Comparative genomic analysis of Kyoto Encyclopedia of Genes and Genomes annotated metabolic pathways identified 350 putative target proteins in E. coli O157:H7 which showed no similarity to human proteins. Further bio-informatic approaches including prediction of subcellular localization, calculation of molecular weight, and web-based investigation of 3D structural characteristics greatly aided in filtering the potential drug targets from 350 to 120. Ultimately, 44 non-homologous essential proteins of E. coli O157:H7 were prioritized and proved to have the eligibility to become novel broad-spectrum antibiotic targets and DNA polymerase III alpha (dnaE) was the top-ranked among these targets. Moreover, druggability of each of the identified drug targets was evaluated by the DrugBank database. In addition, 3D structure of the dnaE was modeled and explored further for in silico docking with ligands having potential druggability. Finally, we confirmed that the compounds N-coeleneterazine and N-(1,4-dihydro-5H-tetrazol-5-ylidene)-9-oxo-9H-xanthene-2-sulfon-amide were the most suitable ligands of dnaE and hence proposed as the potential inhibitors of this target protein. The results of this study could facilitate the discovery and release of new and effective drugs against E

  13. Predicting conserved essential genes in bacteria: in silico identification of putative drug targets.

    PubMed

    Duffield, Melanie; Cooper, Ian; McAlister, Erin; Bayliss, Marc; Ford, Donna; Oyston, Petra

    2010-12-01

    Many genes have been listed as putatively essential for bacterial viability in the Database of Essential Genomes (DEG), although few have been experimentally validated. By prioritising targets according to the criteria suggested by the Research and Training in Tropical Diseases (TDR) Targets database, we have developed a modified down-selection tool to identify essential genes conserved across diverse genera. Using this approach we identified 52 proteins conserved to 7 or more of the 14 genomes in DEG. We confirmed the validity of the down-selection by attempting to make mutants of 8 of these targets in a model organism, Yersinia pseudotuberculosis, which is not closely related to any of the bacteria in DEG. Mutants were recovered for only one of the 8 targets, suggesting that the other 7 were essential in Y. pseudotuberculosis, an impressive success rate compared to other approaches of identification for such targets. Identification of essential proteins common in diverse bacterial genera can then be used to facilitate the selection of effective targets for novel broad-spectrum antibiotics.

  14. Hysteresis prediction inside magnetic shields and application.

    PubMed

    Morić, Igor; De Graeve, Charles-Marie; Grosjean, Olivier; Laurent, Philippe

    2014-07-01

    We have developed a simple model that is able to describe and predict hysteresis behavior inside Mumetal magnetic shields, when the shields are submitted to ultra-low frequency (<0.01 Hz) magnetic perturbations with amplitudes lower than 60 μT. This predictive model has been implemented in a software to perform an active compensation system. With this compensation the attenuation of longitudinal magnetic fields is increased by two orders of magnitude. The system is now integrated in the cold atom space clock called PHARAO. The clock will fly onboard the International Space Station in the frame of the ACES space mission.

  15. Hysteresis prediction inside magnetic shields and application

    SciTech Connect

    Morić, Igor; De Graeve, Charles-Marie; Grosjean, Olivier; Laurent, Philippe

    2014-07-15

    We have developed a simple model that is able to describe and predict hysteresis behavior inside Mumetal magnetic shields, when the shields are submitted to ultra-low frequency (<0.01 Hz) magnetic perturbations with amplitudes lower than 60 μT. This predictive model has been implemented in a software to perform an active compensation system. With this compensation the attenuation of longitudinal magnetic fields is increased by two orders of magnitude. The system is now integrated in the cold atom space clock called PHARAO. The clock will fly onboard the International Space Station in the frame of the ACES space mission.

  16. Predictive microbiology in food packaging applications

    USDA-ARS?s Scientific Manuscript database

    Predictive microbiology including growth, inactivation, surface transfer (or cross-contamination), and survival, plays important roles in understanding microbial food safety. Growth models may involve the growth potential of a specified pathogen under different stresses, e.g., temperature, pH, wate...

  17. Prediction in Sport: Theories and Applications.

    ERIC Educational Resources Information Center

    Disch, James G.; Morrow, James R., Jr.

    The use of various physiological tests and measurements accurately predicts the performance ratings of female athletes. A study involving 180 female intercollegiate volleyball players, 142 female intercollegiate basketball players, and 115 female college-age nonathletes yielded a set of statistical differentials concerning arm length, lean weight…

  18. Clostridium-DT(DB): a comprehensive database for potential drug targets of Clostridium difficile.

    PubMed

    Jadhav, Ankush; Ezhilarasan, Vijayalakshmi; Prakash Sharma, Om; Pan, Archana

    2013-05-01

    Clostridium difficile is considered to be one of the most important causes of health care-associated infections currently. The prevalence and severity of C. difficile infection have increased significantly worldwide in the past decade which has led to the increased research interest. Here, using comparative genomics strategy coupled with bioinformatics tools we have identified potential drug targets in C. difficile and determined their three-dimensional structures in order to develop a database, named Clostridium-DT(DB). Currently, the database comprises the potential drug targets with their structural information from three strains of C. difficile, namely hypervirulent PCR-ribotype 027 strain R20291, PCR-ribotype 012 strain 630, and PCR-ribotype 027 strain CD196. Copyright © 2013 Elsevier Ltd. All rights reserved.

  19. EphB1 as a Novel Drug Target to Combat Pain and Addiction

    DTIC Science & Technology

    2015-09-01

    EphB1  as  a  Novel  Drug  Target  to  Combat  Pain  and   Addiction   Principal  Investigator  Name:   Mark...31 Aug 2015 4. TITLE AND SUBTITLE EphB1 as a Novel Drug Target to Combat Pain and Addiction 5a. CONTRACT NUMBER EphB1 as a Novel Drug Target to...Combat Pain and Addiction 5b. GRANT NUMBER W81XWH-14-1-0220 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER Mark Henkemeyer, Ph.D. 5e

  20. Identification of potential drug targets based on a computational biology algorithm for venous thromboembolism.

    PubMed

    Xie, Ruiqiang; Li, Lei; Chen, Lina; Li, Wan; Chen, Binbin; Jiang, Jing; Huang, Hao; Li, Yiran; He, Yuehan; Lv, Junjie; He, Weiming

    2017-02-01

    Venous thromboembolism (VTE) is a common, fatal and frequently recurrent disease. Changes in the activity of different coagulation factors serve as a pathophysiological basis for the recurrent risk of VTE. Systems biology approaches provide a better understanding of the pathological mechanisms responsible for recurrent VTE. In this study, a novel computational method was presented to identify the recurrent risk modules (RRMs) based on the integration of expression profiles and human signaling network, which hold promise for achieving new and deeper insights into the mechanisms responsible for VTE. The results revealed that the RRMs had good classification performance to discriminate patients with recurrent VTE. The functional annotation analysis demonstrated that the RRMs played a crucial role in the pathogenesis of VTE. Furthermore, a variety of approved drug targets in the RRM M5 were related to VTE. Thus, the M5 may be applied to select potential drug targets for combination therapy and the extended treatment of VTE.

  1. The type III secretion system as a source of novel antibacterial drug targets.

    PubMed

    Kline, Toni; Felise, Heather B; Sanowar, Sarah; Miller, Samuel I

    2012-03-01

    Type III Secretion Systems (T3SSs) are highly organized multi-protein nanomachines which translocate effector proteins from the bacterial cytosol directly into host cells. These systems are required for the pathogenesis of a wide array of Gram-negative bacterial pathogens, and thus have attracted attention as potential antibacterial drug targets. A decade of research has enabled the identification of natural products, conventional small molecule drug-like structures, and proteins that inhibit T3SSs. The mechanism(s) of action and molecular target(s) of the majority of these inhibitors remain to be determined. At the same time, structural biology methods are providing an increasingly detailed picture of the functional arrangement of the T3SS component proteins. The confluence of these two research areas may ultimately identify non-classical drug targets and facilitate the development of novel therapeutics.

  2. Recent advances in the biology and drug targeting of malaria parasite aminoacyl-tRNA synthetases.

    PubMed

    Khan, Sameena

    2016-04-12

    Escalating drug resistance in malaria parasites and lack of vaccine entails the discovery of novel drug targets and inhibitor molecules. The multi-component protein translation machinery is a rich source of such drug targets. Malaria parasites contain three translational compartments: the cytoplasm, apicoplast and mitochondrion, of which the latter two are of the prokaryotic type. Recent explorations by many groups into the malaria parasite protein translation enzymes, aminoacyl-tRNA synthetases (aaRSs), have yielded many promising inhibitors. The understanding of the biology of this unique set of 36 enzymes has become much clearer in recent times. Current review discusses the advances made in understanding of crucial aaRSs from Plasmodium and also the specific inhibitors found against malaria aaRSs.

  3. The gastric H,K ATPase as a drug target: past, present, and future.

    PubMed

    Sachs, George; Shin, Jai Moo; Vagin, Olga; Lambrecht, Nils; Yakubov, Iskandar; Munson, Keith

    2007-07-01

    The recent progress in therapy if acid disease has relied heavily on the performance of drugs targeted against the H,K ATPase of the stomach and the H2 receptor antagonists. It has become apparent in the last decade that the proton pump is the target that has the likelihood of being the most sustainable area of therapeutic application in the regulation of acid suppression. The process of activation of acid secretion requires a change in location of the ATPase from cytoplasmic tubules into the microvilli of the secretory canaliculus of the parietal cell. Stimulation of the resting parietal cell, with involvement of F-actin and ezrin does not use significant numbers of SNARE proteins, because their message is depleted in the pure parietal cell transcriptome. The cell morphology and gene expression suggest a tubule fusion-eversion event. As the active H,K ATPase requires efflux of KCl for activity we have, using the transcriptome derived from 99% pure parietal cells and immunocytochemistry, provided evidence that the KCl pathway is mediated by a KCQ1/KCNE2 complex for supplying K and CLIC6 for supplying the accompanying Cl. The pump has been modeled on the basis of the structures of different conformations of the sr Ca ATPase related to the catalytic cycle. These models use the effects of site directed mutations and identification of the binding domain of the K competitive acid pump antagonists or the defined site of binding for the covalent class of proton pump inhibitors. The pump undergoes conformational changes associated with phosphorylation to allow the ion binding site to change exposure from cytoplasmic to luminal exposure. We have been able to postulate that the very low gastric pH is achieved by lysine 791 motion extruding the hydronium ion bound to carboxylates in the middle of the membrane domain. These models also allow description of the K entry to form the K liganded form of the enzyme and the reformation of the ion site inward conformation thus

  4. Identification of New Drug Targets in Multi-Drug Resistant Bacterial Infections

    DTIC Science & Technology

    2014-10-01

    Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT: A. baumannii is a gram -negative bacillus (GNB) known to cause health-care associated...24! 2 W81XWH-11-2-0218 Annual Report October 2014 Introduction& A. baumannii is a gram -negative bacillus (GNB...approach to drug target discovery in multi-drug resistant gram negative bacilli. Future Microbiology. Posters April 2014. K.M. Armbruster

  5. Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics

    PubMed Central

    2014-01-01

    Background The demand for novel molecularly targeted drugs will continue to rise as we move forward toward the goal of personalizing cancer treatment to the molecular signature of individual tumors. However, the identification of targets and combinations of targets that can be safely and effectively modulated is one of the greatest challenges facing the drug discovery process. A promising approach is to use biological networks to prioritize targets based on their relative positions to one another, a property that affects their ability to maintain network integrity and propagate information-flow. Here, we introduce influence networks and demonstrate how they can be used to generate influence scores as a network-based metric to rank genes as potential drug targets. Results We use this approach to prioritize genes as drug target candidates in a set of ER + breast tumor samples collected during the course of neoadjuvant treatment with the aromatase inhibitor letrozole. We show that influential genes, those with high influence scores, tend to be essential and include a higher proportion of essential genes than those prioritized based on their position (i.e. hubs or bottlenecks) within the same network. Additionally, we show that influential genes represent novel biologically relevant drug targets for the treatment of ER + breast cancers. Moreover, we demonstrate that gene influence differs between untreated tumors and residual tumors that have adapted to drug treatment. In this way, influence scores capture the context-dependent functions of genes and present the opportunity to design combination treatment strategies that take advantage of the tumor adaptation process. Conclusions Influence networks efficiently find essential genes as promising drug targets and combinations of targets to inform the development of molecularly targeted drugs and their use. PMID:24495353

  6. New approaches for the identification of drug targets in protozoan parasites.

    PubMed

    Müller, Joachim; Hemphill, Andrew

    2013-01-01

    Antiparasitic chemotherapy is an important issue for drug development. Traditionally, novel compounds with antiprotozoan activities have been identified by screening of compound libraries in high-throughput systems. More recently developed approaches employ target-based drug design supported by genomics and proteomics of protozoan parasites. In this chapter, the drug targets in protozoan parasites are reviewed. The gene-expression machinery has been among the first targets for antiparasitic drugs and is still under investigation as a target for novel compounds. Other targets include cytoskeletal proteins, proteins involved in intracellular signaling, membranes, and enzymes participating in intermediary metabolism. In apicomplexan parasites, the apicoplast is a suitable target for established and novel drugs. Some drugs act on multiple subcellular targets. Drugs with nitro groups generate free radicals under anaerobic growth conditions, and drugs with peroxide groups generate radicals under aerobic growth conditions, both affecting multiple cellular pathways. Mefloquine and thiazolides are presented as examples for antiprotozoan compounds with multiple (side) effects. The classic approach of drug discovery employing high-throughput physiological screenings followed by identification of drug targets has yielded the mainstream of current antiprotozoal drugs. Target-based drug design supported by genomics and proteomics of protozoan parasites has not produced any antiparasitic drug so far. The reason for this is discussed and a synthesis of both methods is proposed. Copyright © 2013 Elsevier Inc. All rights reserved.

  7. Identification and modeling of a drug target for Clostridium perfringens SM101.

    PubMed

    Chhabra, Gagan; Sharma, Pramila; Anant, Avishek; Deshmukh, Sachin; Kaushik, Himani; Gopal, Keshav; Srivastava, Nutan; Sharma, Neeraj; Garg, Lalit C

    2010-01-17

    In the present study, comparative genome analysis between Clostridium perfringens and the human genome was carried out to identify genes that are essential for the pathogen's survival, and non-homologous to the genes of human host, that can be used as potential drug targets. The study resulted in the identification of 426 such genes. The number of these potential drug targets thus identified is significantly lower than the genome's protein coding capacity (2558 protein coding genes). The 426 genes of C. perfringens were further analyzed for overall similarities with the essential genes of 14 different bacterial species present in Database of Essential Genes (DEG). Our results show that there are only 5 essential genes of C. perfringens that exhibit similarity with 12 species of the 14 different bacterial species present in DEG database. Of these, 1 gene was similar in 12 species and 4 genes were similar in 11 species. Thus, the study opens a new avenue for the development of potential drugs against the highly pathogenic bacterium. Further, by selecting these essential genes of C. perfringens, which are common and essential for other pathogenic microbial species, a broad spectrum anti-microbial drug can be developed. As a case study, we have built a homology model of one of the potential drug targets, ABC transporter-ATP binding protein, which can be employed for in silico docking studies by suitable inhibitors.

  8. Predicting New Materials for Hydrogen Storage Application

    PubMed Central

    Vajeeston, Ponniah; Ravindran, Ponniah; Fjellvåg, Helmer

    2009-01-01

    Knowledge about the ground-state crystal structure is a prerequisite for the rational understanding of solid-state properties of new materials. To act as an efficient energy carrier, hydrogen should be absorbed and desorbed in materials easily and in high quantities. Owing to the complexity in structural arrangements and difficulties involved in establishing hydrogen positions by x-ray diffraction methods, the structural information of hydrides are very limited compared to other classes of materials (like oxides, intermetallics, etc.). This can be overcome by conducting computational simulations combined with selected experimental study which can save environment, money, and man power. The predicting capability of first-principles density functional theory (DFT) is already well recognized and in many cases structural and thermodynamic properties of single/multi component system are predicted. This review will focus on possible new classes of materials those have high hydrogen content, demonstrate the ability of DFT to predict crystal structure, and search for potential meta-stable phases. Stabilization of such meta-stable phases is also discussed.

  9. Application of optimal prediction to molecular dynamics

    SciTech Connect

    Barber, IV, John Letherman

    2004-12-01

    Optimal prediction is a general system reduction technique for large sets of differential equations. In this method, which was devised by Chorin, Hald, Kast, Kupferman, and Levy, a projection operator formalism is used to construct a smaller system of equations governing the dynamics of a subset of the original degrees of freedom. This reduced system consists of an effective Hamiltonian dynamics, augmented by an integral memory term and a random noise term. Molecular dynamics is a method for simulating large systems of interacting fluid particles. In this thesis, I construct a formalism for applying optimal prediction to molecular dynamics, producing reduced systems from which the properties of the original system can be recovered. These reduced systems require significantly less computational time than the original system. I initially consider first-order optimal prediction, in which the memory and noise terms are neglected. I construct a pair approximation to the renormalized potential, and ignore three-particle and higher interactions. This produces a reduced system that correctly reproduces static properties of the original system, such as energy and pressure, at low-to-moderate densities. However, it fails to capture dynamical quantities, such as autocorrelation functions. I next derive a short-memory approximation, in which the memory term is represented as a linear frictional force with configuration-dependent coefficients. This allows the use of a Fokker-Planck equation to show that, in this regime, the noise is δ-correlated in time. This linear friction model reproduces not only the static properties of the original system, but also the autocorrelation functions of dynamical variables.

  10. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    ERIC Educational Resources Information Center

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  11. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    ERIC Educational Resources Information Center

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  12. [Application of predictive microbiology in fungi growth and mycotoxin production].

    PubMed

    Wang, Wei; Yu, Hongxia; Li, Fengqin

    2009-11-01

    Food spoilage and food poisoning caused by fungi is an important issue in the field of food safety. The linear regression equations and predictive models are developed through the study on the effect of environmental factors on fungi growth and mycotoxin production. This is easier to understand fungi activities. Predictive microbiology plays an important role in evaluation of food spoilage and food poisoning. The application of predictive modelling in fungi growth and mycotoxin production are reviewed.

  13. Characterizing Drug-Target Residence Time with Metadynamics: How To Achieve Dissociation Rate Efficiently without Losing Accuracy against Time-Consuming Approaches.

    PubMed

    Sun, Huiyong; Li, Youyong; Shen, Mingyun; Li, Dan; Kang, Yu; Hou, Tingjun

    2017-08-28

    Drug-target residence time plays a vital role in drug efficacy. However, there is still no effective strategy to predict drug residence time. Here, we propose to use the optimized (or minimized) structures derived from holo-state proteins to calculate drug residence time, which could give a comparable or even better prediction accuracy compared with those calculated utilizing a large number of molecular dynamics (MD) structures based on the Poisson process. Besides, in addition to the Poisson process, one may use fewer samples for predicting residence time due to the reason that, in a large extent, the calculated drug residence time is stable and independent of the number of samples used for the prediction. With remarkably reduced computational load, the proposed strategy may be promising for large-scale drug residence time prediction, such as post-processing in virtual screening (VS) and lead compound optimization.

  14. Identification of polycystic ovary syndrome potential drug targets based on pathobiological similarity in the protein-protein interaction network

    PubMed Central

    Li, Wan; Wei, Wenqing; Li, Yiran; Xie, Ruiqiang; Guo, Shanshan; Wang, Yahui; Jiang, Jing; Chen, Binbin; Lv, Junjie; Zhang, Nana; Chen, Lina; He, Weiming

    2016-01-01

    Polycystic ovary syndrome (PCOS) is one of the most common endocrinological disorders in reproductive aged women. PCOS and Type 2 Diabetes (T2D) are closely linked in multiple levels and possess high pathobiological similarity. Here, we put forward a new computational approach based on the pathobiological similarity to identify PCOS potential drug target modules (PPDT-Modules) and PCOS potential drug targets in the protein-protein interaction network (PPIN). From the systems level and biological background, 1 PPDT-Module and 22 PCOS potential drug targets were identified, 21 of which were verified by literatures to be associated with the pathogenesis of PCOS. 42 drugs targeting to 13 PCOS potential drug targets were investigated experimentally or clinically for PCOS. Evaluated by independent datasets, the whole PPDT-Module and 22 PCOS potential drug targets could not only reveal the drug response, but also distinguish the statuses between normal and disease. Our identified PPDT-Module and PCOS potential drug targets would shed light on the treatment of PCOS. And our approach would provide valuable insights to research on the pathogenesis and drug response of other diseases. PMID:27191267

  15. Implant-assisted magnetic drug targeting in permeable microvessels: Comparison of two-fluid statistical transport model with experiment

    NASA Astrophysics Data System (ADS)

    ChiBin, Zhang; XiaoHui, Lin; ZhaoMin, Wang; ChangBao, Wang

    2017-03-01

    In experiments and theoretical analyses, this study examines the capture efficiency (CE) of magnetic drug carrier particles (MDCPs) for implant-assisted magnetic drug targeting (IA-MDT) in microvessels. It also proposes a three-dimensional statistical transport model of MDCPs for IA-MDT in permeable microvessels, which describes blood flow by the two-fluid (Casson and Newtonian) model. The model accounts for the permeable effect of the microvessel wall and the coupling effect between the blood flow and tissue fluid flow. The MDCPs move randomly through the microvessel, and their transport state is described by the Boltzmann equation. The regulated changes and factors affecting the CE of the MDCPs in the assisted magnetic targeting were obtained by solving the theoretical model and by experimental testing. The CE was negatively correlated with the blood flow velocity, and positively correlated with the external magnetic field intensity and microvessel permeability. The predicted CEs of the MDCPs were consistent with the experimental results. Additionally, under the same external magnetic field, the predicted CE was 5-8% higher in the IA-MDT model than in the model ignoring the permeability effect of the microvessel wall.

  16. Epitopemap: a web application for integrated whole proteome epitope prediction.

    PubMed

    Farrell, Damien; Gordon, Stephen V

    2015-07-14

    Predictions of MHC binding affinity are commonly used in immunoinformatics for T cell epitope prediction. There are multiple available methods, some of which provide web access. However there is currently no convenient way to access the results from multiple methods at the same time or to execute predictions for an entire proteome at once. We designed a web application that allows integration of multiple epitope prediction methods for any number of proteins in a genome. The tool is a front-end for various freely available methods. Features include visualisation of results from multiple predictors within proteins in one plot, genome-wide analysis and estimates of epitope conservation. We present a self contained web application, Epitopemap, for calculating and viewing epitope predictions with multiple methods. The tool is easy to use and will assist in computational screening of viral or bacterial genomes.

  17. Drug-target networks for Tanshinone IIA identified by data mining.

    PubMed

    Chen, Shao-Jun

    2015-10-01

    Tanshinone IIA is a pharmacologically active compound isolated from Danshen (Salvia miltiorrhiza), a traditional Chinese herbal medicine for the management of cardiac diseases and other disorders. But its underlying molecular mechanisms of action are still unclear. The present investigation utilized a data mining approach based on network pharmacology to uncover the potential protein targets of Tanshinone IIA. Network pharmacology, an integrated multidisciplinary study, incorporates systems biology, network analysis, connectivity, redundancy, and pleiotropy, providing powerful new tools and insights into elucidating the fine details of drug-target interactions. In the present study, two separate drug-target networks for Tanshinone IIA were constructed using the Agilent Literature Search (ALS) and STITCH (search tool for interactions of chemicals) methods. Analysis of the ALS-constructed network revealed a target network with a scale-free topology and five top nodes (protein targets) corresponding to Fos, Jun, Src, phosphatidylinositol-4, 5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA), and mitogen-activated protein kinase kinase 1 (MAP2K1), whereas analysis of the STITCH-constructed network revealed three top nodes corresponding to cytochrome P450 3A4 (CYP3A4), cytochrome P450 A1 (CYP1A1), and nuclear factor kappa B1 (NFκB1). The discrepancies were probably due to the differences in the divergent computer mining tools and databases employed by the two methods. However, it is conceivable that all eight proteins mediate important biological functions of Tanshinone IIA, contributing to its overall drug-target network. In conclusion, the current results may assist in developing a comprehensive understanding of the molecular mechanisms and signaling pathways of in a simple, compact, and visual manner. Copyright © 2015 China Pharmaceutical University. Published by Elsevier B.V. All rights reserved.

  18. Discovery of Anthelmintic Drug Targets and Drugs Using Chokepoints in Nematode Metabolic Pathways

    PubMed Central

    Taylor, Christina M.; Wang, Qi; Rosa, Bruce A.; Huang, Stanley Ching-Cheng; Powell, Kerrie; Schedl, Tim; Pearce, Edward J.; Abubucker, Sahar; Mitreva, Makedonka

    2013-01-01

    Parasitic roundworm infections plague more than 2 billion people (1/3 of humanity) and cause drastic losses in crops and livestock. New anthelmintic drugs are urgently needed as new drug resistance and environmental concerns arise. A “chokepoint reaction” is defined as a reaction that either consumes a unique substrate or produces a unique product. A chokepoint analysis provides a systematic method of identifying novel potential drug targets. Chokepoint enzymes were identified in the genomes of 10 nematode species, and the intersection and union of all chokepoint enzymes were found. By studying and experimentally testing available compounds known to target proteins orthologous to nematode chokepoint proteins in public databases, this study uncovers features of chokepoints that make them successful drug targets. Chemogenomic screening was performed on drug-like compounds from public drug databases to find existing compounds that target homologs of nematode chokepoints. The compounds were prioritized based on chemical properties frequently found in successful drugs and were experimentally tested using Caenorhabditis elegans. Several drugs that are already known anthelmintic drugs and novel candidate targets were identified. Seven of the compounds were tested in Caenorhabditis elegans and three yielded a detrimental phenotype. One of these three drug-like compounds, Perhexiline, also yielded a deleterious effect in Haemonchus contortus and Onchocerca lienalis, two nematodes with divergent forms of parasitism. Perhexiline, known to affect the fatty acid oxidation pathway in mammals, caused a reduction in oxygen consumption rates in C. elegans and genome-wide gene expression profiles provided an additional confirmation of its mode of action. Computational modeling of Perhexiline and its target provided structural insights regarding its binding mode and specificity. Our lists of prioritized drug targets and drug-like compounds have potential to expedite the discovery

  19. Novel drug targets for the pharmacotherapy of benign prostatic hyperplasia (BPH)

    PubMed Central

    Ventura, S; Oliver, VL; White, CW; Xie, JH; Haynes, JM; Exintaris, B

    2011-01-01

    Benign prostatic hyperplasia (BPH) is the major cause of lower urinary tract symptoms in men aged 50 or older. Symptoms are not normally life threatening, but often drastically affect the quality of life. The number of men seeking treatment for BPH is expected to grow in the next few years as a result of the ageing male population. Estimates of annual pharmaceutical sales of BPH therapies range from $US 3 to 10 billion, yet this market is dominated by two drug classes. Current drugs are only effective in treating mild to moderate symptoms, yet despite this, no emerging contenders appear to be on the horizon. This is remarkable given the increasing number of patients with severe symptoms who are required to undergo invasive and unpleasant surgery. This review provides a brief background on prostate function and the pathophysiology of BPH, followed by a brief description of BPH epidemiology, the burden it places on society, and the current surgical and pharmaceutical therapies. The recent literature on emerging contenders to current therapies and novel drug targets is then reviewed, focusing on drug targets which are able to relax prostatic smooth muscle in a similar way to the α1-adrenoceptor antagonists, as this appears to be the most effective mechanism of action. Other mechanisms which may be of benefit are also discussed. It is concluded that recent basic research has revealed a number of novel drug targets such as muscarinic receptor or P2X-purinoceptor antagonists, which have the potential to produce more effective and safer drug treatments. PMID:21410684

  20. Evaluation of Giardia lamblia thioredoxin reductase as drug activating enzyme and as drug target.

    PubMed

    Leitsch, David; Müller, Joachim; Müller, Norbert

    2016-12-01

    The antioxidative enzyme thioredoxin reductase (TrxR) has been suggested to be a drug target in several pathogens, including the protist parasite Giardia lamblia. TrxR is also believed to catalyse the reduction of nitro drugs, e.g. metronidazole and furazolidone, a reaction required to render these compounds toxic to G. lamblia and other microaerophiles/anaerobes. It was the objective of this study to assess the potential of TrxR as a drug target in G. lamblia and to find direct evidence for the role of this enzyme in the activation of metronidazole and other nitro drugs. TrxR was overexpressed approximately 10-fold in G. lamblia WB C6 cells by placing the trxR gene behind the arginine deiminase (ADI) promoter on a plasmid. Likewise, a mutant TrxR with a defective disulphide reductase catalytic site was strongly expressed in another G. lamblia WB C6 cell line. Susceptibilities to five antigiardial drugs, i.e. metronidazole, furazolidone, nitazoxanide, albendazole and auranofin were determined in both transfectant cell lines and compared to wildtype. Further, the impact of all five drugs on TrxR activity in vivo was measured. Overexpression of TrxR rendered G. lamblia WB C6 more susceptible to metronidazole and furazolidone but not to nitazoxanide, albendazole, and auranofin. Of all five drugs tested, only auranofin had an appreciably negative effect on TrxR activity in vivo, albeit to a much smaller extent than expected. Overexpression of TrxR and mutant TrxR had hardly any impact on growth of G. lamblia WB C6, although the enzyme also exerts a strong NADPH oxidase activity which is a source of oxidative stress. Our results constitute first direct evidence for the notion that TrxR is an activator of metronidazole and furazolidone but rather question that it is a relevant drug target of presently used antigiardial drugs.

  1. Protein Drug Targets of Lavandula angustifolia on treatment of Rat Alzheimer's Disease

    PubMed Central

    Zali, Hakimeh; Zamanian-Azodi, Mona; Rezaei Tavirani, Mostafa; Akbar-zadeh Baghban, Alireza

    2015-01-01

    Different treatment strategies of Alzheimer's disease (AD) are being studied for treating or slowing the progression of AD. Many pharmaceutically important regulation systems operate through proteins as drug targets. Here, we investigate the drug target proteins in beta-amyloid (Aβ) injected rat hippocampus treated with Lavandula angustifolia (LA) by proteomics techniques. The reported study showed that lavender extract (LE) improves the spatial performance in AD animal model by diminishing Aβ production in histopathology of hippocampus, so in this study neuroprotective proteins expressed in Aβ injected rats treated with LE were scrutinized. Rats were divided into three groups including normal, Aβ injected, and Aβ injected that was treated with LE. Protein expression profiles of hippocampus tissue were determined by two-dimensional electrophoresis (2DE) method and dysregulated proteins such as Snca, NF-L, Hspa5, Prdx2, Apoa1, and Atp5a1were identified by MALDI-TOF/TOF. KEGG pathway and gene ontology (GO) categories were used by searching DAVID Bioinformatics Resources. All detected protein spots were used to determine predictedinteractions with other proteins in STRING online database. Different isoforms of important protein, Snca that exhibited neuroprotective effects by anti-apoptotic properties were expressed. NF-L involved in the maintenance of neuronal caliber. Hspa5 likewise Prdx2 displays as anti-apoptotic protein that Prdx2 also involved in the neurotrophic effects. Apoa1 has anti-inflammatory activity and Atp5a1, produces ATP from ADP. To sum up, these proteins as potential drug targets were expressed in hippocampus in response to effective components in LA may have therapeutic properties for the treatment of AD and other neurodegenerative diseases. PMID:25561935

  2. Quantitative Chemical Proteomics Reveals New Potential Drug Targets in Head and Neck Cancer*

    PubMed Central

    Wu, Zhixiang; Doondeea, Jessica B.; Gholami, Amin Moghaddas; Janning, Melanie C.; Lemeer, Simone; Kramer, Karl; Eccles, Suzanne A.; Gollin, Susanne M.; Grenman, Reidar; Walch, Axel; Feller, Stephan M.; Kuster, Bernhard

    2011-01-01

    Tumors of the head and neck represent a molecularly diverse set of human cancers, but relatively few proteins have actually been shown to drive the disease at the molecular level. To identify new targets for individualized diagnosis or therapeutic intervention, we performed a kinase centric chemical proteomics screen and quantified 146 kinases across 34 head and neck squamous cell carcinoma (HNSCC) cell lines using intensity-based label-free mass spectrometry. Statistical analysis of the profiles revealed significant intercell line differences for 42 kinases (p < 0.05), and loss of function experiments using siRNA in high and low expressing cell lines identified kinases including EGFR, NEK9, LYN, JAK1, WEE1, and EPHA2 involved in cell survival and proliferation. EGFR inhibition by the small molecule inhibitors lapatinib, gefitinib, and erlotinib as well as siRNA led to strong reduction of viability in high but not low expressing lines, confirming EGFR as a drug target in 10–20% of HNSCC cell lines. Similarly, high, but not low EPHA2-expressing cells showed strongly reduced viability concomitant with down-regulation of AKT and ERK signaling following EPHA2 siRNA treatment or EPHA1-Fc ligand exposure, suggesting that EPHA2 is a novel drug target in HNSCC. This notion is underscored by immunohistochemical analyses showing that high EPHA2 expression is detected in a subset of HNSCC tissues and is associated with poor prognosis. Given that the approved pan-SRC family kinase inhibitor dasatinib is also a very potent inhibitor of EPHA2, our findings may lead to new therapeutic options for HNSCC patients. Importantly, the strategy employed in this study is generic and therefore also of more general utility for the identification of novel drug targets and molecular pathway markers in tumors. This may ultimately lead to a more rational approach to individualized cancer diagnosis and therapy. PMID:21955398

  3. Adhesion molecules and the extracellular matrix as drug targets for glioma.

    PubMed

    Shimizu, Toshihiko; Kurozumi, Kazuhiko; Ishida, Joji; Ichikawa, Tomotsugu; Date, Isao

    2016-04-01

    The formation of tumor vasculature and cell invasion along white matter tracts have pivotal roles in the development and progression of glioma. A better understanding of the mechanisms of angiogenesis and invasion in glioma will aid the development of novel therapeutic strategies. The processes of angiogenesis and invasion cause the production of an array of adhesion molecules and extracellular matrix (ECM) components. This review focuses on the role of adhesion molecules and the ECM in malignant glioma. The results of clinical trials using drugs targeted against adhesion molecules and the ECM for glioma are also discussed.

  4. Prioritizing drug targets in Clostridium botulinum with a computational systems biology approach.

    PubMed

    Muhammad, Syed Aun; Ahmed, Safia; Ali, Amjad; Huang, Hui; Wu, Xiaogang; Yang, X Frank; Naz, Anam; Chen, Jake

    2014-07-01

    A computational and in silico system level framework was developed to identify and prioritize the antibacterial drug targets in Clostridium botulinum (Clb), the causative agent of flaccid paralysis in humans that can be fatal in 5 to 10% of cases. This disease is difficult to control due to the emergence of drug-resistant pathogenic strains and the only available treatment antitoxin which can target the neurotoxin at the extracellular level and cannot reverse the paralysis. This study framework is based on comprehensive systems-scale analysis of genomic sequence homology and phylogenetic relationships among Clostridium, other infectious bacteria, host and human gut flora. First, the entire 2628-annotated genes of this bacterial genome were categorized into essential, non-essential and virulence genes. The results obtained showed that 39% of essential proteins that functionally interact with virulence proteins were identified, which could be a key to new interventions that may kill the bacteria and minimize the host damage caused by the virulence factors. Second, a comprehensive comparative COGs and blast sequence analysis of these proteins and host proteins to minimize the risks of side effects was carried out. This revealed that 47% of a set of C. botulinum proteins were evolutionary related with Homo sapiens proteins to sort out the non-human homologs. Third, orthology analysis with other infectious bacteria to assess broad-spectrum effects was executed and COGs were mostly found in Clostridia, Bacilli (Firmicutes), and in alpha and beta Proteobacteria. Fourth, a comparative phylogenetic analysis was performed with human microbiota to filter out drug targets that may also affect human gut flora. This reduced the list of candidate proteins down to 131. Finally, the role of these putative drug targets in clostridial biological pathways was studied while subcellular localization of these candidate proteins in bacterial cellular system exhibited that 68% of the

  5. PLP-dependent enzymes as potential drug targets for protozoan diseases.

    PubMed

    Kappes, Barbara; Tews, Ivo; Binter, Alexandra; Macheroux, Peter

    2011-11-01

    The chemical properties of the B(6) vitamers are uniquely suited for wide use as cofactors in essential reactions, such as decarboxylations and transaminations. This review addresses current efforts to explore vitamin B(6) dependent enzymatic reactions as drug targets. Several current targets are described that are found amongst these enzymes. The focus is set on diseases caused by protozoan parasites. Comparison across a range of these organisms allows insight into the distribution of potential targets, many of which may be of interest in the development of broad range anti-protozoan drugs. This article is part of a Special Issue entitled: Pyridoxal Phosphate Enzymology.

  6. Central nervous system myeloid cells as drug targets: current status and translational challenges.

    PubMed

    Biber, Knut; Möller, Thomas; Boddeke, Erik; Prinz, Marco

    2016-02-01

    Myeloid cells of the central nervous system (CNS), which include parenchymal microglia, macrophages at CNS interfaces and monocytes recruited from the circulation during disease, are increasingly being recognized as targets for therapeutic intervention in neurological and psychiatric diseases. The origin of these cells in the immune system distinguishes them from ectodermal neurons and other glia and endows them with potential drug targets distinct from classical CNS target groups. However, despite the identification of several promising therapeutic approaches and molecular targets, no agents directly targeting these cells are currently available. Here, we assess strategies for targeting CNS myeloid cells and address key issues associated with their translation into the clinic.

  7. An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions.

    PubMed

    Deng, Xin; Gumm, Jordan; Karki, Suman; Eickholt, Jesse; Cheng, Jianlin

    2015-07-07

    Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale.

  8. An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

    PubMed Central

    Deng, Xin; Gumm, Jordan; Karki, Suman; Eickholt, Jesse; Cheng, Jianlin

    2015-01-01

    Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale. PMID:26198229

  9. An Approach to Performance Prediction for Parallel Applications

    SciTech Connect

    Ipek, E; de Supinski, B R; Schulz, M; McKee, S A

    2005-05-17

    Accurately modeling and predicting performance for large-scale applications becomes increasingly difficult as system complexity scales dramatically. Analytic predictive models are useful, but are difficult to construct, usually limited in scope, and often fail to capture subtle interactions between architecture and software. In contrast, we employ multilayer neural networks trained on input data from executions on the target platform. This approach is useful for predicting many aspects of performance, and it captures full system complexity. Our models are developed automatically from the training input set, avoiding the difficult and potentially error-prone process required to develop analytic models. This study focuses on the high-performance, parallel application SMG2000, a much studied code whose variations in execution times are still not well understood. Our model predicts performance on two large-scale parallel platforms within 5%-7% error across a large, multi-dimensional parameter space.

  10. Network pharmacology-based prediction of the active ingredients and potential targets of Mahuang Fuzi Xixin decoction for application to allergic rhinitis.

    PubMed

    Tang, Feng; Tang, Qingfa; Tian, Yuanxin; Fan, Qin; Huang, Yao; Tan, Xiaomei

    2015-12-24

    Certain herbal formulae from Traditional Chinese Medicine (TCM) are effective for treating and preventing diseases in clinical practice. Mahuang fuzi Xixin Decoction (MFXD) is a TCM that is used to treat allergic rhinitis (AR); however, the active ingredients and potential targets of its action against AR remain unclear. Therefore, further investigation is required. A network pharmacology approach comprising drug-likeness evaluation, oral bioavailability prediction, multiple drug target prediction, and network analysis has been used in this study. The comprehensive systematic approach was successfully to indentify 41 bioactive ingredients in MFXD, while 37 potential targets hit by these ingredients related to AR. Moreover, wherein four predicted ingredients possess anti-inflammatory effects were found by this technique. Our works successfully predict the active ingredients and potential targets of MFXD for application to allergic rhinitis and helps to illustrate mechanism of action on a systematic level. This study not only provides new insights into the chemical basis and pharmacology of MFXD but also demonstrates a feasible method for discovering potential drugs from herbal medicine. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  11. [Two-component signal transduction as attractive drug targets in pathogenic bacteria].

    PubMed

    Utsumi, Ryutaro; Igarashi, Masayuki

    2012-01-01

    Gene clusters contributing to processes such as cell growth and pathogenicity are often controlled by two-component signal transduction systems (TCSs). TCS consists of a histidine kinase (HK) and a response regulator (RR). TCSs are attractive as drug targets for antimicrobials because many HK and RR genes are coded on the bacterial genome though few are found in lower eukaryotes. The HK/RR signal transduction system is distinct from serine/threonine and tyrosine phosphorylation in higher eukaryotes. Specific inhibitors against TCS systems work differently from conventional antibiotics, and developing them into new drugs that are effective against various drug-resistant bacteria may be possible. Furthermore, inhibitors of TCSs that control virulence factors may reduce virulence without killing the pathogenic bacteria. Previous TCS inhibitors targeting the kinase domain of the histidine kinase sensor suffered from poor selectivity. Recent TCS inhibitors, however, target the sensory domains of the sensors blocking the quorum sensing system, or target the essential response regulator. These new targets are introduced, together with several specific TCSs that have the potential to serve as effective drug targets.

  12. Controllability in cancer metabolic networks according to drug targets as driver nodes.

    PubMed

    Asgari, Yazdan; Salehzadeh-Yazdi, Ali; Schreiber, Falk; Masoudi-Nejad, Ali

    2013-01-01

    Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.

  13. Controllability in Cancer Metabolic Networks According to Drug Targets as Driver Nodes

    PubMed Central

    Asgari, Yazdan; Salehzadeh-Yazdi, Ali; Schreiber, Falk; Masoudi-Nejad, Ali

    2013-01-01

    Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine. PMID:24282504

  14. Architecture and Conservation of the Bacterial DNA Replication Machinery, an Underexploited Drug Target

    PubMed Central

    Robinson, Andrew; Causer, Rebecca J; Dixon, Nicholas E

    2012-01-01

    New antibiotics with novel modes of action are required to combat the growing threat posed by multi-drug resistant bacteria. Over the last decade, genome sequencing and other high-throughput techniques have provided tremendous insight into the molecular processes underlying cellular functions in a wide range of bacterial species. We can now use these data to assess the degree of conservation of certain aspects of bacterial physiology, to help choose the best cellular targets for development of new broad-spectrum antibacterials. DNA replication is a conserved and essential process, and the large number of proteins that interact to replicate DNA in bacteria are distinct from those in eukaryotes and archaea; yet none of the antibiotics in current clinical use acts directly on the replication machinery. Bacterial DNA synthesis thus appears to be an underexploited drug target. However, before this system can be targeted for drug design, it is important to understand which parts are conserved and which are not, as this will have implications for the spectrum of activity of any new inhibitors against bacterial species, as well as the potential for development of drug resistance. In this review we assess similarities and differences in replication components and mechanisms across the bacteria, highlight current progress towards the discovery of novel replication inhibitors, and suggest those aspects of the replication machinery that have the greatest potential as drug targets. PMID:22206257

  15. Diacylglycerol Kinases as Emerging Potential Drug Targets for a Variety of Diseases: An Update

    PubMed Central

    Sakane, Fumio; Mizuno, Satoru; Komenoi, Suguru

    2016-01-01

    Ten mammalian diacylglycerol kinase (DGK) isozymes (α–κ) have been identified to date. Our previous review noted that several DGK isozymes can serve as potential drug targets for cancer, epilepsy, autoimmunity, cardiac hypertrophy, hypertension and type II diabetes (Sakane et al., 2008). Since then, recent genome-wide association studies have implied several new possible relationships between DGK isozymes and diseases. For example, DGKθ and DGKκ have been suggested to be associated with susceptibility to Parkinson's disease and hypospadias, respectively. In addition, the DGKη gene has been repeatedly identified as a bipolar disorder (BPD) susceptibility gene. Intriguingly, we found that DGKη-knockout mice showed lithium (BPD remedy)-sensitive mania-like behaviors, suggesting that DGKη is one of key enzymes of the etiology of BPD. Because DGKs are potential drug targets for a wide variety of diseases, the development of DGK isozyme-specific inhibitors/activators has been eagerly awaited. Recently, we have identified DGKα-selective inhibitors. Because DGKα has both pro-tumoral and anti-immunogenic properties, the DGKα-selective inhibitors would simultaneously have anti-tumoral and pro-immunogenic (anti-tumor immunogenic) effects. Although the ten DGK isozymes are highly similar to each other, our current results have encouraged us to identify and develop specific inhibitors/activators against every DGK isozyme that can be effective regulators and drugs against a wide variety of physiological events and diseases. PMID:27583247

  16. Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as candidate drug targets

    PubMed Central

    2014-01-01

    Background Aberrant DNA methylation is often associated with cancers. Thus, screening genes with cancer-associated aberrant DNA methylation is a useful method to identify candidate cancer-causing genes. Aberrant DNA methylation is also genotype dependent. Thus, the selection of genes with genotype-specific aberrant DNA methylation in cancers is potentially important for tailor-made medicine. The selected genes are important candidate drug targets. Results The recently proposed principal component analysis based selection of genes with aberrant DNA methylation was applied to genotype and DNA methylation patterns in squamous cell carcinoma measured using single nucleotide polymorphism (SNP) arrays. SNPs that are frequently found in cancers are usually highly methylated, and the genes that were selected using this method were reported previously to be related to cancers. Thus, genes with genotype-specific DNA methylation patterns will be good therapeutic candidates. The tertiary structures of the proteins encoded by the selected genes were successfully inferred using two profile-based protein structure servers, FAMS and Phyre2. Candidate drugs for three of these proteins, tyrosine kinase receptor (ALK), EGLN3 protein, and NUAK family SNF1-like kinase 1 (NUAK1), were identified by ChooseLD. Conclusions We detected genes with genotype-specific DNA methylation in squamous cell carcinoma that are candidate drug targets. Using in silico drug discovery, we successfully identified several candidate drugs for the ALK, EGLN3 and NUAK1 genes that displayed genotype-specific DNA methylation. PMID:24565165

  17. ATP synthase: a molecular therapeutic drug target for antimicrobial and antitumor peptides.

    PubMed

    Ahmad, Zulfiqar; Okafor, Florence; Azim, Sofiya; Laughlin, Thomas F

    2013-01-01

    In this review we discuss the role of ATP synthase as a molecular drug target for natural and synthetic antimicrobial/ antitumor peptides. We start with an introduction of the universal nature of the ATP synthase enzyme and its role as a biological nanomotor. Significant structural features required for catalytic activity and motor functions of ATP synthase are described. Relevant details regarding the presence of ATP synthase on the surface of several animal cell types, where it is associated with multiple cellular processes making it a potential drug target with respect to antimicrobial peptides and other inhibitors such as dietary polyphenols, is also reviewed. ATP synthase is known to have about twelve discrete inhibitor binding sites including peptides and other inhibitors located at the interface of α/β subunits on the F(1) sector of the enzyme. Molecular interaction of peptides at the β DEELSEED site on ATP synthase is discussed with specific examples. An inhibitory effect of other natural/synthetic inhibitors on ATP is highlighted to explore the therapeutic roles played by peptides and other inhibitors. Lastly, the effect of peptides on the inhibition of the Escherichia coli model system through their action on ATP synthase is presented.

  18. Structural systems pharmacology: a new frontier in discovering novel drug targets.

    PubMed

    Tan, Hepan; Ge, Xiaoxia; Xie, Lei

    2013-08-01

    The modern target-based drug discovery process, characterized by the one-drug-one-gene paradigm, has been of limited success. In contrast, phenotype-based screening produces thousands of active compounds but gives no hint as to what their molecular targets are or which ones merit further research. This presents a question: What is a suitable target for an efficient and safe drug? In this paper, we argue that target selection should take into account the proteome-wide energetic and kinetic landscape of drug-target interactions, as well as their cellular and organismal consequences. We propose a new paradigm of structural systems pharmacology to deconvolute the molecular targets of successful drugs as well as to identify druggable targets and their drug-like binders. Here we face two major challenges in structural systems pharmacology: How do we characterize and analyze the structural and energetic origins of drug-target interactions on a proteome scale? How do we correlate the dynamic molecular interactions to their in vivo activity? We will review recent advances in developing new computational tools for biophysics, bioinformatics, chemoinformatics, and systems biology related to the identification of genome-wide target profiles. We believe that the integration of these tools will realize structural systems pharmacology, enabling us to both efficiently develop effective therapeutics for complex diseases and combat drug resistance.

  19. Chemical validation of trypanothione synthetase: a potential drug target for human trypanosomiasis.

    PubMed

    Torrie, Leah S; Wyllie, Susan; Spinks, Daniel; Oza, Sandra L; Thompson, Stephen; Harrison, Justin R; Gilbert, Ian H; Wyatt, Paul G; Fairlamb, Alan H; Frearson, Julie A

    2009-12-25

    In the search for new therapeutics for the treatment of human African trypanosomiasis, many potential drug targets in Trypanosoma brucei have been validated by genetic means, but very few have been chemically validated. Trypanothione synthetase (TryS; EC 6.3.1.9; spermidine/glutathionylspermidine:glutathione ligase (ADP-forming)) is one such target. To identify novel inhibitors of T. brucei TryS, we developed an in vitro enzyme assay, which was amenable to high throughput screening. The subsequent screen of a diverse compound library resulted in the identification of three novel series of TryS inhibitors. Further chemical exploration resulted in leads with nanomolar potency, which displayed mixed, uncompetitive, and allosteric-type inhibition with respect to spermidine, ATP, and glutathione, respectively. Representatives of all three series inhibited growth of bloodstream T. brucei in vitro. Exposure to one of our lead compounds (DDD86243; 2 x EC(50) for 72 h) decreased intracellular trypanothione levels to <10% of wild type. In addition, there was a corresponding 5-fold increase in the precursor metabolite, glutathione, providing strong evidence that DDD86243 was acting on target to inhibit TryS. This was confirmed with wild-type, TryS single knock-out, and TryS-overexpressing cell lines showing expected changes in potency to DDD86243. Taken together, these data provide initial chemical validation of TryS as a drug target in T. brucei.

  20. Heat shock protein 90 as a potential drug target against surra.

    PubMed

    Rochani, Ankit K; Mithra, Chandan; Singh, Meetali; Tatu, Utpal

    2014-08-01

    Trypanosomiasis is caused by Trypanosoma species which affect both human and animal populations and pose a major threat to developing countries. The incidence of animal trypanosomiasis is on the rise. Surra is a type of animal trypanosomiasis, caused by Trypanosoma evansi, and has been included in priority list B of significant diseases by the World Organization of Animal Health (OIE). Control of surra has been a challenge due to the lack of effective drugs and vaccines and emergence of resistance towards existing drugs. Our laboratory has previously implicated Heat shock protein 90 (Hsp90) from protozoan parasites as a potential drug target and successfully demonstrated efficacy of an Hsp90 inhibitor in cell culture as well as a pre-clinical mouse model of trypanosomiasis. This article explores the role of Hsp90 in the Trypanosoma life cycle and its potential as a drug target. It appears plausible that the repertoire of Hsp90 inhibitors available in academia and industry may have value for treatment of surra and other animal trypanosomiasis.

  1. ATP Synthase: A Molecular Therapeutic Drug Target for Antimicrobial and Antitumor Peptides

    PubMed Central

    Ahmad, Zulfiqar; Okafor, Florence; Azim, Sofiya; Laughlin, Thomas F.

    2015-01-01

    In this review we discuss the role of ATP synthase as a molecular drug target for natural and synthetic antimi-crobial/antitumor peptides. We start with an introduction of the universal nature of the ATP synthase enzyme and its role as a biological nanomotor. Significant structural features required for catalytic activity and motor functions of ATP synthase are described. Relevant details regarding the presence of ATP synthase on the surface of several animal cell types, where it is associated with multiple cellular processes making it a potential drug target with respect to antimicrobial peptides and other inhibitors such as dietary polyphenols, is also reviewed. ATP synthase is known to have about twelve discrete inhibitor binding sites including peptides and other inhibitors located at the interface of α/β subunits on the F1 sector of the enzyme. Molecular interaction of peptides at the β DEELSEED site on ATP synthase is discussed with specific examples. An inhibitory effect of other natural/synthetic inhibitors on ATP is highlighted to explore the therapeutic roles played by peptides and other inhibitors. Lastly, the effect of peptides on the inhibition of the Escherichia coli model system through their action on ATP synthase is presented. PMID:23432591

  2. Architecture and conservation of the bacterial DNA replication machinery, an underexploited drug target.

    PubMed

    Robinson, Andrew; Causer, Rebecca J; Dixon, Nicholas E

    2012-03-01

    New antibiotics with novel modes of action are required to combat the growing threat posed by multi-drug resistant bacteria. Over the last decade, genome sequencing and other high-throughput techniques have provided tremendous insight into the molecular processes underlying cellular functions in a wide range of bacterial species. We can now use these data to assess the degree of conservation of certain aspects of bacterial physiology, to help choose the best cellular targets for development of new broad-spectrum antibacterials. DNA replication is a conserved and essential process, and the large number of proteins that interact to replicate DNA in bacteria are distinct from those in eukaryotes and archaea; yet none of the antibiotics in current clinical use acts directly on the replication machinery. Bacterial DNA synthesis thus appears to be an underexploited drug target. However, before this system can be targeted for drug design, it is important to understand which parts are conserved and which are not, as this will have implications for the spectrum of activity of any new inhibitors against bacterial species, as well as the potential for development of drug resistance. In this review we assess similarities and differences in replication components and mechanisms across the bacteria, highlight current progress towards the discovery of novel replication inhibitors, and suggest those aspects of the replication machinery that have the greatest potential as drug targets.

  3. ROCK1 is a potential combinatorial drug target for BRAF mutant melanoma

    PubMed Central

    Smit, Marjon A; Maddalo, Gianluca; Greig, Kylie; Raaijmakers, Linsey M; Possik, Patricia A; van Breukelen, Bas; Cappadona, Salvatore; Heck, Albert JR; Altelaar, AF Maarten; Peeper, Daniel S

    2014-01-01

    Treatment of BRAF mutant melanomas with specific BRAF inhibitors leads to tumor remission. However, most patients eventually relapse due to drug resistance. Therefore, we designed an integrated strategy using (phospho)proteomic and functional genomic platforms to identify drug targets whose inhibition sensitizes melanoma cells to BRAF inhibition. We found many proteins to be induced upon PLX4720 (BRAF inhibitor) treatment that are known to be involved in BRAF inhibitor resistance, including FOXD3 and ErbB3. Several proteins were down-regulated, including Rnd3, a negative regulator of ROCK1 kinase. For our genomic approach, we performed two parallel shRNA screens using a kinome library to identify genes whose inhibition sensitizes to BRAF or ERK inhibitor treatment. By integrating our functional genomic and (phospho)proteomic data, we identified ROCK1 as a potential drug target for BRAF mutant melanoma. ROCK1 silencing increased melanoma cell elimination when combined with BRAF or ERK inhibitor treatment. Translating this to a preclinical setting, a ROCK inhibitor showed augmented melanoma cell death upon BRAF or ERK inhibition in vitro. These data merit exploration of ROCK1 as a target in combination with current BRAF mutant melanoma therapies. PMID:25538140

  4. Parallel shRNA and CRISPR-Cas9 screens enable antiviral drug target identification

    PubMed Central

    Deans, Richard M.; Morgens, David W.; Ökesli, Ayşe; Pillay, Sirika; Horlbeck, Max A.; Kampmann, Martin; Gilbert, Luke A.; Li, Amy; Mateo, Roberto; Smith, Mark; Glenn, Jeffrey S.; Carette, Jan E.; Khosla, Chaitan; Bassik, Michael C.

    2016-01-01

    Broad spectrum antiviral drugs targeting host processes could potentially treat a wide range of viruses while reducing the likelihood of emergent resistance. Despite great promise as therapeutics, such drugs remain largely elusive. Here we use parallel genome-wide high-coverage shRNA and CRISPR-Cas9 screens to identify the cellular target and mechanism of action of GSK983, a potent broad spectrum antiviral with unexplained cytotoxicity1–3. We show that GSK983 blocks cell proliferation and dengue virus replication by inhibiting the pyrimidine biosynthesis enzyme dihydroorotate dehydrogenase (DHODH). Guided by mechanistic insights from both genomic screens, we found that exogenous deoxycytidine markedly reduces GSK983 cytotoxicity but not antiviral activity, providing an attractive novel approach to improve the therapeutic window of DHODH inhibitors against RNA viruses. Together, our results highlight the distinct advantages and limitations of each screening method for identifying drug targets and demonstrate the utility of parallel knockdown and knockout screens for comprehensively probing drug activity. PMID:27018887

  5. Drug Synergy Screen and Network Modeling in Dedifferentiated Liposarcoma Identifies CDK4 and IGF1R as Synergistic Drug Targets

    PubMed Central

    Miller, Martin L.; Molinelli, Evan J.; Nair, Jayasree S.; Sheikh, Tahir; Samy, Rita; Jing, Xiaohong; He, Qin; Korkut, Anil; Crago, Aimee M.; Singer, Samuel; Schwartz, Gary K.; Sander, Chris

    2014-01-01

    Dedifferentiated liposarcoma (DDLS) is a rare but aggressive cancer with high recurrence and low response rates to targeted therapies. Increasing treatment efficacy may require combinations of targeted agents that counteract the effects of multiple abnormalities. To identify a possible multicomponent therapy, we performed a combinatorial drug screen in a DDLS-derived cell line and identified cyclin-dependent kinase 4 (CDK4) and insulin-like growth factor 1 receptor (IGF1R) as synergistic drug targets. We measured the phosphorylation of multiple proteins and cell viability in response to systematic drug combinations and derived computational models of the signaling network. These models predict that the observed synergy in reducing cell viability with CDK4 and IGF1R inhibitors depend on activity of the AKT pathway. Experiments confirmed that combined inhibition of CDK4 and IGF1R cooperatively suppresses the activation of proteins within the AKT pathway. Consistent with these findings, synergistic reductions in cell viability were also found when combining CDK4 inhibition with inhibition of either AKT or epidermal growth factor receptor (EGFR), another receptor similar to IGF1R that activates AKT. Thus, network models derived from context-specific proteomic measurements of systematically perturbed cancer cells may reveal cancer-specific signaling mechanisms and aid in the design of effective combination therapies. PMID:24065146

  6. Modelling the Effect of SPION Size in a Stent Assisted Magnetic Drug Targeting System with Interparticle Interactions

    PubMed Central

    Mardinoglu, Adil; Cregg, P. J.

    2015-01-01

    Cancer is a leading cause of death worldwide and it is caused by the interaction of genomic, environmental, and lifestyle factors. Although chemotherapy is one way of treating cancers, it also damages healthy cells and may cause severe side effects. Therefore, it is beneficial in drug delivery in the human body to increase the proportion of the drugs at the target site while limiting its exposure at the rest of body through Magnetic Drug Targeting (MDT). Superparamagnetic iron oxide nanoparticles (SPIONs) are derived from polyol methods and coated with oleic acid and can be used as magnetic drug carrier particles (MDCPs) in an MDT system. Here, we develop a mathematical model for studying the interactions between the MDCPs enriched with three different diameters of SPIONs (6.6, 11.6, and 17.8 nm) in the MDT system with an implanted magnetizable stent using different magnetic field strengths and blood velocities. Our computational analysis allows for the optimal design of the SPIONs enriched MDCPs to be used in clinical applications. PMID:25815370

  7. Bayesian approach to estimate AUC, partition coefficient and drug targeting index for studies with serial sacrifice design.

    PubMed

    Wang, Tianli; Baron, Kyle; Zhong, Wei; Brundage, Richard; Elmquist, William

    2014-03-01

    The current study presents a Bayesian approach to non-compartmental analysis (NCA), which provides the accurate and precise estimate of AUC 0 (∞) and any AUC 0 (∞) -based NCA parameter or derivation. In order to assess the performance of the proposed method, 1,000 simulated datasets were generated in different scenarios. A Bayesian method was used to estimate the tissue and plasma AUC 0 (∞) s and the tissue-to-plasma AUC 0 (∞) ratio. The posterior medians and the coverage of 95% credible intervals for the true parameter values were examined. The method was applied to laboratory data from a mice brain distribution study with serial sacrifice design for illustration. Bayesian NCA approach is accurate and precise in point estimation of the AUC 0 (∞) and the partition coefficient under a serial sacrifice design. It also provides a consistently good variance estimate, even considering the variability of the data and the physiological structure of the pharmacokinetic model. The application in the case study obtained a physiologically reasonable posterior distribution of AUC, with a posterior median close to the value estimated by classic Bailer-type methods. This Bayesian NCA approach for sparse data analysis provides statistical inference on the variability of AUC 0 (∞) -based parameters such as partition coefficient and drug targeting index, so that the comparison of these parameters following destructive sampling becomes statistically feasible.

  8. Application of indoor noise prediction in the real world

    NASA Astrophysics Data System (ADS)

    Lewis, David N.

    2002-11-01

    Predicting indoor noise in industrial workrooms is an important part of the process of designing industrial plants. Predicted levels are used in the design process to determine compliance with occupational-noise regulations, and to estimate levels inside the walls in order to predict community noise radiated from the building. Once predicted levels are known, noise-control strategies can be developed. In this paper an overview of over 20 years of experience is given with the use of various prediction approaches to manage noise in Unilever plants. This work has applied empirical and ray-tracing approaches separately, and in combination, to design various packaging and production plants and other facilities. The advantages of prediction methods in general, and of the various approaches in particular, will be discussed. A case-study application of prediction methods to the optimization of noise-control measures in a food-packaging plant will be presented. Plans to acquire a simplified prediction model for use as a company noise-screening tool will be discussed.

  9. Geospatial application of the Water Erosion Prediction Project (WEPP) model

    USDA-ARS?s Scientific Manuscript database

    At the hillslope profile and/or field scale, a simple Windows graphical user interface (GUI) is available to easily specify the slope, soil, and management inputs for application of the USDA Water Erosion Prediction Project (WEPP) model. Likewise, basic small watershed configurations of a few hillsl...

  10. Geospatial application of the Water Erosion Prediction Project (WEPP) model

    Treesearch

    D. C. Flanagan; J. R. Frankenberger; T. A. Cochrane; C. S. Renschler; W. J. Elliot

    2013-01-01

    At the hillslope profile and/or field scale, a simple Windows graphical user interface (GUI) is available to easily specify the slope, soil, and management inputs for application of the USDA Water Erosion Prediction Project (WEPP) model. Likewise, basic small watershed configurations of a few hillslopes and channels can be created and simulated with this GUI. However,...

  11. NASTRAN application for the prediction of aircraft interior noise

    NASA Technical Reports Server (NTRS)

    Marulo, Francesco; Beyer, Todd B.

    1987-01-01

    The application of a structural-acoustic analogy within the NASTRAN finite element program for the prediction of aircraft interior noise is presented. Some refinements of the method, which reduce the amount of computation required for large, complex structures, are discussed. Also, further improvements are proposed and preliminary comparisons with structural and acoustic modal data obtained for a large, composite cylinder are presented.

  12. Hydrologic ensemble prediction: enhancing science, operation and application through HEPEX

    NASA Astrophysics Data System (ADS)

    Wetterhall, Fredrik; Ramos, Maria-Helena; Wang, Qj; Wood, Andy

    2016-04-01

    HEPEX (Hydrologic Ensemble Prediction Experiment) was established in March 2004 at a workshop hosted by the European Centre for Medium Range Weather Forecasts (ECMWF), co-sponsored by the US National Weather Service (NWS) and the European Commission (EC). HEPEX and the community it represents has over its more than 10 years of existence continuously worked to promote and advance the science of hydrologic ensemble prediction as well as operational systems and water management applications. Through workshops and conference sessions, HEPEX has connected the research community, forecasters and forecast users and facilitated the exchange of ideas, data, methods and experience. In particular, the establishment of an online blog portal has greatly enhanced community interaction and knowledge sharing (www.hepex.org). HEPEX has now a strong and active community of nearly 400 researchers and practitioners around the world. In this poster, we present an overview of recent and planned HEPEX activities, and highlight opportunities to further progress ensemble prediction science, operation and application.

  13. Applications of subseasonal-to-seasonal (S2S) predictions

    NASA Astrophysics Data System (ADS)

    White, Christopher; Lamb, Rob; Carlsen, Henrik; Robertson, Andrew; Klein, Richard; Lazo, Jeffrey; Kumar, Arun; Vitart, Frederic; Coughlan de Perez, Erin; Ray, Andrea; Murray, Virginia; Graham, Richard; Buontempo, Carlo

    2017-04-01

    While long-range seasonal outlooks have been operational for many years, until recently the extended-range timescale - referred to as 'subseasonal-to-seasonal' (S2S) and which sits between the medium- to long-range forecasting timescales - has received relatively little attention. The S2S timescale has long been seen as a 'predictability desert', yet a new generation of S2S predictions are starting to bridge the gap between weather forecasts and longer-range prediction. Decisions in a range of sectors are made in this extended-range lead time, therefore there is a strong demand for this new generation of predictions. At least ten international weather centres now have some capability for issuing experimental or operational S2S predictions, including the European Centre for Medium-Range Weather Forecasting (ECMWF) and the National Oceanic and Atmospheric Administration (NOAA) that now have operational S2S outputs. International efforts are now underway to identify key sources of predictability, improve forecast skill and operationalise aspects of S2S forecasts, however challenges remain in advancing this new frontier. If S2S predictions are to be utilised effectively, it is important that along with science advances, we learn how to develop, communicate and apply these forecasts appropriately. In this study, we present the potential of the emerging operational S2S forecasts to the wider weather and climate applications community by undertaking the first comprehensive review of sectoral applications of S2S predictions, including public health, disaster preparedness, water management, energy and agriculture. We explore the value of applications-relevant S2S predictions, and highlight the opportunities and challenges facing their uptake. We show how social sciences can be integrated with S2S development - from communication to decision-making and valuation of forecasts - to enhance the benefits of 'climate services' approaches for extended-range forecasting. We

  14. The structural properties of non-traditional drug targets present new challenges for virtual screening

    PubMed Central

    Gowthaman, Ragul; Deeds, Eric J.; Karanicolas, John

    2013-01-01

    Traditional drug targets have historically included signaling proteins that respond to small-molecules and enzymes that use small-molecules as substrates. Increasing attention is now being directed towards other types of protein targets, in particular those that exert their function by interacting with nucleic acids or other proteins rather than small-molecule ligands. Here, we systematically compare existing examples of inhibitors of protein–protein interactions to inhibitors of traditional drug targets. While both sets of inhibitors bind with similar potency, we find that the inhibitors of protein–protein interactions typically bury a smaller fraction of their surface area upon binding to their protein targets. The fact that an average atom is less buried suggests that more atoms are needed to achieve a given potency, explaining the observation that ligand efficiency is typically poor for inhibitors of protein– protein interactions. We then carried out a series of docking experiments, and found a further consequence of these relatively exposed binding modes is that structure-based virtual screening may be more difficult: such binding modes do not provide sufficient clues to pick out active compounds from decoy compounds. Collectively, these results suggest that the challenges associated with such non-traditional drug targets may not lie with identifying compounds that potently bind to the target protein surface, but rather with identifying compounds that bind in a sufficiently buried manner to achieve good ligand efficiency, and thus good oral bioavailability. While the number of available crystal structures of distinct protein interaction sites bound to small-molecule inhibitors is relatively small at present (only 21 such complexes were included in this study), these are sufficient to draw conclusions based on the current state of the field; as additional data accumulate it will be exciting to refine the viewpoint presented here. Even with this limited

  15. Endocytosis mediated by monocyte and macrophage membrane lectins--application to antiviral drug targeting.

    PubMed

    Roche, A C; Midoux, P; Pimpaneau, V; Nègre, E; Mayer, R; Monsigny, M

    1990-01-01

    Sugar receptors, or membrane lectins, have been evidenced at the surface of various normal and tumour cells using fluoresceinylated neoglycoproteins (glycosylated bovine serum albumin (BSA]. By flow cytometry we have shown that macrophages bind and internalize mannosylated and 6-phosphomannosylated ligands in acidic compartments. Freshly isolated monocytes and U937, a promonocytic cell line, lack a mannose-specific receptor, but express mannose-6-phosphate (Man-6P) membrane lectin. Neoglycoproteins are potent drug carriers: muramyl dipeptide (MDP), an immunoactivator, when bound to Man-BSA or Man-6P-BSA, is 100 times more efficient than free MDP in activating macrophages; in vivo, it enables eradication of lung metastases in mice. Recently, neutral glycosylated biodegradable and nonimmunogenic polymers, were synthesized and found to be as efficient as neoglycoproteins. Antiviral drug conjugates were more active than the free drug, inhibiting the multiplication of virus (herpes) in human macrophages in vitro.

  16. Conformal prediction to define applicability domain - A case study on predicting ER and AR binding.

    PubMed

    Norinder, U; Rybacka, A; Andersson, P L

    2016-04-01

    A fundamental element when deriving a robust and predictive in silico model is not only the statistical quality of the model in question but, equally important, the estimate of its predictive boundaries. This work presents a new method, conformal prediction, for applicability domain estimation in the field of endocrine disruptors. The method is applied to binders and non-binders related to the oestrogen and androgen receptors. Ensembles of decision trees are used as statistical method and three different sets (dragon, rdkit and signature fingerprints) are investigated as chemical descriptors. The conformal prediction method results in valid models where there is an excellent balance in quality between the internally validated training set and the corresponding external test set, both in terms of validity and with respect to sensitivity and specificity. With this method the level of confidence can be readily altered by the user and the consequences thereof immediately inspected. Furthermore, the predictive boundaries for the derived models are rigorously defined by using the conformal prediction framework, thus no ambiguity exists as to the level of similarity needed for new compounds to be in or out of the predictive boundaries of the derived models where reliable predictions can be expected.

  17. Orphan G protein-coupled receptors (GPCRs): biological functions and potential drug targets

    PubMed Central

    Tang, Xiao-long; Wang, Ying; Li, Da-li; Luo, Jian; Liu, Ming-yao

    2012-01-01

    The superfamily of G protein-coupled receptors (GPCRs) includes at least 800 seven-transmembrane receptors that participate in diverse physiological and pathological functions. GPCRs are the most successful targets of modern medicine, and approximately 36% of marketed pharmaceuticals target human GPCRs. However, the endogenous ligands of more than 140 GPCRs remain unidentified, leaving the natural functions of those GPCRs in doubt. These are the so-called orphan GPCRs, a great source of drug targets. This review focuses on the signaling transduction pathways of the adhesion GPCR family, the LGR subfamily, and the PSGR subfamily, and their potential functions in immunology, development, and cancers. In this review, we present the current approaches and difficulties of orphan GPCR deorphanization and characterization. PMID:22367282

  18. The Bcl10/Malt1 signaling pathway as a drug target in lymphoma.

    PubMed

    Jost, P; Peschel, C; Ruland, J

    2006-10-01

    The development of lymphomas and leukemias is frequently caused by chromosomal translocations that deregulate cellular pathways of differentiation, proliferation or survival. The molecules that are involved in these aberrations provide rational targets for selective drug therapies. Recently, several disease specific translocations have been identified in human MALT lymphoma. These aberrations either upregulate the expression of BCL10 or MALT1 or induce the formation of API2-MALT1 fusion proteins. Genetic and biochemical experiments identified BCL10 and MALT1 as central components of an oligomerization-ubiquitinylation-phosphorylation cascade that activates the transcription factor NF-kappaB in response to antigen receptor ligation. Deregulation of the signaling cascade is directly associated with antigen independent MALT lymphoma growth. Here we provide an overview of the physiological and pathological functions of BCL10/MALT1 signal transduction and discuss the potential of this pathway as a drug target.

  19. Two-component signal transduction as potential drug targets in pathogenic bacteria.

    PubMed

    Gotoh, Yasuhiro; Eguchi, Yoko; Watanabe, Takafumi; Okamoto, Sho; Doi, Akihiro; Utsumi, Ryutaro

    2010-04-01

    Gene clusters contributing to processes such as cell growth and pathogenicity are often controlled by two-component signal transduction systems (TCSs). Specific inhibitors against TCS systems work differently from conventional antibiotics, and developing them into new drugs that are effective against various drug-resistant bacteria may be possible. Furthermore, inhibitors of TCSs that control virulence factors may reduce virulence without killing the pathogenic bacteria. Previous TCS inhibitors targeting the kinase domain of the histidine kinase sensor suffered from poor selectivity. Recent TCS inhibitors, however, target the sensory domains of the sensors blocking the quorum sensing system, or target the essential response regulator. These new targets are introduced, together with several specific TCSs that have the potential to serve as effective drug targets. Copyright 2010 Elsevier Ltd. All rights reserved.

  20. Particle size, magnetic field, and blood velocity effects on particle retention in magnetic drug targeting.

    PubMed

    Cherry, Erica M; Maxim, Peter G; Eaton, John K

    2010-01-01

    A physics-based model of a general magnetic drug targeting (MDT) system was developed with the goal of realizing the practical limitations of MDT when electromagnets are the source of the magnetic field. The simulation tracks magnetic particles subject to gravity, drag force, magnetic force, and hydrodynamic lift in specified flow fields and external magnetic field distributions. A model problem was analyzed to determine the effect of drug particle size, blood flow velocity, and magnetic field gradient strength on efficiency in holding particles stationary in a laminar Poiseuille flow modeling blood flow in a medium-sized artery. It was found that particle retention rate increased with increasing particle diameter and magnetic field gradient strength and decreased with increasing bulk flow velocity. The results suggest that MDT systems with electromagnets are unsuitable for use in small arteries because it is difficult to control particles smaller than about 20 microm in diameter.

  1. Targeting the latest hallmark of cancer: another attempt at 'magic bullet' drugs targeting cancers' metabolic phenotype.

    PubMed

    Cuperlovic-Culf, M; Culf, A S; Touaibia, M; Lefort, N

    2012-10-01

    The metabolism of tumors is remarkably different from the metabolism of corresponding normal cells and tissues. Metabolic alterations are initiated by oncogenes and are required for malignant transformation, allowing cancer cells to resist some cell death signals while producing energy and fulfilling their biosynthetic needs with limiting resources. The distinct metabolic phenotype of cancers provides an interesting avenue for treatment, potentially with minimal side effects. As many cancers show similar metabolic characteristics, drugs targeting the cancer metabolic phenotype are, perhaps optimistically, expected to be 'magic bullet' treatments. Over the last few years there have been a number of potential drugs developed to specifically target cancer metabolism. Several of these drugs are currently in clinical and preclinical trials. This review outlines examples of drugs developed for different targets of significance to cancer metabolism, with a focus on small molecule leads, chemical biology and clinical results for these drugs.

  2. Polyamine homoeostasis as a drug target in pathogenic protozoa: peculiarities and possibilities

    PubMed Central

    Birkholtz, Lyn-Marie; Williams, Marni; Niemand, Jandeli; Louw, Abraham I.; Persson, Lo; Heby, Olle

    2011-01-01

    New drugs are urgently needed for the treatment of tropical and subtropical parasitic diseases, such as African sleeping sickness, Chagas' disease, leishmaniasis and malaria. Enzymes in polyamine biosynthesis and thiol metabolism, as well as polyamine transporters, are potential drug targets within these organisms. In the present review, the current knowledge of unique properties of polyamine metabolism in these parasites is outlined. These properties include prozyme regulation of AdoMetDC (S-adenosylmethionine decarboxylase) activity in trypanosomatids, co-expression of ODC (ornithine decarboxylase) and AdoMetDC activities in a single protein in plasmodia, and formation of trypanothione, a unique compound linking polyamine and thiol metabolism in trypanosomatids. Particularly interesting features within polyamine metabolism in these parasites are highlighted for their potential in selective therapeutic strategies. PMID:21834794

  3. Protective mechanisms of helminths against reactive oxygen species are highly promising drug targets.

    PubMed

    Perbandt, Markus; Ndjonka, Dieudonne; Liebau, Eva

    2014-01-01

    Helminths that are the causative agents of numerous neglected tropical diseases continue to be a major problem for human global health. In the absence of vaccines, control relies solely on pharmacoprophylaxis and pharmacotherapy to reduce transmission and to relieve symptoms. There are only a few drugs available and resistance in helminths of lifestock has been observed to the same drugs that are also used to treat humans. Clearly there is an urgent need to find novel antiparasitic compounds. Not only are helminths confronted with their own metabolically derived toxic and redox-active byproducts but also with the production of reactive oxygen species (ROS) by the host immune system, adding to the overall oxidative burden of the parasite. Antioxidant enzymes of helminths have been identified as essential proteins, some of them biochemically distinct to their host counterpart and thus appealing drug targets. In this review we have selected a few enzymatic antioxidants of helminths that are thought to be druggable.

  4. Metabolic network analysis-based identification of antimicrobial drug targets in category A bioterrorism agents.

    PubMed

    Ahn, Yong-Yeol; Lee, Deok-Sun; Burd, Henry; Blank, William; Kapatral, Vinayak

    2014-01-01

    The 2001 anthrax mail attacks in the United States demonstrated the potential threat of bioterrorism, hence driving the need to develop sophisticated treatment and diagnostic protocols to counter biological warfare. Here, by performing flux balance analyses on the fully-annotated metabolic networks of multiple, whole genome-sequenced bacterial strains, we have identified a large number of metabolic enzymes as potential drug targets for each of the three Category A-designated bioterrorism agents including Bacillus anthracis, Francisella tularensis and Yersinia pestis. Nine metabolic enzymes- belonging to the coenzyme A, folate, phosphatidyl-ethanolamine and nucleic acid pathways common to all strains across the three distinct genera were identified as targets. Antimicrobial agents against some of these enzymes are available. Thus, a combination of cross species-specific antibiotics and common antimicrobials against shared targets may represent a useful combinatorial therapeutic approach against all Category A bioterrorism agents.

  5. Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach

    PubMed Central

    Kaltdorf, Martin; Srivastava, Mugdha; Gupta, Shishir K.; Liang, Chunguang; Binder, Jasmin; Dietl, Anna-Maria; Meir, Zohar; Haas, Hubertus; Osherov, Nir; Krappmann, Sven; Dandekar, Thomas

    2016-01-01

    New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly expressed enzymes, and (III) analysis of enzyme structure, enzyme interconnectedness (“hubs”), and identification of pathogen-specific enzymes using orthology relations. We have identified 64 targets including metabolic enzymes involved in vitamin synthesis, lipid, and amino acid biosynthesis including 18 targets validated from the literature, two validated and five currently examined in own genetic experiments, and 38 further promising novel target proteins which are non-orthologous to human proteins, involved in metabolism and are highly ranked drug targets from these pipelines. PMID:27379244

  6. Pharmaceutical formulation of HSA hybrid coated iron oxide nanoparticles for magnetic drug targeting.

    PubMed

    Zaloga, Jan; Pöttler, Marina; Leitinger, Gerd; Friedrich, Ralf P; Almer, Gunter; Lyer, Stefan; Baum, Eva; Tietze, Rainer; Heimke-Brinck, Ralph; Mangge, Harald; Dörje, Frank; Lee, Geoffrey; Alexiou, Christoph

    2016-04-01

    In this work we present a new formulation of superparamagnetic iron oxide nanoparticles (SPIONs) for magnetic drug targeting. The particles were reproducibly synthesized from current good manufacturing practice (cGMP) - grade substances. They were surface coated using fatty acids as anchoring molecules for human serum albumin. We comprehensively characterized the physicochemical core-shell structure of the particles using sophisticated methods. We investigated biocompatibility and cellular uptake of the particles using an established flow cytometric method in combination with microwave-plasma assisted atomic emission spectroscopy (MP-AES). The cytotoxic drug mitoxantrone was adsorbed on the protein shell and we showed that even in complex media it is slowly released with a close to zero order kinetics. We also describe an in vitro proof-of-concept assay in which we clearly showed that local enrichment of this SPION-drug conjugate with a magnet allows site-specific therapeutic effects.

  7. Comparative genomics of NAD(P) biosynthesis and novel antibiotic drug targets.

    PubMed

    Bi, Jicai; Wang, Honghai; Xie, Jianping

    2011-02-01

    NAD(P) is an indispensable cofactor for all organisms and its biosynthetic pathways are proposed as promising novel antibiotics targets against pathogens such as Mycobacterium tuberculosis. Six NAD(P) biosynthetic pathways were reconstructed by comparative genomics: de novo pathway (Asp), de novo pathway (Try), NmR pathway I (RNK-dependent), NmR pathway II (RNK-independent), Niacin salvage, and Niacin recycling. Three enzymes pivotal to the key reactions of NAD(P) biosynthesis are shared by almost all organisms, that is, NMN/NaMN adenylyltransferase (NMN/NaMNAT), NAD synthetase (NADS), and NAD kinase (NADK). They might serve as ideal broad spectrum antibiotic targets. Studies in M. tuberculosis have in part tested such hypothesis. Three regulatory factors NadR, NiaR, and NrtR, which regulate NAD biosynthesis, have been identified. M. tuberculosis NAD(P) metabolism and regulation thereof, potential drug targets and drug development are summarized in this paper.

  8. Channels and transporters as drug targets in the Plasmodium-infected erythrocyte.

    PubMed

    Kirk, Kiaran

    2004-02-01

    Throughout the intraerythrocytic phase of its lifecycle the malaria parasite is separated from the extracellular medium by the plasma membrane of its host erythrocyte and by the parasitophorous vacuole in which the parasite is enclosed. The intracellular parasite itself has, at its surface, a plasma membrane, and has a variety of membrane-bound organelles which carry out a range of biochemical functions. Each of the various membranes of the infected cell have in them proteins that facilitate the movement of molecules and ions from one side of the membrane to the other. These 'channels' and 'transporters' play a central role in the physiology of the parasitised cell. From a clinical viewpoint they are of interest both as potential targets in their own right, and as potential drug targeting routes capable of mediating the entry of cytotoxic drugs into the appropriate compartment of the infected cell. In this review both of these aspects are considered.

  9. Chemical and genetic validation of thiamine utilization as an antimalarial drug target.

    PubMed

    Chan, Xie Wah Audrey; Wrenger, Carsten; Stahl, Katharina; Bergmann, Bärbel; Winterberg, Markus; Müller, Ingrid B; Saliba, Kevin J

    2013-01-01

    Thiamine is metabolized into an essential cofactor for several enzymes. Here we show that oxythiamine, a thiamine analog, inhibits proliferation of the malaria parasite Plasmodium falciparum in vitro via a thiamine-related pathway and significantly reduces parasite growth in a mouse malaria model. Overexpression of thiamine pyrophosphokinase (the enzyme that converts thiamine into its active form, thiamine pyrophosphate) hypersensitizes parasites to oxythiamine by up to 1,700-fold, consistent with oxythiamine being a substrate for thiamine pyrophosphokinase and its conversion into an antimetabolite. We show that parasites overexpressing the thiamine pyrophosphate-dependent enzymes oxoglutarate dehydrogenase and pyruvate dehydrogenase are up to 15-fold more resistant to oxythiamine, consistent with the antimetabolite inactivating thiamine pyrophosphate-dependent enzymes. Our studies therefore validate thiamine utilization as an antimalarial drug target and demonstrate that a single antimalarial can simultaneously target several enzymes located within distinct organelles.

  10. Mitochondria as a Drug Target in Ischemic Heart Disease and Cardiomyopathy

    PubMed Central

    Walters, Andrew M; Porter, George A; Brookes, Paul S.

    2012-01-01

    Ischemic heart disease (IHD) is a significant cause of morbidity and mortality in Western society. Although interventions such as thrombolysis and percutaneous coronary intervention (PCI) have proven efficacious in ischemia and reperfusion (IR) injury, the underlying pathologic process of IHD, laboratory studies suggest further protection is possible, and an expansive research effort is aimed at bringing new therapeutic options to the clinic. Mitochondrial dysfunction plays a key role in the pathogenesis of IR injury and cardiomyopathy (CM). However, despite promising mitochondria-targeted drugs emerging from the lab, very few have successfully completed clinical trials. As such, the mitochondrion is a potential untapped target for new IHD and CM therapies. Notably, there are a number of overlapping therapies for both these diseases, and as such novel therapeutic options for one condition may find use in the other. This review summarizes efforts to date in targeting mitochondria for IHD and CM therapy, and outlines emerging drug targets in this field. PMID:23065345

  11. Metabolic Network Analysis-Based Identification of Antimicrobial Drug Targets in Category A Bioterrorism Agents

    PubMed Central

    Ahn, Yong-Yeol; Lee, Deok-Sun; Burd, Henry; Blank, William; Kapatral, Vinayak

    2014-01-01

    The 2001 anthrax mail attacks in the United States demonstrated the potential threat of bioterrorism, hence driving the need to develop sophisticated treatment and diagnostic protocols to counter biological warfare. Here, by performing flux balance analyses on the fully-annotated metabolic networks of multiple, whole genome-sequenced bacterial strains, we have identified a large number of metabolic enzymes as potential drug targets for each of the three Category A-designated bioterrorism agents including Bacillus anthracis, Francisella tularensis and Yersinia pestis. Nine metabolic enzymes- belonging to the coenzyme A, folate, phosphatidyl-ethanolamine and nucleic acid pathways common to all strains across the three distinct genera were identified as targets. Antimicrobial agents against some of these enzymes are available. Thus, a combination of cross species-specific antibiotics and common antimicrobials against shared targets may represent a useful combinatorial therapeutic approach against all Category A bioterrorism agents. PMID:24454817

  12. PEG-g-chitosan nanoparticles functionalized with the monoclonal antibody OX26 for brain drug targeting.

    PubMed

    Monsalve, Yuliana; Tosi, Giovanni; Ruozi, Barbara; Belletti, Daniela; Vilella, Antonietta; Zoli, Michele; Vandelli, Maria Angela; Forni, Flavio; López, Betty L; Sierra, Ligia

    2015-01-01

    Drug targeting to the CNS is challenging due to the presence of blood-brain barrier. We investigated chitosan (Cs) nanoparticles (NPs) as drug transporter system across the blood-brain barrier, based on mAb OX26 modified Cs. Cs NPs functionalized with PEG, modified and unmodified with OX26 (Cs-PEG-OX26) were prepared and chemico-physically characterized. These NPs were administered (intraperitoneal) in mice to define their ability to reach the brain. Brain uptake of OX26-conjugated NPs is much higher than of unmodified NPs, because: long-circulating abilities (conferred by PEG), interaction between cationic Cs and brain endothelium negative charges and OX26 TfR receptor affinity. Cs-PEG-OX26 NPs are promising drug delivery system to the CNS.

  13. Plasmodial sugar transporters as anti-malarial drug targets and comparisons with other protozoa

    PubMed Central

    2011-01-01

    Glucose is the primary source of energy and a key substrate for most cells. Inhibition of cellular glucose uptake (the first step in its utilization) has, therefore, received attention as a potential therapeutic strategy to treat various unrelated diseases including malaria and cancers. For malaria, blood forms of parasites rely almost entirely on glycolysis for energy production and, without energy stores, they are dependent on the constant uptake of glucose. Plasmodium falciparum is the most dangerous human malarial parasite and its hexose transporter has been identified as being the major glucose transporter. In this review, recent progress regarding the validation and development of the P. falciparum hexose transporter as a drug target is described, highlighting the importance of robust target validation through both chemical and genetic methods. Therapeutic targeting potential of hexose transporters of other protozoan pathogens is also reviewed and discussed. PMID:21676209

  14. Trypanosoma cruzi trans-sialidase as a drug target against Chagas disease (American trypanosomiasis).

    PubMed

    Miller, Bill R; Roitberg, Adrian E

    2013-10-01

    Chagas disease (or American trypanosomiasis) is a deadly tropical disease that affects millions of people worldwide, primarily in rural regions of South America. Trypanosoma cruzi, the parasitic cause of Chagas disease, possesses a membrane-anchored trans-sialidase enzyme that transfers sialic acids from the host cell surface to the parasitic cell surface, allowing T. cruzi to effectively evade the host's immune system. This enzyme has no analogous human counterpart and thus has become an interesting drug target to combat the parasite. Recent computational efforts have improved our knowledge about the enzyme's structure, dynamics and catalyzed reaction. Many compounds have been tested against trans-sialidase activity, but no strong inhibitors have been identified yet. The current lack of drugs for Chagas disease necessitates more R&D into the design and discovery of strong inhibitors of T. cruzi trans-sialidase.

  15. Important biology events and pathways in Brucella infection and implications for novel antibiotic drug targets.

    PubMed

    Gao, Guangjun; Xu, Jie

    2013-01-01

    Brucellosis caused by Brucella spp. is a common zoonosis in many parts of the world. Humans are infected through contact with infected animals or their dirty products. Many mechanisms are needed for this successful infection, although the mechanisms are still unclear. Host immune response and some signaling molecules play an important role in the infection event. Bacterial pathogens operate by attacking crucial intracellular pathways or some important molecules in each of these pathways for survival in their hosts. The crucial components (molecules) of immunity or pathway play a critical role in the whole process of Brucella infection. Here we summarize the findings of the Brucella-host interactions' immune system and signaling molecular cascades involved in the TLR-initiated immune response to Brucella spp. infection. The paper serves to deepen our understanding of this complex process and to provide some clues regarding the discovery of drug targets for prevention and control.

  16. Drug Target Optimization in Chronic Myeloid Leukemia Using Innovative Computational Platform

    NASA Astrophysics Data System (ADS)

    Chuang, Ryan; Hall, Benjamin A.; Benque, David; Cook, Byron; Ishtiaq, Samin; Piterman, Nir; Taylor, Alex; Vardi, Moshe; Koschmieder, Steffen; Gottgens, Berthold; Fisher, Jasmin

    2015-02-01

    Chronic Myeloid Leukemia (CML) represents a paradigm for the wider cancer field. Despite the fact that tyrosine kinase inhibitors have established targeted molecular therapy in CML, patients often face the risk of developing drug resistance, caused by mutations and/or activation of alternative cellular pathways. To optimize drug development, one needs to systematically test all possible combinations of drug targets within the genetic network that regulates the disease. The BioModelAnalyzer (BMA) is a user-friendly computational tool that allows us to do exactly that. We used BMA to build a CML network-model composed of 54 nodes linked by 104 interactions that encapsulates experimental data collected from 160 publications. While previous studies were limited by their focus on a single pathway or cellular process, our executable model allowed us to probe dynamic interactions between multiple pathways and cellular outcomes, suggest new combinatorial therapeutic targets, and highlight previously unexplored sensitivities to Interleukin-3.

  17. Blood-brain barrier drug targeting: the future of brain drug development.

    PubMed

    Pardridge, William M

    2003-03-01

    As human longevity increases, the likelihood of the onset of diseases of the brain (and other organs) also increases. Clinical therapeutics offer useful long-term treatments, if not cures, if drugs can be delivered appropriately and effectively. Unfortunately, research in drug transport to the brain has not advanced very far. Through better characterization of the transport systems utilized within the blood-brain barrier, a greater understanding of how to exploit these systems will lead to effective treatments for brain disorders. Pardridge reviews the functions of the various known transport systems in the brain and discusses how the development of BBB drug-targeting programs in pharmaceutical and academic settings may lead to more efficacious treatments.

  18. Polyamine homoeostasis as a drug target in pathogenic protozoa: peculiarities and possibilities.

    PubMed

    Birkholtz, Lyn-Marie; Williams, Marni; Niemand, Jandeli; Louw, Abraham I; Persson, Lo; Heby, Olle

    2011-09-01

    New drugs are urgently needed for the treatment of tropical and subtropical parasitic diseases, such as African sleeping sickness, Chagas' disease, leishmaniasis and malaria. Enzymes in polyamine biosynthesis and thiol metabolism, as well as polyamine transporters, are potential drug targets within these organisms. In the present review, the current knowledge of unique properties of polyamine metabolism in these parasites is outlined. These properties include prozyme regulation of AdoMetDC (S-adenosylmethionine decarboxylase) activity in trypanosomatids, co-expression of ODC (ornithine decarboxylase) and AdoMetDC activities in a single protein in plasmodia, and formation of trypanothione, a unique compound linking polyamine and thiol metabolism in trypanosomatids. Particularly interesting features within polyamine metabolism in these parasites are highlighted for their potential in selective therapeutic strategies.

  19. Pathogenesis in tuberculosis: transcriptomic approaches to unraveling virulence mechanisms and finding new drug targets.

    PubMed

    Mukhopadhyay, Sangita; Nair, Shiny; Ghosh, Sudip

    2012-03-01

    Tuberculosis (TB) remains a major health problem worldwide. Attempts to control this disease have proved difficult owing to our poor understanding of the pathobiology of Mycobacterium tuberculosis and the emergence of strains that are resistant to multiple drugs currently available for treatment. Genome-wide expression profiling has provided new insight into the transcriptome signatures of the bacterium during infection, notably of macrophages and dendritic cells. These data indicate that M. tuberculosis expresses numerous genes to evade the host immune responses, to suit its intracellular life style, and to respond to various antibiotic drugs. Among the intracellularly induced genes, several have functions in lipid metabolism, cell wall synthesis, iron uptake, oxidative stress resistance, protein secretion, or inhibition of apoptosis. Herein we review these findings and discuss possible ways to exploit the data to understand the complex etiology of TB and to find new effective drug targets. © 2011 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

  20. Perspective of microsomal prostaglandin E2 synthase-1 as drug target in inflammation-related disorders.

    PubMed

    Koeberle, Andreas; Werz, Oliver

    2015-11-01

    Prostaglandin (PG)E2 encompasses crucial roles in pain, fever, inflammation and diseases with inflammatory component, such as cancer, but is also essential for gastric, renal, cardiovascular and immune homeostasis. Cyclooxygenases (COX) convert arachidonic acid to the intermediate PGH2 which is isomerized to PGE2 by at least three different PGE2 synthases. Inhibitors of COX - non-steroidal anti-inflammatory drugs (NSAIDs) - are currently the only available therapeutics that target PGE2 biosynthesis. Due to adverse effects of COX inhibitors on the cardiovascular system (COX-2-selective), stomach and kidney (COX-1/2-unselective), novel pharmacological strategies are in demand. The inducible microsomal PGE2 synthase (mPGES)-1 is considered mainly responsible for the excessive PGE2 synthesis during inflammation and was suggested as promising drug target for suppressing PGE2 biosynthesis. However, 15 years after intensive research on the biology and pharmacology of mPGES-1, the therapeutic value of mPGES-1 as drug target is still vague and mPGES-1 inhibitors did not enter the market so far. This commentary will first shed light on the structure, mechanism and regulation of mPGES-1 and will then discuss its biological function and the consequence of its inhibition for the dynamic network of eicosanoids. Moreover, we (i) present current strategies for interfering with mPGES-1-mediated PGE2 synthesis, (ii) summarize bioanalytical approaches for mPGES-1 drug discovery and (iii) describe preclinical test systems for the characterization of mPGES-1 inhibitors. The pharmacological potential of selective mPGES-1 inhibitor classes as well as dual mPGES-1/5-lipoxygenase inhibitors is reviewed and pitfalls in their development, including species discrepancies and loss of in vivo activity, are discussed.

  1. Novel Drugs Targeting the c-Ring of the F1FO-ATP Synthase.

    PubMed

    Pagliarani, Alessandra; Nesci, S; Ventrella, V

    2016-01-01

    Increasing evidence highlights the role of the ATP synthase/hydrolase, also known as F1FO-complex, as key molecular and enzymatic switch between cell life and death, thus increasing the enzyme attractiveness as drug target in pharmacology. Being inhibition of ATP production usually linked to antiproliferative properties, drugs targeting the enzyme complex have been mainly considered to fight pathogen parasites and cancer. In recent years, a number of natural macrolides, produced by bacterial fermentation and structurally related to the classical enzyme inhibitor oligomycin, have been shown to bind to the membrane-embedded FO sector and to inhibit the enzyme complex by an oligomycin-like mechanism, namely by interacting with the c-ring. Other than natural macrolide antibiotics, which display variegated inhibition power on different F1FO-complexes, synthetic compounds from the diarylquinoline and organotin families also target the c-ring and strongly inhibit the enzyme. Bioinformatic insights address drug design to target FO subunits. Additionally, the possible modulation of the drug inhibition power, by amino acid substitutions or post-translational modifications of c-subunits, adds further interest to the target. The present survey on compounds targeting the c-ring and bi-directionally blocking the transmembrane proton flux which drives ATP synthesis/hydrolysis, discloses new therapeutic options to fight cancer and infections sustained by therapeutically recalcitrant microorganisms. Additionally, c-ring targeting compounds may constitute new tools to eradicate undesired biofilms and to address at the molecular level the therapy of mammalian diseases linked to mitochondrial dysfunctions. In summary, studies on the only partially known molecular interactions within the c-ring of the F1FO-complex may renew hope to counteract mammalian diseases.

  2. Glycogen synthase kinase 3 is a potential drug target for African trypanosomiasis therapy.

    PubMed

    Ojo, Kayode K; Gillespie, J Robert; Riechers, Aaron J; Napuli, Alberto J; Verlinde, Christophe L M J; Buckner, Frederick S; Gelb, Michael H; Domostoj, Mathias M; Wells, Susan J; Scheer, Alexander; Wells, Timothy N C; Van Voorhis, Wesley C

    2008-10-01

    Development of a safe, effective, and inexpensive therapy for African trypanosomiasis is an urgent priority. In this study, we evaluated the validity of Trypanosoma brucei glycogen synthase kinase 3 (GSK-3) as a potential drug target. Interference with the RNA of either of two GSK-3 homologues in bloodstream-form T. brucei parasites led to growth arrest and altered parasite morphology, demonstrating their requirement for cell survival. Since the growth arrest after RNA interference appeared to be more profound for T. brucei GSK-3 "short" (Tb10.161.3140) than for T. brucei GSK-3 "long" (Tb927.7.2420), we focused on T. brucei GSK-3 short for further studies. T. brucei GSK-3 short with an N-terminal maltose-binding protein fusion was cloned, expressed, and purified in a functional form. The potency of a GSK-3-focused inhibitor library against the recombinant enzyme of T. brucei GSK-3 short, as well as bloodstream-form parasites, was evaluated with the aim of determining if compounds that inhibit enzyme activity could also block the parasites' growth and proliferation. Among the compounds active against the cell, there was an excellent correlation between activity inhibiting the T. brucei GSK-3 short enzyme and the inhibition of T. brucei growth. Thus, there is reasonable genetic and chemical validation of GSK-3 short as a drug target for T. brucei. Finally, selective inhibition may be required for therapy targeting the GSK-3 enzyme, and a molecular model of the T. brucei GSK-3 short enzyme suggests that compounds that selectively inhibit T. brucei GSK-3 short over the human GSK-3 enzymes can be found.

  3. Identification of pyruvate kinase in methicillin-resistant Staphylococcus aureus as a novel antimicrobial drug target.

    PubMed

    Zoraghi, Roya; See, Raymond H; Axerio-Cilies, Peter; Kumar, Nag S; Gong, Huansheng; Moreau, Anne; Hsing, Michael; Kaur, Sukhbir; Swayze, Richard D; Worrall, Liam; Amandoron, Emily; Lian, Tian; Jackson, Linda; Jiang, Jihong; Thorson, Lisa; Labriere, Christophe; Foster, Leonard; Brunham, Robert C; McMaster, William R; Finlay, B Brett; Strynadka, Natalie C; Cherkasov, Artem; Young, Robert N; Reiner, Neil E

    2011-05-01

    Novel classes of antimicrobials are needed to address the challenge of multidrug-resistant bacteria such as methicillin-resistant Staphylococcus aureus (MRSA). Using the architecture of the MRSA interactome, we identified pyruvate kinase (PK) as a potential novel drug target based upon it being a highly connected, essential hub in the MRSA interactome. Structural modeling, including X-ray crystallography, revealed discrete features of PK in MRSA, which appeared suitable for the selective targeting of the bacterial enzyme. In silico library screening combined with functional enzymatic assays identified an acyl hydrazone-based compound (IS-130) as a potent MRSA PK inhibitor (50% inhibitory concentration [IC50] of 0.1 μM) with >1,000-fold selectivity over human PK isoforms. Medicinal chemistry around the IS-130 scaffold identified analogs that more potently and selectively inhibited MRSA PK enzymatic activity and S. aureus growth in vitro (MIC of 1 to 5 μg/ml). These novel anti-PK compounds were found to possess antistaphylococcal activity, including both MRSA and multidrug-resistant S. aureus (MDRSA) strains. These compounds also exhibited exceptional antibacterial activities against other Gram-positive genera, including enterococci and streptococci. PK lead compounds were found to be noncompetitive inhibitors and were bactericidal. In addition, mutants with significant increases in MICs were not isolated after 25 bacterial passages in culture, indicating that resistance may be slow to emerge. These findings validate the principles of network science as a powerful approach to identify novel antibacterial drug targets. They also provide a proof of principle, based upon PK in MRSA, for a research platform aimed at discovering and optimizing selective inhibitors of novel bacterial targets where human orthologs exist, as leads for anti-infective drug development.

  4. Hyperlipidemia, Disease Associations, and Top 10 Potential Drug Targets: A Network View.

    PubMed

    Rai, Sneha; Bhatnagar, Sonika

    2016-03-01

    The prevalence of acquired hyperlipidemia has increased due to sedentary life style and lipid-rich diet. In this work, a lipid-protein-protein interaction network (LPPIN) for acquired hyperlipidemia was prepared by incorporating differentially expressed genes in obese fatty liver as seed nodes, protein interactions from PathwayLinker, and lipid interactions from STITCH4.0. Cholesterol, diacylglycreol, phosphatidylinositol-bis-phosphate, and inositol triphosphate were identified as core lipids that influence the signaling pathways in the LPPIN. RACα serine/threonine-protein kinase (AKT1) was a highly essential central protein. The gastrin-CREB pathway was greatly enriched; all enriched pathways in the LPPIN showed crosstalk with the phosphatidylinositol-3-kinase-Akt pathway, correlating with the central role of AKT1 in the network. The disease clusters identified in the LPPIN were cardiovascular disease, cancer, Alzheimer's disease, and Type II diabetes. In this context, we note that the commercially approved drug targets for hyperlipidemia in each disease cluster may potentially be repurposed for treatment of the specific disease. We report here top 10 potential drug targets that may mediate progression from hyperlipidemia to the respective disease state. ToppGene Suite was employed to identify candidates followed by a) discarding high closeness centrality nodes, and b) selecting nodes with high bridging centrality. Three potential targets could be mapped to specific disease clusters in the LPPIN. Lipids associated with acquired hyperlipidemia and each disease cluster identified may be useful as prognostic fingerprints. These findings provide an integrative view of lipid-protein interactions leading to acquired hyperlipidemia and the associated diseases, and might prove useful in future translational pharmaceutical research.

  5. A new tool for predicting drought: An application over India

    PubMed Central

    Kulkarni, M. N.

    2015-01-01

    This is the first attempt of application of atmospheric electricity for rainfall prediction. The atmospheric electrical columnar resistance based on the model calculations involving satellite data has been proposed as a new predictor. It is physically sound, simple to calculate and not probabilistic like the standardized precipitation index. After applying this new predictor over India, it has been found that the data solely over the Bay of Bengal (BB) are sufficient to predict a drought over the country as a whole. Finally, two independent new methods to predict drought conditions and a preliminary forecast of the same for India for year 2014 have been given. Unlike the existing drought prediction techniques, the identification of drought conditions in a pre-drought year during 1981–1990 and 2001–2013 over India has been achieved 100% successfully using the suggested new methods. The association between rainfall and this new predictor has also been found on the sub-regional scale. So, the present predictor is expected to get global application and application in climate models. From the analysis, generally, a long period rising trend in aerosol concentration over the BB causes weak monsoon over India but that for a short time i.e. in pre-monsoon period strengthens it. PMID:25567244

  6. A new tool for predicting drought: an application over India.

    PubMed

    Kulkarni, M N

    2015-01-08

    This is the first attempt of application of atmospheric electricity for rainfall prediction. The atmospheric electrical columnar resistance based on the model calculations involving satellite data has been proposed as a new predictor. It is physically sound, simple to calculate and not probabilistic like the standardized precipitation index. After applying this new predictor over India, it has been found that the data solely over the Bay of Bengal (BB) are sufficient to predict a drought over the country as a whole. Finally, two independent new methods to predict drought conditions and a preliminary forecast of the same for India for year 2014 have been given. Unlike the existing drought prediction techniques, the identification of drought conditions in a pre-drought year during 1981-1990 and 2001-2013 over India has been achieved 100% successfully using the suggested new methods. The association between rainfall and this new predictor has also been found on the sub-regional scale. So, the present predictor is expected to get global application and application in climate models. From the analysis, generally, a long period rising trend in aerosol concentration over the BB causes weak monsoon over India but that for a short time i.e. in pre-monsoon period strengthens it.

  7. C/sic Life Prediction for Propulsion Applications

    NASA Technical Reports Server (NTRS)

    Levine, Stanley R.; Verrilli, Michael J.; Opila, Elizabeth J.; Halbig, Michael C.; Calomino, Anthony M.; Thomas, David J.

    2003-01-01

    Accurate life prediction is critical to successful use of ceramic matrix composites (CMCs). The tools to accomplish this are immature and not oriented toward the behavior of carbon fiber reinforced silicon carbide (C/SiC), the primary system of interest for many reusable and single mission launch vehicle propulsion and airframe applications. This paper describes an approach and progress made to satisfy the need to develop an integrated life prediction system that addresses mechanical durability and environmental degradation of C/SiC.

  8. Model-based global sensitivity analysis as applied to identification of anti-cancer drug targets and biomarkers of drug resistance in the ErbB2/3 network

    PubMed Central

    Lebedeva, Galina; Sorokin, Anatoly; Faratian, Dana; Mullen, Peter; Goltsov, Alexey; Langdon, Simon P.; Harrison, David J.; Goryanin, Igor

    2012-01-01

    High levels of variability in cancer-related cellular signalling networks and a lack of parameter identifiability in large-scale network models hamper translation of the results of modelling studies into the process of anti-cancer drug development. Recently global sensitivity analysis (GSA) has been recognised as a useful technique, capable of addressing the uncertainty of the model parameters and generating valid predictions on parametric sensitivities. Here we propose a novel implementation of model-based GSA specially designed to explore how multi-parametric network perturbations affect signal propagation through cancer-related networks. We use area-under-the-curve for time course of changes in phosphorylation of proteins as a characteristic for sensitivity analysis and rank network parameters with regard to their impact on the level of key cancer-related outputs, separating strong inhibitory from stimulatory effects. This allows interpretation of the results in terms which can incorporate the effects of potential anti-cancer drugs on targets and the associated biological markers of cancer. To illustrate the method we applied it to an ErbB signalling network model and explored the sensitivity profile of its key model readout, phosphorylated Akt, in the absence and presence of the ErbB2 inhibitor pertuzumab. The method successfully identified the parameters associated with elevation or suppression of Akt phosphorylation in the ErbB2/3 network. From analysis and comparison of the sensitivity profiles of pAkt in the absence and presence of targeted drugs we derived predictions of drug targets, cancer-related biomarkers and generated hypotheses for combinatorial therapy. Several key predictions have been confirmed in experiments using human ovarian carcinoma cell lines. We also compared GSA-derived predictions with the results of local sensitivity analysis and discuss the applicability of both methods. We propose that the developed GSA procedure can serve as a

  9. The evolution of regulators of G protein signalling proteins as drug targets - 20 years in the making: IUPHAR Review 21.

    PubMed

    Sjögren, B

    2017-03-01

    Regulators of G protein signalling (RGS) proteins are celebrating the 20th anniversary of their discovery. The unveiling of this new family of negative regulators of G protein signalling in the mid-1990s solved a persistent conundrum in the G protein signalling field, in which the rate of deactivation of signalling cascades in vivo could not be replicated in exogenous systems. Since then, there has been tremendous advancement in the knowledge of RGS protein structure, function, regulation and their role as novel drug targets. RGS proteins play an important modulatory role through their GTPase-activating protein (GAP) activity at active, GTP-bound Gα subunits of heterotrimeric G proteins. They also possess many non-canonical functions not related to G protein signalling. Here, an update on the status of RGS proteins as drug targets is provided, highlighting advances that have led to the inclusion of RGS proteins in the IUPHAR/BPS Guide to PHARMACOLOGY database of drug targets.

  10. Cos-Seq for high-throughput identification of drug target and resistance mechanisms in the protozoan parasite Leishmania.

    PubMed

    Gazanion, Élodie; Fernández-Prada, Christopher; Papadopoulou, Barbara; Leprohon, Philippe; Ouellette, Marc

    2016-05-24

    Innovative strategies are needed to accelerate the identification of antimicrobial drug targets and resistance mechanisms. Here we develop a sensitive method, which we term Cosmid Sequencing (or "Cos-Seq"), based on functional cloning coupled to next-generation sequencing. Cos-Seq identified >60 loci in the Leishmania genome that were enriched via drug selection with methotrexate and five major antileishmanials (antimony, miltefosine, paromomycin, amphotericin B, and pentamidine). Functional validation highlighted both known and previously unidentified drug targets and resistance genes, including novel roles for phosphatases in resistance to methotrexate and antimony, for ergosterol and phospholipid metabolism genes in resistance to miltefosine, and for hypothetical proteins in resistance to paromomycin, amphothericin B, and pentamidine. Several genes/loci were also found to confer resistance to two or more antileishmanials. This screening method will expedite the discovery of drug targets and resistance mechanisms and is easily adaptable to other microorganisms.

  11. Cos-Seq for high-throughput identification of drug target and resistance mechanisms in the protozoan parasite Leishmania

    PubMed Central

    Gazanion, Élodie; Papadopoulou, Barbara; Leprohon, Philippe; Ouellette, Marc

    2016-01-01

    Innovative strategies are needed to accelerate the identification of antimicrobial drug targets and resistance mechanisms. Here we develop a sensitive method, which we term Cosmid Sequencing (or “Cos-Seq”), based on functional cloning coupled to next-generation sequencing. Cos-Seq identified >60 loci in the Leishmania genome that were enriched via drug selection with methotrexate and five major antileishmanials (antimony, miltefosine, paromomycin, amphotericin B, and pentamidine). Functional validation highlighted both known and previously unidentified drug targets and resistance genes, including novel roles for phosphatases in resistance to methotrexate and antimony, for ergosterol and phospholipid metabolism genes in resistance to miltefosine, and for hypothetical proteins in resistance to paromomycin, amphothericin B, and pentamidine. Several genes/loci were also found to confer resistance to two or more antileishmanials. This screening method will expedite the discovery of drug targets and resistance mechanisms and is easily adaptable to other microorganisms. PMID:27162331

  12. Drug target identification in sphingolipid metabolism by computational systems biology tools: metabolic control analysis and metabolic pathway analysis.

    PubMed

    Ozbayraktar, F Betül Kavun; Ulgen, Kutlu O

    2010-08-01

    Sphingolipids regulate cellular processes that are critically important in cell's fate and function in cancer development and progression. This fact underlies the basics of the novel cancer therapy approach. The pharmacological manipulation of the sphingolipid metabolism in cancer therapeutics necessitates the detailed understanding of the pathway. Two computational systems biology tools are used to identify potential drug target enzymes among sphingolipid pathway that can be further utilized in drug design studies for cancer therapy. The enzymes in sphingolipid pathway were ranked according to their roles in controlling the metabolic network by metabolic control analysis. The physiologically connected reactions, i.e. biologically significant and functional modules of network, were identified by metabolic pathway analysis. The final set of candidate drug target enzymes are selected such that their manipulation leads to ceramide accumulation and long chain base phosphates depletion. The mathematical tools' efficiency for drug target identification performed in this study is validated by clinically available drugs. Copyright 2010 Elsevier Inc. All rights reserved.

  13. A stand-alone tidal prediction application for mobile devices

    NASA Astrophysics Data System (ADS)

    Tsai, Cheng-Han; Fan, Ren-Ye; Yang, Yi-Chung

    2017-04-01

    It is essential for people conducting fishing, leisure, or research activities at the coasts to have timely and handy tidal information. Although tidal information can be found easily on the internet or using mobile device applications, this information is all applicable for only certain specific locations, not anywhere on the coast, and they need an internet connection. We have developed an application for Android devices, which allows the user to obtain hourly tidal height anywhere on the coast for the next 24 hours without having to have any internet connection. All the necessary information needed for the tidal height calculation is stored in the application. To develop this application, we first simulate tides in the Taiwan Sea using the hydrodynamic model (MIKE21 HD) developed by the DHI. The simulation domain covers the whole coast of Taiwan and the surrounding seas with a grid size of 1 km by 1 km. This grid size allows us to calculate tides with high spatial resolution. The boundary conditions for the simulation domain were obtained from the Tidal Model Driver of the Oregon State University, using its tidal constants of eight constituents: M2, S2, N2, K2, K1, O1, P1, and Q1. The simulation calculates tides for 183 days so that the tidal constants for the above eight constituents of each water grid can be extracted by harmonic analysis. Using the calculated tidal constants, we can predict the tides in each grid of our simulation domain, which is useful when one needs the tidal information for any location in the Taiwan Sea. However, for the mobile application, we only store the eight tidal constants for the water grids on the coast. Once the user activates the application, it reads the longitude and latitude from the GPS sensor in the mobile device and finds the nearest coastal grid which has our tidal constants. Then, the application calculates tidal height variation based on the harmonic analysis. The application also allows the user to input location and

  14. Are Pharmaceuticals with Evolutionary Conserved Molecular Drug Targets More Potent to Cause Toxic Effects in Non-Target Organisms?

    PubMed Central

    Furuhagen, Sara; Fuchs, Anne; Lundström Belleza, Elin; Breitholtz, Magnus; Gorokhova, Elena

    2014-01-01

    The ubiquitous use of pharmaceuticals has resulted in a continuous discharge into wastewater and pharmaceuticals and their metabolites are found in the environment. Due to their design towards specific drug targets, pharmaceuticals may be therapeutically active already at low environmental concentrations. Several human drug targets are evolutionary conserved in aquatic organisms, raising concerns about effects of these pharmaceuticals in non-target organisms. In this study, we hypothesized that the toxicity of a pharmaceutical towards a non-target invertebrate depends on the presence of the human drug target orthologs in this species. This was tested by assessing toxicity of pharmaceuticals with (miconazole and promethazine) and without (levonorgestrel) identified drug target orthologs in the cladoceran Daphnia magna. The toxicity was evaluated using general toxicity endpoints at individual (immobility, reproduction and development), biochemical (RNA and DNA content) and molecular (gene expression) levels. The results provide evidence for higher toxicity of miconazole and promethazine, i.e. the drugs with identified drug target orthologs. At the individual level, miconazole had the lowest effect concentrations for immobility and reproduction (0.3 and 0.022 mg L−1, respectively) followed by promethazine (1.6 and 0.18 mg L−1, respectively). At the biochemical level, individual RNA content was affected by miconazole and promethazine already at 0.0023 and 0.059 mg L−1, respectively. At the molecular level, gene expression for cuticle protein was significantly suppressed by exposure to both miconazole and promethazine; moreover, daphnids exposed to miconazole had significantly lower vitellogenin expression. Levonorgestrel did not have any effects on any endpoints in the concentrations tested. These results highlight the importance of considering drug target conservation in environmental risk assessments of pharmaceuticals. PMID:25140792

  15. Determining the prediction limits of models and classifiers with applications for disruption prediction in JET

    NASA Astrophysics Data System (ADS)

    Murari, A.; Peluso, E.; Vega, J.; Gelfusa, M.; Lungaroni, M.; Gaudio, P.; Martínez, F. J.; Contributors, JET

    2017-01-01

    Understanding the many aspects of tokamak physics requires the development of quite sophisticated models. Moreover, in the operation of the devices, prediction of the future evolution of discharges can be of crucial importance, particularly in the case of the prediction of disruptions, which can cause serious damage to various parts of the machine. The determination of the limits of predictability is therefore an important issue for modelling, classifying and forecasting. In all these cases, once a certain level of performance has been reached, the question typically arises as to whether all the information available in the data has been exploited, or whether there are still margins for improvement of the tools being developed. In this paper, a theoretical information approach is proposed to address this issue. The excellent properties of the developed indicator, called the prediction factor (PF), have been proved with the help of a series of numerical tests. Its application to some typical behaviour relating to macroscopic instabilities in tokamaks has shown very positive results. The prediction factor has also been used to assess the performance of disruption predictors running in real time in the JET system, including the one systematically deployed in the feedback loop for mitigation purposes. The main conclusion is that the most advanced predictors basically exploit all the information contained in the locked mode signal on which they are based. Therefore, qualitative improvements in disruption prediction performance in JET would need the processing of additional signals, probably profiles.

  16. Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database.

    PubMed

    Barneh, Farnaz; Jafari, Mohieddin; Mirzaie, Mehdi

    2016-11-01

    Network pharmacology elucidates the relationship between drugs and targets. As the identified targets for each drug increases, the corresponding drug-target network (DTN) evolves from solely reflection of the pharmaceutical industry trend to a portrait of polypharmacology. The aim of this study was to evaluate the potentials of DrugBank database in advancing systems pharmacology. We constructed and analyzed DTN from drugs and targets associations in the DrugBank 4.0 database. Our results showed that in bipartite DTN, increased ratio of identified targets for drugs augmented density and connectivity of drugs and targets and decreased modular structure. To clear up the details in the network structure, the DTNs were projected into two networks namely, drug similarity network (DSN) and target similarity network (TSN). In DSN, various classes of Food and Drug Administration-approved drugs with distinct therapeutic categories were linked together based on shared targets. Projected TSN also showed complexity because of promiscuity of the drugs. By including investigational drugs that are currently being tested in clinical trials, the networks manifested more connectivity and pictured the upcoming pharmacological space in the future years. Diverse biological processes and protein-protein interactions were manipulated by new drugs, which can extend possible target combinations. We conclude that network-based organization of DrugBank 4.0 data not only reveals the potential for repurposing of existing drugs, also allows generating novel predictions about drugs off-targets, drug-drug interactions and their side effects. Our results also encourage further effort for high-throughput identification of targets to build networks that can be integrated into disease networks. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  17. A Target Repurposing Approach Identifies N-myristoyltransferase as a New Candidate Drug Target in Filarial Nematodes

    PubMed Central

    Villemaine, Estelle; Poole, Catherine B.; Chapman, Melissa S.; Pollastri, Michael P.; Wyatt, Paul G.; Carlow, Clotilde K. S.

    2014-01-01

    Myristoylation is a lipid modification involving the addition of a 14-carbon unsaturated fatty acid, myristic acid, to the N-terminal glycine of a subset of proteins, a modification that promotes their binding to cell membranes for varied biological functions. The process is catalyzed by myristoyl-CoA:protein N-myristoyltransferase (NMT), an enzyme which has been validated as a drug target in human cancers, and for infectious diseases caused by fungi, viruses and protozoan parasites. We purified Caenorhabditis elegans and Brugia malayi NMTs as active recombinant proteins and carried out kinetic analyses with their essential fatty acid donor, myristoyl-CoA and peptide substrates. Biochemical and structural analyses both revealed that the nematode enzymes are canonical NMTs, sharing a high degree of conservation with protozoan NMT enzymes. Inhibitory compounds that target NMT in protozoan species inhibited the nematode NMTs with IC50 values of 2.5–10 nM, and were active against B. malayi microfilariae and adult worms at 12.5 µM and 50 µM respectively, and C. elegans (25 µM) in culture. RNA interference and gene deletion in C. elegans further showed that NMT is essential for nematode viability. The effects observed are likely due to disruption of the function of several downstream target proteins. Potential substrates of NMT in B. malayi are predicted using bioinformatic analysis. Our genetic and chemical studies highlight the importance of myristoylation in the synthesis of functional proteins in nematodes and have shown for the first time that NMT is required for viability in parasitic nematodes. These results suggest that targeting NMT could be a valid approach for the development of chemotherapeutic agents against nematode diseases including filariasis. PMID:25188325

  18. LIBP-Pred: web server for lipid binding proteins using structural network parameters; PDB mining of human cancer biomarkers and drug targets in parasites and bacteria.

    PubMed

    González-Díaz, Humberto; Munteanu, Cristian R; Postelnicu, Lucian; Prado-Prado, Francisco; Gestal, Marcos; Pazos, Alejandro

    2012-03-01

    Lipid-Binding Proteins (LIBPs) or Fatty Acid-Binding Proteins (FABPs) play an important role in many diseases such as different types of cancer, kidney injury, atherosclerosis, diabetes, intestinal ischemia and parasitic infections. Thus, the computational methods that can predict LIBPs based on 3D structure parameters became a goal of major importance for drug-target discovery, vaccine design and biomarker selection. In addition, the Protein Data Bank (PDB) contains 3000+ protein 3D structures with unknown function. This list, as well as new experimental outcomes in proteomics research, is a very interesting source to discover relevant proteins, including LIBPs. However, to the best of our knowledge, there are no general models to predict new LIBPs based on 3D structures. We developed new Quantitative Structure-Activity Relationship (QSAR) models based on 3D electrostatic parameters of 1801 different proteins, including 801 LIBPs. We calculated these electrostatic parameters with the MARCH-INSIDE software and they correspond to the entire protein or to specific protein regions named core, inner, middle, and surface. We used these parameters as inputs to develop a simple Linear Discriminant Analysis (LDA) classifier to discriminate 3D structure of LIBPs from other proteins. We implemented this predictor in the web server named LIBP-Pred, freely available at , along with other important web servers of the Bio-AIMS portal. The users can carry out an automatic retrieval of protein structures from PDB or upload their custom protein structural models from their disk created with LOMETS server. We demonstrated the PDB mining option performing a predictive study of 2000+ proteins with unknown function. Interesting results regarding the discovery of new Cancer Biomarkers in humans or drug targets in parasites have been discussed here in this sense.

  19. Application of Avco data analysis and prediction techniques (ADAPT) to prediction of sunspot activity

    NASA Technical Reports Server (NTRS)

    Hunter, H. E.; Amato, R. A.

    1972-01-01

    The results are presented of the application of Avco Data Analysis and Prediction Techniques (ADAPT) to derivation of new algorithms for the prediction of future sunspot activity. The ADAPT derived algorithms show a factor of 2 to 3 reduction in the expected 2-sigma errors in the estimates of the 81-day running average of the Zurich sunspot numbers. The report presents: (1) the best estimates for sunspot cycles 20 and 21, (2) a comparison of the ADAPT performance with conventional techniques, and (3) specific approaches to further reduction in the errors of estimated sunspot activity and to recovery of earlier sunspot historical data. The ADAPT programs are used both to derive regression algorithm for prediction of the entire 11-year sunspot cycle from the preceding two cycles and to derive extrapolation algorithms for extrapolating a given sunspot cycle based on any available portion of the cycle.

  20. Comparative genomics allowed the identification of drug targets against human fungal pathogens

    PubMed Central

    2011-01-01

    Background The prevalence of invasive fungal infections (IFIs) has increased steadily worldwide in the last few decades. Particularly, there has been a global rise in the number of infections among immunosuppressed people. These patients present severe clinical forms of the infections, which are commonly fatal, and they are more susceptible to opportunistic fungal infections than non-immunocompromised people. IFIs have historically been associated with high morbidity and mortality, partly because of the limitations of available antifungal therapies, including side effects, toxicities, drug interactions and antifungal resistance. Thus, the search for alternative therapies and/or the development of more specific drugs is a challenge that needs to be met. Genomics has created new ways of examining genes, which open new strategies for drug development and control of human diseases. Results In silico analyses and manual mining selected initially 57 potential drug targets, based on 55 genes experimentally confirmed as essential for Candida albicans or Aspergillus fumigatus and other 2 genes (kre2 and erg6) relevant for fungal survival within the host. Orthologs for those 57 potential targets were also identified in eight human fungal pathogens (C. albicans, A. fumigatus, Blastomyces dermatitidis, Paracoccidioides brasiliensis, Paracoccidioides lutzii, Coccidioides immitis, Cryptococcus neoformans and Histoplasma capsulatum). Of those, 10 genes were present in all pathogenic fungi analyzed and absent in the human genome. We focused on four candidates: trr1 that encodes for thioredoxin reductase, rim8 that encodes for a protein involved in the proteolytic activation of a transcriptional factor in response to alkaline pH, kre2 that encodes for α-1,2-mannosyltransferase and erg6 that encodes for Δ(24)-sterol C-methyltransferase. Conclusions Our data show that the comparative genomics analysis of eight fungal pathogens enabled the identification of four new potential drug

  1. Biosynthetic pathways of plastid-derived organelles as potential drug targets against parasitic apicomplexa.

    PubMed

    Seeber, Frank

    2003-06-01

    Apicomplexan parasites are a large phylum of unicellular and obligate intracellular organisms of great medical importance. They include the human pathogens Plasmodium spp., the causative agent of malaria, and Toxoplasma gondii, an opportunistic parasite of immunosuppressed individuals and a common cause of congenital disease, together affecting several hundred million people worldwide. The search for new and effective drugs against these pathogens has been boosted during the last years by an unexpected finding. Through molecular and cell biological analysis it was realized that probably most members of this phylum harbor a plastid-like organelle, called the apicoplast, which probably is derived from the engulfment of a red alga in ancient times. Although the apicoplast itself contains a small circular genome, most of the proteome of this organelle is encoded in the nuclear genome, and the proteins are subsequently transported to the apicoplast. It is assumed to contain a number of unique metabolic pathways not found in the vertebrate host, making it an ideal "playground" for those interested in drug targets. Recent reports have shown that the rationale of this approach is valid and that new drugs which are urgently needed especially for plasmodial infections, might be developed in the near future based on these targets. Amongst them are three enzymes of the plant-like fatty acid synthesis machinery and enzymes of the non-mevalonat isoprenoid biosynthesis pathway. From their presence in the apicoplast it can be concluded that fatty acid and lipid biosynthesis seems to be a major function of the apicoplast. Another recently described apicoplast enzyme, ferredoxin-NADP(+)-reductase and its redox partner, ferredoxin, points to another interesting organelle-specific biosynthetic pathway, namely [Fe-S] cluster biosynthesis. In the present review, the fundamental aspects of the apicoplast as drug target will be described, together with the specific pathways and their

  2. Giardia fatty acyl-CoA synthetases as potential drug targets

    PubMed Central

    Guo, Fengguang; Ortega-Pierres, Guadalupe; Argüello-García, Raúl; Zhang, Haili; Zhu, Guan

    2015-01-01

    Giardiasis caused by Giardia intestinalis (syn. G. lamblia, G. duodenalis) is one of the leading causes of diarrheal parasitic diseases worldwide. Although limited drugs to treat giardiasis are available, there are concerns regarding toxicity in some patients and the emerging drug resistance. By data-mining genome sequences, we observed that G. intestinalis is incapable of synthesizing fatty acids (FA) de novo. However, this parasite has five long-chain fatty acyl-CoA synthetases (GiACS1 to GiACS5) to activate FA scavenged from the host. ACS is an essential enzyme because FA need to be activated to form acyl-CoA thioesters before they can enter subsequent metabolism. In the present study, we performed experiments to explore whether some GiACS enzymes could serve as drug targets in Giardia. Based on the high-throughput datasets and protein modeling analyses, we initially studied the GiACS1 and GiACS2, because genes encoding these two enzymes were found to be more consistently expressed in varied parasite life cycle stages and when interacting with host cells based on previously reported transcriptome data. These two proteins were cloned and expressed as recombinant proteins. Biochemical analysis revealed that both had apparent substrate preference toward palmitic acid (C16:0) and myristic acid (C14:0), and allosteric or Michaelis–Menten kinetics on palmitic acid or ATP. The ACS inhibitor triacsin C inhibited the activity of both enzymes (IC50 = 1.56 μM, Ki = 0.18 μM for GiACS1, and IC50 = 2.28 μM, Ki = 0.23 μM for GiACS2, respectively) and the growth of G. intestinalis in vitro (IC50 = 0.8 μM). As expected from giardial evolutionary characteristics, both GiACSs displayed differences in overall folding structure as compared with their human counterparts. These observations support the notion that some of the GiACS enzymes may be explored as drug targets in this parasite. PMID:26257723

  3. Leveraging structure determination with fragment screening for infectious disease drug targets: MECP synthase from Burkholderia pseudomallei

    SciTech Connect

    Begley, Darren W.; Hartley, Robert C.; Davies, Douglas R.; Edwards, Thomas E.; Leonard, Jess T.; Abendroth, Jan; Burris, Courtney A.; Bhandari, Janhavi; Myler, Peter J.; Staker, Bart L.; Stewart, Lance J.

    2011-09-28

    As part of the Seattle Structural Genomics Center for Infectious Disease, we seek to enhance structural genomics with ligand-bound structure data which can serve as a blueprint for structure-based drug design. We have adapted fragment-based screening methods to our structural genomics pipeline to generate multiple ligand-bound structures of high priority drug targets from pathogenic organisms. In this study, we report fragment screening methods and structure determination results for 2C-methyl-D-erythritol-2,4-cyclo-diphosphate (MECP) synthase from Burkholderia pseudomallei, the gram-negative bacterium which causes melioidosis. Screening by nuclear magnetic resonance spectroscopy as well as crystal soaking followed by X-ray diffraction led to the identification of several small molecules which bind this enzyme in a critical metabolic pathway. A series of complex structures obtained with screening hits reveal distinct binding pockets and a range of small molecules which form complexes with the target. Additional soaks with these compounds further demonstrate a subset of fragments to only bind the protein when present in specific combinations. This ensemble of fragment-bound complexes illuminates several characteristics of MECP synthase, including a previously unknown binding surface external to the catalytic active site. These ligand-bound structures now serve to guide medicinal chemists and structural biologists in rational design of novel inhibitors for this enzyme.

  4. Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures

    PubMed Central

    Morrison, Eamonn; Wai, Patty; Leonidou, Andri; Bland, Philip; Khalique, Saira; Farnie, Gillian; Daley, Frances; Peck, Barrie; Natrajan, Rachael

    2016-01-01

    The identification of functional driver events in cancer is central to furthering our understanding of cancer biology and indispensable for the discovery of the next generation of novel drug targets. It is becoming apparent that more complex models of cancer are required to fully appreciate the contributing factors that drive tumorigenesis in vivo and increase the efficacy of novel therapies that make the transition from pre-clinical models to clinical trials. Here we present a methodology for generating uniform and reproducible tumor spheroids that can be subjected to siRNA functional screening. These spheroids display many characteristics that are found in solid tumors that are not present in traditional two-dimension culture. We show that several commonly used breast cancer cell lines are amenable to this protocol. Furthermore, we provide proof-of-principle data utilizing the breast cancer cell line BT474, confirming their dependency on amplification of the epidermal growth factor receptor HER2 and mutation of phosphatidylinositol-4,5-biphosphate 3-kinase (PIK3CA) when grown as tumor spheroids. Finally, we are able to further investigate and confirm the spatial impact of these dependencies using immunohistochemistry. PMID:28060271

  5. The periplasmic protein TolB as a potential drug target in Pseudomonas aeruginosa.

    PubMed

    Lo Sciuto, Alessandra; Fernández-Piñar, Regina; Bertuccini, Lucia; Iosi, Francesca; Superti, Fabiana; Imperi, Francesco

    2014-01-01

    The Gram-negative bacterium Pseudomonas aeruginosa is one of the most dreaded pathogens in the hospital setting, and represents a prototype of multi-drug resistant "superbug" for which effective therapeutic options are very limited. The identification and characterization of new cellular functions that are essential for P. aeruginosa viability and/or virulence could drive the development of anti-Pseudomonas compounds with novel mechanisms of action. In this study we investigated whether TolB, the periplasmic component of the Tol-Pal trans-envelope protein complex of Gram-negative bacteria, represents a potential drug target in P. aeruginosa. By combining conditional mutagenesis with the analysis of specific pathogenicity-related phenotypes, we demonstrated that TolB is essential for P. aeruginosa growth, both in laboratory and clinical strains, and that TolB-depleted P. aeruginosa cells are strongly defective in cell-envelope integrity, resistance to human serum and several antibiotics, as well as in the ability to cause infection and persist in an insect model of P. aeruginosa infection. The essentiality of TolB for P. aeruginosa growth, resistance and pathogenicity highlights the potential of TolB as a novel molecular target for anti-P. aeruginosa drug discovery.

  6. The Proteomics Big Challenge for Biomarkers and New Drug-Targets Discovery

    PubMed Central

    Savino, Rocco; Paduano, Sergio; Preianò, Mariaimmacolata; Terracciano, Rosa

    2012-01-01

    In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets. PMID:23203042

  7. DYRK1A: a potential drug target for multiple Down syndrome neuropathologies.

    PubMed

    Becker, Walter; Soppa, Ulf; Tejedor, Francisco J

    2014-02-01

    Down syndrome (DS), the most common genetic cause of intellectual disability, is caused by the trisomy of chromosome 21. MNB/DYRK1A (Minibrain/dual specificity tyrosine phosphorylation-regulated kinase 1A) has possibly been the most extensively studied chromosome 21 gene during the last decade due to the remarkable correlation of its functions in the brain with important DS neuropathologies, such as neuronal deficits, dendrite atrophy, spine dysgenesis, precocious Alzheimer's-like neurodegeneration, and cognitive deficits. MNB/DYRK1A has become an attractive drug target because increasing evidence suggests that its overexpression may induce DS-like neurobiological alterations, and several small-molecule inhibitors of its protein kinase activity are available. Here, we summarize the functional complexity of MNB/DYRK1A from a DS-research perspective, paying particular attention to the capacity of different MNB/DYRK1A inhibitors to reverse the neurobiological alterations caused by the increased activity of MNB/DYRK1A in experimental models. Finally, we discuss the advantages and drawbacks of possible MNB/DYRK1A-based therapeutic strategies that result from the functional, molecular, and pharmacological complexity of MNB/DYRK1A.

  8. Streptococcus pneumoniae TIGR4 Flavodoxin: Structural and Biophysical Characterization of a Novel Drug Target

    PubMed Central

    Rodríguez-Cárdenas, Ángela; Rojas, Adriana L.; Conde-Giménez, María; Velázquez-Campoy, Adrián; Hurtado-Guerrero, Ramón; Sancho, Javier

    2016-01-01

    Streptococcus pneumoniae (Sp) strain TIGR4 is a virulent, encapsulated serotype that causes bacteremia, otitis media, meningitis and pneumonia. Increased bacterial resistance and limited efficacy of the available vaccine to some serotypes complicate the treatment of diseases associated to this microorganism. Flavodoxins are bacterial proteins involved in several important metabolic pathways. The Sp flavodoxin (Spfld) gene was recently reported to be essential for the establishment of meningitis in a rat model, which makes SpFld a potential drug target. To facilitate future pharmacological studies, we have cloned and expressed SpFld in E. coli and we have performed an extensive structural and biochemical characterization of both the apo form and its active complex with the FMN cofactor. SpFld is a short-chain flavodoxin containing 146 residues. Unlike the well-characterized long-chain apoflavodoxins, the Sp apoprotein displays a simple two-state thermal unfolding equilibrium and binds FMN with moderate affinity. The X-ray structures of the apo and holo forms of SpFld differ at the FMN binding site, where substantial rearrangement of residues at the 91–100 loop occurs to permit cofactor binding. This work will set up the basis for future studies aiming at discovering new potential drugs to treat S. pneumoniae diseases through the inhibition of SpFld. PMID:27649488

  9. The non-neuronal cholinergic system as novel drug target in the airways.

    PubMed

    Pieper, Michael Paul

    2012-11-27

    The parasympathetic nervous system is a key regulator of the human organism involved in the pathophysiology of various disorders through cholinergic mechanisms. In the lungs, acetylcholine (ACh) released by vagal nerve endings stimulates muscarinic receptors thereby increasing airway smooth muscle tone. Contraction of airway smooth muscle cells leads to increased respiratory resistance and dyspnea. An additional branch of the cholinergic system is the non-neuronal cholinergic system expressed in nearly all cell types present in the airways. Activation of this system may contribute to an increased cholinergic tone in the lungs, inducing pathophysiological processes like inflammation, remodeling, mucus hypersecretion and chronic cough. Selective muscarinic receptor antagonists specifically inhibit acetylcholine at the receptor inducing bronchodilation in patients with obstructive airway diseases. This paper reviews preclinical pharmacological research activities on anticholinergics including experimental models of asthma and chronic obstructive pulmonary disease, COPD. It discloses various options to follow up the non-neuronal cholinergic system as a novel drug target for the treatment of key aspects of obstructive airway diseases, in particular those of a chronic nature.

  10. NPY signalling pathway in bone homeostasis: Y1 receptor as a potential drug target.

    PubMed

    Sousa, D M; Herzog, H; Lamghari, M

    2009-01-01

    Neuropeptide (NPY) is a neurotransmitter widely distributed in central and peripheral nervous system that has been implicated in several physiological processes through activation of five different Y receptors: Y1, Y2, Y4, Y5, and y6. NPY system has been extensively studied for the last decades due to its implications in a wide variety of physiological processes. For this purpose a diversity of sophisticated animal models and receptors agonists and antagonists has been developed to better understand its actions throughout body homeostasis. Consequently, NPY and its receptors have recently emerged as a potential regulator of bone homeostasis. This is supported by the demonstration of an increase of bone mass in mice lacking Y1 or Y2 receptor genes. Recent findings revealed Y1 receptor as a potential drug target candidate for prevention and treatment of bone loss. Indeed, it has been demonstrated that osteoblasts express Y1 receptor while no other Y receptor was detected in these cells, implicating Y1 receptor signalling in the local control of bone turnover. In this review, we have summarized the findings obtained from studies on NPY system in skeletogenesis focusing on Y1 receptor.

  11. Biodegradable nanocomposite magnetite stent for implant-assisted magnetic drug targeting

    NASA Astrophysics Data System (ADS)

    Mangual, Jan O.; Li, Shigeng; Ploehn, Harry J.; Ebner, Armin D.; Ritter, James A.

    2010-10-01

    This study shows, for the first time, the fabrication of a biodegradable polymer nanocomposite magnetic stent and the feasibility of its use in implant-assisted-magnetic drug targeting (IA-MDT). The nanocomposite magnetic stent was made from PLGA, a biodegradable copolymer, and iron oxide nanopowder via melt mixing and extrusion into fibers. Degradation and dynamic mechanical thermal analyses showed that the addition of the iron oxide nanopowder increased the polymer's glass transition temperature ( Tg) and its modulus but had no notable effect on its degradation rate in PBS buffer solution. IA-MDT in vitro experiments were carried out with the nanocomposite magnetic fiber molded into a stent coil. These stent prototypes were used in the presence of a homogeneous magnetic field of 0.3 T to capture 100 nm magnetic drug carrier particles (MDCPs) from an aqueous solution. Increasing the amount of magnetite in the stent nanocomposite (0, 10 and 40 w/w%) resulted in an increase in the MDCP capture efficiency (CE). Reducing the MDCP concentrations (0.75 and 1.5 mg/mL) in the flowing fluid and increasing the fluid velocities (20 and 40 mL/min) both resulted in decrease in the MDCP CE. These results show that the particle capture performance of PLGA-based, magnetic nanocomposite stents are similar to those exhibited by a variety of different non-polymeric magnetic stent materials studied previously.

  12. Adaptable setups for magnetic drug targeting in human muscular arteries: Design and implementation

    NASA Astrophysics Data System (ADS)

    Hajiaghajani, Amirhossein; Hashemi, Soheil; Abdolali, Ali

    2017-09-01

    Magnetic drug targeting has been used to steer magnetic therapeutic agents and has received much attention for capillaries and human brain arteries. In this paper, we focus on noninvasive targeting of nanoparticles in muscular arteries, in where the vessel diameter and blood flow are much challengingly higher than brain capillaries. We aim to design a low intensity magnetic field which avoids potential side effects on blood cells while steers particles with high targeting rate. The setup design procedure is considerably flexible to be used in a wide variety of large vessels. Using particle tracing, a new method is proposed to connect the geometry of the vessel under the action of targeting to the required magnetic force. Specifications of the coil which is placed outside the body are derived based on this required force. Mutual effects of coil dimensions on the produced magnetic force are elaborated and summarized in a design flowchart to be used for arbitrary muscular vessel sizes. The performance of the optimized coil is validated by in vitro experiments and it is shown that particles are steered with the average efficiency of 80.2% for various conditions.

  13. Numerical simulation of magnetic nano drug targeting in a patient-specific coeliac trunk

    NASA Astrophysics Data System (ADS)

    Boghi, Andrea; Russo, Flavia; Gori, Fabio

    2017-09-01

    Magnetic nano drug targeting, through the use of an external magnetic field, is a new technique for the treatment of several diseases, which can potentially avoid the dispersion of drugs in undesired locations of the body. Nevertheless, due to the limitations on the intensity of the magnetic field applied, the hydrodynamic forces can reduce the effectiveness of the procedure. This technique is studied in this paper with the Computational Fluid Dynamics (CFD), focusing on the influence of the magnetic probe position, and the direction of the circulating electric current. A single rectangular coil is used to generate the external magnetic field. A patient-specific geometry of the coeliac trunk is reconstructed from DICOM images, with the use of VMTK. A new solver, coupling the Lagrangian dynamics of the nanoparticles with the Eulerian dynamics of the blood, is implemented in OpenFOAM to perform the simulations. The resistive pressure, the Womersley's profile for the inlet velocity and the magnetic field of a rectangular coil are implemented in the software as boundary conditions. The results show the influence of the position of the probe, as well as the limitations associated with the rectangular coil configuration.

  14. Hybrid imbalanced data classifier models for computational discovery of antibiotic drug targets.

    PubMed

    Kocyigit, Yucel; Seker, Huseyin

    2014-01-01

    Identification of drug candidates is an important but also difficult process. Given drug resistance bacteria that we face, this process has become more important to identify protein candidates that demonstrate antibacterial activity. The aim of this study is therefore to develop a bioinformatics approach that is more capable of identifying a small but effective set of proteins that are expected to show antibacterial activity, subsequently to be used as antibiotic drug targets. As this is regarded as an imbalanced data classification problem due to smaller number of antibiotic drugs available, a hybrid classification model was developed and applied to the identification of antibiotic drugs. The model was developed by taking into account of various statistical models leading to the development of six different hybrid models. The best model has reached the accuracy of as high as 50% compared to earlier study with the accuracy of less than 1% as far as the proportion of the candidates identified and actual antibiotics in the candidate list is concerned.

  15. Medicinal Chemistry of ATP Synthase: A Potential Drug Target of Dietary Polyphenols and Amphibian Antimicrobial Peptides

    PubMed Central

    Ahmad, Zulfiqar; Laughlin, Thomas F.

    2015-01-01

    In this review we discuss the inhibitory effects of dietary polyphenols and amphibian antimicrobial/antitumor peptides on ATP synthase. In the beginning general structural features highlighting catalytic and motor functions of ATP synthase will be described. Some details on the presence of ATP synthase on the surface of several animal cell types, where it is associated with multiple cellular processes making it an interesting drug target with respect to dietary polyphenols and amphibian antimicrobial peptides will also be reviewed. ATP synthase is known to have distinct polyphenol and peptide binding sites at the interface of α/β subunits. Molecular interaction of polyphenols and peptides with ATP synthase at their respective binding sites will be discussed. Binding and inhibition of other proteins or enzymes will also be covered so as to understand the therapeutic roles of both types of molecules. Lastly, the effects of polyphenols and peptides on the inhibition of Escherichia coli cell growth through their action on ATP synthase will also be presented. PMID:20586714

  16. Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets.

    PubMed

    Vinayagam, Arunachalam; Gibson, Travis E; Lee, Ho-Joon; Yilmazel, Bahar; Roesel, Charles; Hu, Yanhui; Kwon, Young; Sharma, Amitabh; Liu, Yang-Yu; Perrimon, Norbert; Barabási, Albert-László

    2016-05-03

    The protein-protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as "indispensable," "neutral," or "dispensable," which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network's control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets.

  17. A Perspective on Monoamine Oxidase Enzyme as Drug Target: Challenges and Opportunities.

    PubMed

    Kumar, Bhupinder; Gupta, Vivek Prakash; Kumar, Vinod

    2017-01-01

    The monoamine oxidase (MAO) enzyme is responsible for the deamination of monoamine neurotransmitters and regulates their concentration in the central and peripheral nervous systems. Imbalance in the concentration of neurotransmitters in the brain and central nervous system is linked with the biochemical pathology of various neurogenic disorders. Irreversible MAO inhibitors were the first line drugs developed for the management of severe depression but most of these were withdrawn from the clinical practice due to their fatal side effects including food-drug interactions. New generations of MAO inhibitors were developed which were reversible and selective for one of the enzyme isoform and showed improved pharmacological profile. The discovery of crystal structure of MAO-A & MAO-B isoforms helped in understanding the drug-receptor interactions at the molecular level and designing of ligands with selectivity for either of the isoforms. The current article provides an overview on the MAO enzyme as potential drug target for different disease states. The article describes catalytic mechanism of MAO enzyme, crystal structures of the two MAO isoforms, traditional MAO inhibitors and various problems associated with their use, new developments in the MAO inhibitors and their potential as therapeutic agents especially in neurological disorders.

  18. Neurofibrillary degeneration in Alzheimer's disease: from molecular mechanisms to identification of drug targets.

    PubMed

    Pei, Jin-Jing; Sjögren, Magnus; Winblad, Bengt

    2008-11-01

    Great progress has been made in understanding the pathogenesis of neurofibrillary degeneration in Alzheimer's disease brains in the last two decades. In this review we summarize how neurons are degenerated in Alzheimer's disease brains and highlight the evidence of using kinases such as glycogen synthase kinase 3 and p70 S6 kinase and phosphatases such as protein phosphatase 2A as drug targets to prohibit the formation of neurofibrillary degeneration of Alzheimer's disease. In general there are two types of neuronal degeneration in Alzheimer's disease brains: neurofibrillary formation and apoptosis. The microtubule-associated protein tau that stabilizes neuronal microtubules under normal physiological conditions is abnormally hyperphosphorylated in Alzheimer's disease brains, resulting in the generation of aberrant aggregates that are toxic to neurons. The processes of tau hyperphosphorylation and the formation of neurofibrillary tangles are caused by the imbalance of the activities of protein kinases and protein phosphatases in Alzheimer's disease brains. Recent findings from our and other groups have suggested glycogen synthase kinase 3 and p70 S6 kinase as main tau kinases and protein phosphatase 2A as the main tau phosphatase involved in the formation of these processes. Activities of these targets are implicated by Abeta peptide, the major component of another hallmark in Alzheimer's disease brains, senile plaques. To prevent the clinical progression of neurodegeneration, a combination strategy is suggested to target both senile plaques with immunization and neurofibrillary tangles with drugs to prevent the synthesis and phosphorylation of tau.

  19. Charting the molecular network of the drug target Bcr-Abl

    PubMed Central

    Brehme, Marc; Hantschel, Oliver; Colinge, Jacques; Kaupe, Ines; Planyavsky, Melanie; Köcher, Thomas; Mechtler, Karl; Bennett, Keiryn L.; Superti-Furga, Giulio

    2009-01-01

    The tyrosine kinase Bcr-Abl causes chronic myeloid leukemia and is the cognate target of tyrosine kinase inhibitors like imatinib. We have charted the protein–protein interaction network of Bcr-Abl by a 2-pronged approach. Using a monoclonal antibody we have first purified endogenous Bcr-Abl protein complexes from the CML K562 cell line and characterized the set of most tightly-associated interactors by MS. Nine interactors were subsequently subjected to tandem affinity purifications/MS analysis to obtain a molecular interaction network of some hundred cellular proteins. The resulting network revealed a high degree of interconnection of 7 “core” components around Bcr-Abl (Grb2, Shc1, Crk-I, c-Cbl, p85, Sts-1, and SHIP-2), and their links to different signaling pathways. Quantitative proteomics analysis showed that tyrosine kinase inhibitors lead to a disruption of this network. Certain components still appear to interact with Bcr-Abl in a phosphotyrosine-independent manner. We propose that Bcr-Abl and other drug targets, rather than being considered as single polypeptides, can be considered as complex protein assemblies that remodel upon drug action. PMID:19380743

  20. Capture Efficiency of Biocompatible Magnetic Nanoparticles in Arterial Flow: A Computer Simulation for Magnetic Drug Targeting

    NASA Astrophysics Data System (ADS)

    Lunnoo, Thodsaphon; Puangmali, Theerapong

    2015-10-01

    The primary limitation of magnetic drug targeting (MDT) relates to the strength of an external magnetic field which decreases with increasing distance. Small nanoparticles (NPs) displaying superparamagnetic behaviour are also required in order to reduce embolization in the blood vessel. The small NPs, however, make it difficult to vector NPs and keep them in the desired location. The aims of this work were to investigate parameters influencing the capture efficiency of the drug carriers in mimicked arterial flow. In this work, we computationally modelled and evaluated capture efficiency in MDT with COMSOL Multiphysics 4.4. The studied parameters were (i) magnetic nanoparticle size, (ii) three classes of magnetic cores (Fe3O4, Fe2O3, and Fe), and (iii) the thickness of biocompatible coating materials (Au, SiO2, and PEG). It was found that the capture efficiency of small particles decreased with decreasing size and was less than 5 % for magnetic particles in the superparamagnetic regime. The thickness of non-magnetic coating materials did not significantly influence the capture efficiency of MDT. It was difficult to capture small drug carriers ( D<200 nm) in the arterial flow. We suggest that the MDT with high-capture efficiency can be obtained in small vessels and low-blood velocities such as micro-capillary vessels.

  1. Monoglyceride lipase as a drug target: At the crossroads of arachidonic acid metabolism and endocannabinoid signaling.

    PubMed

    Grabner, Gernot F; Zimmermann, Robert; Schicho, Rudolf; Taschler, Ulrike

    2017-07-01

    Monoglyerides (MGs) are short-lived, intermediary lipids deriving from the degradation of phospho- and neutral lipids, and monoglyceride lipase (MGL), also designated as monoacylglycerol lipase (MAGL), is the major enzyme catalyzing the hydrolysis of MGs into glycerol and fatty acids. This distinct function enables MGL to regulate a number of physiological and pathophysiological processes since both MGs and fatty acids can act as signaling lipids or precursors thereof. The most prominent MG species acting as signaling lipid is 2-arachidonoyl glycerol (2-AG) which is the most abundant endogenous agonist of cannabinoid receptors in the body. Importantly, recent observations demonstrate that 2-AG represents a quantitatively important source for arachidonic acid, the precursor of prostaglandins and other inflammatory mediators. Accordingly, MGL-mediated 2-AG degradation affects lipid signaling by cannabinoid receptor-dependent and independent mechanisms. Recent genetic and pharmacological studies gave important insights into MGL's role in (patho-)physiological processes, and the enzyme is now considered as a promising drug target for a number of disorders including cancer, neurodegenerative and inflammatory diseases. This review summarizes the basics of MG (2-AG) metabolism and provides an overview on the therapeutic potential of MGL. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Quadruplex DNA: a promising drug target for the medicinal inorganic chemist.

    PubMed

    Ralph, Stephen F

    2011-01-01

    Compounds that can bind to and stabilize quadruplex DNA structures in telomeres, or induce formation of such structures from ssDNA, represent an attractive general approach to the treatment of cancer. Until recently most effort in this area has been directed towards the synthesis of organic compounds for this purpose. More recently there has been growing recognition that metal complexes offer a number of potential advantages for the preparation of lead complexes that bind with high affinity and selectivity for quadruplex DNA. This review seeks to discuss the work that has been reported in this area to date. While most early studies focused on metal complexes of porphyrin ligands, during the past 4 years there has been a dramatic increase in the number of papers in the literature examining the potential of mononuclear complexes of a variety of other ligands, particularly Schiff base ligands and those based on phenanthroline, as quadruplex DNA binders and telomerase inhibitors. In addition, there has been growing interest in exploiting supramolecular chemistry to prepare novel multinuclear complexes that bind to this new drug target.

  3. Insights on how the Mycobacterium tuberculosis heme uptake pathway can be used as a drug target

    PubMed Central

    Owens, Cedric P; Chim, Nicholas; Goulding, Celia W

    2013-01-01

    Mycobacterium tuberculosis (Mtb) acquires non-heme iron through salicylate-derived siderophores termed mycobactins whereas heme iron is obtained through a cascade of heme uptake proteins. Three proteins are proposed to mediate Mtb heme iron uptake, a secreted heme transporter (Rv0203), and MmpL3 and MmpL11, which are potential transmembrane heme transfer proteins. Furthermore, MhuD, a cytoplasmic heme-degrading enzyme, has been identified. Rv0203, MmpL3 and MmpL11 are mycobacteria-specific proteins, making them excellent drug targets. Importantly, MmpL3, a necessary protein, has also been implicated in trehalose monomycolate export. Recent drug-discovery efforts revealed that MmpL3 is the target of several compounds with antimycobacterial activity. Inhibition of the Mtb heme uptake pathway has yet to be explored. We propose that inhibitor design could focus on heme analogs, with the goal of blocking specific steps of this pathway. In addition, heme uptake could be hijacked as a method of importing drugs into the mycobacterial cytosol. PMID:23919550

  4. Detection of real-time dynamics of drug-target interactions by ultralong nanowalls.

    PubMed

    Menzel, Andreas; Gübeli, Raphael J; Güder, Firat; Weber, Wilfried; Zacharias, Margit

    2013-11-07

    Detecting drug-target interactions in real-time is a powerful approach for drug discovery and analytics. We show here for the first time the ultra fast electrical real-time detection and quantification of antibiotics using a novel biohybrid nanosensor. The biomolecular sensing is performed on ultralong (mm range) high aspect ratio nanowall (50 nm width) surfaces functionalized with operator DNA tetO which is specifically bound by the sensor protein TetR. This sensor protein is released from the operator DNA in a dose dependent manner by exposing the device functionalized with this bound DNA-protein complex to tetracycline antibiotics. As a result, the electrical conductance is accordingly modulated by these surface net charge changes. The switching mechanism of sensor proteins attached at the functionalized surfaces and releasing them again by antibiotics is demonstrated. With the here presented device the detection limit is below the limits of prevailing detection methods. Moreover, the study is extended to detect antibiotic residues in spiked organic milk from cows far below the maximum residual level of the European Union. In spiked milk samples a detection limit for tetracycline concentrations in the 100 fM level was achieved. The nanowall devices are fabricated by atomic layer deposition-based spacer lithography on full wafer scale which is a simple approach capable for mass production.

  5. Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets

    PubMed Central

    Vinayagam, Arunachalam; Gibson, Travis E.; Lee, Ho-Joon; Yilmazel, Bahar; Roesel, Charles; Hu, Yanhui; Kwon, Young; Sharma, Amitabh; Liu, Yang-Yu; Perrimon, Norbert; Barabási, Albert-László

    2016-01-01

    The protein–protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as “indispensable,” “neutral,” or “dispensable,” which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network’s control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets. PMID:27091990

  6. Medicinal chemistry based approaches and nanotechnology-based systems to improve CNS drug targeting and delivery.

    PubMed

    Vlieghe, Patrick; Khrestchatisky, Michel

    2013-05-01

    The central nervous system (CNS) is protected by various barriers, which regulate nervous tissue homeostasis and control the selective and specific uptake, efflux, and metabolism of endogenous and exogenous molecules. Among these barriers is the blood-brain barrier (BBB), a physical and physiological barrier that filters very efficiently and selectively the entry of compounds from the blood to the brain and protects nervous tissue from harmful substances and infectious agents present in the bloodstream. The BBB also prevents the entry of potential drugs. As a result, various drug targeting and delivery strategies are currently being developed to enhance the transport of drugs from the blood to the brain. Following a general introduction, we briefly overview in this review article the fundamental physiological properties of the BBB. Then, we describe current strategies to bypass the BBB (i.e., invasive methods, alternative approaches, and temporary opening) and to cross it (i.e., noninvasive approaches). This section is followed by a chapter addressing the chemical and technological solutions developed to cross the BBB. A special emphasis is given to prodrug-targeting approaches and targeted nanotechnology-based systems, two promising strategies for BBB targeting and delivery of drugs to the brain.

  7. [Collaborative use of neutron and X-ray for determination of drug target proteins].

    PubMed

    Kuroki, Ryota; Tamada, Taro; Kurihara, Kazuo; Ohhara, Takashi; Adachi, Motoyasu

    2010-05-01

    Crystallography enables us to obtain accurate atomic positions within proteins. High resolution X-ray crystallography provides information for most of the atoms comprising a protein, with the exception of hydrogens. Neutron diffraction data can provide information of the location of hydrogen atoms, and is complementary to the structural information determined by X-ray crystallography. Here, we show the recent result of the structural determination of drug-target proteins, porcine pancreatic elastase and human immuno-deficiency virus type-1 protease by both X-ray and neutron diffraction. The structure of porcine pancreatic elastase with its potent inhibitor was determined to 0.94 A resolution by X-ray diffraction and 1.65 A resolution by neutron diffraction. The structure of HIV-PR with its potent inhibitor was also determined to 0.93 A resolution by X-ray diffraction and 1.9 A resolution by neutron diffraction. The ionization state and the location of hydrogen atoms of the catalytic residue in these enzymes were determined by neutron diffraction. Furthermore, collaborative use of both X-ray and neutron to identify the location of ambiguous hydrogen atoms will be shown.

  8. Oxidized macrophage migration inhibitory factor is a potential new tissue marker and drug target in cancer

    PubMed Central

    Schinagl, Alexander; Thiele, Michael; Douillard, Patrice; Völkel, Dirk; Kenner, Lukas; Kazemi, Zahra; Freissmuth, Michael; Scheiflinger, Friedrich; Kerschbaumer, Randolf J.

    2016-01-01

    Macrophage migration inhibitory factor (MIF) is a pleiotropic cytokine, which was shown to be upregulated in cancers and to exhibit tumor promoting properties. Unlike other cytokines, MIF is ubiquitously present in the circulation and tissue of healthy subjects. We recently described a previously unrecognized, disease-related isoform of MIF, designated oxMIF, which is present in the circulation of patients with different inflammatory diseases. In this article, we report that oxMIF is also linked to different solid tumors as it is specifically expressed in tumor tissue from patients with colorectal, pancreatic, ovarian and lung cancer. Furthermore, oxMIF can be specifically targeted by a subset of phage display-derived fully human, monoclonal anti-MIF antibodies (mAbs) that were shown to neutralize pro-tumorigenic activities of MIF in vivo. We further demonstrate that anti-oxMIF mAbs sensitize human cancer cell lines (LNCaP, PC3, A2780 and A2780ADR) to the action of cytotoxic drugs (mitoxantrone, cisplatin and doxorubicin) in vitro and in an A2780 xenograft mouse model of ovarian cancer. We conclude that oxMIF is the disease related isoform of MIF in solid tumors and a potential new diagnostic marker and drug target in cancer. PMID:27636991

  9. Data on overlapping brain disorders and emerging drug targets in human Dopamine Receptors Interaction Network.

    PubMed

    Podder, Avijit; Latha, N

    2017-06-01

    Intercommunication of Dopamine Receptors (DRs) with their associate protein partners is crucial to maintain regular brain function in human. Majority of the brain disorders arise due to malfunctioning of such communication process. Hence, contributions of genetic factors, as well as phenotypic indications for various neurological and psychiatric disorders are often attributed as sharing in nature. In our earlier research article entitled "Human Dopamine Receptors Interaction Network (DRIN): a systems biology perspective on topology, stability and functionality of the network" (Podder et al., 2014) [1], we had depicted a holistic interaction map of human Dopamine Receptors. Given emphasis on the topological parameters, we had characterized the functionality along with the vulnerable properties of the network. In support of this, we hereby provide an additional data highlighting the genetic overlapping of various brain disorders in the network. The data indicates the sharing nature of disease genes for various neurological and psychiatric disorders in dopamine receptors connecting protein-protein interactions network. The data also indicates toward an alternative approach to prioritize proteins for overlapping brain disorders as valuable drug targets in the network.

  10. Heat shock protein 90 as a drug target: some like it hot.

    PubMed

    Banerji, Udai

    2009-01-01

    Heat shock protein 90 (HSP90) is a ubiquitously expressed chaperone that is involved in the posttranslational folding and stability of proteins. Inhibition at the NH(2)-terminal ATP-binding site leads to the degradation of client proteins by the ubiquitin proteasome pathway. Inhibition of HSP90 leads to the degradation of known oncogenes, such as ERB-B2, BRAF, and BCR-ABL, leading to the combinatorial blockade of multiple signal transduction pathways, such as the RAS-RAF-mitogen-activated protein/extracellular signal-regulated kinase kinase-extracellular signal-regulated kinase and phosphatidylinositol 3-kinase pathways. Multiple structurally diverse HSP90 inhibitors are undergoing early clinical evaluation. The clinical focus of these drugs should be solid tumors, such as breast, prostate, and lung cancers, along with malignant melanoma, in addition to hematologic malignancies, such as chronic myeloid leukemia and multiple myeloma. HSP90 inhibitors can be used as single agents or in combination with other targeted treatments or conventional forms of treatment such as chemotherapy and radiotherapy. Clinical trials evaluating efficacy of these agents should include innovative designs to capture cytostasis evidenced by clinical nonprogression and enrichment of patient populations by molecular characterization. The results of clinical trials evaluating the efficacy of drugs targeting this exciting target are awaited.

  11. Microbial Peptidyl-Prolyl cis/trans Isomerases (PPIases): Virulence Factors and Potential Alternative Drug Targets

    PubMed Central

    2014-01-01

    SUMMARY Initially discovered in the context of immunomodulation, peptidyl-prolyl cis/trans isomerases (PPIases) were soon identified as enzymes catalyzing the rate-limiting protein folding step at peptidyl bonds preceding proline residues. Intense searches revealed that PPIases are a superfamily of proteins consisting of three structurally distinguishable families with representatives in every described species of prokaryote and eukaryote and, recently, even in some giant viruses. Despite the clear-cut enzymatic activity and ubiquitous distribution of PPIases, reports on solely PPIase-dependent biological roles remain scarce. Nevertheless, they have been found to be involved in a plethora of biological processes, such as gene expression, signal transduction, protein secretion, development, and tissue regeneration, underscoring their general importance. Hence, it is not surprising that PPIases have also been identified as virulence-associated proteins. The extent of contribution to virulence is highly variable and dependent on the pleiotropic roles of a single PPIase in the respective pathogen. The main objective of this review is to discuss this variety in virulence-related bacterial and protozoan PPIases as well as the involvement of host PPIases in infectious processes. Moreover, a special focus is given to Legionella pneumophila macrophage infectivity potentiator (Mip) and Mip-like PPIases of other pathogens, as the best-characterized virulence-related representatives of this family. Finally, the potential of PPIases as alternative drug targets and first tangible results are highlighted. PMID:25184565

  12. Application of linear gauss pseudospectral method in model predictive control

    NASA Astrophysics Data System (ADS)

    Yang, Liang; Zhou, Hao; Chen, Wanchun

    2014-03-01

    This paper presents a model predictive control(MPC) method aimed at solving the nonlinear optimal control problem with hard terminal constraints and quadratic performance index. The method combines the philosophies of the nonlinear approximation model predictive control, linear quadrature optimal control and Gauss Pseudospectral method. The current control is obtained by successively solving linear algebraic equations transferred from the original problem via linearization and the Gauss Pseudospectral method. It is not only of high computational efficiency since it does not need to solve nonlinear programming problem, but also of high accuracy though there are a few discrete points. Therefore, this method is suitable for on-board applications. A design of terminal impact with a specified direction is carried out to evaluate the performance of this method. Augmented PN guidance law in the three-dimensional coordinate system is applied to produce the initial guess. And various cases for target with straight-line movements are employed to demonstrate the applicability in different impact angles. Moreover, performance of the proposed method is also assessed by comparison with other guidance laws. Simulation results indicate that this method is not only of high computational efficiency and accuracy, but also applicable in the framework of guidance design.

  13. Applications of Machine Learning in Cancer Prediction and Prognosis

    PubMed Central

    Cruz, Joseph A.; Wishart, David S.

    2006-01-01

    Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression. PMID:19458758

  14. [Statistical prediction methods in violence risk assessment and its application].

    PubMed

    Liu, Yuan-Yuan; Hu, Jun-Mei; Yang, Min; Li, Xiao-Song

    2013-06-01

    It is an urgent global problem how to improve the violence risk assessment. As a necessary part of risk assessment, statistical methods have remarkable impacts and effects. In this study, the predicted methods in violence risk assessment from the point of statistics are reviewed. The application of Logistic regression as the sample of multivariate statistical model, decision tree model as the sample of data mining technique, and neural networks model as the sample of artificial intelligence technology are all reviewed. This study provides data in order to contribute the further research of violence risk assessment.

  15. Prediction of silicon-based layered structures for optoelectronic applications.

    PubMed

    Luo, Wei; Ma, Yanming; Gong, Xingao; Xiang, Hongjun

    2014-11-12

    A method based on the particle swarm optimization algorithm is presented to design quasi-two-dimensional materials. With this development, various single-layer and bilayer materials of C, Si, Ge, Sn, and Pb were predicted. A new Si bilayer structure is found to have a more favored energy than the previously widely accepted configuration. Both single-layer and bilayer Si materials have small band gaps, limiting their usages in optoelectronic applications. Hydrogenation has therefore been used to tune the electronic and optical properties of Si layers. We discover two hydrogenated materials of layered Si8H2 and Si6H2 possessing quasidirect band gaps of 0.75 and 1.59 eV, respectively. Their potential applications for light-emitting diode and photovoltaics are proposed and discussed. Our study opened up the possibility of hydrogenated Si layered materials as next-generation optoelectronic devices.

  16. Predictive Gyrokinetic Transport Simulations and Application of Synthetic Diagnostics

    NASA Astrophysics Data System (ADS)

    Candy, J.

    2009-11-01

    In this work we make use of the gyrokinetic transport solver TGYRO [1] to predict kinetic plasma profiles consistent with energy and particle fluxes in the DIII-D tokamak. TGYRO uses direct nonlinear and neoclassical fluxes calculated by the GYRO and NEO codes, respectively, to solve for global, self-consistent temperature and density profiles via Newton iteration. Previous work has shown that gyrokinetic simulation results for DIII-D discharge 128913 match experimental data rather well in the plasma core, but with a discrepancy in both fluxes and fluctuation levels emerging closer to the edge (r/a > 0.8). The present work will expand on previous results by generating model predictions across the entire plasma core, rather than at isolated test radii. We show that TGYRO predicts temperature and density profiles in good agreement with experimental observations which simultaneously yield near-exact (to within experimental uncertainties) agreement with power balance calculations of the particle and energy fluxes for r/a <=0.8. Moreover, we use recently developed synthetic diagnostic algorithms [2] to show that TGYRO also predicts density and electron temperature fluctuation levels in close agreement with experimental measurements across the simulated plasma volume. 8pt [1] J. Candy, C. Holland, R.E. Waltz, M.R. Fahey, and E. Belli, ``Tokamak profile prediction using direct gyrokinetic and neoclassical simulation," Phys. Plasmas 16, 060704 (2009). [2] C. Holland, A.E. White, G.R. McKee, M.W. Shafer, J. Candy, R.E. Waltz, L. Schmitz, and G.R. Tynan, ``Implementation and application of two synthetic diagnostics for validating simulations of core tokamak turbulence," Phys. Plasmas 16, 052301 (2009).

  17. Case for predictable media quality in networked multimedia applications

    NASA Astrophysics Data System (ADS)

    Bouch, Anna; Sasse, M. A.

    1999-12-01

    Shared networks are now able to support a wide range of applications, including real-time multimedia. This has led the networking community to consider a wider range of network Quality of Service (QoS) guarantees and pricing schemes. To date, the QoS required by networked multimedia applications has been described in terms of technical parameters. We argue that, in order to maximize the realized quality of any network, the QoS requirements of networked multimedia applications should be based on the value that users ascribe to the media quality they receive in the content of a particular task. This argument is supported with results from a set of studies in which users' perceptions of media quality was investigated for a listening task. We found that users' expectancies of quality directly influenced their ratings: low expectancies produce higher ratings for the same level of objective quality-- provided that quality is predictable. In conclusion, we outline the implications of our studies for the design of networked multimedia applications and the network services that support them.

  18. Transient receptor potential (TRP) channels as drug targets for diseases of the digestive system

    PubMed Central

    Holzer, Peter

    2011-01-01

    Approximately 20 of the 30 mammalian transient receptor potential (TRP) channel subunits are expressed by specific neurons and cells within the alimentary canal. They subserve important roles in taste, chemesthesis, mechanosensation, pain and hyperalgesia and contribute to the regulation of gastrointestinal motility, absorptive and secretory processes, blood flow, and mucosal homeostasis. In a cellular perspective, TRP channels operate either as primary detectors of chemical and physical stimuli, as secondary transducers of ionotropic or metabotropic receptors, or as ion transport channels. The polymodal sensory function of TRPA1, TRPM5, TRPM8, TRPP2, TRPV1, TRPV3 and TRPV4 enables the digestive system to survey its physical and chemical environment, which is relevant to all processes of digestion. TRPV5 and TRPV6 as well as TRPM6 and TRPM7 contribute to the absorption of Ca2+ and Mg2+, respectively. TRPM7 participates in intestinal pacemaker activity, and TRPC4 transduces muscarinic acetylcholine receptor activation to smooth muscle contraction. Changes in TRP channel expression or function are associated with a variety of diseases/disorders of the digestive system, notably gastro-esophageal reflux disease, inflammatory bowel disease, pain and hyperalgesia in heartburn, functional dyspepsia and irritable bowel syndrome, cholera, hypomagnesemia with secondary hypocalcemia, infantile hypertrophic pyloric stenosis, esophageal, gastrointestinal and pancreatic cancer, and polycystic liver disease. These implications identify TRP channels as promising drug targets for the management of a number of gastrointestinal pathologies. As a result, major efforts are put into the development of selective TRP channel agonists and antagonists and the assessment of their therapeutic potential. PMID:21420431

  19. FK506-Binding Protein 10, a Potential Novel Drug Target for Idiopathic Pulmonary Fibrosis

    PubMed Central

    Staab-Weijnitz, Claudia A.; Fernandez, Isis E.; Knüppel, Larissa; Maul, Julia; Heinzelmann, Katharina; Juan-Guardela, Brenda M.; Hennen, Elisabeth; Preissler, Gerhard; Winter, Hauke; Neurohr, Claus; Hatz, Rudolf; Lindner, Michael; Behr, Jürgen; Kaminski, Naftali

    2015-01-01

    secretion by phLF. Conclusions: FKBP10 might be a novel drug target for IPF. PMID:26039104

  20. Sphingolipids Are Dual Specific Drug Targets for the Management of Pulmonary Infections: Perspective

    PubMed Central

    Sharma, Lalita; Prakash, Hridayesh

    2017-01-01

    bactericidal potential in macrophages for the control of TB. In this review, we have discussed and emphasized that sphingolipids may represent effective novel, yet dual specific drug targets for controlling pulmonary infections. PMID:28400772

  1. Plasmodium falciparum glutamate dehydrogenase a is dispensable and not a drug target during erythrocytic development

    PubMed Central

    2011-01-01

    Background Plasmodium falciparum contains three genes encoding potential glutamate dehydrogenases. The protein encoded by gdha has previously been biochemically and structurally characterized. It was suggested that it is important for the supply of reducing equivalents during intra-erythrocytic development of Plasmodium and, therefore, a suitable drug target. Methods The gene encoding the NADP(H)-dependent GDHa has been disrupted by reverse genetics in P. falciparum and the effect on the antioxidant and metabolic capacities of the resulting mutant parasites was investigated. Results No growth defect under low and elevated oxygen tension, no up- or down-regulation of a number of antioxidant and NADP(H)-generating proteins or mRNAs and no increased levels of GSH were detected in the D10Δgdha parasite lines. Further, the fate of the carbon skeleton of [13C] labelled glutamine was assessed by metabolomic studies, revealing no differences in the labelling of α-ketoglutarate and other TCA pathway intermediates between wild type and mutant parasites. Conclusions First, the data support the conclusion that D10Δgdha parasites are not experiencing enhanced oxidative stress and that GDHa function may not be the provision of NADP(H) for reductive reactions. Second, the results imply that the cytosolic, NADP(H)-dependent GDHa protein is not involved in the oxidative deamination of glutamate but that the protein may play a role in ammonia assimilation as has been described for other NADP(H)-dependent GDH from plants and fungi. The lack of an obvious phenotype in the absence of GDHa may point to a regulatory role of the protein providing glutamate (as nitrogen storage molecule) in situations where the parasites experience a limiting supply of carbon sources and, therefore, under in vitro conditions the enzyme is unlikely to be of significant importance. The data imply that the protein is not a suitable target for future drug development against intra-erythrocytic parasite

  2. Understanding of known drug-target interactions in the catalytic pocket of neuraminidase subtype N1.

    PubMed

    Malaisree, Maturos; Rungrotmongkol, Thanyada; Decha, Panita; Intharathep, Pathumwadee; Aruksakunwong, Ornjira; Hannongbua, Supot

    2008-06-01

    To provide detailed information and insight into the drug-target interaction, structure, solvation, and dynamic and thermodynamic properties, the three known-neuraminidase inhibitors-oseltamivir (OTV), zanamivir (ZNV), and peramivir (PRV)-embedded in the catalytic site of neuraminidase (NA) subtype N1 were studied using molecular dynamics simulations. In terms of ligand conformation, there were major differences in the structures of the guanidinium and the bulky groups. The atoms of the guanidinium group of PRV were observed to form many more hydrogen bonds with the surrounded residues and were much less solvated by water molecules, in comparison with the other two inhibitors. Consequently, D151 lying on the 150-loop (residues 147-152) of group-1 neuraminidase (N1, N4, N5, and N8) was considerably shifted to form direct hydrogen bonds with the --OH group of the PRV, which was located rather far from the 150-loop. For the bulky group, direct hydrogen bonds were detected only between the hydrophilic side chain of ZNV and residues R224, E276, and E277 of N1 with rather weak binding, 20-70% occupation. This is not the case for OTV and PRV, in which flexibility and steric effects due to the hydrophobic side chain lead to the rearrangement of the surrounded residues, that is, the negatively charged side chain of E276 was shifted and rotated to form hydrogen bonds with the positively charged moiety of R224. Taking into account all the ligand-enzyme interaction data, the gas phase MM interaction energy of -282.2 kcal/mol as well as the binding free energy (DeltaG(binding)) of -227.4 kcal/mol for the PRV-N1 are significantly lower than those of the other inhibitors. The ordering of DeltaG(binding) of PRV < ZNV < OTV agrees well with the ordering of experimental IC(50) value. (c) 2008 Wiley-Liss, Inc.

  3. Free fatty acids-sensing G protein-coupled receptors in drug targeting and therapeutics.

    PubMed

    Yonezawa, Tomo; Kurata, Riho; Yoshida, Kaori; Murayama, Masanori A; Cui, Xiaofeng; Hasegawa, Akihiko

    2013-01-01

    G protein-coupled receptor (GPCR) (also known as seven-transmembrane domain receptor) superfamily represents the largest protein family in the human genome. These receptors respond to various physiological ligands such as photons, odors, pheromones, hormones, ions, and small molecules including amines, amino acids to large peptides and steroids. Thus, GPCRs are involved in many diseases and the target of around half of all conventional drugs. The physiological roles of free fatty acids (FFAs), in particular, long-chain FFAs, are important for the development of many metabolic disease including obesity, diabetes, and atherosclerosis. In the past half decade, deorphanization of several GPCRs has revealed that GPR40, GPR41, GPR43, GPR84 and GPR120 sense concentration of extracellular FFAs with various carbon chain lengths. GPR40 and GPR120 are activated by medium- and long-chain FFAs. GPR84 is activated by medium- chain, but not long-chain, FFAs. GPR41 and GPR43 are activated by short-chain FFAs. GPR40 is highly expressed in pancreatic beta cells and plays a crucial role in FFAs-induced insulin secretion. GPR120 is mainly expressed in enteroendocrine cells and plays an important role for FFAs-induced glucagon-like peptide-1. GPR43 is abundant in leukocytes and adipose tissue, whilst GPR41 is highly expressed in adipose tissue, the pancreas and leukocytes. GPR84 is expressed in leukocytes and monocyte/macrophage. This review aims to shed light on the physiological roles and development of drugs targeting these receptors.

  4. Discovery of novel vaccine candidates and drug targets against visceral leishmaniasis using proteomics and transcriptomics.

    PubMed

    Kumari, Shraddha; Kumar, Awanish; Samant, Mukesh; Singh, Neeloo; Dube, Anuradha

    2008-11-01

    Among the three clinical forms (cutaneous, mucosal and visceral) of leishmaniasis visceral (VL) one is the most devastating type caused by the invasion of the reticuloendothelial system of human by Leishmania donovani, L. infantum and L. chagasi. India and Sudan account for about half the world's burden of VL. Current control strategy is based on chemotherapy, which is difficult to administer, expensive and becoming ineffective due to the emergence of drug resistance. An understanding of resistance mechanism(s) operating in clinical isolates might provide additional leads for the development of new drugs. Further, due to the lack of fully effective treatment the search for novel immune targets is also needed. So far, no vaccine exists for VL despite indications of naturally developing immunity. Therefore, an urgent need for new and effective leishmanicidal agents and for this identification of novel drug and vaccine targets is imperative. The availability of the complete genome sequence of Leishmania has revolutionised many areas of leishmanial research and facilitated functional genomic studies as well as provided a wide range of novel targets for drug designing. Most notably, proteomics and transcriptomics have become important tools in gaining increased understanding of the biology of Leishmania to be explored on a global scale, thus accelerating the pace of discovery of vaccine/drug targets. In addition, these approaches provide the information regarding genes and proteins that are expressed and under which conditions. This review provides a comprehensive view about those proteins/genes identified using proteomics and transcriptomic tools for the development of vaccine/drug against VL.

  5. A tale of two drug targets: the evolutionary history of BACE1 and BACE2

    PubMed Central

    Southan, Christopher; Hancock, John M.

    2013-01-01

    The beta amyloid (APP) cleaving enzyme (BACE1) has been a drug target for Alzheimer's Disease (AD) since 1999 with lead inhibitors now entering clinical trials. In 2011, the paralog, BACE2, became a new target for type II diabetes (T2DM) having been identified as a TMEM27 secretase regulating pancreatic β cell function. However, the normal roles of both enzymes are unclear. This study outlines their evolutionary history and new opportunities for functional genomics. We identified 30 homologs (UrBACEs) in basal phyla including Placozoans, Cnidarians, Choanoflagellates, Porifera, Echinoderms, Annelids, Mollusks and Ascidians (but not Ecdysozoans). UrBACEs are predominantly single copy, show 35–45% protein sequence identity with mammalian BACE1, are ~100 residues longer than cathepsin paralogs with an aspartyl protease domain flanked by a signal peptide and a C-terminal transmembrane domain. While multiple paralogs in Trichoplax and Monosiga pre-date the nervous system, duplication of the UrBACE in fish gave rise to BACE1 and BACE2 in the vertebrate lineage. The latter evolved more rapidly as the former maintained the emergent neuronal role. In mammals, Ka/Ks for BACE2 is higher than BACE1 but low ratios for both suggest purifying selection. The 5' exons show higher Ka/Ks than the catalytic section. Model organism genomes show the absence of certain BACE human substrates when the UrBACE is present. Experiments could thus reveal undiscovered substrates and roles. The human protease double-target status means that evolutionary trajectories and functional shifts associated with different substrates will have implications for the development of clinical candidates for both AD and T2DM. A rational basis for inhibition specificity ratios and assessing target-related side effects will be facilitated by a more complete picture of BACE1 and BACE2 functions informed by their evolutionary context. PMID:24381583

  6. Molecular and biochemical characterization of methionine aminopeptidase of Babesia bovis as a potent drug target.

    PubMed

    Munkhjargal, Tserendorj; Ishizaki, Takahiro; Guswanto, Azirwan; Takemae, Hitoshi; Yokoyama, Naoaki; Igarashi, Ikuo

    2016-05-15

    Aminopeptidases are increasingly being investigated as therapeutic targets in various diseases. In this study, we cloned, expressed, and biochemically characterized a member of the methionine aminopeptidase (MAP) family from Babesia bovis (B. bovis) to develop a potential molecular drug target. Recombinant B. bovis MAP (rBvMAP) was expressed in Escherichia coli (E. coli) as a glutathione S-transferase (GST)-fusion protein, and we found that it was antigenic. An antiserum against the rBvMAP protein was generated in mice, and then a native B. bovis MAP was identified in B. bovis by Western blot assay. Further, an immunolocalization assay showed that MAP is present in the cytoplasm of the B. bovis merozoite. Analysis of the biochemical properties of rBvMAP revealed that it was enzymatically active, with optimum activity at pH 7.5. Enhanced enzymatic activity was observed in the presence of divalent manganese cations and was effectively inhibited by a metal chelator, ethylenediaminetetraacetic acid (EDTA). Moreover, the enzymatic activity of BvMAP was inhibited by amastatin and bestatin as inhibitors of MAP (MAPi) in a dose-dependent manner. Importantly, MAPi was also found to significantly inhibit the growth of Babesia parasites both in vitro and in vivo; additionally, they induced high levels of cytokines and immunoglobulin (IgG) titers in the host. Therefore, our results suggest that BvMAP is a molecular target of amastatin and bestatin, and those inhibitors may be drug candidates for the treatment of babesiosis, though more studies are required to confirm this.

  7. Identification of New Drug Targets and Resistance Mechanisms in Mycobacterium tuberculosis

    PubMed Central

    Ioerger, Thomas R.; O’Malley, Theresa; Liao, Reiling; Guinn, Kristine M.; Hickey, Mark J.; Mohaideen, Nilofar; Murphy, Kenan C.; Boshoff, Helena I. M.; Mizrahi, Valerie; Rubin, Eric J.; Sassetti, Christopher M.; Barry, Clifton E.; Sherman, David R.; Parish, Tanya; Sacchettini, James C.

    2013-01-01

    Identification of new drug targets is vital for the advancement of drug discovery against Mycobacterium tuberculosis, especially given the increase of resistance worldwide to first- and second-line drugs. Because traditional target-based screening has largely proven unsuccessful for antibiotic discovery, we have developed a scalable platform for target identification in M. tuberculosis that is based on whole-cell screening, coupled with whole-genome sequencing of resistant mutants and recombineering to confirm. The method yields targets paired with whole-cell active compounds, which can serve as novel scaffolds for drug development, molecular tools for validation, and/or as ligands for co-crystallization. It may also reveal other information about mechanisms of action, such as activation or efflux. Using this method, we identified resistance-linked genes for eight compounds with anti-tubercular activity. Four of the genes have previously been shown to be essential: AspS, aspartyl-tRNA synthetase, Pks13, a polyketide synthase involved in mycolic acid biosynthesis, MmpL3, a membrane transporter, and EccB3, a component of the ESX-3 type VII secretion system. AspS and Pks13 represent novel targets in protein translation and cell-wall biosynthesis. Both MmpL3 and EccB3 are involved in membrane transport. Pks13, AspS, and EccB3 represent novel candidates not targeted by existing TB drugs, and the availability of whole-cell active inhibitors greatly increases their potential for drug discovery. PMID:24086479

  8. Voltage-Gated Proton Channels as Novel Drug Targets: From NADPH Oxidase Regulation to Sperm Biology

    PubMed Central

    Demaurex, Nicolas; Krause, Karl-Heinz

    2015-01-01

    Abstract Significance: Voltage-gated proton channels are increasingly implicated in cellular proton homeostasis. Proton currents were originally identified in snail neurons less than 40 years ago, and subsequently shown to play an important auxiliary role in the functioning of reactive oxygen species (ROS)-generating nicotinamide adenine dinucleotide phosphate (NADPH) oxidases. Molecular identification of voltage-gated proton channels was achieved less than 10 years ago. Interestingly, so far, only one gene coding for voltage-gated proton channels has been identified, namely hydrogen voltage-gated channel 1 (HVCN1), which codes for the HV1 proton channel protein. Over the last years, the first picture of putative physiological functions of HV1 has been emerging. Recent Advances: The best-studied role remains charge and pH compensation during the respiratory burst of the phagocyte NADPH oxidase (NOX). Strong evidence for a role of HV1 is also emerging in sperm biology, but the relationship with the sperm NOX5 remains unclear. Probably in many instances, HV1 functions independently of NOX: for example in snail neurons, basophils, osteoclasts, and cancer cells. Critical Issues: Generally, ion channels are good drug targets; however, this feature has so far not been exploited for HV1, and hitherto no inhibitors compatible with clinical use exist. However, there are emerging indications for HV1 inhibitors, ranging from diseases with a strong activation of the phagocyte NOX (e.g., stroke) to infertility, osteoporosis, and cancer. Future Directions: Clinically useful HV1-active drugs should be developed and might become interesting drugs of the future. Antioxid. Redox Signal. 23, 490–513. PMID:24483328

  9. The α7 nicotinic acetylcholine receptor complex: one, two or multiple drug targets?

    PubMed

    Thomsen, Morten S; Mikkelsen, Jens D

    2012-05-01

    The α7 nicotinic acetylcholine receptor (nAChR) is a promising drug target for a number of diseases ranging from schizophrenia and Alzheimer's disease to chronic pain and inflammatory diseases. Focusing on the central nervous system, we describe how endogenous and experimental compounds and proteins regulate expression and function of the α7 nAChR. Drug development efforts have hitherto focused on direct manipulation of the α7 nAChR, but it is still not clear, whether agonism/antagonism or allosteric modulation is preferable as a potential drug therapy. In addition, the action of such compounds in vivo is highly dependent on α7 nAChR-interacting proteins, such as RIC-3 and lynx1, which modulate expression and function of the receptor. These regulatory proteins are often not expressed in in vitro models used to study α7 nAChR function, and it is not known to what extent they are involved in diseases such as schizophrenia and Alzheimer's disease. Furthermore, α7 nAChR agonists and allosteric modulators differentially alter expression and functionality of the α7 nAChR with repeated administration, which suggests that there may be fundamentally different outcomes of long-term administration with these different types of compounds. Finally, we describe the special case of Aβ1-42 binding to the α7 nAChR, which may pose a unique challenge to drug development of α7 nAChR-specific ligands for Alzheimer's disease. Hopefully, a greater knowledge of the many factors influencing α7 nAChR function as well as an increasing pipeline of specific drug candidates, enabling a more subtle manipulation of α7 nAChR function, may facilitate α7 nAChR drug development efforts.

  10. A Critical Review of Pro-Cognitive Drug Targets in Psychosis: Convergence on Myelination and Inflammation

    PubMed Central

    Kroken, Rune A.; Løberg, Else-Marie; Drønen, Tore; Grüner, Renate; Hugdahl, Kenneth; Kompus, Kristiina; Skrede, Silje; Johnsen, Erik

    2014-01-01

    Antipsychotic drugs have thus far focused on dopaminergic antagonism at the D2 receptors, as counteracting the hyperdopaminergia in nigrostriatal and mesolimbic projections has been considered mandatory for the antipsychotic action of the drugs. Current drugs effectively target the positive symptoms of psychosis such as hallucinations and delusions in the majority of patients, whereas effect sizes are smaller for negative symptoms and cognitive dysfunctions. With the understanding that neurocognitive dysfunction associated with schizophrenia have a greater impact on functional outcome than the positive symptoms, the focus in pharmacotherapy for schizophrenia has shifted to the potential effect of future drugs on cognitive enhancement. A major obstacle is, however, that the biological underpinnings of cognitive dysfunction remain largely unknown. With the availability of increasingly sophisticated techniques in molecular biology and brain imaging, this situation is about to change with major advances being made in identifying the neuronal substrates underlying schizophrenia, and putative pro-cognitive drug targets may be revealed. In relation to cognitive effects, this review focuses on evidence from basic neuroscience and clinical studies, taking two separate perspectives. One perspective is the identification of previously under-recognized treatment targets for existing antipsychotic drugs, including myelination and mediators of inflammation. A second perspective is the development of new drugs or novel treatment targets for well-known drugs, which act on recently discovered treatment targets for cognitive enhancement, and which may complement the existing drugs. This might pave the way for personalized treatment regimens for patients with schizophrenia aimed at improved functional outcome. The review also aims at identifying major current constraints for pro-cognitive drug development for patients with schizophrenia. PMID:24550848

  11. Chemical and Genetic Validation of the Statin Drug Target to Treat the Helminth Disease, Schistosomiasis

    PubMed Central

    Rojo-Arreola, Liliana; Long, Thavy; Asarnow, Dan; Suzuki, Brian M.; Singh, Rahul; Caffrey, Conor R.

    2014-01-01

    The mevalonate pathway is essential in eukaryotes and responsible for a diversity of fundamental synthetic activities. 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) is the rate-limiting enzyme in the pathway and is targeted by the ubiquitous statin drugs to treat hypercholesterolemia. Independent reports have indicated the cidal effects of statins against the flatworm parasite, S. mansoni, and the possibility that SmHMGR is a useful drug target to develop new statin-based anti-schistosome therapies. For six commercially available statins, we demonstrate concentration- and time-dependent killing of immature (somule) and adult S. mansoni in vitro at sub-micromolar and micromolar concentrations, respectively. Cidal activity trends with statin lipophilicity whereby simvastatin and pravastatin are the most and least active, respectively. Worm death is preventable by excess mevalonate, the product of HMGR. Statin activity against somules was quantified both manually and automatically using a new, machine learning-based automated algorithm with congruent results. In addition, to chemical targeting, RNA interference (RNAi) of HMGR also kills somules in vitro and, again, lethality is blocked by excess mevalonate. Further, RNAi of HMGR of somules in vitro subsequently limits parasite survival in a mouse model of infection by up to 80%. Parasite death, either via statins or specific RNAi of HMGR, is associated with activation of apoptotic caspase activity. Together, our genetic and chemical data confirm that S. mansoni HMGR is an essential gene and the relevant target of statin drugs. We discuss our findings in context of a potential drug development program and the desired product profile for a new schistosomiasis drug. PMID:24489942

  12. Nucleoside transporter proteins as biomarkers of drug responsiveness and drug targets

    PubMed Central

    Pastor-Anglada, Marçal; Pérez-Torras, Sandra

    2015-01-01

    Nucleoside and nucleobase analogs are currently used in the treatment of solid tumors, lymphoproliferative diseases, viral infections such as hepatitis and AIDS, and some inflammatory diseases such as Crohn. Two gene families are implicated in the uptake of nucleosides and nucleoside analogs into cells, SCL28 and SLC29. The former encodes hCNT1, hCNT2, and hCNT3 proteins. They translocate nucleosides in a Na+ coupled manner with high affinity and some substrate selectivity, being hCNT1 and hCNT2 pyrimidine- and purine-preferring, respectively, and hCNT3 a broad selectivity transporter. SLC29 genes encode four members, being hENT1 and hENT2 the only two which are unequivocally implicated in the translocation of nucleosides and nucleobases (the latter mostly via hENT2) at the cell plasma membrane. Some nucleoside-derived drugs can also interact with and be translocated by members of the SLC22 gene family, particularly hOCT and hOAT proteins. Inter-individual differences in transporter function and perhaps, more importantly, altered expression associated with the disease itself might modulate the transporter profile of target cells, thereby determining drug bioavailability and action. Drug transporter pharmacology has been periodically reviewed. Thus, with this contribution we aim at providing a state-of-the-art overview of the clinical evidence generated so far supporting the concept that these membrane proteins can indeed be biomarkers suitable for diagnosis and/or prognosis. Last but not least, some of these transporter proteins can also be envisaged as drug targets, as long as they can show “transceptor” functions, in some cases related to their role as modulators of extracellular adenosine levels, thereby providing a functional link between P1 receptors and transporters. PMID:25713533

  13. Utilizing Chemical Genomics to Identify Cytochrome b as a Novel Drug Target for Chagas Disease

    PubMed Central

    Khare, Shilpi; Roach, Steven L.; Barnes, S. Whitney; Hoepfner, Dominic; Walker, John R.; Chatterjee, Arnab K.; Neitz, R. Jeffrey; Arkin, Michelle R.; McNamara, Case W.; Ballard, Jaime; Lai, Yin; Fu, Yue; Molteni, Valentina; Yeh, Vince; McKerrow, James H.; Glynne, Richard J.; Supek, Frantisek

    2015-01-01

    Unbiased phenotypic screens enable identification of small molecules that inhibit pathogen growth by unanticipated mechanisms. These small molecules can be used as starting points for drug discovery programs that target such mechanisms. A major challenge of the approach is the identification of the cellular targets. Here we report GNF7686, a small molecule inhibitor of Trypanosoma cruzi, the causative agent of Chagas disease, and identification of cytochrome b as its target. Following discovery of GNF7686 in a parasite growth inhibition high throughput screen, we were able to evolve a GNF7686-resistant culture of T. cruzi epimastigotes. Clones from this culture bore a mutation coding for a substitution of leucine by phenylalanine at amino acid position 197 in cytochrome b. Cytochrome b is a component of complex III (cytochrome bc1) in the mitochondrial electron transport chain and catalyzes the transfer of electrons from ubiquinol to cytochrome c by a mechanism that utilizes two distinct catalytic sites, QN and QP. The L197F mutation is located in the QN site and confers resistance to GNF7686 in both parasite cell growth and biochemical cytochrome b assays. Additionally, the mutant cytochrome b confers resistance to antimycin A, another QN site inhibitor, but not to strobilurin or myxothiazol, which target the QP site. GNF7686 represents a promising starting point for Chagas disease drug discovery as it potently inhibits growth of intracellular T. cruzi amastigotes with a half maximal effective concentration (EC50) of 0.15 µM, and is highly specific for T. cruzi cytochrome b. No effect on the mammalian respiratory chain or mammalian cell proliferation was observed with up to 25 µM of GNF7686. Our approach, which combines T. cruzi chemical genetics with biochemical target validation, can be broadly applied to the discovery of additional novel drug targets and drug leads for Chagas disease. PMID:26186534

  14. A tale of two drug targets: the evolutionary history of BACE1 and BACE2.

    PubMed

    Southan, Christopher; Hancock, John M

    2013-01-01

    The beta amyloid (APP) cleaving enzyme (BACE1) has been a drug target for Alzheimer's Disease (AD) since 1999 with lead inhibitors now entering clinical trials. In 2011, the paralog, BACE2, became a new target for type II diabetes (T2DM) having been identified as a TMEM27 secretase regulating pancreatic β cell function. However, the normal roles of both enzymes are unclear. This study outlines their evolutionary history and new opportunities for functional genomics. We identified 30 homologs (UrBACEs) in basal phyla including Placozoans, Cnidarians, Choanoflagellates, Porifera, Echinoderms, Annelids, Mollusks and Ascidians (but not Ecdysozoans). UrBACEs are predominantly single copy, show 35-45% protein sequence identity with mammalian BACE1, are ~100 residues longer than cathepsin paralogs with an aspartyl protease domain flanked by a signal peptide and a C-terminal transmembrane domain. While multiple paralogs in Trichoplax and Monosiga pre-date the nervous system, duplication of the UrBACE in fish gave rise to BACE1 and BACE2 in the vertebrate lineage. The latter evolved more rapidly as the former maintained the emergent neuronal role. In mammals, Ka/Ks for BACE2 is higher than BACE1 but low ratios for both suggest purifying selection. The 5' exons show higher Ka/Ks than the catalytic section. Model organism genomes show the absence of certain BACE human substrates when the UrBACE is present. Experiments could thus reveal undiscovered substrates and roles. The human protease double-target status means that evolutionary trajectories and functional shifts associated with different substrates will have implications for the development of clinical candidates for both AD and T2DM. A rational basis for inhibition specificity ratios and assessing target-related side effects will be facilitated by a more complete picture of BACE1 and BACE2 functions informed by their evolutionary context.

  15. Beta-secretase (BACE) as a drug target for Alzheimer's disease.

    PubMed

    Vassar, Robert

    2002-12-07

    Evidence suggests that the beta-amyloid peptide (Abeta) is central to the pathophysiology of Alzheimer's Disease (AD). Amyloid plaques, primarily composed of Abeta, progressively develop in the brains of AD patients, and mutations in three genes (APP, PS1, and PS2) cause early on-set familial AD (FAD) by increasing synthesis of the toxic Abeta42 peptide. Given the strong association between Abeta and AD, therapeutic strategies to lower the concentration of Abeta in the brain should prove beneficial for the treatment of AD. Abeta is a proteolytic product of the large TypeI membrane protein, amyloid precursor protein (APP). Two proteases, called beta- and gamma-secretase, cleave APP to generate the Abeta peptide. For over a decade, the molecular identities of these proteases were unknown. Recently, the gamma-secretase has been tentatively identified as the presenilin proteins, PS1 and PS2, and the beta-secretase has been shown to be the novel transmembrane aspartic protease, beta-site APP Cleaving Enzyme 1 (BACE1; also called Asp2 and memapsin2). BACE2, a novel protease homologous to BACE1, was also identified, and the two BACE enzymes define a new family of transmembrane aspartic proteases. BACE1 exhibits all the properties of the beta-secretase, and as the key enzyme that initiates the formation of Abeta, BACE1 is an attractive drug target for AD. This review discusses the identification and initial characterization of BACE1 and BACE2, and summarizes our current understanding of BACE1 post-translational processing and intracellular trafficking. Finally, recent studies of BACE1 knockout mice, the BACE1 X-ray structure, and implications for BACE1 drug development will be discussed.

  16. Characterization of parasite-specific indels and their proposed relevance for selective anthelminthic drug targeting.

    PubMed

    Wang, Qi; Heizer, Esley; Rosa, Bruce A; Wildman, Scott A; Janetka, James W; Mitreva, Makedonka

    2016-04-01

    Insertions and deletions (indels) are important sequence variants that are considered as phylogenetic markers that reflect evolutionary adaptations in different species. In an effort to systematically study indels specific to the phylum Nematoda and their structural impact on the proteins bearing them, we examined over 340,000 polypeptides from 21 nematode species spanning the phylum, compared them to non-nematodes and identified indels unique to nematode proteins in more than 3000 protein families. Examination of the amino acid composition revealed uneven usage of amino acids for insertions and deletions. The amino acid composition and cost, along with the secondary structure constitution of the indels, were analyzed in the context of their biological pathway associations. Species-specific indels could enable indel-based targeting for drug design in pathogens/parasites. Therefore, we screened the spatial locations of the indels in the parasite's protein 3D structures, determined the location of the indel and identified potential unique drug targeting sites. These indels could be confirmed by RNA-Seq data. Examples are presented illustrating the close proximity of some indels to established small-molecule binding pockets that can potentially facilitate selective targeting to the parasites and bypassing their host, thus reducing or eliminating the toxicity of the potential drugs. This study presents an approach for understanding the adaptation of pathogens/parasites at a molecular level, and outlines a strategy to identify such nematode-selective targets that remain essential to the organism. With further experimental characterization and validation, it opens a possible channel for the development of novel treatments with high target specificity, addressing both host toxicity and resistance concerns. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. Isolated swine heart ventricle perfusion model for implant assisted-magnetic drug targeting.

    PubMed

    Avilés, Misael O; Mangual, Jan O; Ebner, Armin D; Ritter, James A

    2008-09-01

    An isolated swine heart ventricle perfusion model was developed and used under physiologically relevant conditions to study implant assisted-magnetic drug targeting (IA-MDT). A stent coil was fabricated from a ferromagnetic SS 430 wire and used to capture 100-nm diameter magnetite particles that mimicked magnetic drug carrier particles (MDCPs). Four key cases were studied: (1) no stent and no magnet (control), (2) no magnet but with a stent, (3) no stent but with a magnet (traditional MDT), and (4) with a stent and a magnet (IA-MDT). When applied, the magnetic field was fixed at 0.125T. The performance of the system was based on the capture efficiency (CE) of the magnetite nanoparticles. The experiments done in the absence of the magnetic field showed minimal retention of any nanoparticles whether the stent was present or not. The experiments done in the presence of the magnetic field showed a statistically significant increase in the retention of the nanoparticles, with a marked difference between the traditional and IA-MDT cases. Compared to the control case, in one case there was nearly an 11-fold increase in CE for the IA-MDT case compared to only a threefold increase in CE for the traditional MDT case. This enhanced performance by the IA-MDT case was typical of all the experiments. Histology images of the cross-section of the coronary artery revealed that the nanoparticles were captured mainly in the vicinity of the stent. Overall, the IA-MDT results from this work with actual tissue were very encouraging and similar to those obtained from other non-tissue and theoretical studies; but, they did point to the need for further studies of IA-MDT.

  18. Characterization of parasite-specific indels and their proposed relevance for selective anthelminthic drug targeting

    PubMed Central

    Wang, Qi; Heizer, Esley; Rosa, Bruce A.; Wildman, Scott A.; Janetka, James W.; Mitreva, Makedonka

    2016-01-01

    Insertions and deletions (indels) are important sequence variants that are considered as phylogenetic markers that reflect evolutionary adaptations in different species. In an effort to systematically study indels specific to the phylum Nematoda and their structural impact on the proteins bearing them, we examined over 340,000 polypeptides from 21 nematode species spanning the phylum, compared them to non-nematodes and identified indels unique to nematode proteins in more than 3,000 protein families. Examination of the amino acid composition revealed uneven usage of amino acids for insertions and deletions. The amino acid composition and cost, along with the secondary structure constitution of the indels, were analyzed in the context of their biological pathway associations. Species-specific indels could enable indel-based targeting for drug design in pathogens/parasites. Therefore, we screened the spatial locations of the indels in the parasite’s protein 3D structures, determined the location of the indel and identified potential unique drug targeting sites. These indels could be confirmed by RNA-Seq data. Examples are presented that illustrate the close proximity of the indel to established small-molecule binding pockets that can potentially facilitate selective targeting to the parasites and bypassing their host, thus reducing or eliminating the toxicity of the potential drugs. The study presents an approach for understanding the adaptation of pathogens/parasites at a molecular level, and outlines a strategy to identify such nematode-selective targets that remain essential to the organism. With further experimental characterization and validation, it opens a possible channel for the development of novel treatments with high target specificity, addressing both host toxicity and resistance concerns. PMID:26829384

  19. Control of bladder function by peripheral nerves: avenues for novel drug targets.

    PubMed

    Fry, Christopher H; Ikeda, Youko; Harvey, Richard; Wu, Changhao; Sui, Gui-Ping

    2004-03-01

    , the cellular and molecular regulation of bladder fullness sensation and efferent transmitter release are becoming better understood and represent potential drug targets for the management of detrusor overactivity.

  20. The tuberculosis drug discovery and development pipeline and emerging drug targets.

    PubMed

    Mdluli, Khisimuzi; Kaneko, Takushi; Upton, Anna

    2015-01-29

    The recent accelerated approval for use in extensively drug-resistant and multidrug-resistant-tuberculosis (MDR-TB) of two first-in-class TB drugs, bedaquiline and delamanid, has reinvigorated the TB drug discovery and development field. However, although several promising clinical development programs are ongoing to evaluate new TB drugs and regimens, the number of novel series represented is few. The global early-development pipeline is also woefully thin. To have a chance of achieving the goal of better, shorter, safer TB drug regimens with utility against drug-sensitive and drug-resistant disease, a robust and diverse global TB drug discovery pipeline is key, including innovative approaches that make use of recently acquired knowledge on the biology of TB. Fortunately, drug discovery for TB has resurged in recent years, generating compounds with varying potential for progression into developable leads. In parallel, advances have been made in understanding TB pathogenesis. It is now possible to apply the lessons learned from recent TB hit generation efforts and newly validated TB drug targets to generate the next wave of TB drug leads. Use of currently underexploited sources of chemical matter and lead-optimization strategies may also improve the efficiency of future TB drug discovery. Novel TB drug regimens with shorter treatment durations must target all subpopulations of Mycobacterium tuberculosis existing in an infection, including those responsible for the protracted TB treatment duration. This review summarizes the current TB drug development pipeline and proposes strategies for generating improved hits and leads in the discovery phase that could help achieve this goal.

  1. Production evaluation of automated reticle defect printability prediction application

    NASA Astrophysics Data System (ADS)

    Howard, William B.; Pomeroy, Scott; Moses, Raphael; Thaler, Thomas

    2007-02-01

    The growing complexity of reticles and continual tightening of defect specifications causes the reticle defect disposition function to become increasingly difficult. No longer can defect specifications be distilled to a single number, nor can past simple classification rules be employed due to the effects of MEEF on actual printing behavior. The mask maker now requires lithography-based rules and capabilities for making these go/no-go decisions at the reticle inspection step. We have evaluated an automated system that predicts the lithographic significance of reticle defects using PROLITHTM technology. This printability prediction tool was evaluated and tested in a production environment using both standard test reticles and production samples in an advanced reticle manufacturing environment. Reference measurements on Zeiss AIMSTM systems were used to assess the accuracy of predicted results. The application, called the Automated Mask Defect Disposition System, or AMDD, models defective and non-defective test and reference images generated by a high-resolution inspection system. The results were calculated according to the wafer exposure conditions given at setup such that the reticle could be judged for its 'fitness-for-use' from a lithographic standpoint rather than from a simple physical measurement of the film materials. We present the methods and empirical results comparing 1D and 2D Intensity Difference Metrics (IDMs) with respect to AIMS and discuss the results of usability and productivity studies as they apply to manufacturing environments.

  2. Predictive modeling of addiction lapses in a mobile health application.

    PubMed

    Chih, Ming-Yuan; Patton, Timothy; McTavish, Fiona M; Isham, Andrew J; Judkins-Fisher, Chris L; Atwood, Amy K; Gustafson, David H

    2014-01-01

    The chronically relapsing nature of alcoholism leads to substantial personal, family, and societal costs. Addiction-comprehensive health enhancement support system (A-CHESS) is a smartphone application that aims to reduce relapse. To offer targeted support to patients who are at risk of lapses within the coming week, a Bayesian network model to predict such events was constructed using responses on 2,934 weekly surveys (called the Weekly Check-in) from 152 alcohol-dependent individuals who recently completed residential treatment. The Weekly Check-in is a self-monitoring service, provided in A-CHESS, to track patients' recovery progress. The model showed good predictability, with the area under receiver operating characteristic curve of 0.829 in the 10-fold cross-validation and 0.912 in the external validation. The sensitivity/specificity table assists the tradeoff decisions necessary to apply the model in practice. This study moves us closer to the goal of providing lapse prediction so that patients might receive more targeted and timely support.

  3. Predictive Modeling of Addiction Lapses in a Mobile Health Application

    PubMed Central

    Chih, Ming-Yuan; Patton, Timothy; McTavish, Fiona M.; Isham, Andrew; Judkins-Fisher, Chris L.; Atwood, Amy K.; Gustafson, David H.

    2013-01-01

    The chronically relapsing nature of alcoholism leads to substantial personal, family, and societal costs. Addiction-Comprehensive Health Enhancement Support System (A-CHESS) is a smartphone application that aims to reduce relapse. To offer targeted support to patients who are at risk of lapses within the coming week, a Bayesian network model to predict such events was constructed using responses on 2,934 weekly surveys (called the Weekly Check-in) from 152 alcohol-dependent individuals who recently completed residential treatment. The Weekly Check-in is a self-monitoring service, provided in A-CHESS, to track patients’ recovery progress. The model showed good predictability, with the area under receiver operating characteristic curve of 0.829 in the 10-fold cross-validation and 0.912 in the external validation. The sensitivity/specificity table assists the tradeoff decisions necessary to apply the model in practice. This study moves us closer to the goal of providing lapse prediction so that patients might receive more targeted and timely support. PMID:24035143

  4. Hydroplasticization of polymers: model predictions and application to emulsion polymers.

    PubMed

    Tsavalas, John G; Sundberg, Donald C

    2010-05-18

    The plasticization of a polymer by solvent has a dramatic impact on both its thermal and mechanical behavior. With increasing demand for zero volatile organic compound materials and coatings, water is often the sole solvent used both in the polymer synthesis and in formulation and application; latex colloids derived from emulsion polymerization are a good example. The impact of water on the glass transition temperature of a polymer thus becomes a critical physical property to predict. It has been shown here that in order to do so, one simply needs the dry state glass transition temperature (T(g)) of the (co)polymer, the T(g) of water, and the saturated weight fraction of water for the sample in question. Facile calculation of the later can be achieved using water sorption data and the group additivity method. With these readily available data, we show that a form of the Flory-Fox equation can be used to predict the hydroplasticized state of copolymers in exceptional agreement with direct experimental measurement. Furthermore, extending the prediction to include the impact of the degree of ionization for pH responsive components, only with extra knowledge of the pK(a), was also validated by experiment.

  5. Complete genome-wide screening and subtractive genomic approach revealed new virulence factors, potential drug targets against bio-war pathogen Brucella melitensis 16M.

    PubMed

    Pradeepkiran, Jangampalli Adi; Sainath, Sri Bhashyam; Kumar, Konidala Kranthi; Bhaskar, Matcha

    2015-01-01

    Brucella melitensis 16M is a Gram-negative coccobacillus that infects both animals and humans. It causes a disease known as brucellosis, which is characterized by acute febrile illness in humans and causes abortions in livestock. To prevent and control brucellosis, identification of putative drug targets is crucial. The present study aimed to identify drug targets in B. melitensis 16M by using a subtractive genomic approach. We used available database repositories (Database of Essential Genes, Kyoto Encyclopedia of Genes and Genomes Automatic Annotation Server, and Kyoto Encyclopedia of Genes and Genomes) to identify putative genes that are nonhomologous to humans and essential for pathogen B. melitensis 16M. The results revealed that among 3 Mb genome size of pathogen, 53 putative characterized and 13 uncharacterized hypothetical genes were identified; further, from Basic Local Alignment Search Tool protein analysis, one hypothetical protein showed a close resemblance (50%) to Silicibacter pomeroyi DUF1285 family protein (2RE3). A further homology model of the target was constructed using MODELLER 9.12 and optimized through variable target function method by molecular dynamics optimization with simulating annealing. The stereochemical quality of the restrained model was evaluated by PROCHECK, VERIFY-3D, ERRAT, and WHATIF servers. Furthermore, structure-based virtual screening was carried out against the predicted active site of the respective protein using the glycerol structural analogs from the PubChem database. We identified five best inhibitors with strong affinities, stable interactions, and also with reliable drug-like properties. Hence, these leads might be used as the most effective inhibitors of modeled protein. The outcome of the present work of virtual screening of putative gene targets might facilitate design of potential drugs for better treatment against brucellosis.

  6. Complete genome-wide screening and subtractive genomic approach revealed new virulence factors, potential drug targets against bio-war pathogen Brucella melitensis 16M

    PubMed Central

    Pradeepkiran, Jangampalli Adi; Sainath, Sri Bhashyam; Kumar, Konidala Kranthi; Bhaskar, Matcha

    2015-01-01

    Brucella melitensis 16M is a Gram-negative coccobacillus that infects both animals and humans. It causes a disease known as brucellosis, which is characterized by acute febrile illness in humans and causes abortions in livestock. To prevent and control brucellosis, identification of putative drug targets is crucial. The present study aimed to identify drug targets in B. melitensis 16M by using a subtractive genomic approach. We used available database repositories (Database of Essential Genes, Kyoto Encyclopedia of Genes and Genomes Automatic Annotation Server, and Kyoto Encyclopedia of Genes and Genomes) to identify putative genes that are nonhomologous to humans and essential for pathogen B. melitensis 16M. The results revealed that among 3 Mb genome size of pathogen, 53 putative characterized and 13 uncharacterized hypothetical genes were identified; further, from Basic Local Alignment Search Tool protein analysis, one hypothetical protein showed a close resemblance (50%) to Silicibacter pomeroyi DUF1285 family protein (2RE3). A further homology model of the target was constructed using MODELLER 9.12 and optimized through variable target function method by molecular dynamics optimization with simulating annealing. The stereochemical quality of the restrained model was evaluated by PROCHECK, VERIFY-3D, ERRAT, and WHATIF servers. Furthermore, structure-based virtual screening was carried out against the predicted active site of the respective protein using the glycerol structural analogs from the PubChem database. We identified five best inhibitors with strong affinities, stable interactions, and also with reliable drug-like properties. Hence, these leads might be used as the most effective inhibitors of modeled protein. The outcome of the present work of virtual screening of putative gene targets might facilitate design of potential drugs for better treatment against brucellosis. PMID:25834405

  7. Antifungal Resistance, Metabolic Routes as Drug Targets, and New Antifungal Agents: An Overview about Endemic Dimorphic Fungi.

    PubMed

    Parente-Rocha, Juliana Alves; Bailão, Alexandre Melo; Amaral, André Correa; Taborda, Carlos Pelleschi; Paccez, Juliano Domiraci; Borges, Clayton Luiz; Pereira, Maristela

    2017-01-01

    Diseases caused by fungi can occur in healthy people, but immunocompromised patients are the major risk group for invasive fungal infections. Cases of fungal resistance and the difficulty of treatment make fungal infections a public health problem. This review explores mechanisms used by fungi to promote fungal resistance, such as the mutation or overexpression of drug targets, efflux and degradation systems, and pleiotropic drug responses. Alternative novel drug targets have been investigated; these include metabolic routes used by fungi during infection, such as trehalose and amino acid metabolism and mitochondrial proteins. An overview of new antifungal agents, including nanostructured antifungals, as well as of repositioning approaches is discussed. Studies focusing on the development of vaccines against antifungal diseases have increased in recent years, as these strategies can be applied in combination with antifungal therapy to prevent posttreatment sequelae. Studies focused on the development of a pan-fungal vaccine and antifungal drugs can improve the treatment of immunocompromised patients and reduce treatment costs.

  8. Identification of Multiple Cryptococcal Fungicidal Drug Targets by Combined Gene Dosing and Drug Affinity Responsive Target Stability Screening

    PubMed Central

    Park, Yoon-Dong; Sun, Wei; Salas, Antonio; Antia, Avan; Carvajal, Cindy; Wang, Amy; Xu, Xin; Meng, Zhaojin; Zhou, Ming; Tawa, Gregory J.; Dehdashti, Jean; Zheng, Wei; Henderson, Christina M.; Zelazny, Adrian M.

    2016-01-01

    ABSTRACT Cryptococcus neoformans is a pathogenic fungus that is responsible for up to half a million cases of meningitis globally, especially in immunocompromised individuals. Common fungistatic drugs, such as fluconazole, are less toxic for patients but have low efficacy for initial therapy of the disease. Effective therapy against the disease is provided by the fungicidal drug amphotericin B; however, due to its high toxicity and the difficulty in administering its intravenous formulation, it is imperative to find new therapies targeting the fungus. The antiparasitic drug bithionol has been recently identified as having potent fungicidal activity. In this study, we used a combined gene dosing and drug affinity responsive target stability (GD-DARTS) screen as well as protein modeling to identify a common drug binding site of bithionol within multiple NAD-dependent dehydrogenase drug targets. This combination genetic and proteomic method thus provides a powerful method for identifying novel fungicidal drug targets for further development. PMID:27486194

  9. Detecting Drug-Target Binding in Cells using Fluorescence Activated Cell Sorting Coupled with Mass Spectrometry Analysis.

    PubMed

    Wilson, Kris; Webster, Scott P; Iredale, John Peter; Zheng, Xiaozhong; Homer, Natalie Z M; Pham, Nhan; Auer, Manfred; Mole, Damian James

    2017-09-13

    The assessment of drug-target engagement for determining the efficacy of a compound inside cells remains challenging, particularly for difficult target proteins. Existing techniques are more suited to soluble protein targets. Difficult target proteins include those with challenging in vitro solubility, stability or purification properties that preclude target isolation. Here, we report a novel technique that measures intracellular compound-target complex formation, as well as cellular permeability, specificity and cytotoxicity - the Toxicity-Affinity-Permeability-Selectivity (TAPS) technique. The TAPS assay is exemplified here using human kynurenine 3-monooxygenase (KMO), a challenging intracellular membrane protein target of significant current interest. TAPS confirmed target binding of known KMO inhibitors inside cells. We conclude that the TAPS assay can be used to facilitate intracellular hit validation on most, if not all intracellular drug targets. . Creative Commons Attribution license.

  10. Association of hypertension drug target genes with blood pressure and hypertension in 86,588 individuals.

    PubMed

    Johnson, Andrew D; Newton-Cheh, Christopher; Chasman, Daniel I; Ehret, Georg B; Johnson, Toby; Rose, Lynda; Rice, Kenneth; Verwoert, Germaine C; Launer, Lenore J; Gudnason, Vilmundur; Larson, Martin G; Chakravarti, Aravinda; Psaty, Bruce M; Caulfield, Mark; van Duijn, Cornelia M; Ridker, Paul M; Munroe, Patricia B; Levy, Daniel

    2011-05-01

    We previously conducted genome-wide association meta-analysis of systolic blood pressure, diastolic blood pressure, and hypertension in 29,136 people from 6 cohort studies in the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium. Here we examine associations of these traits with 30 gene regions encoding known antihypertensive drug targets. We find nominal evidence of association of ADRB1, ADRB2, AGT, CACNA1A, CACNA1C, and SLC12A3 polymorphisms with 1 or more BP traits in the Cohorts for Heart and Aging Research in Genomic Epidemiology genome-wide association meta-analysis. We attempted replication of the top meta-analysis single nucleotide polymorphisms for these genes in the Global BPgen Consortium (n=34,433) and the Women's Genome Health Study (n=23,019) and found significant results for rs1801253 in ADRB1 (Arg389Gly), with the Gly allele associated with a lower mean systolic blood pressure (β: 0.57 mm Hg; SE: 0.09 mm Hg; meta-analysis: P=4.7×10(-10)), diastolic blood pressure (β: 0.36 mm Hg; SE: 0.06 mm Hg; meta-analysis: P=9.5×10(-10)), and prevalence of hypertension (β: 0.06 mm Hg; SE: 0.02 mm Hg; meta-analysis: P=3.3×10(-4)). Variation in AGT (rs2004776) was associated with systolic blood pressure (β: 0.42 mm Hg; SE: 0.09 mm Hg; meta-analysis: P=3.8×10(-6)), as well as diastolic blood pressure (P=5.0×10(-8)) and hypertension (P=3.7×10(-7)). A polymorphism in ACE (rs4305) showed modest replication of association with increased hypertension (β: 0.06 mm Hg; SE: 0.01 mm Hg; meta-analysis: P=3.0×10(-5)). Two loci, ADRB1 and AGT, contain single nucleotide polymorphisms that reached a genome-wide significance threshold in meta-analysis for the first time. Our findings suggest that these genes warrant further studies of their genetic effects on blood pressure, including pharmacogenetic interactions.

  11. One-dimensional boron nanostructures: Prediction, synthesis, characterizations, and applications.

    PubMed

    Tian, Jifa; Xu, Zhichuan; Shen, Chengmin; Liu, Fei; Xu, Ningsheng; Gao, Hong-Jun

    2010-08-01

    One-dimensional (1D) boron nanostructures are very potential for nanoscale electronic devices since their physical properties including electric transport and field emission have been found very promising as compared to other well-developed 1D nanomaterials. In this article, we review the current progress that has been made on 1D boron nanostructures in terms of theoretical prediction, synthetic techniques, characterizations and potential applications. To date, the synthesis of 1D boron nanostructures has been well-developed. The popular structures include nanowires, nanobelts, and nanocones. Some of these 1D nanostructures exhibited improved electric transport properties over bulk boron materials as well as promising field emission properties. By current experimental findings, 1D boron nanostructures are promising to be one of core materials for future nanodevices. More efforts are expected to be made in future on the controlled growth of 1D boron nanostructures and tailoring their physical properties.

  12. Identification of Multiple Cryptococcal Fungicidal Drug Targets by Combined Gene Dosing and Drug Affinity Responsive Target Stability Screening.

    PubMed

    Park, Yoon-Dong; Sun, Wei; Salas, Antonio; Antia, Avan; Carvajal, Cindy; Wang, Amy; Xu, Xin; Meng, Zhaojin; Zhou, Ming; Tawa, Gregory J; Dehdashti, Jean; Zheng, Wei; Henderson, Christina M; Zelazny, Adrian M; Williamson, Peter R

    2016-08-02

    Cryptococcus neoformans is a pathogenic fungus that is responsible for up to half a million cases of meningitis globally, especially in immunocompromised individuals. Common fungistatic drugs, such as fluconazole, are less toxic for patients but have low efficacy for initial therapy of the disease. Effective therapy against the disease is provided by the fungicidal drug amphotericin B; however, due to its high toxicity and the difficulty in administering its intravenous formulation, it is imperative to find new therapies targeting the fungus. The antiparasitic drug bithionol has been recently identified as having potent fungicidal activity. In this study, we used a combined gene dosing and drug affinity responsive target stability (GD-DARTS) screen as well as protein modeling to identify a common drug binding site of bithionol within multiple NAD-dependent dehydrogenase drug targets. This combination genetic and proteomic method thus provides a powerful method for identifying novel fungicidal drug targets for further development. Cryptococcosis is a neglected fungal meningitis that causes approximately half a million deaths annually. The most effective antifungal agent, amphotericin B, was developed in the 1950s, and no effective medicine has been developed for this disease since that time. A key aspect of amphotericin B's effectiveness is thought to be because of its ability to kill the fungus (fungicidal activity), rather than just stop or slow its growth. The present study utilized a recently identified fungicidal agent, bithionol, to identify potential fungicidal drug targets that can be used in developing modern fungicidal agents. A combined protein and genetic analysis approach was used to identify a class of enzymes, dehydrogenases, that the fungus uses to maintain homeostasis with regard to sugar nutrients. Similarities in the drug target site were found that resulted in simultaneous inhibition and killing of the fungus by bithionol. These studies thus

  13. The influence of drug distribution and drug-target binding on target occupancy: The rate-limiting step approximation.

    PubMed

    de Witte, W E A; Vauquelin, G; van der Graaf, P H; de Lange, E C M

    2017-05-12

    The influence of drug-target binding kinetics on target occupancy can be influenced by drug distribution and diffusion around the target, often referred to as "rebinding" or "diffusion-limited binding". This gives rise to a decreased decline of the drug-target complex concentration as a result of a locally higher drug concentration that arises around the target, which leads to prolonged target exposure to the drug. This phenomenon has been approximated by the steady-state approximation, assuming a steady-state concentration around the target. Recently, a rate-limiting step approximation of drug distribution and drug-target binding has been published. However, a comparison between both approaches has not been made so far. In this study, the rate-limiting step approximation has been rewritten into the same mathematical format as the steady-state approximation in order to compare the performance of both approaches for the investigation of the influence of drug-target binding kinetics on target occupancy. While both approximations clearly indicated the importance of kon and high target concentrations, it was shown that the rate-limiting step approximation is more accurate than the steady-state approximation, especially when dissociation is fast compared to association and distribution out of the binding compartment. It is therefore concluded that the new rate-limiting step approximation is to be preferred for assessing the influence of binding kinetics on local target site concentrations and target occupancy. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  14. Biotechnology Conference: Drug Delivery and Drug Targeting Systems Held in London, United Kingdom on 14-15 December 1987

    DTIC Science & Technology

    1988-07-10

    release catalysts in comparison to 116,800 without catalysts. systems have an advantage over other systems in obviat- When acidic p-Toluene sulphonic acid ...systems, amino salicylic acid . Drug targeting - liposome for- biological problems can occur, according to Davis. Some mulations for Pentostam, colloidal...achieved close to ambient conditions and the gel can be Anionic formed into the shape of the container (mold). Accord- acrylic acid derivatives ing to

  15. Machine learning applications in cancer prognosis and prediction.

    PubMed

    Kourou, Konstantina; Exarchos, Themis P; Exarchos, Konstantinos P; Karamouzis, Michalis V; Fotiadis, Dimitrios I

    2015-01-01

    Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

  16. Case Studies of Predictive Analysis Applications in Law Enforcement

    DTIC Science & Technology

    2015-12-01

    or prevent crime in those locations. In this thesis, theories and methodologies behind predictive policing are described, and the case study method...crime control, predictive analysis, predictive policing, homeland security, community resilience, crime prevention, data mining, criminal justice ...In this thesis, theories and methodologies behind predictive policing are described, and the case study method is used to review current predictive

  17. Turbulent heat transfer prediction method for application to scramjet engines

    NASA Technical Reports Server (NTRS)

    Pinckney, S. Z.

    1974-01-01

    An integral method for predicting boundary layer development in turbulent flow regions on two-dimensional or axisymmetric bodies was developed. The method has the capability of approximating nonequilibrium velocity profiles as well as the local surface friction in the presence of a pressure gradient. An approach was developed for the problem of predicting the heat transfer in a turbulent boundary layer in the presence of a high pressure gradient. The solution was derived with particular emphasis on its applicability to supersonic combustion; thus, the effects of real gas flows were included. The resulting integrodifferential boundary layer method permits the estimation of cooling reguirements for scramjet engines. Theoretical heat transfer results are compared with experimental combustor and noncombustor heat transfer data. The heat transfer method was used in the development of engine design concepts which will produce an engine with reduced cooling requirements. The Langley scramjet engine module was designed by utilizing these design concepts and this engine design is discussed along with its corresponding cooling requirements. The heat transfer method was also used to develop a combustor cooling correlation for a combustor whose local properties are computed one dimensionally by assuming a linear area variation and a given heat release schedule.

  18. Predicting indoor pollutant concentrations, and applications to air quality management

    SciTech Connect

    Lorenzetti, David M.

    2002-10-01

    Because most people spend more than 90% of their time indoors, predicting exposure to airborne pollutants requires models that incorporate the effect of buildings. Buildings affect the exposure of their occupants in a number of ways, both by design (for example, filters in ventilation systems remove particles) and incidentally (for example, sorption on walls can reduce peak concentrations, but prolong exposure to semivolatile organic compounds). Furthermore, building materials and occupant activities can generate pollutants. Indoor air quality depends not only on outdoor air quality, but also on the design, maintenance, and use of the building. For example, ''sick building'' symptoms such as respiratory problems and headaches have been related to the presence of air-conditioning systems, to carpeting, to low ventilation rates, and to high occupant density (1). The physical processes of interest apply even in simple structures such as homes. Indoor air quality models simulate the processes, such as ventilation and filtration, that control pollutant concentrations in a building. Section 2 describes the modeling approach, and the important transport processes in buildings. Because advection usually dominates among the transport processes, Sections 3 and 4 describe methods for predicting airflows. The concluding section summarizes the application of these models.

  19. Predicting aquifer response time for application in catchment modeling.

    PubMed

    Walker, Glen R; Gilfedder, Mat; Dawes, Warrick R; Rassam, David W

    2015-01-01

    It is well established that changes in catchment land use can lead to significant impacts on water resources. Where land-use changes increase evapotranspiration there is a resultant decrease in groundwater recharge, which in turn decreases groundwater discharge to streams. The response time of changes in groundwater discharge to a change in recharge is a key aspect of predicting impacts of land-use change on catchment water yield. Predicting these impacts across the large catchments relevant to water resource planning can require the estimation of groundwater response times from hundreds of aquifers. At this scale, detailed site-specific measured data are often absent, and available spatial data are limited. While numerical models can be applied, there is little advantage if there are no detailed data to parameterize them. Simple analytical methods are useful in this situation, as they allow the variability in groundwater response to be incorporated into catchment hydrological models, with minimal modeling overhead. This paper describes an analytical model which has been developed to capture some of the features of real, sloping aquifer systems. The derived groundwater response timescale can be used to parameterize a groundwater discharge function, allowing groundwater response to be predicted in relation to different broad catchment characteristics at a level of complexity which matches the available data. The results from the analytical model are compared to published field data and numerical model results, and provide an approach with broad application to inform water resource planning in other large, data-scarce catchments. © 2014, CommonWealth of Australia. Groundwater © 2014, National Ground Water Association.

  20. C/sic Life Prediction for Propulsion Applications

    NASA Technical Reports Server (NTRS)

    Levine, Stanley R.; Verrilli, Michael J.; Opila, Elizabeth J.; Halbig, Michael C.; Calomino, Anthony M.; Thomas, David J.

    2002-01-01

    Accurate life prediction is critical to successful use of ceramic matrix composites (CMC). The tools to accomplish this are immature and not oriented toward the behavior of carbon fiber reinforced silicon carbide (C/SiC), the primary system of interest for many reusable and single mission launch vehicle propulsion and airframe applications. This paper describes an approach and process made to satisfy the need to develop an integrated life prediction system that addresses mechanical durability and environmental degradation of C/SiC. Issues such as oxidation, steam and hydrogen effects on material behavior are discussed. Preliminary tests indicate that steam will aggressively remove SiC seal coat and matrix in line with past experience. The kinetics of water vapor reaction with carbon fibers is negligible at 600 C, but comparable to air attack at 1200 C. The mitigating effect of steam observed in fiber oxidation studies has also been observed in stress rupture tests. Detailed microscopy of oxidized specimens is being carried out to develop the oxidation model. Carbon oxidation kinetics are reaction controlled at intermediate temperatures and diffusion controlled at high temperatures (approximately 1000 C). Activation energies for T-300 and interface pyrolytic carbon were determined as key inputs to the oxidation model. Crack opening as a function of temperature and stress was calculated. Mechanical property tests to develop and verify the probabilistic life model are very encouraging except for residual strength prediction. Gage width is a key variable governing edge oxidation of seal coated specimens. Future efforts will include architectural effects, enhanced coatings, biaxial tests, and LCF. Modeling will need to account for combined effects.

  1. C/SIC Life Prediction for Propulsion Applications

    NASA Technical Reports Server (NTRS)

    Levine, Stanley R.; Verrilli, Michael J.; Opula, Elizabeth J.; Halbig, Michael C.; Calomino, Anthony M.; Thomas, David J.

    2002-01-01

    Accurate life prediction is critical to successful use of ceramic matrix composites (CMC). The tools to accomplish this are immature and not oriented toward the behavior of carbon fiber reinforced silicon carbide (C/SiC), the primary system of interest for many reusable and single mission launch vehicle propulsion and airframe applications. This paper describes an approach and progress made to satisfy the need to develop an integrated life prediction system that addresses mechanical durability and environmental degradation of C/SiC. Issues such as oxidation, steam and hydrogen effects on material behavior are discussed. Preliminary tests indicate that stream will aggressively remove SiC seal coat and matrix in line with past experience. The kinetics of water vapor reaction with carbon fibers is negligible at 600 C, but comparable to air attack at 1200 C. The mitigating effect of steam observed in fiber oxidation studies has also been observed in stress rupture tests. Detailed microscopy of oxidized specimens is being carried out to develop the oxidation model. Carbon oxidation kinetics are reaction controlled at intermediate temperatures and diffusion controlled at high temperatures (approx. 1000 C). Activation energies for T-300 and interface pyrolytic carbon were determined as key inputs to the oxidation model. Crack opening as a function of temperature and stress was calculated. Mechanical property tests to develop and verify the probabilistic life model are very encouraging except for residual strength prediction. Gage width is a key variable governing edge oxidation of seal coated specimens. Future efforts will include architectural effects, enhanced coatings, biaxial tests, and LCF. Modeling will need to account for combined effects.

  2. SynSysNet: integration of experimental data on synaptic protein–protein interactions with drug-target relations

    PubMed Central

    von Eichborn, Joachim; Dunkel, Mathias; Gohlke, Björn O.; Preissner, Sarah C.; Hoffmann, Michael F.; Bauer, Jakob M. J.; Armstrong, J. D.; Schaefer, Martin H.; Andrade-Navarro, Miguel A.; Le Novere, Nicolas; Croning, Michael D. R.; Grant, Seth G. N.; van Nierop, Pim; Smit, August B.; Preissner, Robert

    2013-01-01

    We created SynSysNet, available online at http://bioinformatics.charite.de/synsysnet, to provide a platform that creates a comprehensive 4D network of synaptic interactions. Neuronal synapses are fundamental structures linking nerve cells in the brain and they are responsible for neuronal communication and information processing. These processes are dynamically regulated by a network of proteins. New developments in interaction proteomics and yeast two-hybrid methods allow unbiased detection of interactors. The consolidation of data from different resources and methods is important to understand the relation to human behaviour and disease and to identify new therapeutic approaches. To this end, we established SynSysNet from a set of ∼1000 synapse specific proteins, their structures and small-molecule interactions. For two-thirds of these, 3D structures are provided (from Protein Data Bank and homology modelling). Drug-target interactions for 750 approved drugs and 50 000 compounds, as well as 5000 experimentally validated protein–protein interactions, are included. The resulting interaction network and user-selected parts can be viewed interactively and exported in XGMML. Approximately 200 involved pathways can be explored regarding drug-target interactions. Homology-modelled structures are downloadable in Protein Data Bank format, and drugs are available as MOL-files. Protein–protein interactions and drug-target interactions can be viewed as networks; corresponding PubMed IDs or sources are given. PMID:23143269

  3. Prediction of Silicon-Based Layered Structures for Optoelectronic Applications

    NASA Astrophysics Data System (ADS)

    Luo, Wei; Ma, Yanming; Gong, Xingao; Xiang, Hongjun; CCMG Team

    2015-03-01

    A method based on the particle swarm optimization (PSO) algorithm is presented to design quasi-two-dimensional (Q2D) materials. With this development, various single-layer and bi-layer materials in C, Si, Ge, Sn, and Pb were predicted. A new Si bi-layer structure is found to have a much-favored energy than the previously widely accepted configuration. Both single-layer and bi-layer Si materials have small band gaps, limiting their usages in optoelectronic applications. Hydrogenation has therefore been used to tune the electronic and optical properties of Si layers. We discover two hydrogenated materials of layered Si8H2andSi6H2 possessing quasi-direct band gaps of 0.75 eV and 1.59 eV, respectively. Their potential applications for light emitting diode and photovoltaics are proposed and discussed. Our study opened up the possibility of hydrogenated Si layered materials as next-generation optoelectronic devices.

  4. RICTOR amplification identifies a subgroup in small cell lung cancer and predicts response to drugs targeting mTOR

    PubMed Central

    Sakre, Nneha; Wildey, Gary; Behtaj, Mohadese; Kresak, Adam; Yang, Michael; Fu, Pingfu; Dowlati, Afshin

    2017-01-01

    Small cell lung cancer (SCLC) is an aggressive cancer that represents ~15% of all lung cancers. Currently there are no targeted therapies to treat SCLC. Our genomic analysis of a metastatic SCLC cohort identified recurrent RICTOR amplification. Here, we examine the translational potential of this observation. RICTOR was the most frequently amplified gene observed (~14% patients), and co-amplified with FGF10 and IL7R on chromosome 5p13. RICTOR copy number variation correlated with RICTOR protein expression in SCLC cells. In parallel, cells with RICTOR copy number (CN) gain showed increased sensitivity to three mTOR inhibitors, AZD8055, AZD2014 and INK128 in cell growth assays, with AZD2014 demonstrating the best inhibition of downstream signaling. SCLC cells with RICTOR CN gain also migrated more rapidly in chemotaxis and scratch wound assays and were again more sensitive to mTOR inhibitors. The overall survival in SCLC patients with RICTOR amplification was significantly decreased (p = 0.021). Taken together, our results suggest that SCLC patients with RICTOR amplification may constitute a clinically important subgroup because of their potential response to mTORC1/2 inhibitors. PMID:27863413

  5. Functional Genomics Identifies Tis21-Dependent Mechanisms and Putative Cancer Drug Targets Underlying Medulloblastoma Shh-Type Development

    PubMed Central

    Gentile, Giulia; Ceccarelli, Manuela; Micheli, Laura; Tirone, Felice; Cavallaro, Sebastiano

    2016-01-01

    We have recently generated a novel medulloblastoma (MB) mouse model with activation of the Shh pathway and lacking the MB suppressor Tis21 (Patched1+/−/Tis21KO). Its main phenotype is a defect of migration of the cerebellar granule precursor cells (GCPs). By genomic analysis of GCPs in vivo, we identified as drug target and major responsible of this defect the down-regulation of the promigratory chemokine Cxcl3. Consequently, the GCPs remain longer in the cerebellum proliferative area, and the MB frequency is enhanced. Here, we further analyzed the genes deregulated in a Tis21-dependent manner (Patched1+/−/Tis21 wild-type vs. Ptch1+/−/Tis21 knockout), among which are a number of down-regulated tumor inhibitors and up-regulated tumor facilitators, focusing on pathways potentially involved in the tumorigenesis and on putative new drug targets. The data analysis using bioinformatic tools revealed: (i) a link between the Shh signaling and the Tis21-dependent impairment of the GCPs migration, through a Shh-dependent deregulation of the clathrin-mediated chemotaxis operating in the primary cilium through the Cxcl3-Cxcr2 axis; (ii) a possible lineage shift of Shh-type GCPs toward retinal precursor phenotype, i.e., the neural cell type involved in group 3 MB; (iii) the identification of a subset of putative drug targets for MB, involved, among the others, in the regulation of Hippo signaling and centrosome assembly. Finally, our findings define also the role of Tis21 in the regulation of gene expression, through epigenetic and RNA processing mechanisms, influencing the fate of the GCPs. PMID:27965576

  6. Novel Drug Targets for Food-Borne Pathogen Campylobacter jejuni: An Integrated Subtractive Genomics and Comparative Metabolic Pathway Study

    PubMed Central

    Mehla, Kusum

    2015-01-01

    Abstract Campylobacters are a major global health burden and a cause of food-borne diarrheal illness and economic loss worldwide. In developing countries, Campylobacter infections are frequent in children under age two and may be associated with mortality. In developed countries, they are a common cause of bacterial diarrhea in early adulthood. In the United States, antibiotic resistance against Campylobacter is notably increased from 13% in 1997 to nearly 25% in 2011. Novel drug targets are urgently needed but remain a daunting task to accomplish. We suggest that omics-guided drug discovery is timely and worth considering in this context. The present study employed an integrated subtractive genomics and comparative metabolic pathway analysis approach. We identified 16 unique pathways from Campylobacter when compared against H. sapiens with 326 non-redundant proteins; 115 of these were found to be essential in the Database of Essential Genes. Sixty-six proteins among these were non-homologous to the human proteome. Six membrane proteins, of which four are transporters, have been proposed as potential vaccine candidates. Screening of 66 essential non-homologous proteins against DrugBank resulted in identification of 34 proteins with drug-ability potential, many of which play critical roles in bacterial growth and survival. Out of these, eight proteins had approved drug targets available in DrugBank, the majority serving crucial roles in cell wall synthesis and energy metabolism and therefore having the potential to be utilized as drug targets. We conclude by underscoring that screening against these proteins with inhibitors may aid in future discovery of novel therapeutics against campylobacteriosis in ways that will be pathogen specific, and thus have minimal toxic effect on host. Omics-guided drug discovery and bioinformatics analyses offer the broad potential for veritable advances in global health relevant novel therapeutics. PMID:26061459

  7. The application of geological advanced prediction in tunnel construction

    NASA Astrophysics Data System (ADS)

    Youzhong, LU

    2017-04-01

    This article studied the theoretical composition of tunnel geological prediction (TGP), put forth the concepts of broad-sense TGP, narrow-sense TGP and comprehensive TGP and established a technical solution of broad-sense TGP on this basis, including three steps: macroscopic prediction of unfavorable geologies in the tunnel area, prediction of unfavorable geological bodies in the tunnel (i.e.: narrow-sense TGP) and alarm on approaching of geological hazards in the tunnel. The article also researched the methods and technical means of short-term geological prediction and long-term geological prediction of narrow-sense TGP and proposed a fault parameter prediction method, a surface geological body projection prediction method and a precursor prediction method of unfavorable geological bodies in the tunnel. In addition, the article also introduced the concept of alarm on approaching of geological hazards in tunnel construction and established a method for alarm on approaching of geological hazards in the tunnel. In the end, the article concluded that the implementation of comprehensive prediction is the most effective means to achieve expected prediction effect and increase prediction accuracy.

  8. Group-regularized individual prediction: theory and application to pain.

    PubMed

    Lindquist, Martin A; Krishnan, Anjali; López-Solà, Marina; Jepma, Marieke; Woo, Choong-Wan; Koban, Leonie; Roy, Mathieu; Atlas, Lauren Y; Schmidt, Liane; Chang, Luke J; Reynolds Losin, Elizabeth A; Eisenbarth, Hedwig; Ashar, Yoni K; Delk, Elizabeth; Wager, Tor D

    2017-01-15

    Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or 'decode' psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction-based on population-level predictive maps from prior groups-and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N=180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker-in this case, the Neurologic Pain Signature (NPS)-improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. Structure of pyrR (Rv1379) from Mycobacterium tuberculosis: A persistence gene and protein drug target

    SciTech Connect

    Kantardjieff, K A; Vasquez, C; Castro, P; Warfel, N M; Rho, B; Lekin, T; Kim, C; Segelke, B W; Terwilliger, T C; Rupp, B

    2004-09-24

    The 1.9 {angstrom} native structure of pyrimidine biosynthesis regulatory protein encoded by the Mycobacterium tuberculosis pyrR gene (Rv1379) is reported. Because pyrimidine biosynthesis is an essential step in the progression of TB, pyrR is an attractive antitubercular drug target. The Mycobacterium tuberculosis pyrR gene (Rv1379) encodes a protein that regulates expression of pyrimidine nucleotide biosynthesis (pyr) genes in a UMP-dependent manner. Because pyrimidine biosynthesis is an essential step in the progression of TB, the gene product pyrR is an attractive antitubercular drug target. We report the 1.9 {angstrom} native structure of Mtb pyrR determined by the TB Structural Genomics Consortium facilities (PDB entry 1W30) in trigonal space group P3{sub 1}21, with cell dimensions at 120K of a = 66.64 {angstrom}, c = 154.72 {angstrom}, and two molecules in the asymmetric unit. The 3D structure and residual uracil phosphoribosyltransferase activity point to a common PRTase ancestor for pyrR. However, while PRPP and UMP binding sites have been retained in Mtb pyrR, a novel dimer interaction among subunits creates a deep, positively charged cleft capable of binding pyr mRNA. In silico screening of pyrimidine nucleoside analogs has revealed a number of potential leads compounds that, if bound to Mtb pyrR, could facilitate transcriptional attenuation, particularly cyclopentenyl nucleosides.

  10. Characterizing and optimizing human anticancer drug targets based on topological properties in the context of biological pathways.

    PubMed

    Zhang, Jian; Wang, Yan; Shang, Desi; Yu, Fulong; Liu, Wei; Zhang, Yan; Feng, Chenchen; Wang, Qiuyu; Xu, Yanjun; Liu, Yuejuan; Bai, Xuefeng; Li, Xuecang; Li, Chunquan

    2015-04-01

    One of the challenging problems in drug discovery is to identify the novel targets for drugs. Most of the traditional methods for drug targets optimization focused on identifying the particular families of "druggable targets", but ignored their topological properties based on the biological pathways. In this study, we characterized the topological properties of human anticancer drug targets (ADTs) in the context of biological pathways. We found that the ADTs tended to present the following seven topological properties: influence the number of the pathways related to cancer, be localized at the start or end of the pathways, interact with cancer related genes, exhibit higher connectivity, vulnerability, betweenness, and closeness than other genes. We first ranked ADTs based on their topological property values respectively, then fused them into one global-rank using the joint cumulative distribution of an N-dimensional order statistic to optimize human ADTs. We applied the optimization method to 13 anticancer drugs, respectively. Results demonstrated that over 70% of known ADTs were ranked in the top 20%. Furthermore, the performance for mercaptopurine was significant: 6 known targets (ADSL, GMPR2, GMPR, HPRT1, AMPD3, AMPD2) were ranked in the top 15 and other four out of the top 15 (MAT2A, CDKN1A, AREG, JUN) have the potentialities to become new targets for cancer therapy. Copyright © 2015 Elsevier Inc. All rights reserved.

  11. Antifungal Resistance, Metabolic Routes as Drug Targets, and New Antifungal Agents: An Overview about Endemic Dimorphic Fungi

    PubMed Central

    Parente-Rocha, Juliana Alves; Bailão, Alexandre Melo; Amaral, André Correa; Paccez, Juliano Domiraci; Borges, Clayton Luiz

    2017-01-01

    Diseases caused by fungi can occur in healthy people, but immunocompromised patients are the major risk group for invasive fungal infections. Cases of fungal resistance and the difficulty of treatment make fungal infections a public health problem. This review explores mechanisms used by fungi to promote fungal resistance, such as the mutation or overexpression of drug targets, efflux and degradation systems, and pleiotropic drug responses. Alternative novel drug targets have been investigated; these include metabolic routes used by fungi during infection, such as trehalose and amino acid metabolism and mitochondrial proteins. An overview of new antifungal agents, including nanostructured antifungals, as well as of repositioning approaches is discussed. Studies focusing on the development of vaccines against antifungal diseases have increased in recent years, as these strategies can be applied in combination with antifungal therapy to prevent posttreatment sequelae. Studies focused on the development of a pan-fungal vaccine and antifungal drugs can improve the treatment of immunocompromised patients and reduce treatment costs. PMID:28694566

  12. A Review: The Current In Vivo Models for the Discovery and Utility of New Anti-leishmanial Drugs Targeting Cutaneous Leishmaniasis

    PubMed Central

    Mears, Emily Rose; Modabber, Farrokh; Don, Robert; Johnson, George E.

    2015-01-01

    The current in vivo models for the utility and discovery of new potential anti-leishmanial drugs targeting Cutaneous Leishmaniasis (CL) differ vastly in their immunological responses to the disease and clinical presentation of symptoms. Animal models that show similarities to the human form of CL after infection with Leishmania should be more representative as to the effect of the parasite within a human. Thus, these models are used to evaluate the efficacy of new anti-leishmanial compounds before human clinical trials. Current animal models aim to investigate (i) host–parasite interactions, (ii) pathogenesis, (iii) biochemical changes/pathways, (iv) in vivo maintenance of parasites, and (v) clinical evaluation of drug candidates. This review focuses on the trends of infection observed between Leishmania parasites, the predictability of different strains, and the determination of parasite load. These factors were used to investigate the overall effectiveness of the current animal models. The main aim was to assess the efficacy and limitations of the various CL models and their potential for drug discovery and evaluation. In conclusion, we found that the following models are the most suitable for the assessment of anti-leishmanial drugs: L. major–C57BL/6 mice (or–vervet monkey, or–rhesus monkeys), L. tropica–CsS-16 mice, L. amazonensis–CBA mice, L. braziliensis–golden hamster (or–rhesus monkey). We also provide in-depth guidance for which models are not suitable for these investigations. PMID:26334763

  13. Predicted Residual Error Sum of Squares of Mixed Models: An Application for Genomic Prediction

    PubMed Central

    Xu, Shizhong

    2017-01-01

    Genomic prediction is a statistical method to predict phenotypes of polygenic traits using high-throughput genomic data. Most diseases and behaviors in humans and animals are polygenic traits. The majority of agronomic traits in crops are also polygenic. Accurate prediction of these traits can help medical professionals diagnose acute diseases and breeders to increase food products, and therefore significantly contribute to human health and global food security. The best linear unbiased prediction (BLUP) is an important tool to analyze high-throughput genomic data for prediction. However, to judge the efficacy of the BLUP model with a particular set of predictors for a given trait, one has to provide an unbiased mechanism to evaluate the predictability. Cross-validation (CV) is an essential tool to achieve this goal, where a sample is partitioned into K parts of roughly equal size, one part is predicted using parameters estimated from the remaining K – 1 parts, and eventually every part is predicted using a sample excluding that part. Such a CV is called the K-fold CV. Unfortunately, CV presents a substantial increase in computational burden. We developed an alternative method, the HAT method, to replace CV. The new method corrects the estimated residual errors from the whole sample analysis using the leverage values of a hat matrix of the random effects to achieve the predicted residual errors. Properties of the HAT method were investigated using seven agronomic and 1000 metabolomic traits of an inbred rice population. Results showed that the HAT method is a very good approximation of the CV method. The method was also applied to 10 traits in 1495 hybrid rice with 1.6 million SNPs, and to human height of 6161 subjects with roughly 0.5 million SNPs of the Framingham heart study data. Predictabilities of the HAT and CV methods were all similar. The HAT method allows us to easily evaluate the predictabilities of genomic prediction for large numbers of traits in

  14. Geospatial application of the Water Erosion Prediction Project (WEPP) model

    USDA-ARS?s Scientific Manuscript database

    The Water Erosion Prediction Project (WEPP) model is a process-based technology for prediction of soil erosion by water at hillslope profile, field, and small watershed scales. In particular, WEPP utilizes observed or generated daily climate inputs to drive the surface hydrology processes (infiltrat...

  15. Geospatial application of the Water Erosion Prediction Project (WEPP) Model

    Treesearch

    D. C. Flanagan; J. R. Frankenberger; T. A. Cochrane; C. S. Renschler; W. J. Elliot

    2011-01-01

    The Water Erosion Prediction Project (WEPP) model is a process-based technology for prediction of soil erosion by water at hillslope profile, field, and small watershed scales. In particular, WEPP utilizes observed or generated daily climate inputs to drive the surface hydrology processes (infiltration, runoff, ET) component, which subsequently impacts the rest of the...

  16. MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development

    PubMed Central

    Moran, Josue D.; Giuste, Felipe O.; Du, Yuhong; Ivanov, Andrei A.; Johns, Margaret A.; Khuri, Fadlo R.; Fu, Haian

    2017-01-01

    Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology. Here we introduce a computational method (MEDICI) to predict PPI essentiality by combining gene knockdown studies with network models of protein interaction pathways in an analytic framework. Our method uses network topology to model how gene silencing can disrupt PPIs, relating the unknown essentialities of individual PPIs to experimentally observed protein essentialities. This model is then deconvolved to recover the unknown essentialities of individual PPIs. We demonstrate the validity of our approach via prediction of sensitivities to compounds based on PPI essentiality and differences in essentiality based on genetic mutations. We further show that lung cancer patients have improved overall survival when specific PPIs are no longer present, suggesting that these PPIs may be potentially new targets for therapeutic development. Software is freely available at https://github.com/cooperlab/MEDICI. Datasets are available at https://ctd2.nci.nih.gov/dataPortal. PMID:28118365

  17. MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development.

    PubMed

    Harati, Sahar; Cooper, Lee A D; Moran, Josue D; Giuste, Felipe O; Du, Yuhong; Ivanov, Andrei A; Johns, Margaret A; Khuri, Fadlo R; Fu, Haian; Moreno, Carlos S

    2017-01-01

    Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology. Here we introduce a computational method (MEDICI) to predict PPI essentiality by combining gene knockdown studies with network models of protein interaction pathways in an analytic framework. Our method uses network topology to model how gene silencing can disrupt PPIs, relating the unknown essentialities of individual PPIs to experimentally observed protein essentialities. This model is then deconvolved to recover the unknown essentialities of individual PPIs. We demonstrate the validity of our approach via prediction of sensitivities to compounds based on PPI essentiality and differences in essentiality based on genetic mutations. We further show that lung cancer patients have improved overall survival when specific PPIs are no longer present, suggesting that these PPIs may be potentially new targets for therapeutic development. Software is freely available at https://github.com/cooperlab/MEDICI. Datasets are available at https://ctd2.nci.nih.gov/dataPortal.

  18. Predictive analytics in mental health: applications, guidelines, challenges and perspectives.

    PubMed

    Hahn, T; Nierenberg, A A; Whitfield-Gabrieli, S

    2017-01-01

    The emerging field of 'predictive analytics in mental health' has recently generated tremendous interest with the bold promise to revolutionize clinical practice in psychiatry paralleling similar developments in personalized and precision medicine. Here, we provide an overview of the key questions and challenges in the field, aiming to (1) propose general guidelines for predictive analytics projects in psychiatry, (2) provide a conceptual introduction to core aspects of predictive modeling technology, and (3) foster a broad and informed discussion involving all stakeholders including researchers, clinicians, patients, funding bodies and policymakers.

  19. SR μCT and MRX analysis of ferrofluid accumulation in bovine arteries: a step further in the understanding of Magnetic Drug Targeting

    NASA Astrophysics Data System (ADS)

    Rahn, H.; Lyer, S.; Tietze, R.; Richter, H.; Alexiou, Ch.; Eberbeck, D.; Wiekhorst, F.; Trahms, L.; Beckmann, F.; Odenbach, S.

    Cancer is one of the most deadly and widespread diseases nowadays. Local cancer treatments are gaining strong importance. Magnetic Drug Targeting and magnetic heating treatment are minimal invasive cancer treatments that make use of magnetic nanoparticles as drug carriers in the first case and to induce heat transfer in the second case. For MDT the application of drugs, bound to magnetic nanoparticles, is realized via the tumor supplying vessels while a strong magnetic field gradient influences the flow and directs the nanoparticles to the respective region. In order to understand the mechanisms behind this magnetically controlled drug delivery, an ex-vivo artery model has been established. To study the amount and the distribution of magnetic nanoparticles within the arteries, microcomputed tomography and magnetorelaxometry have been used as analytical techniques. MRX provides very sensitively the amount of magnetic nanoparticles, while microcomputed tomography based on a synchrotron beam line enables 3-dimensional non-destructive examination of the distribution of the magnetic nanoparticles at a spatial resolution of a few micrometers. In this paper we present first results concerning the quantitative study of accumulation of nanoparticles within bovine arteries streamed with magnetic nanoparticles.

  20. Quantitative imaging for development of companion diagnostics to drugs targeting HGF/MET

    PubMed Central

    Huang, Fangjin; Ma, Zhaoxuan; Pollan, Sara; Yuan, Xiaopu; Swartwood, Steven; Gertych, Arkadiusz; Rodriguez, Maria; Mallick, Jayati; Bhele, Sanica; Guindi, Maha; Dhall, Deepti; Walts, Ann E; Bose, Shikha; de Peralta Venturina, Mariza; Marchevsky, Alberto M; Luthringer, Daniel J; Feller, Stephan M; Berman, Benjamin; Freeman, Michael R; Alvord, W Gregory; Vande Woude, George; Amin, Mahul B

    2016-01-01

    Abstract The limited clinical success of anti‐HGF/MET drugs can be attributed to the lack of predictive biomarkers that adequately select patients for treatment. We demonstrate here that quantitative digital imaging of formalin fixed paraffin embedded tissues stained by immunohistochemistry can be used to measure signals from weakly staining antibodies and provides new opportunities to develop assays for detection of MET receptor activity. To establish a biomarker panel of MET activation, we employed seven antibodies measuring protein expression in the HGF/MET pathway in 20 cases and up to 80 cores from 18 human cancer types. The antibodies bind to epitopes in the extra (EC)‐ and intracellular (IC) domains of MET (MET4EC, SP44_METIC, D1C2_METIC), to MET‐pY1234/pY1235, a marker of MET kinase activation, as well as to HGF, pSFK or pMAPK. Expression of HGF was determined in tumour cells (T_HGF) as well as in stroma surrounding cancer (St_HGF). Remarkably, MET4EC correlated more strongly with pMET (r = 0.47) than SP44_METIC (r = 0.21) or D1C2_METIC (r = 0.08) across 18 cancer types. In addition, correlation coefficients of pMET and T_HGF (r = 0.38) and pMET and pSFK (r = 0.56) were high. Prediction models of MET activation reveal cancer‐type specific differences in performance of MET4EC, SP44_METIC and anti‐HGF antibodies. Thus, we conclude that assays to predict the response to HGF/MET inhibitors require a cancer‐type specific antibody selection and should be developed in those cancer types in which they are employed clinically. PMID:27785366

  1. Quantitative imaging for development of companion diagnostics to drugs targeting HGF/MET.

    PubMed

    Huang, Fangjin; Ma, Zhaoxuan; Pollan, Sara; Yuan, Xiaopu; Swartwood, Steven; Gertych, Arkadiusz; Rodriguez, Maria; Mallick, Jayati; Bhele, Sanica; Guindi, Maha; Dhall, Deepti; Walts, Ann E; Bose, Shikha; de Peralta Venturina, Mariza; Marchevsky, Alberto M; Luthringer, Daniel J; Feller, Stephan M; Berman, Benjamin; Freeman, Michael R; Alvord, W Gregory; Vande Woude, George; Amin, Mahul B; Knudsen, Beatrice S

    2016-10-01

    The limited clinical success of anti-HGF/MET drugs can be attributed to the lack of predictive biomarkers that adequately select patients for treatment. We demonstrate here that quantitative digital imaging of formalin fixed paraffin embedded tissues stained by immunohistochemistry can be used to measure signals from weakly staining antibodies and provides new opportunities to develop assays for detection of MET receptor activity. To establish a biomarker panel of MET activation, we employed seven antibodies measuring protein expression in the HGF/MET pathway in 20 cases and up to 80 cores from 18 human cancer types. The antibodies bind to epitopes in the extra (EC)- and intracellular (IC) domains of MET (MET4(EC), SP44_MET(IC), D1C2_MET(IC)), to MET-pY1234/pY1235, a marker of MET kinase activation, as well as to HGF, pSFK or pMAPK. Expression of HGF was determined in tumour cells (T_HGF) as well as in stroma surrounding cancer (St_HGF). Remarkably, MET4(EC) correlated more strongly with pMET (r = 0.47) than SP44_MET(IC) (r = 0.21) or D1C2_MET(IC) (r = 0.08) across 18 cancer types. In addition, correlation coefficients of pMET and T_HGF (r = 0.38) and pMET and pSFK (r = 0.56) were high. Prediction models of MET activation reveal cancer-type specific differences in performance of MET4(EC), SP44_MET(IC) and anti-HGF antibodies. Thus, we conclude that assays to predict the response to HGF/MET inhibitors require a cancer-type specific antibody selection and should be developed in those cancer types in which they are employed clinically.

  2. Application of array backprojection to tsunami prediction and early warning