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Sample records for applications predicting drug-target

  1. 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.

  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. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction.

    PubMed

    Liu, Yong; Wu, Min; Miao, Chunyan; Zhao, Peilin; Li, Xiao-Li

    2016-02-01

    In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches.

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. 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.

  18. 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

  19. 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

  20. 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

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. 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

  15. 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

  16. 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.

  17. 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

  18. 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

  19. 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/).

  20. 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

  1. 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

  2. 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

  3. 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.

  4. 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

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  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. 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

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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

  6. 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

  7. 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.

  8. 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

  9. 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

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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.

  16. Sirtuins as potential drug targets for metablic diseases

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  17. 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

  18. 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.

  19. 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

  20. 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.

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

    DTIC Science & Technology

    2015-04-01

    Release; Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Sporozo ite infection of the liver is the first obl igate step of the Plasmodium...goal is to find drugs that prevent or control liver infection. Development of such drugs will be faci l itated by identification of parasite proteins...required for l iver infection. These proteins are potential drug targets for development of therapies that restrict Plasmodium liver infection. The

  2. 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

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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

  13. 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.

  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. 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

  16. 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.

  17. 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

  18. 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

  19. 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

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. Predictive Microbiology and Food Safety Applications

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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 ...

  5. 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

  6. Experimental and theoretical studies of implant assisted magnetic drug targeting

    NASA Astrophysics Data System (ADS)

    Aviles, Misael O.

    One way to achieve drug targeting in the body is to incorporate magnetic nanoparticles into drug carriers and then retain them at the site using an externally applied magnetic field. This process is referred to as magnetic drug targeting (MDT). However, the main limitation of MDT is that an externally applied magnetic field alone may not be able to retain a sufficient number of magnetic drug carrier particles (MDCPs) to justify its use. Such a limitation might not exist when high gradient magnetic separation (HGMS) principles are applied to assist MDT by means of ferromagnetic implants. It was hypothesized that an Implant Assisted -- MDT (IA-MDT) system would increase the retention of the MDCPs at a target site where an implant had been previously located, since the magnetic forces are produced internally. With this in mind, the overall objective of this work was to demonstrate the feasibility of an IA-MDT system through mathematical modeling and in vitro experimentation. The mathematical models were developed and used to demonstrate the behavior and limitations of IA-MDT, and the in vitro experiments were designed and used to validate the models and to further elucidate the important parameters that affect the performance of the system. IA-MDT was studied with three plausible implants, ferromagnetic stents, seed particles, and wires. All implants were studied theoretically and experimentally using flow through systems with polymer particles containing magnetite nanoparticles as MDCPs. In the stent studies, a wire coil or mesh was simply placed in a flow field and the capture of the MDCPs was studied. In the other cases, a porous polymer matrix was used as a surrogate capillary tissue scaffold to study the capture of the MDCPs using wires or particle seeds as the implant, with the seeds either fixed within the polymer matrix or captured prior to capturing the MDCPs. An in vitro heart tissue perfusion model was also used to study the use of stents. In general, all

  7. "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.

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. Essential Gene Identification and Drug Target Prioritization in Aspergillus fumigatus

    PubMed Central

    Hu, Wenqi; Sillaots, Susan; Lemieux, Sebastien; Davison, John; Kauffman, Sarah; Breton, Anouk; Linteau, Annie; Xin, Chunlin; Bowman, Joel; Becker, Jeff; Jiang, Bo; Roemer, Terry

    2007-01-01

    Aspergillus fumigatus is the most prevalent airborne filamentous fungal pathogen in humans, causing severe and often fatal invasive infections in immunocompromised patients. Currently available antifungal drugs to treat invasive aspergillosis have limited modes of action, and few are safe and effective. To identify and prioritize antifungal drug targets, we have developed a conditional promoter replacement (CPR) strategy using the nitrogen-regulated A. fumigatus NiiA promoter (pNiiA). The gene essentiality for 35 A. fumigatus genes was directly demonstrated by this pNiiA-CPR strategy from a set of 54 genes representing broad biological functions whose orthologs are confirmed to be essential for growth in Candida albicans and Saccharomyces cerevisiae. Extending this approach, we show that the ERG11 gene family (ERG11A and ERG11B) is essential in A. fumigatus despite neither member being essential individually. In addition, we demonstrate the pNiiA-CPR strategy is suitable for in vivo phenotypic analyses, as a number of conditional mutants, including an ERG11 double mutant (erg11BΔ, pNiiA-ERG11A), failed to establish a terminal infection in an immunocompromised mouse model of systemic aspergillosis. Collectively, the pNiiA-CPR strategy enables a rapid and reliable means to directly identify, phenotypically characterize, and facilitate target-based whole cell assays to screen A. fumigatus essential genes for cognate antifungal inhibitors. PMID:17352532

  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. 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

  16. 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.

  17. 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

  18. 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

  19. 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.

  20. Predictive microbiology for food packaging applications

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. 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

  6. 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.

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

    DTIC Science & Technology

    2014-10-01

    Identification of New Drug Targets in Multi-Drug Resistant Bacterial Infections PRINCIPAL INVESTIGATOR: Andrew M. Gulick, PhD...Identification of New Drug Targets in Multi-Drug Resistant Bacterial Infections 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-11-2-0218 5c... infections . Recently, community-acquired infections , infections in wounded U.S. service members, and infections in residents of long-term care facilities

  8. 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.

  9. 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.

  10. 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

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. 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

  2. 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

  3. 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

  4. 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.

  5. 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

  6. 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

  7. 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.

  8. 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...molecular weight drug -like compounds that antagonize the EphB1:NR1 protein-protein interaction. In year 1 of the project we have cloned, expressed, and

  9. 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.

  10. 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.

  11. Applications for predictive microbiology to food packaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  12. 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.

  13. 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

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. Predictive microbiology in food packaging applications

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  3. 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…

  4. 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

  5. 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.

  6. 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)

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. 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

  15. 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.

  16. 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

  17. Systematic Analysis of Drug Targets Confirms Expression in Disease-Relevant Tissues

    PubMed Central

    Kumar, Vinod; Sanseau, Philippe; Simola, Daniel F.; Hurle, Mark R.; Agarwal, Pankaj

    2016-01-01

    It is commonly assumed that drug targets are expressed in tissues relevant to their indicated diseases, even under normal conditions. While multiple anecdotal cases support this hypothesis, a comprehensive study has not been performed to verify it. We conducted a systematic analysis to assess gene and protein expression for all targets of marketed and phase III drugs across a diverse collection of normal human tissues. For 87% of gene-disease pairs, the target is expressed in a disease-affected tissue under healthy conditions. This result validates the importance of confirming expression of a novel drug target in an appropriate tissue for each disease indication and strengthens previous findings showing that targets of efficacious drugs should be expressed in relevant tissues under normal conditions. Further characterization of the remaining 13% of gene-disease pairs revealed that most genes are expressed in a different tissue linked to another disease. Our analysis demonstrates the value of extensive tissue specific expression resources.both in terms of tissue and cell diversity as well as techniques used to measure gene expression. PMID:27824084

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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

  4. 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.

  5. 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

  6. 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.

  7. 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

  8. 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.

  9. 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.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. 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

  18. 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.

  19. 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.

  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.

    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.

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. Improved drug targeting of cancer cells by utilizing actively targetable folic acid-conjugated albumin nanospheres.

    PubMed

    Shen, Zheyu; Li, Yan; Kohama, Kazuhiro; Oneill, Brian; Bi, Jingxiu

    2011-01-01

    Folic acid-conjugated albumin nanospheres (FA-AN) have been developed to provide an actively targetable drug delivery system for improved drug targeting of cancer cells with reduced side effects. The nanospheres were prepared by conjugating folic acid onto the surface of albumin nanospheres using 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDAC) as a catalyst. To test the efficacy of these nanospheres as a potential delivery platform, doxorubicin-loaded albumin nanospheres (DOX-AN) and doxorubicin-loaded FA-AN (FA-DOX-AN) were prepared by entrapping DOX (an anthracycline, antibiotic drug widely used in cancer chemotherapy that works by intercalating DNA) into AN and FA-AN nanoparticles. Cell uptake of the DOX was then measured. The results show that FA-AN was incorporated into HeLa cells (tumor cells) only after 2.0h incubation, whereas HeLa cells failed to incorporate albumin nanospheres without conjugated folic acid after 4.0h incubation. When HeLa cells were treated with the DOX-AN, FA-DOX-AN nanoparticles or free DOX, cell viability decreased with increasing culture time (i.e. cell death increases with time) over a 70h period. Cell viability was always the lowest for free DOX followed by FA-DOX-AN4 and then DOX-AN. In a second set of experiments, HeLa cells washed to remove excess DOX after an initial incubation for 2h were incubated for 70h. The corresponding cell viability was slightly higher when the cells were treated with FA-DOX-AN or free DOX whilst cells treated with DOX-AN nanoparticles remained viable. The above experiments were repeated for non-cancerous, aortic smooth muscle cells (AoSMC). As expected, cell viability of the HeLa cells (with FA receptor alpha, FRα) and AoSMC cells (without FRα) decreased rapidly with time in the presence of free DOX, but treatment with FA-DOX-AN resulted in selective killing of the tumor cells. These results indicated that FA-AN may be used as a promising actively targetable drug delivery system to improve drug

  9. 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.

  10. 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.

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. [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.

  16. 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

  17. 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.

  18. 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.

  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. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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

  5. 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

  6. 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.

  7. 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

  8. 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

  9. 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.

  10. 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

  11. [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.

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. 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

  17. 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

  18. 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.

  19. 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

  20. 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.

  1. 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

  2. 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.

  3. 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

  4. 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.

  5. 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

  6. 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

  7. 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.

  8. 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

  9. 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).

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  16. 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

  17. 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.

  18. Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome.

    PubMed

    Zhao, Huiying; Wang, Jihua; Zhou, Yaoqi; Yang, Yuedong

    2014-01-01

    As more and more protein sequences are uncovered from increasingly inexpensive sequencing techniques, an urgent task is to find their functions. This work presents a highly reliable computational technique for predicting DNA-binding function at the level of protein-DNA complex structures, rather than low-resolution two-state prediction of DNA-binding as most existing techniques do. The method first predicts protein-DNA complex structure by utilizing the template-based structure prediction technique HHblits, followed by binding affinity prediction based on a knowledge-based energy function (Distance-scaled finite ideal-gas reference state for protein-DNA interactions). A leave-one-out cross validation of the method based on 179 DNA-binding and 3797 non-binding protein domains achieves a Matthews correlation coefficient (MCC) of 0.77 with high precision (94%) and high sensitivity (65%). We further found 51% sensitivity for 82 newly determined structures of DNA-binding proteins and 56% sensitivity for the human proteome. In addition, the method provides a reasonably accurate prediction of DNA-binding residues in proteins based on predicted DNA-binding complex structures. Its application to human proteome leads to more than 300 novel DNA-binding proteins; some of these predicted structures were validated by known structures of homologous proteins in APO forms. The method [SPOT-Seq (DNA)] is available as an on-line server at http://sparks-lab.org.

  19. Drug target identification using network analysis: Taking active components in Sini decoction as an example.

    PubMed

    Chen, Si; Jiang, Hailong; Cao, Yan; Wang, Yun; Hu, Ziheng; Zhu, Zhenyu; Chai, Yifeng

    2016-04-20

    Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

  20. Drug target identification using network analysis: Taking active components in Sini decoction as an example

    NASA Astrophysics Data System (ADS)

    Chen, Si; Jiang, Hailong; Cao, Yan; Wang, Yun; Hu, Ziheng; Zhu, Zhenyu; Chai, Yifeng

    2016-04-01

    Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

  1. Drug target identification using network analysis: Taking active components in Sini decoction as an example

    PubMed Central

    Chen, Si; Jiang, Hailong; Cao, Yan; Wang, Yun; Hu, Ziheng; Zhu, Zhenyu; Chai, Yifeng

    2016-01-01

    Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound. PMID:27095146

  2. 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.

  3. On Predictive Understanding of Extreme Events: Pattern Recognition Approach; Prediction Algorithms; Applications to Disaster Preparedness

    NASA Astrophysics Data System (ADS)

    Keilis-Borok, V. I.; Soloviev, A.; Gabrielov, A.

    2011-12-01

    We describe a uniform approach to predicting different extreme events, also known as critical phenomena, disasters, or crises. The following types of such events are considered: strong earthquakes; economic recessions (their onset and termination); surges of unemployment; surges of crime; and electoral changes of the governing party. A uniform approach is possible due to the common feature of these events: each of them is generated by a certain hierarchical dissipative complex system. After a coarse-graining, such systems exhibit regular behavior patterns; we look among them for "premonitory patterns" that signal the approach of an extreme event. We introduce methodology, based on the optimal control theory, assisting disaster management in choosing optimal set of disaster preparedness measures undertaken in response to a prediction. Predictions with their currently realistic (limited) accuracy do allow preventing a considerable part of the damage by a hierarchy of preparedness measures. Accuracy of prediction should be known, but not necessarily high.

  4. Application of array backprojection to tsunami prediction and early warning

    NASA Astrophysics Data System (ADS)

    An, Chao; Meng, Lingsen

    2016-04-01

    Teleseismic and static geodetic data have weak constraints on the offshore slip while tsunami data are limited by their availability, so predictions of tsunami waves in the near-field remain challenging. In this study, we develop a near-field tsunami prediction approach based on seismic array backprojections (BP). In this approach, the rupture area is first estimated by enclosing the BP radiators. Then slip models with uniform slip are constructed based on statistical scaling relations between rupture area and seismic moment to predict the near-field tsunami waveforms. The method is applied to the 2011 Tohoku, 2014 Iquique, and 2015 Illapel tsunami events, and the model predictions are compared with tsunami recordings at 57 tidal gauges and nine DART stations. Results show that the average error of arrival time and amplitude nearshore is approximately -15 to +5 min and 0.5 m, respectively, which are sufficiently small for tsunami warning purposes.

  5. Prediction of Chemical Function: Model Development and Application

    EPA Science Inventory

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (...

  6. Radio Interference Modeling and Prediction for Satellite Operation Applications

    DTIC Science & Technology

    2015-08-25

    in Task 4 into existing Intelligent Fusion Technology (IFT, a sub-contractor) SATCOM tools to display the RFI prediction and detection results. IFT...how the model was ported into existing IFT ( Intelligent Fusion Technology, Inc) SATCOM tools to display the RFI detection and prediction results...which included North Carolina State University (NCSU) and Intelligent Fusion Technology (IFT) on radio frequency interference (RFI) modeling and

  7. Application of Machine Learning to the Prediction of Vegetation Health

    NASA Astrophysics Data System (ADS)

    Burchfield, Emily; Nay, John J.; Gilligan, Jonathan

    2016-06-01

    This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI). The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. Though the tool was built to increase capacity to monitor vegetation health in data scarce regions like South Asia, users may include ancillary spatiotemporal datasets relevant to their region of interest to increase predictive power and to facilitate interpretation of model results. The tool can automatically update predictions as new MODIS data is made available by NASA. The tool is particularly well-suited for decision makers interested in understanding and predicting vegetation health dynamics in countries in which environmental data is scarce and cloud cover is a significant concern.

  8. Reverse Chemical Genetics: Comprehensive Fitness Profiling Reveals the Spectrum of Drug Target Interactions

    PubMed Central

    Sinha, Sunita; Bergeron, Julien R.; Mellor, Joseph C.; Giaever, Guri; Nislow, Corey

    2016-01-01

    The emergence and prevalence of drug resistance demands streamlined strategies to identify drug resistant variants in a fast, systematic and cost-effective way. Methods commonly used to understand and predict drug resistance rely on limited clinical studies from patients who are refractory to drugs or on laborious evolution experiments with poor coverage of the gene variants. Here, we report an integrative functional variomics methodology combining deep sequencing and a Bayesian statistical model to provide a comprehensive list of drug resistance alleles from complex variant populations. Dihydrofolate reductase, the target of methotrexate chemotherapy drug, was used as a model to identify functional mutant alleles correlated with methotrexate resistance. This systematic approach identified previously reported resistance mutations, as well as novel point mutations that were validated in vivo. Use of this systematic strategy as a routine diagnostics tool widens the scope of successful drug research and development. PMID:27588687

  9. SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets.

    PubMed

    Guo, Jing; Liu, Hui; Zheng, Jie

    2016-01-04

    Synthetic lethality (SL) is a type of genetic interaction between two genes such that simultaneous perturbations of the two genes result in cell death or a dramatic decrease of cell viability, while a perturbation of either gene alone is not lethal. SL reflects the biologically endogenous difference between cancer cells and normal cells, and thus the inhibition of SL partners of genes with cancer-specific mutations could selectively kill cancer cells but spare normal cells. Therefore, SL is emerging as a promising anticancer strategy that could potentially overcome the drawbacks of traditional chemotherapies by reducing severe side effects. Researchers have developed experimental technologies and computational prediction methods to identify SL gene pairs on human and a few model species. However, there has not been a comprehensive database dedicated to collecting SL pairs and related knowledge. In this paper, we propose a comprehensive database, SynLethDB (http://histone.sce.ntu.edu.sg/SynLethDB/), which contains SL pairs collected from biochemical assays, other related databases, computational predictions and text mining results on human and four model species, i.e. mouse, fruit fly, worm and yeast. For each SL pair, a confidence score was calculated by integrating individual scores derived from different evidence sources. We also developed a statistical analysis module to estimate the druggability and sensitivity of cancer cells upon drug treatments targeting human SL partners, based on large-scale genomic data, gene expression profiles and drug sensitivity profiles on more than 1000 cancer cell lines. To help users access and mine the wealth of the data, we developed other practical functionalities, such as search and filtering, orthology search, gene set enrichment analysis. Furthermore, a user-friendly web interface has been implemented to facilitate data analysis and interpretation. With the integrated data sets and analytics functionalities, SynLethDB would

  10. The Mycobacterium tuberculosis MEP (2C-methyl-D-erythritol 4-phosphate) pathway as a new drug target

    PubMed Central

    Eoh, Hyungjin; Brennan, Patrick J.; Crick, Dean C.

    2009-01-01

    Tuberculosis (TB) is still a major public health problem, compounded by the human immunodeficiency virus (HIV)-TB co-infection and recent emergence of multidrug-resistant (MDR) and extensive drug resistant (XDR)-TB. Novel anti-TB drugs are urgently required. In this context, the 2C-methyl-D-erythritol 4-phosphate (MEP) pathway of Mycobacterium tuberculosis has drawn attention; it is one of several pathways vital for M. tuberculosis viability and the human host lacks homologous enzymes. Thus, the MEP pathway promises bacterium-specific drug targets and the potential for identification of lead compounds unencumbered by target-based toxicity. Indeed, fosmidomycin is now known to inhibit the second step in the MEP pathway. This review describes the cardinal features of the main enzymes of the MEP pathway in M. tuberculosis and how these can be manipulated in high throughput screening campaigns in the search for new anti-infectives against TB. PMID:18793870

  11. Computational 3D structures of drug-targeting proteins in the 2009-H1N1 influenza A virus

    NASA Astrophysics Data System (ADS)

    Du, Qi-Shi; Wang, Shu-Qing; Huang, Ri-Bo; Chou, Kuo-Chen

    2010-01-01

    The neuraminidase (NA) and M2 proton channel of influenza virus are the drug-targeting proteins, based on which several drugs were developed. However these once powerful drugs encountered drug-resistant problem to the H5N1 and H1N1 flu. To address this problem, the computational 3D structures of NA and M2 proteins of 2009-H1N1 influenza virus were built using the molecular modeling technique and computational chemistry method. Based on the models the structure features of NA and M2 proteins were analyzed, the docking structures of drug-protein complexes were computed, and the residue mutations were annotated. The results may help to solve the drug-resistant problem and stimulate designing more effective drugs against 2009-H1N1 influenza pandemic.

  12. Economic decision making and the application of nonparametric prediction models

    USGS Publications Warehouse

    Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

    2007-01-01

    Sustained increases in energy prices have focused attention on gas resources in low permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are large. Planning and development decisions for extraction of such resources must be area-wide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm the decision to enter such plays depends on reconnaissance level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional scale cost functions. The context of the worked example is the Devonian Antrim shale gas play, Michigan Basin. One finding relates to selection of the resource prediction model to be used with economic models. Models which can best predict aggregate volume over larger areas (many hundreds of sites) may lose granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined by extraneous factors. The paper also shows that when these simple prediction models are used to strategically order drilling prospects, the gain in gas volume over volumes associated with simple random site selection amounts to 15 to 20 percent. It also discusses why the observed benefit of updating predictions from results of new drilling, as opposed to following static predictions, is somewhat smaller. Copyright 2007, Society of Petroleum Engineers.

  13. Fan Noise Prediction with Applications to Aircraft System Noise Assessment

    NASA Technical Reports Server (NTRS)

    Nark, Douglas M.; Envia, Edmane; Burley, Casey L.

    2009-01-01

    This paper describes an assessment of current fan noise prediction tools by comparing measured and predicted sideline acoustic levels from a benchmark fan noise wind tunnel test. Specifically, an empirical method and newly developed coupled computational approach are utilized to predict aft fan noise for a benchmark test configuration. Comparisons with sideline noise measurements are performed to assess the relative merits of the two approaches. The study identifies issues entailed in coupling the source and propagation codes, as well as provides insight into the capabilities of the tools in predicting the fan noise source and subsequent propagation and radiation. In contrast to the empirical method, the new coupled computational approach provides the ability to investigate acoustic near-field effects. The potential benefits/costs of these new methods are also compared with the existing capabilities in a current aircraft noise system prediction tool. The knowledge gained in this work provides a basis for improved fan source specification in overall aircraft system noise studies.

  14. RNA Editing TUTase 1: structural foundation of substrate recognition, complex interactions and drug targeting

    PubMed Central

    Rajappa-Titu, Lional; Suematsu, Takuma; Munoz-Tello, Paola; Long, Marius; Demir, Özlem; Cheng, Kevin J.; Stagno, Jason R.; Luecke, Hartmut; Amaro, Rommie E.; Aphasizheva, Inna; Aphasizhev, Ruslan; Thore, Stéphane

    2016-01-01

    Terminal uridyltransferases (TUTases) execute 3′ RNA uridylation across protists, fungi, metazoan and plant species. Uridylation plays a particularly prominent role in RNA processing pathways of kinetoplastid protists typified by the causative agent of African sleeping sickness, Trypanosoma brucei. In mitochondria of this pathogen, most mRNAs are internally modified by U-insertion/deletion editing while guide RNAs and rRNAs are U-tailed. The founding member of TUTase family, RNA editing TUTase 1 (RET1), functions as a subunit of the 3′ processome in uridylation of gRNA precursors and mature guide RNAs. Along with KPAP1 poly(A) polymerase, RET1 also participates in mRNA translational activation. RET1 is divergent from human TUTases and is essential for parasite viability in the mammalian host and the insect vector. Given its robust in vitro activity, RET1 represents an attractive target for trypanocide development. Here, we report high-resolution crystal structures of the RET1 catalytic core alone and in complex with UTP analogs. These structures reveal a tight docking of the conserved nucleotidyl transferase bi-domain module with a RET1-specific C2H2 zinc finger and RNA recognition (RRM) domains. Furthermore, we define RET1 region required for incorporation into the 3′ processome, determinants for RNA binding, subunit oligomerization and processive UTP incorporation, and predict druggable pockets. PMID:27744351

  15. Convection and retro-convection enhanced delivery: some theoretical considerations related to drug targeting.

    PubMed

    Motion, J P Michael; Huynh, Grace H; Szoka, Francis C; Siegel, Ronald A

    2011-03-01

    Delivery of drugs and macromolecules into the brain is a challenging problem, due in part to the blood-brain barrier. In this article, we focus on the possibilities and limitations of two infusion techniques devised to bypass the blood-brain barrier: convection enhanced delivery (CED) and retro-convection enhanced delivery (R-CED). CED infuses fluid directly into the interstitial space of brain or tumor, whereas R-CED removes fluid from the interstitial space, which results in the transfer of drugs from the vascular compartment into the brain or tumor. Both techniques have shown promising results for the delivery of drugs into large volumes of tissue. Theoretical approaches of varying complexity have been developed to better understand and predict brain interstitial pressures and drug distribution for these techniques. These theoretical models of flow and diffusion can only be solved explicitly in simple geometries, and spherical symmetry is usually assumed for CED, while axial symmetry has been assumed for R-CED. This perspective summarizes features of these models and provides physical arguments and numerical simulations to support the notion that spherical symmetry is a reasonable approximation for modeling CED and R-CED. We also explore the potential of multi-catheter arrays for delivering and compartmentalizing drugs using CED and R-CED.

  16. Cancer stem cell drugs target K-ras signaling in a stemness context

    PubMed Central

    Najumudeen, A K; Jaiswal, A; Lectez, B; Oetken-Lindholm, C; Guzmán, C; Siljamäki, E; Posada, I M D; Lacey, E; Aittokallio, T; Abankwa, D

    2016-01-01

    Cancer stem cells (CSCs) are considered to be responsible for treatment relapse and have therefore become a major target in cancer research. Salinomycin is the most established CSC inhibitor. However, its primary mechanistic target is still unclear, impeding the discovery of compounds with similar anti-CSC activity. Here, we show that salinomycin very specifically interferes with the activity of K-ras4B, but not H-ras, by disrupting its nanoscale membrane organization. We found that caveolae negatively regulate the sensitivity to this drug. On the basis of this novel mechanistic insight, we defined a K-ras-associated and stem cell-derived gene expression signature that predicts the drug response of cancer cells to salinomycin. Consistent with therapy resistance of CSC, 8% of tumor samples in the TCGA-database displayed our signature and were associated with a significantly higher mortality. Using our K-ras-specific screening platform, we identified several new candidate CSC drugs. Two of these, ophiobolin A and conglobatin A, possessed a similar or higher potency than salinomycin. Finally, we established that the most potent compound, ophiobolin A, exerts its K-ras4B-specific activity through inactivation of calmodulin. Our data suggest that specific interference with the K-ras4B/calmodulin interaction selectively inhibits CSC. PMID:26973241

  17. RNA structures as mediators of neurological diseases and as drug targets

    PubMed Central

    Bernat, Viachaslau; Disney, Matthew D.

    2015-01-01

    RNAs adopt diverse folded structures that are essential for function and thus play critical roles in cellular biology. A striking example of this is the ribosome, a complex, three-dimensionally folded macromolecular machine that orchestrates protein synthesis. Advances in RNA biochemistry, structural and molecular biology, and bioinformatics have revealed other non-coding RNAs whose functions are dictated by their structure. It is not surprising that aberrantly folded RNA structures contribute to disease. In this review, we provide a brief introduction into RNA structural biology and then describe how RNA structures function in cells and cause or contribute to neurological disease. Finally, we highlight successful applications of rational design principles to provide chemical probes and lead compounds targeting structured RNAs. Based on several examples of well-characterized RNA-driven neurological disorders, we demonstrate how designed small molecules can facilitate study of RNA dysfunction, elucidating previously unknown roles for RNA in disease, and provide lead therapeutics. PMID:26139368

  18. RNA Structures as Mediators of Neurological Diseases and as Drug Targets.

    PubMed

    Bernat, Viachaslau; Disney, Matthew D

    2015-07-01

    RNAs adopt diverse folded structures that are essential for function and thus play critical roles in cellular biology. A striking example of this is the ribosome, a complex, three-dimensionally folded macromolecular machine that orchestrates protein synthesis. Advances in RNA biochemistry, structural and molecular biology, and bioinformatics have revealed other non-coding RNAs whose functions are dictated by their structure. It is not surprising that aberrantly folded RNA structures contribute to disease. In this Review, we provide a brief introduction into RNA structural biology and then describe how RNA structures function in cells and cause or contribute to neurological disease. Finally, we highlight successful applications of rational design principles to provide chemical probes and lead compounds targeting structured RNAs. Based on several examples of well-characterized RNA-driven neurological disorders, we demonstrate how designed small molecules can facilitate the study of RNA dysfunction, elucidating previously unknown roles for RNA in disease, and provide lead therapeutics.

  19. An application of site response functions to ground motion prediction

    NASA Astrophysics Data System (ADS)

    Tsuda, K.; Archuleta, R.; Steidl, J.; Koketsu, K.

    2006-12-01

    The prediction of ground motion from large future earthquakes is very important for hazard mitigation in urban areas of Japan. Because the observed ground motions are affected by three factors; the seismic source, attenuation (quality factor) of seismic wave propagation inside the earth, and the effects of the local surface geology, understanding each factor is essential for the ground motion prediction. The effect of surface geology (local soil conditions) on ground motions was documented as early as the 1906 San Francisco earthquake. The correlation between soil type and the degree of damage was again recognized in the 1923 Kanto earthquake. Additionally, accelerometer records from almost all recent large events also have reinforced the role of site effects in the level of strong shaking. Because most cities in Japan are located on thick sedimentary basins, accounting for site response is essential for realistic predictions of ground motion. However, predicting ground motion has uncertainties that arise from all three factors: source, path, and site. The analysis of well-recorded data from dense seismograph arrays can reduce these uncertainties for ground motion prediction. The new technique presented here provides a site response correlation function for estimation of the spatial distribution of site response. This function is based on the known site responses at instrumented sites and is used to estimate the site response at a site for which there is no instrumental records. We initially predict the level of ground motion by using this estimate with the assumption of linear wave propagation. This method is applied to the data from a relatively dense seismic array located near the city of Sendai, Japan by using moderate sized earthquakes with small ground motion levels to estimate linear site response. The array consists of 29 stations: 20 managed by Tohoku Institute of Technology, 6 by Building Research Institute, and 3 by NIED within an area of 20 x 30 km

  20. Identification and Validation of Small-Gatekeeper Kinases as Drug Targets in Giardia lamblia.

    PubMed

    Hennessey, Kelly M; Smith, Tess R; Xu, Jennifer W; Alas, Germain C M; Ojo, Kayode K; Merritt, Ethan A; Paredez, Alexander R

    2016-11-01

    Giardiasis is widely acknowledged to be a neglected disease in need of new therapeutics to address toxicity and resistance issues associated with the limited available treatment options. We examined seven protein kinases in the Giardia lamblia genome that are predicted to share an unusual structural feature in their active site. This feature, an expanded active site pocket resulting from an atypically small gatekeeper residue, confers sensitivity to "bumped" kinase inhibitors (BKIs), a class of compounds that has previously shown good pharmacological properties and minimal toxicity. An initial phenotypic screen for biological activity using a subset of an in-house BKI library found that 5 of the 36 compounds tested reduced trophozoite growth by at least 50% at a concentration of 5 μM. The cellular localization and the relative expression levels of the seven protein kinases of interest were determined after endogenously tagging the kinases. Essentiality of these kinases for parasite growth and infectivity were evaluated genetically using morpholino knockdown of protein expression to establish those that could be attractive targets for drug design. Two of the kinases were critical for trophozoite growth and attachment. Therefore, recombinant enzymes were expressed, purified and screened against a BKI library of >400 compounds in thermal stability assays in order to identify high affinity compounds. Compounds with substantial thermal stabilization effects on recombinant protein were shown to have good inhibition of cell growth in wild-type G. lamblia and metronidazole-resistant strains of G. lamblia. Our data suggest that BKIs are a promising starting point for the development of new anti-giardiasis therapeutics that do not overlap in mechanism with current drugs.

  1. Identification and Validation of Small-Gatekeeper Kinases as Drug Targets in Giardia lamblia

    PubMed Central

    Hennessey, Kelly M.; Smith, Tess R.; Xu, Jennifer W.; Alas, Germain C. M.; Ojo, Kayode K.; Merritt, Ethan A.

    2016-01-01

    Giardiasis is widely acknowledged to be a neglected disease in need of new therapeutics to address toxicity and resistance issues associated with the limited available treatment options. We examined seven protein kinases in the Giardia lamblia genome that are predicted to share an unusual structural feature in their active site. This feature, an expanded active site pocket resulting from an atypically small gatekeeper residue, confers sensitivity to “bumped” kinase inhibitors (BKIs), a class of compounds that has previously shown good pharmacological properties and minimal toxicity. An initial phenotypic screen for biological activity using a subset of an in-house BKI library found that 5 of the 36 compounds tested reduced trophozoite growth by at least 50% at a concentration of 5 μM. The cellular localization and the relative expression levels of the seven protein kinases of interest were determined after endogenously tagging the kinases. Essentiality of these kinases for parasite growth and infectivity were evaluated genetically using morpholino knockdown of protein expression to establish those that could be attractive targets for drug design. Two of the kinases were critical for trophozoite growth and attachment. Therefore, recombinant enzymes were expressed, purified and screened against a BKI library of >400 compounds in thermal stability assays in order to identify high affinity compounds. Compounds with substantial thermal stabilization effects on recombinant protein were shown to have good inhibition of cell growth in wild-type G. lamblia and metronidazole-resistant strains of G. lamblia. Our data suggest that BKIs are a promising starting point for the development of new anti-giardiasis therapeutics that do not overlap in mechanism with current drugs. PMID:27806042

  2. The fibrogenic actions of lung fibroblast-derived urokinase: a potential drug target in IPF

    PubMed Central

    Schuliga, Michael; Jaffar, Jade; Harris, Trudi; Knight, Darryl A; Westall, Glen; Stewart, Alastair G

    2017-01-01

    The role of urokinase plasminogen activator (uPA) in idiopathic pulmonary fibrosis (IPF) remains unclear. uPA-generated plasmin has potent fibrogenic actions involving protease activated receptor-1 (PAR-1) and interleukin-6 (IL-6). Here we characterize uPA distribution or levels in lung tissue and sera from IPF patients to establish the mechanism of its fibrogenic actions on lung fibroblasts (LFs). uPA immunoreactivity was detected in regions of fibrosis including fibroblasts of lung tissue from IPF patients (n = 7). Serum uPA levels and activity were also higher in IPF patients (n = 18) than controls (n = 18) (P < 0.05), being negatively correlated with lung function as measured by forced vital capacity (FVC) %predicted (P < 0.05). The culture supernatants of LFs from IPF patients, as compared to controls, showed an increase in plasmin activity after plasminogen incubation (5–15 μg/mL), corresponding with increased levels of uPA and IL-6 (n = 5–6, P < 0.05). Plasminogen-induced increases in plasmin activity and IL-6 levels were attenuated by reducing uPA and/or PAR-1 expression by RNAi. Plasmin(ogen)-induced mitogenesis was also attenuated by targeting uPA, PAR-1 or IL-6. Our data shows uPA is formed in active regions of fibrosis in IPF lung and contributes to LF plasmin generation, IL-6 production and proliferation. Urokinase is a potential target for the treatment of lung fibrosis. PMID:28139758

  3. Predicting radionuclide leaching from root zone soil for assessment applications

    SciTech Connect

    Baes, C.F. III.; Sharp, R.D.

    1981-01-01

    A simple leaching model is given for estimation of leaching rates of thirteen radioisotopes. A literature search with radionuclide migration data is presented in tabular form. This data must be considered in any model for predicting radiation doses from agricultural products or drinking water obtained from contaminated soils. (PSB)

  4. A Study on Predictive Analytics Application to Ship Machinery Maintenance

    DTIC Science & Technology

    2013-09-01

    whole ship stop running. 19 Controllable pitch propeller ship with shaft generator or offshore drilling unit circumstances It is quite normal...Data Warehouse Institute. Last modified May 27, 2013. http://tdwi.org/articles/2007/05/10/predictive-analytics.aspx?sc_lang=en. Fair Isaac Corporation

  5. 2-Aryl-5-carboxytetrazole as a New Photoaffinity Label for Drug Target Identification

    PubMed Central

    2016-01-01

    Photoaffinity labels are powerful tools for dissecting ligand–protein interactions, and they have a broad utility in medicinal chemistry and drug discovery. Traditional photoaffinity labels work through nonspecific C–H/X–H bond insertion reactions with the protein of interest by the highly reactive photogenerated intermediate. Herein, we report a new photoaffinity label, 2-aryl-5-carboxytetrazole (ACT), that interacts with the target protein via a unique mechanism in which the photogenerated carboxynitrile imine reacts with a proximal nucleophile near the target active site. In two distinct case studies, we demonstrate that the attachment of ACT to a ligand does not significantly alter the binding affinity and specificity of the parent drug. Compared with diazirine and benzophenone, two commonly used photoaffinity labels, in two case studies ACT showed higher photo-cross-linking yields toward their protein targets in vitro based on mass spectrometry analysis. In the in situ target identification studies, ACT successfully captured the desired targets with an efficiency comparable to the diazirine. We expect that further development of this class of photoaffinity labels will lead to a broad range of applications across target identification, and validation and elucidation of the binding site in drug discovery. PMID:27740749

  6. Histone deacetylase 3 (HDAC 3) as emerging drug target in NF-κB-mediated inflammation

    PubMed Central

    Leus, Niek G.J.; Zwinderman, Martijn R.H.; Dekker, Frank J.

    2016-01-01

    Activation of inflammatory gene expression is regulated, among other factors, by post-translational modifications of histone proteins. The most investigated type of histone modifications are lysine acetylations. Histone deacetylases (HDACs) remove acetylations from lysines, thereby influencing (inflammatory) gene expression. Intriguingly, apart from histones, HDACs also target non-histone proteins. The nuclear factor κB (NF-κB) pathway is an important regulator in the expression of numerous inflammatory genes, and acetylation plays a crucial role in regulating its responses. Several studies have shed more light on the role of HDAC 1-3 in inflammation with a particular pro-inflammatory role for HDAC 3. Nevertheless, the HDAC-NF-κB interactions in inflammatory signalling have not been fully understood. An important challenge in targeting the regulatory role of HDACs in the NF-κB pathway is the development of highly potent small molecules that selectively target HDAC iso-enzymes. This review focuses on the role of HDAC 3 in (NF-κB-mediated) inflammation and NF-κB lysine acetylation. In addition, we address the application of frequently used small molecule HDAC inhibitors as an approach to attenuate inflammatory responses, and their potential as novel therapeutics. Finally, recent progress and future directions in medicinal chemistry efforts aimed at HDAC 3-selective inhibitors are discussed. PMID:27371876

  7. Integrative exploration of genomic profiles for triple negative breast cancer identifies potential drug targets

    PubMed Central

    Wang, Xiaosheng; Guda, Chittibabu

    2016-01-01

    different types of genomic data to molecularly characterize TNBC and identify potential targets for TNBC therapy. The integrative analysis of genomic profiles for TNBC could assist in identifying potential new therapeutic targets and predicting the effectiveness of a targeted treatment strategy for TNBC therapy. PMID:27472710

  8. The Response Regulator BfmR Is a Potential Drug Target for Acinetobacter baumannii

    PubMed Central

    Manohar, Akshay; Beanan, Janet M.; Olson, Ruth; MacDonald, Ulrike; Graham, Jessica

    2016-01-01

    ABSTRACT Identification and validation is the first phase of target-based antimicrobial development. BfmR (RstA), a response regulator in a two-component signal transduction system (TCS) in Acinetobacter baumannii, is an intriguing potential antimicrobial target. A unique characteristic of BfmR is that its inhibition would have the dual benefit of significantly decreasing in vivo survival and increasing sensitivity to selected antimicrobials. Studies on the clinically relevant strain AB307-0294 have shown BfmR to be essential in vivo. Here, we demonstrate that this phenotype in strains AB307-0294 and AB908 is mediated, in part, by enabling growth in human ascites fluid and serum. Further, BfmR conferred resistance to complement-mediated bactericidal activity that was independent of capsular polysaccharide. Importantly, BfmR also increased resistance to the clinically important antimicrobials meropenem and colistin. BfmR was highly conserved among A. baumannii strains. The crystal structure of the receiver domain of BfmR was determined, lending insight into putative ligand binding sites. This enabled an in silico ligand binding analysis and a blind docking strategy to assess use as a potential druggable target. Predicted binding hot spots exist at the homodimer interface and the phosphorylation site. These data support pursuing the next step in the development process, which includes determining the degree of inhibition needed to impact growth/survival and the development a BfmR activity assay amenable to high-throughput screening for the identification of inhibitors. Such agents would represent a new class of antimicrobials active against A. baumannii which could be active against other Gram-negative bacilli that possess a TCS with shared homology. IMPORTANCE Increasing antibiotic resistance in bacteria, particularly Gram-negative bacilli, has significantly affected the ability of physicians to treat infections, with resultant increased morbidity, mortality, and

  9. Multistep prediction of physiological tremor for surgical robotics applications.

    PubMed

    Veluvolu, Kalyana C; Tatinati, Sivanagaraja; Hong, Sun-Mog; Ang, Wei Tech

    2013-11-01

    Accurate canceling of physiological tremor is extremely important in robotics-assisted surgical instruments/procedures. The performance of robotics-based hand-held surgical devices degrades in real time due to the presence of phase delay in sensors (hardware) and filtering (software) processes. Effective tremor compensation requires zero-phase lag in filtering process so that the filtered tremor signal can be used to regenerate an opposing motion in real time. Delay as small as 20 ms degrades the performance of human-machine interference. To overcome this phase delay, we employ multistep prediction in this paper. Combined with the existing tremor estimation methods, the procedure improves the overall accuracy by 60% for tremor estimation compared to single-step prediction methods in the presence of phase delay. Experimental results with developed methods for 1-DOF tremor estimation highlight the improvement.

  10. Multi-step prediction of physiological tremor for robotics applications.

    PubMed

    Veluvolu, K C; Tatinati, S; Hong, S M; Ang, W T

    2013-01-01

    The performance of surgical robotic devices in real-time mainly depends on phase-delay in sensors and filtering process. A phase delay of 16-20 ms is unavoidable in these robotics procedures due to the presence of hardware low pass filter in sensors and pre-filtering required in later stages of cancellation. To overcome this phase delay, we employ multi-step prediction with band limited multiple Fourier linear combiner (BMFLC) and Autoregressive (AR) methods. Results show that the overall accuracy is improved by 60% for tremor estimation compared to single-step prediction methods in the presence of phase delay. Experimental results with the proposed methods for 1-DOF tremor estimation highlight the improvement.

  11. Predictability of the coherent-noise model and its applications.

    PubMed

    Sarlis, N V; Christopoulos, S-R G

    2012-05-01

    We study the threshold distribution function of the coherent-noise model for the case of infinite number of agents. This function is piecewise constant with a finite number of steps n. The latter exhibits a 1/f behavior as a function of the order of occurrence of an avalanche and hence versus natural time. An analytic expression of the expectation value E(S) for the size S of the next avalanche is obtained and used for the prediction of the next avalanche. Apart from E(S), the number of steps n can also serve for this purpose. This enables the construction of a similar prediction scheme which can be applied to real earthquake aftershock data.

  12. RFI modeling and prediction approach for SATOPS applications

    NASA Astrophysics Data System (ADS)

    Nguyen, Tien M.; Tran, Hien T.; Wang, Zhonghai; Coons, Amanda; Nguyen, Charles C.; Lane, Steven A.; Pham, Khanh D.; Chen, Genshe; Wang, Gang

    2015-05-01

    This paper describes innovative frameworks to develop RFI modeling and prediction models for (i) estimating the RFI characteristics, (ii) evaluating effectiveness of the existing Unified S-Band (USB) command waveforms employed by civil, commercial and military SATOPS ground stations, and (iii) predicting the impacts of RFI on USB command systems. The approach presented here will allow the communications designer to characterize both friendly and unfriendly RFI sources, and evaluate the impacts of RFI on civil, commercial and military USB SATOPS systems. In addition, the proposed frameworks allow the designer to estimate the optimum transmitted signal power to maintain a required USB SATOPS Quality-of-Service (QoS) in the presence of both friendly and unfriendly RFI sources.

  13. Economic decision making and the application of nonparametric prediction models

    USGS Publications Warehouse

    Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

    2008-01-01

    Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly. Copyright ?? 2008 Society of Petroleum Engineers.

  14. RFI modeling and prediction approach for SATOP applications: RFI prediction models

    NASA Astrophysics Data System (ADS)

    Nguyen, Tien M.; Tran, Hien T.; Wang, Zhonghai; Coons, Amanda; Nguyen, Charles C.; Lane, Steven A.; Pham, Khanh D.; Chen, Genshe; Wang, Gang

    2016-05-01

    This paper describes a technical approach for the development of RFI prediction models using carrier synchronization loop when calculating Bit or Carrier SNR degradation due to interferences for (i) detecting narrow-band and wideband RFI signals, and (ii) estimating and predicting the behavior of the RFI signals. The paper presents analytical and simulation models and provides both analytical and simulation results on the performance of USB (Unified S-Band) waveforms in the presence of narrow-band and wideband RFI signals. The models presented in this paper will allow the future USB command systems to detect the RFI presence, estimate the RFI characteristics and predict the RFI behavior in real-time for accurate assessment of the impacts of RFI on the command Bit Error Rate (BER) performance. The command BER degradation model presented in this paper also allows the ground system operator to estimate the optimum transmitted SNR to maintain a required command BER level in the presence of both friendly and un-friendly RFI sources.

  15. Application of a predictive Bayesian model to environmental accounting.

    PubMed

    Anex, R P; Englehardt, J D

    2001-03-30

    Environmental accounting techniques are intended to capture important environmental costs and benefits that are often overlooked in standard accounting practices. Environmental accounting methods themselves often ignore or inadequately represent large but highly uncertain environmental costs and costs conditioned by specific prior events. Use of a predictive Bayesian model is demonstrated for the assessment of such highly uncertain environmental and contingent costs. The predictive Bayesian approach presented generates probability distributions for the quantity of interest (rather than parameters thereof). A spreadsheet implementation of a previously proposed predictive Bayesian model, extended to represent contingent costs, is described and used to evaluate whether a firm should undertake an accelerated phase-out of its PCB containing transformers. Variability and uncertainty (due to lack of information) in transformer accident frequency and severity are assessed simultaneously using a combination of historical accident data, engineering model-based cost estimates, and subjective judgement. Model results are compared using several different risk measures. Use of the model for incorporation of environmental risk management into a company's overall risk management strategy is discussed.

  16. Cell-Based Selection Expands the Utility of DNA-Encoded Small-Molecule Library Technology to Cell Surface Drug Targets: Identification of Novel Antagonists of the NK3 Tachykinin Receptor.

    PubMed

    Wu, Zining; Graybill, Todd L; Zeng, Xin; Platchek, Michael; Zhang, Jean; Bodmer, Vera Q; Wisnoski, David D; Deng, Jianghe; Coppo, Frank T; Yao, Gang; Tamburino, Alex; Scavello, Genaro; Franklin, G Joseph; Mataruse, Sibongile; Bedard, Katie L; Ding, Yun; Chai, Jing; Summerfield, Jennifer; Centrella, Paolo A; Messer, Jeffrey A; Pope, Andrew J; Israel, David I

    2015-12-14

    DNA-encoded small-molecule library technology has recently emerged as a new paradigm for identifying ligands against drug targets. To date, this technology has been used with soluble protein targets that are produced and used in a purified state. Here, we describe a cell-based method for identifying small-molecule ligands from DNA-encoded libraries against integral membrane protein targets. We use this method to identify novel, potent, and specific inhibitors of NK3, a member of the tachykinin family of G-protein coupled receptors (GPCRs). The method is simple and broadly applicable to other GPCRs and integral membrane proteins. We have extended the application of DNA-encoded library technology to membrane-associated targets and demonstrate the feasibility of selecting DNA-tagged, small-molecule ligands from complex combinatorial libraries against targets in a heterogeneous milieu, such as the surface of a cell.

  17. Computational repositioning of ethno medicine elucidated gB-gH-gL complex as novel anti herpes drug target

    PubMed Central

    2013-01-01

    Background Herpes viruses are important human pathogens that can cause mild to severe lifelong infections with high morbidity. They remain latent in the host cells and can cause recurrent infections that might prove fatal. These viruses are known to infect the host cells by causing the fusion of viral and host cell membrane proteins. Fusion is achieved with the help of conserved fusion machinery components, glycoproteins gB, heterodimer gH-gL complex along with other non-conserved components. Whereas, another important glycoprotein gD without which viral entry to the cell is not possible, acts as a co-activator for the gB-gH-gL complex formation. Thus, this complex formation interface is the most promising drug target for the development of novel anti-herpes drug candidates. In the present study, we propose a model for binding of gH-gL to gB glycoprotein leading from pre to post conformational changes during gB-gH-gL complex formation and reported the key residues involved in this binding activity along with possible binding site locations. To validate the drug targetability of our proposed binding site, we have repositioned some of the most promising in vitro, in vivo validated anti-herpes molecules onto the proposed binding site of gH-gL complex in a computational approach. Methods Hex 6.3 standalone software was used for protein-protein docking studies. Arguslab 4.0.1 and Accelrys® Discovery Studio 3.1 Visualizer softwares were used for semi-flexible docking studies and visualizing the interactions respectively. Protein receptors and ethno compounds were retrieved from Protein Data Bank (PDB) and Pubchem databases respectively. Lipinski’s Filter, Osiris Property Explorer and Lazar online servers were used to check the pharmaceutical fidelity of the drug candidates. Results Through protein-protein docking studies, it was identified that the amino acid residues VAL342, GLU347, SER349, TYR355, SER388, ASN395, HIS398 and ALA387 of gH-gL complex play an active

  18. A Nonlinear Model Predictive Observer for Smart Projectile Applications

    DTIC Science & Technology

    2008-03-01

    Because of a large number of applications, a significant literature base has amassed for nonlinear observers (12, 13, 14). Algebraic methods have...Conference, Snowmass, CO, 1985. 6. Lam, Q.; Pal, P.; Welch, R.; Grossman , W. Magnetometer Based Attitude Determination System using a Reduced Kalman

  19. Fire occurrence prediction in the Mediterranean: Application to Southern France

    NASA Astrophysics Data System (ADS)

    Papakosta, Panagiota; Öster, Jan; Scherb, Anke; Straub, Daniel

    2013-04-01

    The areas that extend in the Mediterranean basin have a long fire history. The climatic conditions of wet winters and long hot drying summers support seasonal fire events, mainly ignited by humans. Extended land fragmentation hinders fire spread, but seasonal winds (e.g. Mistral in South France or Meltemia in Greece) can drive fire events to become uncontrollable fires with severe impacts to humans and the environment [1]. Prediction models in these areas should incorporate both natural and anthropogenic factors. Several indices have been developed worldwide to express fire weather conditions. The Canadian Fire Weather Index (FWI) is currently adapted by many countries in Europe due to the easily observable input weather parameters (temperature, wind speed, relative humidity, precipitation) and the easy-to-implement algorithms of the Canadian formulation describing fuel moisture relations [2],[3]. Human influence can be expressed directly by human presence (e.g. population density) or indirectly by proxy indicators (e.g. street density [4], land cover type). The random nature of fire occurrences and the uncertainties associated with the influencing factors motivate probabilistic prediction models. The aim of this study is to develop a prediction model of fire occurrence probability under natural and anthropogenic influence in Southern France and to compare it with earlier developed predictions in other Mediterranean areas [5]. Fire occurrence is modeled as a Poisson process. Two interpolation methods (Kriging and Inverse Distance Weighting) are used to interpolate daily weather observations from weather stations to a 1 km² spatial grid and their results are compared. Poisson regression estimates the parameters of the model and the resulting daily predictions are provided in terms of maps displaying fire occurrence rates. The model is applied to the regions Provence-Alpes-Côtes D'Azur und Languedoc-Roussillon in the South of France. Weather data are obtained from

  20. Fundamental Mechanisms, Predictive Modeling, and Novel Aerospace Applications of Plasma Assisted Combustion

    DTIC Science & Technology

    2011-11-01

    Fundamental Mechanisms, Predictive Modeling, and Novel Aerospace Applications of Plasma Assisted Combustion Yiguang Ju AFOSR MURI Review Meeting...SUBTITLE Fundamental Mechanisms, Predictive Modeling, and Novel Aerospace Applications of Plasma Assisted Combustion 5a. CONTRACT NUMBER 5b. GRANT...stabilization • Combustion completion F135 engine: (F35, 2011) Mach 6-8 Ignition instability Plasma assisted combustion Plasma Ions/electrons Excited species

  1. Nonlinear system identification with applications to space weather prediction

    NASA Astrophysics Data System (ADS)

    Palanthandalam-Madapusi, Harish J.

    2007-02-01

    System identification is the process of constructing empirical mathematical models of dynamcal systems using measured data. Since data represents a key link between mathematical principles and physical processes, system identification is an important research area that can benefit all disciplines. In this dissertation, we develop identification methods for Hammerstein-Wiener models, which are model structures based on the interconnection of linear dynamics and static nonlinearities. These identification methods identify models in state-space form and use known basis functions to represent the unknown nonlinear maps. Next, we use these methods to identify periodically- switching Hammerstein-Wiener models for predicting magnetic-field fluctuation on the surface of the Earth, 30 to 90 minutes into the future. These magnetic- field fluctuations caused by the solar wind (ejections of charged plasma from the surface of the Sun) can damage critical systems aboard satellites and drive currents in power grids that can overwhelm and damage transformers. By predicting magnetic-field fluctuations on the Earth, we obtain advance warning of future disturbances. Furthermore, to predict solar wind conditions 27 days in advance, we use solar wind measurements and image measurements to construct nonlinear time-series models. We propose a class of radial basis functions to represent the nonlinear maps, which have fewer parameters that need to be tuned by the user. Additionally, we develop an identification algorithm that simultaneously identifies the state space matrices of an unknown model and reconstructs the unknown input, using output measurements and known inputs. For this purpose, we formulate the concept of input and state observability, that is, conditions under which both the unknown input and initial state of a known model can be determined from output measurements. We provide necessary and sufficient conditions for input and state observability in discrete-time systems.

  2. Life prediction of advanced materials for gas turbine application

    SciTech Connect

    Zamrik, S.Y.; Ray, A.; Koss, D.A.

    1995-10-01

    Most of the studies on the low cycle fatigue life prediction have been reported under isothermal conditions where the deformation of the material is strain dependent. In the development of gas turbines, components such as blades and vanes are exposed to temperature variations in addition to strain cycling. As a result, the deformation process becomes temperature and strain dependent. Therefore, the life of the component becomes sensitive to temperature-strain cycling which produces a process known as {open_quotes}thermomechanical fatigue, or TMF{close_quotes}. The TMF fatigue failure phenomenon has been modeled using conventional fatigue life prediction methods, which are not sufficiently accurate to quantitatively establish an allowable design procedure. To add to the complexity of TMF life prediction, blade and vane substrates are normally coated with aluminide, overlay or thermal barrier type coatings (TBC) where the durability of the component is dominated by the coating/substrate constitutive response and by the fatigue behavior of the coating. A number of issues arise from TMF depending on the type of temperature/strain phase cycle: (1) time-dependent inelastic behavior can significantly affect the stress response. For example, creep relaxation during a tensile or compressive loading at elevated temperatures leads to a progressive increase in the mean stress level under cyclic loading. (2) the mismatch in elastic and thermal expansion properties between the coating and the substrate can lead to significant deviations in the coating stress levels due to changes in the elastic modulii. (3) the {open_quotes}dry{close_quotes} corrosion resistance coatings applied to the substrate may act as primary crack initiation sites. Crack initiation in the coating is a function of the coating composition, its mechanical properties, creep relaxation behavior, thermal strain range and the strain/temperature phase relationship.

  3. Applications of URANS on predicting unsteady turbulent separated flows

    NASA Astrophysics Data System (ADS)

    Xu, Jinglei; Ma, Huiyang

    2009-06-01

    Accurate prediction of unsteady separated turbulent flows remains one of the toughest tasks and a practical challenge for turbulence modeling. In this paper, a 2D flow past a circular cylinder at Reynolds number 3,900 is numerically investigated by using the technique of unsteady RANS (URANS). Some typical linear and nonlinear eddy viscosity turbulence models (LEVM and NLEVM) and a quadratic explicit algebraic stress model (EASM) are evaluated. Numerical results have shown that a high-performance cubic NLEVM, such as CLS, are superior to the others in simulating turbulent separated flows with unsteady vortex shedding.

  4. Artificial Neural Networks: A New Approach for Predicting Application Behavior. AIR 2001 Annual Forum Paper.

    ERIC Educational Resources Information Center

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

    This paper examines how predictive modeling can be used to study application behavior. A relatively new technique, artificial neural networks (ANNs), was applied to help predict which students were likely to get into a large Research I university. Data were obtained from a university in Iowa. Two cohorts were used, each containing approximately…

  5. Application of Sampling Based Model Predictive Control to an Autonomous Underwater Vehicle

    DTIC Science & Technology

    2010-07-01

    55 Application of Sampling Based Model Predictive Control to an Autonomous Underwater Vehicle Unmanned Underwater Vehicles (UUVs) can be utilized...the vehicle can feasibly traverse. As a result, Sampling- Based Model Predictive Control (SBMPC) is proposed to simultaneously generate control...inputs and system trajectories for an autonomous underwater vehicle (AUV). The algorithm combines the benefits of sampling- based motion planning with

  6. Predicting Adolescent Sexual and Contraceptive Behavior: An Application and Test of the Fishbein Model.

    ERIC Educational Resources Information Center

    Jorgensen, Stephen R.; Sonstegard, Janet S.

    1984-01-01

    Presents a test of the Fishbein model of behavior prediction applied to predict the pregnancy risk-taking behavior of adolescent females (N=244). Analyses of data showed that the Fishbein model of attitude-behavior consistency seems to be applicable to the fertility-related behavior of adolescent females. (LLL)

  7. Understanding of Drug-Target Interactions: A case Study in Influenza Virus A Subtype H5N1

    NASA Astrophysics Data System (ADS)

    Rungrotmongkol, Thanyada; Malaisree, Maturos; Decha, Panita; Laohpongspaisan, Chittima; Aruksakunwong, Ornjira; Intharathep, Pathumwadee; Pianwanit, Somsak; Sompornpisut, Pornthep; Parasuk, Vudhichai; Megnassan, Eugene; Frecer, Vladimir; Miertus, Stanislav; Hannongbua, Supot

    2007-12-01

    This study aims at gaining insight into molecular mechanisms of action of three drug targets of the life cycle of influenza virus A subtype H5N1, namely Hemagglutinin (H5), Neuraminidase (N1) and M2 ion channel (M2), using molecular mechanics and molecular dynamics techniques. In hemagglutinin, interest is focused on the high pathogenicity of the H5 due to the -RRRKK- insertion. MD simulations carried out for H5 in both high and low pathogenic forms (HPH5 and LPH5), aimed at understanding why HPH5 was experimentally observed to be 5-fold better cleaved by furin relative to the non-inserted sequence of LPH5. As the results, the cleavage loop of HPH5 was found to fit well and bind strongly into the catalytic site of human furin, serving as a conformation suitable for the proteolytic reaction. The second target, neuraminidase was studied by two different approaches. Firstly with MD simulations, rotation of the -NHAc and—OCHEt2 side chains of oseltamivir (OTV), leading directly to rearrangement of the catalytic cavity, was found to be a primary source of the lower susceptibility of OTV to neuraminidase subtype N1 than to N2 and N9. In addition, three inhibitiors, OTV, zanamivir (ZNV) and peramivir (PRV), complexed with neuraminidase subtype N1 were studied to understand the drug-target interactions. The structural properties, position and conformation of PRV and its side chains are uniformly preferential, i.e., its conformation fits very well with the N1 active site. At the N1 target, another approach, combinatorial chemistry, was used to design a library of new potent inhibitors, which well fit to the active site and the 150-loop residues of N1. Investigation was also extended to the M2 proton channel. Five different protonation states of the selectivity filter residue (His) where 0H, 1H, 2aH, 2dH and 4H represent the systems with none, mono-protonated, di-protonated at adjacent and opposite positions, and tetra-protonated, respectively, were taken into account both

  8. Cloud-based Predictive Modeling System and its Application to Asthma Readmission Prediction.

    PubMed

    Chen, Robert; Su, Hang; Khalilia, Mohammed; Lin, Sizhe; Peng, Yue; Davis, Tod; Hirsh, Daniel A; Searles, Elizabeth; Tejedor-Sojo, Javier; Thompson, Michael; Sun, Jimeng

    The predictive modeling process is time consuming and requires clinical researchers to handle complex electronic health record (EHR) data in restricted computational environments. To address this problem, we implemented a cloud-based predictive modeling system via a hybrid setup combining a secure private server with the Amazon Web Services (AWS) Elastic MapReduce platform. EHR data is preprocessed on a private server and the resulting de-identified event sequences are hosted on AWS. Based on user-specified modeling configurations, an on-demand web service launches a cluster of Elastic Compute 2 (EC2) instances on AWS to perform feature selection and classification algorithms in a distributed fashion. Afterwards, the secure private server aggregates results and displays them via interactive visualization. We tested the system on a pediatric asthma readmission task on a de-identified EHR dataset of 2,967 patients. We conduct a larger scale experiment on the CMS Linkable 2008-2010 Medicare Data Entrepreneurs' Synthetic Public Use File dataset of 2 million patients, which achieves over 25-fold speedup compared to sequential execution.

  9. Prediction of three sigma maximum dispersed density for aerospace applications

    NASA Technical Reports Server (NTRS)

    Charles, Terri L.; Nitschke, Michael D.

    1993-01-01

    Free molecular heating (FMH) is caused by the transfer of energy during collisions between the upper atmosphere molecules and a space vehicle. The dispersed free molecular heating on a surface is an important constraint for space vehicle thermal analyses since it can be a significant source of heating. To reduce FMH to a spacecraft, the parking orbit is often designed to a higher altitude at the expense of payload capability. Dispersed FMH is a function of both space vehicle velocity and atmospheric density, however, the space vehicle velocity variations are insignificant when compared to the atmospheric density variations. The density of the upper atmosphere molecules is a function of altitude, but also varies with other environmental factors, such as solar activity, geomagnetic activity, location, and time. A method has been developed to predict three sigma maximum dispersed density for up to 15 years into the future. This method uses a state-of-the-art atmospheric density code, MSIS 86, along with 50 years of solar data, NASA and NOAA solar activity predictions for the next 15 years, and an Aerospace Corporation correlation to account for density code inaccuracies to generate dispersed maximum density ratios denoted as 'K-factors'. The calculated K-factors can be used on a mission unique basis to calculate dispersed density, and hence dispersed free molecular heating rates. These more accurate K-factors can allow lower parking orbit altitudes, resulting in increased payload capability.

  10. Application of Functional Use Predictions to Aid in Structure ...

    EPA Pesticide Factsheets

    Humans are potentially exposed to thousands of anthropogenic chemicals in commerce. Recent work has shown that the bulk of this exposure may occur in near-field indoor environments (e.g., home, school, work, etc.). Advances in suspect screening analyses (SSA) now allow an improved understanding of the chemicals present in these environments. However, due to the nature of suspect screening techniques, investigators are often left with chemical formula predictions, with the possibility of many chemical structures matching to each formula. Here, newly developed quantitative structure-use relationship (QSUR) models are used to identify potential exposure sources for candidate structures. Previously, a suspect screening workflow was introduced and applied to house dust samples collected from the U.S. Department of Housing and Urban Development’s American Healthy Homes Survey (AHHS) [Rager, et al., Env. Int. 88 (2016)]. This workflow utilized the US EPA’s Distributed Structure-Searchable Toxicity (DSSTox) Database to link identified molecular features to molecular formulas, and ultimately chemical structures. Multiple QSUR models were applied to support the evaluation of candidate structures. These QSURs predict the likelihood of a chemical having a functional use commonly associated with consumer products having near-field use. For 3,228 structures identified as possible chemicals in AHHS house dust samples, we were able to obtain the required descriptors to appl

  11. Non-linear aeroelastic prediction for aircraft applications

    NASA Astrophysics Data System (ADS)

    de C. Henshaw, M. J.; Badcock, K. J.; Vio, G. A.; Allen, C. B.; Chamberlain, J.; Kaynes, I.; Dimitriadis, G.; Cooper, J. E.; Woodgate, M. A.; Rampurawala, A. M.; Jones, D.; Fenwick, C.; Gaitonde, A. L.; Taylor, N. V.; Amor, D. S.; Eccles, T. A.; Denley, C. J.

    2007-05-01

    Current industrial practice for the prediction and analysis of flutter relies heavily on linear methods and this has led to overly conservative design and envelope restrictions for aircraft. Although the methods have served the industry well, it is clear that for a number of reasons the inclusion of non-linearity in the mathematical and computational aeroelastic prediction tools is highly desirable. The increase in available and affordable computational resources, together with major advances in algorithms, mean that non-linear aeroelastic tools are now viable within the aircraft design and qualification environment. The Partnership for Unsteady Methods in Aerodynamics (PUMA) Defence and Aerospace Research Partnership (DARP) was sponsored in 2002 to conduct research into non-linear aeroelastic prediction methods and an academic, industry, and government consortium collaborated to address the following objectives: To develop useable methodologies to model and predict non-linear aeroelastic behaviour of complete aircraft. To evaluate the methodologies on real aircraft problems. To investigate the effect of non-linearities on aeroelastic behaviour and to determine which have the greatest effect on the flutter qualification process. These aims have been very effectively met during the course of the programme and the research outputs include: New methods available to industry for use in the flutter prediction process, together with the appropriate coaching of industry engineers. Interesting results in both linear and non-linear aeroelastics, with comprehensive comparison of methods and approaches for challenging problems. Additional embryonic techniques that, with further research, will further improve aeroelastics capability. This paper describes the methods that have been developed and how they are deployable within the industrial environment. We present a thorough review of the PUMA aeroelastics programme together with a comprehensive review of the relevant research

  12. Decision tree methods: applications for classification and prediction

    PubMed Central

    SONG, Yan-yan; LU, Ying

    2015-01-01

    Summary Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure. PMID:26120265

  13. Application of artificial neural networks for prediction of photocatalytic reactor.

    PubMed

    Delnavaz, Mohammad

    2015-02-01

    In this paper, forecasting of kinetic constant and efficiency of photocatalytic process of TiO2 nano powder immobilized on light expanded clay aggregates (LECA) was investigated. Synthetic phenolic wastewater, which is toxic and not easily biodegradable, was selected as the pollutant. The efficiency of the process in various operation conditions, including initial phenol concentration, pH, TiO2 concentration, retention time, and UV lamp intensity, was then measured. The TiO2 nano powder was immobilized on LECA using slurry and sol-gel methods. Kinetics of photocatalytic reactions has been proposed to follow the Langmuir-Hinshelwood model in different initial phenol concentration and pH. Several steps of training and testing of the models were used to determine the appropriate architecture of the artificial neural network models (ANNs). The ANN-based models were found to provide an efficient and robust tool in predicting photocatalytic reactor efficiency and kinetic constant for treating phenolic compounds.

  14. Identification and Evaluation of Novel Drug Targets against the Human Fungal Pathogen Aspergillus fumigatus with Elaboration on the Possible Role of RNA-Binding Protein

    PubMed Central

    Malekzadeh, Saeid; Sardari, Soroush; Azerang, Parisa; Khorasanizadeh, Dorsa; Amiri, Solmaz Agha; Azizi, Mohammad; Mohajerani, Nazanin; Khalaj, Vahid

    2017-01-01

    Bakground: Aspergillus fumigatus is an airborne opportunistic fungal pathogen that can cause fatal infections in immunocompromised patients. Although the current anti-fungal therapies are relatively efficient, some issues such as drug toxicity, drug interactions, and the emergence of drug-resistant fungi have promoted the intense research toward finding the novel drug targets. Methods: In search of new antifungal drug targets, we have used a bioinformatics approach to identify novel drug targets. We compared the whole proteome of this organism with yeast Saccharomyces cerevisiae to come up with 153 specific proteins. Further screening of these proteins revealed 50 potential molecular targets in A. fumigatus. Amongst them, RNA-binding protein (RBP) was selected for further examination. The aspergillus fumigatus RBP (AfuRBP), as a peptidylprolyl isomerase, was evaluated by homology modeling and bioinformatics tools. RBP-deficient mutant strains of A. fumigatus were generated and characterized. Furthermore, the susceptibility of these strains to known peptidylprolyl isomerase inhibitors was assessed. Results: AfuRBP-deficient mutants demonstrated a normal growth phenotype. MIC assay results using inhibitors of peptidylprolyl isomerase confirmed a higher sensitivity of these mutants compared to the wild type. Conclusion: Our bioinformatics approach revealed a number of fungal-specific proteins that may be considered as new targets for drug discovery purposes. Peptidylprolyl isomerase, as a possible drug target, was evaluated against two potential inhibitors, and the promising results were investigated mechanistically. Future studies would confirm the impact of such target on the antifungal discovery investigations PMID:28000798

  15. Proteome mining for the identification and in-silico characterization of putative drug targets of multi-drug resistant Clostridium difficile strain 630.

    PubMed

    Lohani, Mohtashim; Dhasmana, Anupam; Haque, Shafiul; Wahid, Mohd; Jawed, Arshad; Dar, Sajad A; Mandal, Raju K; Areeshi, Mohammed Y; Khan, Saif

    2017-05-01

    Clostridium difficile is an enteric pathogen that causes approximately 20% to 30% of antibiotic-associated diarrhea. In recent years, there has been a substantial rise in the rate of C. difficile infections as well as the emergence of virulent and antibiotic resistant C. difficile strains. So, there is an urgent need for the identification of therapeutic potential targets and development of new drugs for the treatment and prevention of C. difficile infections. In the current study, we used a hybrid approach by combining sequence similarity-based approach and protein-protein interaction network topology-based approach to identify and characterize the potential drug targets of C. difficile. A total of 155 putative drug targets of C. difficile were identified and the metabolic pathway analysis of these putative drug targets using DAVID revealed that 46 of them are involved in 9 metabolic pathways. In-silico characterization of these proteins identified seven proteins involved in pathogen-specific peptidoglycan biosynthesis pathway. Three promising targets viz. homoserine dehydrogenase, aspartate-semialdehyde dehydrogenase and aspartokinase etc. were found to be involved in multiple enzymatic pathways of the pathogen. These 3 drug targets are of particular interest as they can be used for developing effective drugs against multi-drug resistant C. difficile strain 630 in the near future.

  16. Integrated analysis of numerous heterogeneous gene expression profiles for detecting robust disease-specific biomarkers and proposing drug targets.

    PubMed

    Amar, David; Hait, Tom; Izraeli, Shai; Shamir, Ron

    2015-09-18

    Genome-wide expression profiling has revolutionized biomedical research; vast amounts of expression data from numerous studies of many diseases are now available. Making the best use of this resource in order to better understand disease processes and treatment remains an open challenge. In particular, disease biomarkers detected in case-control studies suffer from low reliability and are only weakly reproducible. Here, we present a systematic integrative analysis methodology to overcome these shortcomings. We assembled and manually curated more than 14,000 expression profiles spanning 48 diseases and 18 expression platforms. We show that when studying a particular disease, judicious utilization of profiles from other diseases and information on disease hierarchy improves classification quality, avoids overoptimistic evaluation of that quality, and enhances disease-specific biomarker discovery. This approach yielded specific biomarkers for 24 of the analyzed diseases. We demonstrate how to combine these biomarkers with large-scale interaction, mutation and drug target data, forming a highly valuable disease summary that suggests novel directions in disease understanding and drug repurposing. Our analysis also estimates the number of samples required to reach a desired level of biomarker stability. This methodology can greatly improve the exploitation of the mountain of expression profiles for better disease analysis.

  17. Molecular drug targets in myeloproliferative neoplasms: mutant ABL1, JAK2, MPL, KIT, PDGFRA, PDGFRB and FGFR1

    PubMed Central

    Tefferi, Ayalew

    2009-01-01

    Abstract Therapeutically validated oncoproteins in myeloproliferative neoplasms (MPN) include BCR-ABL1 and rearranged PDGFR proteins. The latter are products of intra- (e.g. FIP1L1-PDGFRA) or inter-chromosomal (e.g.ETV6-PDGFRB) gene fusions. BCR-ABL1 is associated with chronic myelogenous leukaemia (CML) and mutant PDGFR with an MPN phenotype characterized by eosinophilia and in addition, in case of FIP1L1-PDGFRA, bone marrow mastocytosis. These genotype-phenotype associations have been effectively exploited in the development of highly accurate diagnostic assays and molecular targeted therapy. It is hoped that the same will happen in other MPN with specific genetic alterations: polycythemia vera (JAK2V617F and other JAK2 mutations), essential thrombocythemia (JAK2V617F and MPL515 mutations), primary myelofibrosis (JAK2V617F and MPL515 mutations), systemic mastocytosis (KITD816V and other KIT mutations) and stem cell leukaemia/lymphoma (ZNF198-FGFR1 and other FGFR1 fusion genes). The current review discusses the above-listed mutant molecules in the context of their value as drug targets. PMID:19175693

  18. Sequence-motif Detection of NAD(P)-binding Proteins: Discovery of a Unique Antibacterial Drug Target

    NASA Astrophysics Data System (ADS)

    Hua, Yun Hao; Wu, Chih Yuan; Sargsyan, Karen; Lim, Carmay

    2014-09-01

    Many enzymes use nicotinamide adenine dinucleotide or nicotinamide adenine dinucleotide phosphate (NAD(P)) as essential coenzymes. These enzymes often do not share significant sequence identity and cannot be easily detected by sequence homology. Previously, we determined all distinct locally conserved pyrophosphate-binding structures (3d motifs) from NAD(P)-bound protein structures, from which 1d sequence motifs were derived. Here, we aim to establish the precision of these 3d and 1d motifs to annotate NAD(P)-binding proteins. We show that the pyrophosphate-binding 3d motifs are characteristic of NAD(P)-binding proteins, as they are rarely found in nonNAD(P)-binding proteins. Furthermore, several 1d motifs could distinguish between proteins that bind only NAD and those that bind only NADP. They could also distinguish between NAD(P)-binding proteins from nonNAD(P)-binding ones. Interestingly, one of the pyrophosphate-binding 3d and corresponding 1d motifs was found only in enoyl-acyl carrier protein reductases, which are enzymes essential for bacterial fatty acid biosynthesis. This unique 3d motif serves as an attractive novel drug target, as it is conserved across many bacterial species and is not found in human proteins.

  19. The Inosine Monophosphate Dehydrogenase, GuaB2, Is a Vulnerable New Bactericidal Drug Target for Tuberculosis

    PubMed Central

    2016-01-01

    VCC234718, a molecule with growth inhibitory activity against Mycobacterium tuberculosis (Mtb), was identified by phenotypic screening of a 15344-compound library. Sequencing of a VCC234718-resistant mutant identified a Y487C substitution in the inosine monophosphate dehydrogenase, GuaB2, which was subsequently validated to be the primary molecular target of VCC234718 in Mtb. VCC234718 inhibits Mtb GuaB2 with a Ki of 100 nM and is uncompetitive with respect to IMP and NAD+. This compound binds at the NAD+ site, after IMP has bound, and makes direct interactions with IMP; therefore, the inhibitor is by definition uncompetitive. VCC234718 forms strong pi interactions with the Y487 residue side chain from the adjacent protomer in the tetramer, explaining the resistance-conferring mutation. In addition to sensitizing Mtb to VCC234718, depletion of GuaB2 was bactericidal in Mtb in vitro and in macrophages. When supplied at a high concentration (≥125 μM), guanine alleviated the toxicity of VCC234718 treatment or GuaB2 depletion via purine salvage. However, transcriptional silencing of guaB2 prevented Mtb from establishing an infection in mice, confirming that Mtb has limited access to guanine in this animal model. Together, these data provide compelling validation of GuaB2 as a new tuberculosis drug target. PMID:27726334

  20. Associating Drugs, Targets and Clinical Outcomes into an Integrated Network Affords a New Platform for Computer-Aided Drug Repurposing

    PubMed Central

    Oprea, Tudor I.; Nielsen, Sonny Kim; Ursu, Oleg; Yang, Jeremy J.; Taboureau, Olivier; Mathias, Stephen L.; Kouskoumvekaki, lrene; Sklar, Larry A.; Bologa, Cristian G.

    2012-01-01

    Finding new uses for old drugs is a strategy embraced by the pharmaceutical industry, with increasing participation from the academic sector. Drug repurposing efforts focus on identifying novel modes of action, but not in a systematic manner. With intensive data mining and curation, we aim to apply bio- and cheminformatics tools using the DRUGS database, containing 3,837 unique small molecules annotated on 1,750 proteins. These are likely to serve as drug targets and antitargets (i.e., associated with side effects, SE). The academic community, the pharmaceutical sector and clinicians alike could benefit from an integrated, semantic-web compliant computer-aided drug repurposing (CADR) effort, one that would enable deep data mining of associations between approved drugs (D), targets (T), clinical outcomes (CO) and SE. We report preliminary results from text mining and multivariate statistics, based on 7,684 approved drug labels, ADL (Dailymed) via text mining. From the ADL corresponding to 988 unique drugs, the “adverse reactions” section was mapped onto 174 SE, then clustered via principal component analysis into a 5x5 self-organizing map that was integrated into a Cytoscape network of SE-D-T-CO. This type of data can be used to streamline drug repurposing and may result in novel insights that can lead to the identification of novel drug actions. PMID:22287994

  1. Influenza A Virus Nucleoprotein: A Highly Conserved Multi-Functional Viral Protein As A Hot Antiviral Drug Target.

    PubMed

    Hu, Yanmei; Sneyd, Hannah; Dekant, Raphael; Wang, Jun

    2017-02-24

    Prevention and treatment of influenza virus infection is an ongoing unmet medical need. Each year, thousands of deaths and millions of hospitalizations are attributed to influenza virus infection, which poses a tremendous health and economic burden to the society. Aside from the annual influenza season, influenza viruses also lead to occasional influenza pandemics as a result of emerging or re-emerging influenza strains. Influenza viruses are RNA viruses that exist in quasispecies, meaning that they have a very diverse genetic background. Such a feature creates a grand challenge in devising therapeutic intervention strategies to inhibit influenza virus replication, as a single agent might not be able to inhibit all influenza virus strains. Both classes of currently approved anti-influenza drugs have limitations: the M2 channel blockers amantadine and rimantadine are no longer recommended for use in the U.S. due to predominant drug resistance, and resistance to the neuraminidase inhibitor oseltamivir is continuously on the rise. In pursuing the next generation of antiviral drugs with broad-spectrum activity and higher genetic barrier of drug resistance, the influenza virus nucleoprotein (NP) stands out as a high-profile drug target. This review summarizes recent developments in designing inhibitors targeting influenza NP and their mechanisms of action.

  2. FGF23-FGF Receptor/Klotho Pathway as a New Drug Target for Disorders of Bone and Mineral Metabolism.

    PubMed

    Fukumoto, Seiji

    2016-04-01

    Fibroblast growth factor 23 (FGF23) is a phosphaturic hormone produced by bone and works by binding to Klotho-FGF receptor complex. Excessive and deficient actions of FGF23 result in hypophosphatemic and hyperphosphatemic diseases, respectively. Therefore, it is reasonable to think that modulating FGF23 activities may be a novel therapeutic measure for these diseases. Several preclinical reports indicate that the inhibition of FGF23 activities ameliorates hypophosphatemic rickets/osteomalacia caused by excessive actions of FGF23. In addition, phase I-II clinical trials of anti-FGF23 antibody in adult patients with X-linked hypophosphatemia rickets, the most prevalent cause of genetic FGF23-related hypophosphatemic rickets, indicated that the antibody enhances renal tubular phosphate reabsorption and increases serum phosphate. However, it is not known whether the inhibition of FGF23 activities actually brings clinical improvement of rickets and osteomalacia. Available data indicate that FGF23-FGF receptor/Klotho pathway can be a new drug target for disorders of phosphate and bone metabolism.

  3. Biochemical and Structural Characterization of Selective Allosteric Inhibitors of the Plasmodium falciparum Drug Target, Prolyl-tRNA-synthetase

    PubMed Central

    2016-01-01

    Plasmodium falciparum (Pf) prolyl-tRNA synthetase (ProRS) is one of the few chemical-genetically validated drug targets for malaria, yet highly selective inhibitors have not been described. In this paper, approximately 40,000 compounds were screened to identify compounds that selectively inhibit PfProRS enzyme activity versus Homo sapiens (Hs) ProRS. X-ray crystallography structures were solved for apo, as well as substrate- and inhibitor-bound forms of PfProRS. We identified two new inhibitors of PfProRS that bind outside the active site. These two allosteric inhibitors showed >100 times specificity for PfProRS compared to HsProRS, demonstrating this class of compounds could overcome the toxicity related to HsProRS inhibition by halofuginone and its analogues. Initial medicinal chemistry was performed on one of the two compounds, guided by the cocrystallography of the compound with PfProRS, and the results can instruct future medicinal chemistry work to optimize these promising new leads for drug development against malaria. PMID:27798837

  4. The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands

    PubMed Central

    Pawson, Adam J.; Sharman, Joanna L.; Benson, Helen E.; Faccenda, Elena; Alexander, Stephen P.H.; Buneman, O. Peter; Davenport, Anthony P.; McGrath, John C.; Peters, John A.; Southan, Christopher; Spedding, Michael; Yu, Wenyuan; Harmar, Anthony J.

    2014-01-01

    The International Union of Basic and Clinical Pharmacology/British Pharmacological Society (IUPHAR/BPS) Guide to PHARMACOLOGY (http://www.guidetopharmacology.org) is a new open access resource providing pharmacological, chemical, genetic, functional and pathophysiological data on the targets of approved and experimental drugs. Created under the auspices of the IUPHAR and the BPS, the portal provides concise, peer-reviewed overviews of the key properties of a wide range of established and potential drug targets, with in-depth information for a subset of important targets. The resource is the result of curation and integration of data from the IUPHAR Database (IUPHAR-DB) and the published BPS ‘Guide to Receptors and Channels’ (GRAC) compendium. The data are derived from a global network of expert contributors, and the information is extensively linked to relevant databases, including ChEMBL, DrugBank, Ensembl, PubChem, UniProt and PubMed. Each of the ∼6000 small molecule and peptide ligands is annotated with manually curated 2D chemical structures or amino acid sequences, nomenclature and database links. Future expansion of the resource will complete the coverage of all the targets of currently approved drugs and future candidate targets, alongside educational resources to guide scientists and students in pharmacological principles and techniques. PMID:24234439

  5. Plausible Drug Targets in the Streptococcus mutans Quorum Sensing Pathways to Combat Dental Biofilms and Associated Risks.

    PubMed

    Kaur, Gurmeet; Rajesh, Shrinidhi; Princy, S Adline

    2015-12-01

    Streptococcus mutans, a Gram positive facultative anaerobe, is one among the approximately seven hundred bacterial species to exist in human buccal cavity and cause dental caries. Quorum sensing (QS) is a cell-density dependent communication process that respond to the inter/intra-species signals and elicit responses to show behavioral changes in the bacteria to an aggressive forms. In accordance to this phenomenon, the S. mutans also harbors a Competing Stimulating Peptide (CSP)-mediated quorum sensing, ComCDE (Two-component regulatory system) to regulate several virulence-associated traits that includes the formation of the oral biofilm (dental plaque), genetic competence and acidogenicity. The QS-mediated response of S. mutans adherence on tooth surface (dental plaque) imparts antibiotic resistance to the bacterium and further progresses to lead a chronic state, known as periodontitis. In recent years, the oral streptococci, S. mutans are not only recognized for its cariogenic potential but also well known to worsen the infective endocarditis due to its inherent ability to colonize and form biofilm on heart valves. The review significantly appreciate the increasing complexity of the CSP-mediated quorum-sensing pathway with a special emphasis to identify the plausible drug targets within the system for the development of anti-quorum drugs to control biofilm formation and associated risks.

  6. Systemic Analysis of Gene Expression Profiles Identifies ErbB3 as a Potential Drug Target in Pediatric Alveolar Rhabdomyosarcoma

    PubMed Central

    Nordberg, Janne; Mpindi, John Patrick; Iljin, Kristiina; Pulliainen, Arto Tapio; Kallajoki, Markku; Kallioniemi, Olli; Elenius, Klaus; Elenius, Varpu

    2012-01-01

    Pediatric sarcomas, including rhabdomyosarcomas, Ewing’s sarcoma, and osteosarcoma, are aggressive tumors with poor survival rates. To overcome problems associated with nonselectivity of the current therapeutic approaches, targeted therapeutics have been developed. Currently, an increasing number of such drugs are used for treating malignancies of adult patients but little is known about their effects in pediatric patients. We analyzed expression of 24 clinically approved target genes in a wide variety of pediatric normal and malignant tissues using a novel high-throughput systems biology approach. Analysis of the Genesapiens database of human transcriptomes demonstrated statistically significant up-regulation of VEGFC and EPHA2 in Ewing’s sarcoma, and ERBB3 in alveolar rhabdomyosarcomas. In silico data for ERBB3 was validated by demonstrating ErbB3 protein expression in pediatric rhabdomyosarcoma in vitro and in vivo. ERBB3 overexpression promoted whereas ERBB3-targeted siRNA suppressed rhabdomyosarcoma cell gowth, indicating a functional role for ErbB3 signaling in rhabdomyosarcoma. These data suggest that drugs targeting ErbB3, EphA2 or VEGF-C could be further tested as therapeutic targets for pediatric sarcomas. PMID:23227212

  7. Biochemical and Structural Characterization of Selective Allosteric Inhibitors of the Plasmodium falciparum Drug Target, Prolyl-tRNA-synthetase.

    PubMed

    Hewitt, Stephen Nakazawa; Dranow, David M; Horst, Benjamin G; Abendroth, Jan A; Forte, Barbara; Hallyburton, Irene; Jansen, Chimed; Baragaña, Beatriz; Choi, Ryan; Rivas, Kasey L; Hulverson, Matthew A; Dumais, Mitchell; Edwards, Thomas E; Lorimer, Donald D; Fairlamb, Alan H; Gray, David W; Read, Kevin D; Lehane, Adele M; Kirk, Kiaran; Myler, Peter J; Wernimont, Amy; Walpole, Chris; Stacy, Robin; Barrett, Lynn K; Gilbert, Ian H; Van Voorhis, Wesley C

    2017-01-13

    Plasmodium falciparum (Pf) prolyl-tRNA synthetase (ProRS) is one of the few chemical-genetically validated drug targets for malaria, yet highly selective inhibitors have not been described. In this paper, approximately 40,000 compounds were screened to identify compounds that selectively inhibit PfProRS enzyme activity versus Homo sapiens (Hs) ProRS. X-ray crystallography structures were solved for apo, as well as substrate- and inhibitor-bound forms of PfProRS. We identified two new inhibitors of PfProRS that bind outside the active site. These two allosteric inhibitors showed >100 times specificity for PfProRS compared to HsProRS, demonstrating this class of compounds could overcome the toxicity related to HsProRS inhibition by halofuginone and its analogues. Initial medicinal chemistry was performed on one of the two compounds, guided by the cocrystallography of the compound with PfProRS, and the results can instruct future medicinal chemistry work to optimize these promising new leads for drug development against malaria.

  8. Associating Drugs, Targets and Clinical Outcomes into an Integrated Network Affords a New Platform for Computer-Aided Drug Repurposing.

    PubMed

    Oprea, Tudor I; Nielsen, Sonny Kim; Ursu, Oleg; Yang, Jeremy J; Taboureau, Olivier; Mathias, Stephen L; Kouskoumvekaki, Lrene; Sklar, Larry A; Bologa, Cristian G

    2011-03-14

    Finding new uses for old drugs is a strategy embraced by the pharmaceutical industry, with increasing participation from the academic sector. Drug repurposing efforts focus on identifying novel modes of action, but not in a systematic manner. With intensive data mining and curation, we aim to apply bio- and cheminformatics tools using the DRUGS database, containing 3,837 unique small molecules annotated on 1,750 proteins. These are likely to serve as drug targets and antitargets (i.e., associated with side effects, SE). The academic community, the pharmaceutical sector and clinicians alike could benefit from an integrated, semantic-web compliant computer-aided drug repurposing (CADR) effort, one that would enable deep data mining of associations between approved drugs (D), targets (T), clinical outcomes (CO) and SE. We report preliminary results from text mining and multivariate statistics, based on 7,684 approved drug labels, ADL (Dailymed) via text mining. From the ADL corresponding to 988 unique drugs, the "adverse reactions" section was mapped onto 174 SE, then clustered via principal component analysis into a 5x5 self-organizing map that was integrated into a Cytoscape network of SE-D-T-CO. This type of data can be used to streamline drug repurposing and may result in novel insights that can lead to the identification of novel drug actions.

  9. Autonomous CaMKII Activity as a Drug Target for Histological and Functional Neuroprotection after Resuscitation from Cardiac Arrest.

    PubMed

    Deng, Guiying; Orfila, James E; Dietz, Robert M; Moreno-Garcia, Myriam; Rodgers, Krista M; Coultrap, Steve J; Quillinan, Nidia; Traystman, Richard J; Bayer, K Ulrich; Herson, Paco S

    2017-01-31

    The Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) is a major mediator of physiological glutamate signaling, but its role in pathological glutamate signaling (excitotoxicity) remains less clear, with indications for both neuro-toxic and neuro-protective functions. Here, the role of CaMKII in ischemic injury is assessed utilizing our mouse model of cardiac arrest and cardiopulmonary resuscitation (CA/CPR). CaMKII inhibition (with tatCN21 or tatCN19o) at clinically relevant time points (30 min after resuscitation) greatly reduces neuronal injury. Importantly, CaMKII inhibition also works in combination with mild hypothermia, the current standard of care. The relevant drug target is specifically Ca(2+)-independent "autonomous" CaMKII activity generated by T286 autophosphorylation, as indicated by substantial reduction in injury in autonomy-incompetent T286A mutant mice. In addition to reducing cell death, tatCN19o also protects the surviving neurons from functional plasticity impairments and prevents behavioral learning deficits, even at extremely low doses (0.01 mg/kg), further highlighting the clinical potential of our findings.

  10. Phenotypic Screening of Small-Molecule Inhibitors: Implications for Therapeutic Discovery and Drug Target Development in Traumatic Brain Injury.

    PubMed

    Al-Ali, Hassan; Lemmon, Vance P; Bixby, John L

    2016-01-01

    The inability of central nervous system (CNS) neurons to regenerate damaged axons and dendrites following traumatic brain injury (TBI) creates a substantial obstacle for functional recovery. Apoptotic cell death, deposition of scar tissue, and growth-repressive molecules produced by glia further complicate the problem and make it challenging for re-growing axons to extend across injury sites. To date, there are no approved drugs for the treatment of TBI, accentuating the need for relevant leads. Cell-based and organotypic bioassays can better mimic outcomes within the native CNS microenvironment than target-based screening methods and thus should speed the discovery of therapeutic agents that induce axon or dendrite regeneration. Additionally, when used to screen focused chemical libraries such as small-molecule protein kinase inhibitors, these assays can help elucidate molecular mechanisms involved in neurite outgrowth and regeneration as well as identify novel drug targets. Here, we describe a phenotypic cellular (high content) screening assay that utilizes brain-derived primary neurons for screening small-molecule chemical libraries.

  11. Application of Newtonian physics to predict the speed of a gravity racer

    NASA Astrophysics Data System (ADS)

    Driscoll, H. F.; Bullas, A. M.; King, C. E.; Senior, T.; Haake, S. J.; Hart, J.

    2016-07-01

    Gravity racing can be studied using numerical solutions to the equations of motion derived from Newton’s second law. This allows students to explore the physics of gravity racing and to understand how design and course selection influences vehicle speed. Using Euler’s method, we have developed a spreadsheet application that can be used to predict the speed of a gravity powered vehicle. The application includes the effects of air and rolling resistance. Examples of the use of the application for designing a gravity racer are presented and discussed. Predicted speeds are compared to the results of an official world record attempt.

  12. Identification of Phosphoribosyl-AMP cyclohydrolase, as drug target and its inhibitors in Brucella melitensis bv. 1 16M using metabolic pathway analysis.

    PubMed

    Gupta, Money; Prasad, Yamuna; Sharma, Sanjeev Kumar; Jain, Chakresh Kumar

    2017-02-01

    Brucella melitensis is a pathogenic Gram-negative bacterium which is known for causing zoonotic diseases (Brucellosis). The organism is highly contagious and has been reported to be used as bioterrorism agent against humans. Several antibiotics and vaccines have been developed but these antibiotics have exhibited the sign of antibiotic resistance or ineffective at lower concentrations, which imposes an urgent need to identify the novel drugs/drug targets against this organism. In this work, metabolic pathways analysis has been performed with different filters such as non-homology with humans, essentially of genes and choke point analysis, leading to identification of novel drug targets. A total of 18 potential drug target proteins were filtered out and used to develop the high confidence protein-protein interaction network The Phosphoribosyl-AMP cyclohydrolase (HisI) protein has been identified as potential drug target on the basis of topological parameters. Further, a homology model of (HisI) protein has been developed using Modeller with multiple template (1W6Q (48%), 1ZPS (55%), and 2ZKN (48%)) approach and validated using PROCHECK and Verify3D. The virtual high throughput screening (vHTS) using DockBlaster tool has been performed against 16,11,889 clean fragments from ZINC database. Top 500 molecules from DockBlaster were docked using Vina. The docking analysis resulted in ZINC04880153 showing the lowest binding energy (-9.1 kcal/mol) with the drug target. The molecular dynamics study of the complex HisI-ZINC04880153 was conducted to analyze the stability and fluctuation of ligand within the binding pocket of HisI. The identified ligand could be analyzed in the wet-lab based experiments for future drug discovery.

  13. Predicting the reliability of polyisobutylene seal for photovoltaic application

    NASA Astrophysics Data System (ADS)

    Liu, Hua; Feng, Jie; Nicoli, Edoardo; López, Leonardo; Kauffmann, Keith; Yang, Kwanho; Ramesh, Narayan

    2012-10-01

    Polyisobutylene (PIB) or butyl rubber has been used widely in applications such as construction materials, adhesives and sealants, agricultural chemicals, medical devices, personal care products, and fuel additives. Due to the unique low gas permeability, flexibility, and excellent weathering resistance, PIB or PIB based materials are frequently employed in photovoltaic (PV) industry as sealant to protect the electrical assembly in the package as well as moisture sensitive PV cells from aggressive environments. Long term behavior of the PIB sealant within the operating temperature range of the PV devices thus becomes a critical factor to the reliability of the device. In this paper, an experimental study of the temperature dependent fatigue behavior of a PIB based joint is presented. A finite element model capturing the joint region geometry is developed and an approach to estimate lifetime is proposed.

  14. Velocity ratio and its application to predicting velocities

    USGS Publications Warehouse

    Lee, Myung W.

    2003-01-01

    The velocity ratio of water-saturated sediment derived from the Biot-Gassmann theory depends mainly on the Biot coefficient?a property of dry rock?for consolidated sediments with porosity less than the critical porosity. With this theory, the shear moduli of dry sediments are the same as the shear moduli of water-saturated sediments. Because the velocity ratio depends on the Biot coefficient explicitly, Biot-Gassmann theory accurately predicts velocity ratios with respect to differential pressure for a given porosity. However, because the velocity ratio is weakly related to porosity, it is not appropriate to investigate the velocity ratio with respect to porosity (f). A new formulation based on the assumption that the velocity ratio is a function of (1?f)n yields a velocity ratio that depends on porosity, but not on the Biot coefficient explicitly. Unlike the Biot-Gassmann theory, the shear moduli of water-saturated sediments depend not only on the Biot coefficient but also on the pore fluid. This nonclassical behavior of the shear modulus of water-saturated sediment is speculated to be an effect of interaction between fluid and the solid matrix, resulting in softening or hardening of the rock frame and an effect of velocity dispersion owing to local fluid flow. The exponent n controls the degree of softening/hardening of the formation. Based on laboratory data measured near 1 MHz, this theory is extended to include the effect of differential pressure on the velocity ratio by making n a function of differential pressure and consolidation. However, the velocity dispersion and anisotropy are not included in the formulation.

  15. [Application research of data assimilation in air pollution numerical prediction].

    PubMed

    Bai, Xiao-ping; Li, Hong; Fang, Dong; Costabile, Francesca; Liu, Feng-lei

    2008-02-01

    Based on an air pollution modeling system coupling with the non-hydrostatic fifth generation mesoscale meteorological model (MM5) and the regional modeling system for aerosols and deposition (REMSAD), the forecast results of NOx and SO2 in August and September 2002 in Nanjing were assimilated with the optimal interpolation method and the ensemble Kalman filter. The results show that the improvement rates of deviation mean value of NOx and SO2 after assimilated with the optimal interpolation method are 34.20% and 47.53%, and the improvement rates of root mean square errors are 31.95% and 42.04% respectively. It is also demonstrated that the improvement rates of deviation mean value of NOx and SO2 after assimilated with the ensemble Kalman filter with 30 ensemble members are 26.73% and 60.75%, and the improvement rates of root mean square errors are 25.20% and 55.16% respectively. So, the optimal interpolation method and the ensemble Kalman filter both can improve the quality of the initial state from the air pollution numerical prediction model. The comparative experiments on the assimilation performance with the optimal interpolation method and the ensemble Kalman filter with 61 ensemble members were performed, and the experiments demonstrate that the assimilation performance of the ensemble Kalman filter with 61 ensemble members were improved compared with 30 ensemble members, and with the increase of the ensemble members, the improvement to the initial state of NOx and SO2 with the ensemble Kalman filter will be better than the optimal interpolation method.

  16. Improved prediction of accessible surface area results in efficient energy function application.

    PubMed

    Iqbal, Sumaiya; Mishra, Avdesh; Hoque, Md Tamjidul

    2015-09-07

    An accurate prediction of real value accessible surface area (ASA) from protein sequence alone has wide application in the field of bioinformatics and computational biology. ASA has been helpful in understanding the 3-dimensional structure and function of a protein, acting as high impact feature in secondary structure prediction, disorder prediction, binding region identification and fold recognition applications. To enhance and support broad applications of ASA, we have made an attempt to improve the prediction accuracy of absolute accessible surface area by developing a new predictor paradigm, namely REGAd(3)p, for real value prediction through classical Exact Regression with Regularization and polynomial kernel of degree 3 which was further optimized using Genetic Algorithm. ASA assisting effective energy function, motivated us to enhance the accuracy of predicted ASA for better energy function application. Our ASA prediction paradigm was trained and tested using a new benchmark dataset, proposed in this work, consisting of 1001 and 298 protein chains, respectively. We achieved maximum Pearson Correlation Coefficient (PCC) of 0.76 and 1.45% improved PCC when compared with existing top performing predictor, SPINE-X, in ASA prediction on independent test set. Furthermore, we modeled the error between actual and predicted ASA in terms of energy and combined this energy linearly with the energy function 3DIGARS which resulted in an effective energy function, namely 3DIGARS2.0, outperforming all the state-of-the-art energy functions. Based on Rosetta and Tasser decoy-sets 3DIGARS2.0 resulted 80.78%, 73.77%, 141.24%, 16.52%, and 32.32% improvement over DFIRE, RWplus, dDFIRE, GOAP and 3DIGARS respectively.

  17. Ensemble Prediction System Development for Hydrometeorological Testbed (HMT) Application

    NASA Astrophysics Data System (ADS)

    Jankov, I.; Albers, S.; Yuan, H.; Wharton, L.; Toth, Z.; Schneider, T.; White, A.; Ralph, M.

    2010-09-01

    Significant precipitation events in California during the winter season are often caused by land-falling "atmospheric rivers" associated with extratropical cyclones from the Pacific Ocean. Atmospheric rivers are narrow, elongated plumes of enhanced water vapor transport over the Pacific and Atlantic oceans that can extend from the tropics and subtropics into the extratropics. Large values of integrated water vapor are advected within the warm sector of extratropical cyclones immediately ahead of polar cold fronts, although the source of these vapor plumes can originate in the tropics beyond the cyclone warm sector. When an atmospheric river makes a landfall on the coast of California, the northwest to southeast orientation of the Sierra Mountain chain exerts orographic forcing on the southwesterly low-level flow in the warm sector of approaching extratropical cyclones. As a result, sustained precipitation is typically enhanced and modified by the complex terrain. This has major hydrological consequences. The National Oceanic Atmospheric Administration (NOAA) has established the Hydrometeorological Testbed (HMT) to design and support a series of field and numerical modeling experiments to better understand and forecast precipitation in the Central Valley. The main role of the Forecast Application Branch (NOAA/ESRL/GSD) in HMT has been in supporting the real time numerical forecasts as well as research activities targeting better understanding and improvement of Quantitative Precipitation Forecasts (QPF). For this purpose ensemble modeling system has been developed. The ensemble system consists of mixed dynamic cores, mixed physics and mixed lateral boundary conditions. Details related to the ensemble setting and performance will be presented at the conference.

  18. The Ascaris suum nicotinic receptor, ACR-16, as a drug target: Four novel negative allosteric modulators from virtual screening

    PubMed Central

    Zheng, Fudan; Robertson, Alan P.; Abongwa, Melanie; Yu, Edward W.; Martin, Richard J.

    2016-01-01

    Soil-transmitted helminth infections in humans and livestock cause significant debility, reduced productivity and economic losses globally. There are a limited number of effective anthelmintic drugs available for treating helminths infections, and their frequent use has led to the development of resistance in many parasite species. There is an urgent need for novel therapeutic drugs for treating these parasites. We have chosen the ACR-16 nicotinic acetylcholine receptor of Ascaris suum (Asu-ACR-16), as a drug target and have developed three-dimensional models of this transmembrane protein receptor to facilitate the search for new bioactive compounds. Using the human α7 nAChR chimeras and Torpedo marmorata nAChR for homology modeling, we defined orthosteric and allosteric binding sites on the Asu-ACR-16 receptor for virtual screening. We identified four ligands that bind to sites on Asu-ACR-16 and tested their activity using electrophysiological recording from Asu-ACR-16 receptors expressed in Xenopus oocytes. The four ligands were acetylcholine inhibitors (SB-277011-A, IC50, 3.12 ± 1.29 μM; (+)-butaclamol Cl, IC50, 9.85 ± 2.37 μM; fmoc-1, IC50, 10.00 ± 1.38 μM; fmoc-2, IC50, 16.67 ± 1.95 μM) that behaved like negative allosteric modulators. Our work illustrates a structure-based in silico screening method for seeking anthelmintic hits, which can then be tested electrophysiologically for further characterization. PMID:27054065

  19. The Ascaris suum nicotinic receptor, ACR-16, as a drug target: Four novel negative allosteric modulators from virtual screening.

    PubMed

    Zheng, Fudan; Robertson, Alan P; Abongwa, Melanie; Yu, Edward W; Martin, Richard J

    2016-04-01

    Soil-transmitted helminth infections in humans and livestock cause significant debility, reduced productivity and economic losses globally. There are a limited number of effective anthelmintic drugs available for treating helminths infections, and their frequent use has led to the development of resistance in many parasite species. There is an urgent need for novel therapeutic drugs for treating these parasites. We have chosen the ACR-16 nicotinic acetylcholine receptor of Ascaris suum (Asu-ACR-16), as a drug target and have developed three-dimensional models of this transmembrane protein receptor to facilitate the search for new bioactive compounds. Using the human α7 nAChR chimeras and Torpedo marmorata nAChR for homology modeling, we defined orthosteric and allosteric binding sites on the Asu-ACR-16 receptor for virtual screening. We identified four ligands that bind to sites on Asu-ACR-16 and tested their activity using electrophysiological recording from Asu-ACR-16 receptors expressed in Xenopus oocytes. The four ligands were acetylcholine inhibitors (SB-277011-A, IC50, 3.12 ± 1.29 μM; (+)-butaclamol Cl, IC50, 9.85 ± 2.37 μM; fmoc-1, IC50, 10.00 ± 1.38 μM; fmoc-2, IC50, 16.67 ± 1.95 μM) that behaved like negative allosteric modulators. Our work illustrates a structure-based in silico screening method for seeking anthelmintic hits, which can then be tested electrophysiologically for further characterization.

  20. Tyrosine kinase, aurora kinase and leucine aminopeptidase as attractive drug targets in anticancer therapy - characterisation of their inhibitors.

    PubMed

    Ziemska, Joanna; Solecka, Jolanta

    Cancers are the leading cause of deaths all over the world. Available anticancer agents used in clinics exhibit low therapeutic index and usually high toxicity. Wide spreading drug resistance of cancer cells induce a demanding need to search for new drug targets. Currently, many on-going studies on novel compounds with potent anticancer activity, high selectivity as well as new modes of action are conducted. In this work, we describe in details three enzyme groups, which are at present of extensive interest to medical researchers and pharmaceutical companies. These include receptor tyrosine kinases (e.g. EGFR enzymes) and non-receptor tyrosine kinases (Src enzymes), type A, B and C Aurora kinases and aminopeptidases, especially leucine aminopeptidase. We discuss classification of these enzymes, biochemistry as well as their role in the cell cycle under normal conditions and during cancerogenesis. Further on, the work describes enzyme inhibitors that are under in vitro, preclinical, clinical studies as well as drugs available on the market. Both, chemical structures of discovered inhibitors and the role of chemical moieties in novel drug design are discussed. Described enzymes play essential role in cell cycle, especially in mitosis (Aurora kinases), cell differentiation, growth and apoptosis (tyrosine kinases) as well as G1/S transition (leucine aminopeptidase). In cancer cells, they are overexpressed and only their inhibition may stop tumor progression. This review presents the clinical outcomes of selected inhibitors and argues the safety of drug usage in human volunteers. Clinical studies of EGFR and Src kinase inhibitors in different tumors clearly show the need for molecular selection of patients (to those with mutations in genes coding EGFR and Src) to achieve positive clinical response. Current data indicates the great necessity for new anticancer treatment and actions to limit off-target activity.

  1. Clinicopathological significance and potential drug target of CDH1 in breast cancer: a meta-analysis and literature review.

    PubMed

    Huang, Ruixue; Ding, Ping; Yang, Fei

    2015-01-01

    CDH1, as a tumor suppressor gene, contributes sporadic breast cancer (BC) progression. However, the association between CDH1 hypermethylation and BC, and its clinicopathological significance remains unclear. We conducted a meta-analysis to investigate the relationship between the CDH1 methylation profile and the major clinicopathological features. A detailed literature was searched through the electronic databases PubMed, Web of Science™, and EMBASE™ for related research publications. The data were extracted and assessed by two reviewers independently. Odds ratios (ORs) with corresponding confidence intervals (CIs) were calculated and summarized respectively. The frequency of CDH1 methylation was significantly higher in invasive ductal carcinoma than in normal breast tissues (OR =5.83, 95% CI 3.76-9.03, P<0.00001). CDH1 hypermethylation was significantly higher in estrogen receptor (ER)-negative BC than in ER-positive BC (OR =0.62, 95% CI 0.43-0.87, P=0.007). In addition, we found that the CDH1 was significantly methylated in HER2-negative BC than in HER2-positive BC (OR =0.26, 95% CI 0.15-0.44, P<0.00001). However, CDH1 methylation frequency was not associated with progesterone receptor (PR) status, or with grades, stages, or lymph node metastasis of BC patients. Our results indicate that CDH1 hypermethylation is a potential novel drug target for developing personalized therapy. CDH1 hypermethylation is strongly associated with ER-negative and HER2-negative BC, respectively, suggesting CDH1 methylation status could contribute to the development of novel therapeutic approaches for the treatment of ER-negative or HER2-negative BC with aggressive tumor biology.

  2. Applications Determine the Best Model to Predict Flow Duration Curves in Ungauged Basins

    NASA Astrophysics Data System (ADS)

    Muller, M. F.; Thompson, S. E.

    2014-12-01

    Flow duration curves (FDCs) are an important tool for watershed management and their prediction in ungauged catchments is a challenging problem. Selecting the most appropriate model for prediction the FDC is itself a challenge that determines how theoretical improvements in prediction are transferred into engineering practice. Available performance metrics (e.g., Nash Sutcliffe Coefficient, error on flow moments) typically consider the aggregated ability of the model to predict all streamflow quantiles. These metrics may be inappropriate for model selection in practice because watershed management decisions are typically driven by a limited number of streamflow quantiles that may be poorly represented by an aggregate performance metric. As an illustrative case study, the performance of three distinct FDC prediction approaches -- graphical, statistical and process-based -- are compared for ungauged streams in Nepal. The practical application of these predictions is to inform the design of run-of-river hydropower plants. The process-based approach provides the best prediction of the observed flow distribution and results in significantly higher Nash coefficients. However, the graphical approach provides the best prediction of the flow quantiles that are most relevant for hydropower design and reduces the design error caused by streamflow estimation. To assist in an application driven model selection process, we propose a novel model selection framework.

  3. Prediction of User's Web-Browsing Behavior: Application of Markov Model.

    PubMed

    Awad, M A; Khalil, I

    2012-08-01

    Web prediction is a classification problem in which we attempt to predict the next set of Web pages that a user may visit based on the knowledge of the previously visited pages. Predicting user's behavior while serving the Internet can be applied effectively in various critical applications. Such application has traditional tradeoffs between modeling complexity and prediction accuracy. In this paper, we analyze and study Markov model and all- Kth Markov model in Web prediction. We propose a new modified Markov model to alleviate the issue of scalability in the number of paths. In addition, we present a new two-tier prediction framework that creates an example classifier EC, based on the training examples and the generated classifiers. We show that such framework can improve the prediction time without compromising prediction accuracy. We have used standard benchmark data sets to analyze, compare, and demonstrate the effectiveness of our techniques using variations of Markov models and association rule mining. Our experiments show the effectiveness of our modified Markov model in reducing the number of paths without compromising accuracy. Additionally, the results support our analysis conclusions that accuracy improves with higher orders of all- Kth model.

  4. Application and life prediction of titanium alloys in military gas turbine engines

    SciTech Connect

    Cowles, B.A.

    1999-07-01

    Since initial introduction in the 1950's, application of titanium alloys has steadily increased in aircraft gas turbine engines. The low density and high specific strength of titanium alloys have contributed significantly toward attainment of today's high thrust, lightweight, fuel-efficient engines. Today, titanium alloys comprise more than one-third of total engine weight, much of it in structurally critical parts such as fan and compressor rotors and airfoils, and engine mainframe structures. Materials processing, and structural design, durability, and life prediction practices have continuously evolved to facilitate such applications. This paper presents an overview of current titanium applications in gas turbine engines, the current durability and life prediction challenges and areas that appear significant for future applications.

  5. Emory University: MEDICI (Mining Essentiality Data to Identify Critical Interactions) for Cancer Drug Target Discovery and Development | Office of Cancer Genomics

    Cancer.gov

    The CTD2 Center at Emory University has developed a computational methodology to combine high-throughput knockdown data with known protein network topologies to infer the importance of protein-protein interactions (PPIs) for the survival of cancer cells.  Applying these data to the Achilles shRNA results, the CCLE cell line characterizations, and known and newly identified PPIs provides novel insights for potential new drug targets for cancer therapies and identifies important PPI hubs.

  6. Distinct prognostic values and potential drug targets of ALDH1 isoenzymes in non-small-cell lung cancer

    PubMed Central

    You, Qinghua; Guo, Huanchen; Xu, Dongxiang

    2015-01-01

    Increased aldehyde dehydrogenase 1 (ALDH1) activity has been found in the stem cell populations of leukemia and some solid tumors including non-small-cell lung cancer (NSCLC). However, which ALDH1’s isoenzymes are contributing to ALDH1 activity remains elusive. In addition, the prognostic value of individual ALDH1 isoenzyme is not clear. In the current study, we investigated the prognostic value of ALDH1 isoenzymes in NSCLC patients through the Kaplan–Meier plotter database, which contains updated gene expression data and survival information from a total of 1,926 NSCLC patients. High expression of ALDH1A1 mRNA was found to be correlated to a better overall survival (OS) in all NSCLC patients followed for 20 years (hazard ratio [HR] 0.88 [0.77–0.99], P=0.039). In addition, high expression of ALDH1A1 mRNA was also found to be correlated to better OS in adenocarcinoma (Ade) patients (HR 0.71 [0.57–0.9], P=0.0044) but not in squamous cell carcinoma (SCC) patients (HR 0.92 [0.72–1.16], P=0.48). High expression of ALDH1A2 and ALDH1B1 mRNA was found to be correlated to worser OS in all NSCLC patients, as well as in Ade, but not in SCC patients. High expression of both ALDH1A3 and ALDH1L1 mRNA was not found to be correlated to OS in all NSCLC patients. These results strongly support that ALDH1A1 mRNA in NSCLC is associated with better prognosis. In addition, our current study also supports that ALDH1A2 and ALDH1B1 might be major contributors to the ALDH1 activity in NSCLC, since high expression of ALDH1A2 and ALDH1B1 mRNA was found to be significantly correlated to worser OS in all NSCLC patients. Based on our study, ALDH1A2 and ALDH1B1 might be excellent potential drug targets for NSCLC patients. PMID:26366059

  7. A novel blood-brain barrier co-culture system for drug targeting of Alzheimer's disease: establishment by using acitretin as a model drug.

    PubMed

    Freese, Christian; Reinhardt, Sven; Hefner, Gudrun; Unger, Ronald E; Kirkpatrick, C James; Endres, Kristina

    2014-01-01

    In the pathogenesis of Alzheimer's disease (AD) the homeostasis of amyloid precursor protein (APP) processing in the brain is impaired. The expression of the competing proteases ADAM10 (a disintegrin and metalloproteinase 10) and BACE-1 (beta site APP cleaving enzyme 1) is shifted in favor of the A-beta generating enzyme BACE-1. Acitretin--a synthetic retinoid-e.g., has been shown to increase ADAM10 gene expression, resulting in a decreased level of A-beta peptides within the brain of AD model mice and thus is of possible value for AD therapy. A striking challenge in evaluating novel therapeutically applicable drugs is the analysis of their potential to overcome the blood-brain barrier (BBB) for central nervous system targeting. In this study, we established a novel cell-based bio-assay model to test ADAM10-inducing drugs for their ability to cross the BBB. We therefore used primary porcine brain endothelial cells (PBECs) and human neuroblastoma cells (SH-SY5Y) transfected with an ADAM10-promoter luciferase reporter vector in an indirect co-culture system. Acitretin served as a model substance that crosses the BBB and induces ADAM10 expression. We ensured that ADAM10-dependent constitutive APP metabolism in the neuronal cells was unaffected under co-cultivation conditions. Barrier properties established by PBECs were augmented by co-cultivation with SH-SY5Y cells and they remained stable during the treatment with acitretin as demonstrated by electrical resistance measurement and permeability-coefficient determination. As a consequence of transcellular acitretin transport measured by HPLC, the activity of the ADAM10-promoter reporter gene was significantly increased in co-cultured neuronal cells as compared to vehicle-treated controls. In the present study, we provide a new bio-assay system relevant for the study of drug targeting of AD. This bio-assay can easily be adapted to analyze other Alzheimer- or CNS disease-relevant targets in neuronal cells, as their

  8. The Human Kinome Targeted by FDA Approved Multi-Target Drugs and Combination Products: A Comparative Study from the Drug-Target Interaction Network Perspective

    PubMed Central

    Yu, Chun Yan; Yang, Hong; Zhou, Jin; Xue, Wei Wei; Tan, Jun; Zhu, Feng

    2016-01-01

    The human kinome is one of the most productive classes of drug target, and there is emerging necessity for treating complex diseases by means of polypharmacology (multi-target drugs and combination products). However, the advantages of the multi-target drugs and the combination products are still under debate. A comparative analysis between FDA approved multi-target drugs and combination products, targeting the human kinome, was conducted by mapping targets onto the phylogenetic tree of the human kinome. The approach of network medicine illustrating the drug-target interactions was applied to identify popular targets of multi-target drugs and combination products. As identified, the multi-target drugs tended to inhibit target pairs in the human kinome, especially the receptor tyrosine kinase family, while the combination products were able to against targets of distant homology relationship. This finding asked for choosing the combination products as a better solution for designing drugs aiming at targets of distant homology relationship. Moreover, sub-networks of drug-target interactions in specific disease were generated, and mechanisms shared by multi-target drugs and combination products were identified. In conclusion, this study performed an analysis between approved multi-target drugs and combination products against the human kinome, which could assist the discovery of next generation polypharmacology. PMID:27828998

  9. Application of predictive modelling techniques in industry: from food design up to risk assessment.

    PubMed

    Membré, Jeanne-Marie; Lambert, Ronald J W

    2008-11-30

    In this communication, examples of applications of predictive microbiology in industrial contexts (i.e. Nestlé and Unilever) are presented which cover a range of applications in food safety from formulation and process design to consumer safety risk assessment. A tailor-made, private expert system, developed to support safe product/process design assessment is introduced as an example of how predictive models can be deployed for use by non-experts. Its use in conjunction with other tools and software available in the public domain is discussed. Specific applications of predictive microbiology techniques are presented relating to investigations of either growth or limits to growth with respect to product formulation or process conditions. An example of a probabilistic exposure assessment model for chilled food application is provided and its potential added value as a food safety management tool in an industrial context is weighed against its disadvantages. The role of predictive microbiology in the suite of tools available to food industry and some of its advantages and constraints are discussed.

  10. Application of short-term water demand prediction model to Seoul.

    PubMed

    Joo, C N; Koo, J Y; Yu, M J

    2002-01-01

    To predict daily water demand for Seoul, Korea, the artificial neural network (ANN) was used. For the cross correlation, the factors affecting water demand such as maximum temperature, humidity, and wind speed as natural factors, holidays as a social factor and daily demand 1 day before were used. From the results of learning using various hidden layers and units in order to establish the structure of optimal ANN, the case of 3 hidden layers and numbers of unit with the same number of input factors showed the best result and, therefore, it was applied to seasonal water demand prediction. The performance of ANN was compared with a multiple regression method. We discuss the representation ability of the model building process and the applicability of the ANN approach for the daily water demand prediction. ANN provided reasonable results for time series prediction.

  11. Pointing-Vector and Velocity Based Frequency Predicts for Deep-Space Uplink Array Applications

    NASA Technical Reports Server (NTRS)

    Tsao, P.; Vilnrotter, Victor A.; Jamnejad, V.

    2008-01-01

    Uplink array technology is currently being developed for NASA's Deep Space Network (DSN) to provide greater range and data throughput for future NASA missions, including manned missions to Mars and exploratory missions to the outer planets, the Kuiper belt, and beyond. Here we describe a novel technique for generating the frequency predicts that are used to compensate for relative Doppler, derived from interpolated earth position and spacecraft ephemerides. The method described here guarantees velocity and range estimates that are consistent with each other, hence one can always be recovered from the other. Experimental results have recently proven that these frequency predicts are accurate enough to maintain the phase of a three element array at the EPOXI spacecraft for three hours. Previous methods derive frequency predicts directly from interpolated relative velocities. However, these velocities were found to be inconsistent with the corresponding spacecraft range, meaning that range could not always be recovered accurately from the velocity predicts, and vice versa. Nevertheless, velocity-based predicts are also capable of maintaining uplink array phase calibration for extended periods, as demonstrated with the EPOXI spacecraft, however with these predicts important range and phase information may be lost. A comparison of the steering-vector method with velocity-based techniques for generating precise frequency predicts specifically for uplink array applications is provided in the following sections.

  12. Disorder Prediction Methods, Their Applicability to Different Protein Targets and Their Usefulness for Guiding Experimental Studies

    PubMed Central

    Atkins, Jennifer D.; Boateng, Samuel Y.; Sorensen, Thomas; McGuffin, Liam J.

    2015-01-01

    The role and function of a given protein is dependent on its structure. In recent years, however, numerous studies have highlighted the importance of unstructured, or disordered regions in governing a protein’s function. Disordered proteins have been found to play important roles in pivotal cellular functions, such as DNA binding and signalling cascades. Studying proteins with extended disordered regions is often problematic as they can be challenging to express, purify and crystallise. This means that interpretable experimental data on protein disorder is hard to generate. As a result, predictive computational tools have been developed with the aim of predicting the level and location of disorder within a protein. Currently, over 60 prediction servers exist, utilizing different methods for classifying disorder and different training sets. Here we review several good performing, publicly available prediction methods, comparing their application and discussing how disorder prediction servers can be used to aid the experimental solution of protein structure. The use of disorder prediction methods allows us to adopt a more targeted approach to experimental studies by accurately identifying the boundaries of ordered protein domains so that they may be investigated separately, thereby increasing the likelihood of their successful experimental solution. PMID:26287166

  13. A Foundation for the Accurate Prediction of the Soft Error Vulnerability of Scientific Applications

    SciTech Connect

    Bronevetsky, G; de Supinski, B; Schulz, M

    2009-02-13

    Understanding the soft error vulnerability of supercomputer applications is critical as these systems are using ever larger numbers of devices that have decreasing feature sizes and, thus, increasing frequency of soft errors. As many large scale parallel scientific applications use BLAS and LAPACK linear algebra routines, the soft error vulnerability of these methods constitutes a large fraction of the applications overall vulnerability. This paper analyzes the vulnerability of these routines to soft errors by characterizing how their outputs are affected by injected errors and by evaluating several techniques for predicting how errors propagate from the input to the output of each routine. The resulting error profiles can be used to understand the fault vulnerability of full applications that use these routines.

  14. A hybrid statistical-dynamical framework for meteorological drought prediction: Application to the southwestern United States

    NASA Astrophysics Data System (ADS)

    Madadgar, Shahrbanou; AghaKouchak, Amir; Shukla, Shraddhanand; Wood, Andrew W.; Cheng, Linyin; Hsu, Kou-Lin; Svoboda, Mark

    2016-07-01

    Improving water management in water stressed-regions requires reliable seasonal precipitation predication, which remains a grand challenge. Numerous statistical and dynamical model simulations have been developed for predicting precipitation. However, both types of models offer limited seasonal predictability. This study outlines a hybrid statistical-dynamical modeling framework for predicting seasonal precipitation. The dynamical component relies on the physically based North American Multi-Model Ensemble (NMME) model simulations (99 ensemble members). The statistical component relies on a multivariate Bayesian-based model that relates precipitation to atmosphere-ocean teleconnections (also known as an analog-year statistical model). Here the Pacific Decadal Oscillation (PDO), Multivariate ENSO Index (MEI), and Atlantic Multidecadal Oscillation (AMO) are used in the statistical component. The dynamical and statistical predictions are linked using the so-called Expert Advice algorithm, which offers an ensemble response (as an alternative to the ensemble mean). The latter part leads to the best precipitation prediction based on contributing statistical and dynamical ensembles. It combines the strength of physically based dynamical simulations and the capability of an analog-year model. An application of the framework in the southwestern United States, which has suffered from major droughts over the past decade, improves seasonal precipitation predictions (3-5 month lead time) by 5-60% relative to the NMME simulations. Overall, the hybrid framework performs better in predicting negative precipitation anomalies (10-60% improvement over NMME) than positive precipitation anomalies (5-25% improvement over NMME). The results indicate that the framework would likely improve our ability to predict droughts such as the 2012-2014 event in the western United States that resulted in significant socioeconomic impacts.

  15. Performance Prediction of Service-Oriented Applications based on an Enterprise Service Bus

    SciTech Connect

    Liu, Yan; Gorton, Ian; Zhu, Liming

    2007-07-27

    An Enterprise Service Bus (ESB) is a standards-based integration platform that combines messaging, web services, data transformation, and intelligent routing in a highly distributed environment. The ESB has been adopted as a key component of SOA infrastructures. For SOA implementations with large number of users, services, or traffic, maintaining the necessary performance levels of applications integrated using an ESB presents a substantial challenge, both to the architects who design the infrastructure as well as to IT professionals who are responsible for administration. In this paper, we develop a performance model for analyzing and predicting the runtime performance of service applications composed on a COTS ESB platform. Our approach utilizes benchmarking techniques to measure primitive performance overheads of service routing activities in the ESB. The performance characteristics of the ESB and services running on the ESB are modeled in a queuing network, which facilitates the performance prediction of service oriented applications. This model is validated by an example ESB based service application modeled from real world loan broking business application.

  16. On the importance of identifying, characterizing, and predicting fundamental phenomena towards microbial electrochemistry applications.

    PubMed

    Torres, César Iván

    2014-06-01

    The development of microbial electrochemistry research toward technological applications has increased significantly in the past years, leading to many process configurations. This short review focuses on the need to identify and characterize the fundamental phenomena that control the performance of microbial electrochemical cells (MXCs). Specifically, it discusses the importance of recent efforts to discover and characterize novel microorganisms for MXC applications, as well as recent developments to understand transport limitations in MXCs. As we increase our understanding of how MXCs operate, it is imperative to continue modeling efforts in order to effectively predict their performance, design efficient MXC technologies, and implement them commercially. Thus, the success of MXC technologies largely depends on the path of identifying, understanding, and predicting fundamental phenomena that determine MXC performance.

  17. MetaTox: Web Application for Predicting Structure and Toxicity of Xenobiotics' Metabolites.

    PubMed

    Rudik, Anastasia V; Bezhentsev, Vladislav M; Dmitriev, Alexander V; Druzhilovskiy, Dmitry S; Lagunin, Alexey A; Filimonov, Dmitry A; Poroikov, Vladimir V

    2017-04-03

    A new freely available web-application MetaTox ( http://www.way2drug.com/mg ) for prediction of xenobiotic's metabolism and calculation toxicity of metabolites based on the structural formula of chemicals has been developed. MetaTox predicts metabolites, which are formed by nine classes of reactions (aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation). The calculation of probability for generated metabolites is based on analyses of "structure-biotransformation reactions" and "structure-modified atoms" relationships using a Bayesian approach. Prediction of LD50 values is performed by GUSAR software for the parent compound and each of the generated metabolites using quantitative structure-activity relationahip (QSAR) models created for acute rat toxicity with the intravenous type of administration.

  18. Combined migration velocity model-building and its application in tunnel seismic prediction

    NASA Astrophysics Data System (ADS)

    Gong, Xiang-Bo; Han, Li-Guo; Niu, Jian-Jun; Zhang, Xiao-Pei; Wang, De-Li; Du, Li-Zhi

    2010-09-01

    We propose a combined migration velocity analysis and imaging method based on Kirchhoff integral migration and reverse time migration, using the residual curvature analysis and layer stripping strategy to build the velocity model. This method improves the image resolution of Kirchhoff integral migration and reduces the computations of the reverse time migration. It combines the advantages of efficiency and accuracy of the two migration methods. Its application in tunnel seismic prediction shows good results. Numerical experiments show that the imaging results of reverse time migration are better than the imaging results of Kirchhoff integral migration in many aspects of tunnel prediction. Field data show that this method has efficient computations and can establish a reasonable velocity model and a high quality imaging section. Combination with geological information can make an accurate prediction of the front of the tunnel geological structure.

  19. Delivered Performance Predictions and Trends for RISC Processors in Radar Applications

    DTIC Science & Technology

    2007-11-02

    Delivered Performance Predictions and Trends for RISC Processors in Radar Applications Luke Cico and Mark Merritt Mercury Computer Systems, Inc...NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Mercury Computer Systems, Inc. Chelmsford, MA 01824...vendors will be examined, as will the effects of interconnect technologies and the network interface devices. © 2003 Mercury Computer Systems, Inc

  20. Application of Predictive Nursing Reduces Psychiatric Complications in ICU Patients after Neurosurgery

    PubMed Central

    LIU, Qiong; ZHU, Hui

    2016-01-01

    Background: Our aim was to investigate the effects of clinical application of perioperative predictive nursing on reducing psychiatric complications in Intensive Care Unit (ICU) patients after neurosurgery. Methods: A total of 129 patients who underwent neurosurgery and received intensive care were enrolled in our study from February 2013 to February 2014. These patients were divided into two groups: the experimental group (n=68) receiving predictive nursing before and after operation, and the control group (n=61) with general nursing. Clinical data including length of ICU stay, duration of the patients’ psychiatric symptoms, form and incidence of adverse events, and patient satisfaction ratings were recorded, and their differences between the two groups were analyzed. Results: The duration of psychiatric symptoms and the length of ICU stay for patients in the experimental group were significantly shorter than those in the control group (P<0.05). The incidence of adverse events and psychiatric symptoms, such as sensory and intuition disturbance, thought disturbance, emotional disorder, and consciousness disorder, in the experimental group was significantly lower than that in the control group (P<0.05). Patient satisfaction ratings were significantly higher in the experimental group than those in the control group (P<0.05). Conclusion: Application of predictive nursing on ICU patients who undergo neurosurgery could effectively reduce the incidence of psychiatric symptoms as well as other adverse events. Our study provided clinical evidences to encourage predictive nursing in routine settings for patients in critical conditions. PMID:27252916

  1. Structural and logical analysis of a comprehensive hedgehog signaling pathway to identify alternative drug targets for glioma, colon and pancreatic cancer.

    PubMed

    Chowdhury, Saikat; Pradhan, Rachana N; Sarkar, Ram Rup

    2013-01-01

    Hedgehog is an evolutionarily conserved developmental pathway, widely implicated in controlling various cellular responses such as cellular proliferation and stem cell renewal in human and other organisms, through external stimuli. Aberrant activation of this pathway in human adult stem cell line may cause different types of cancers. Hence, targeting this pathway in cancer therapy has become indispensable, but the non availability of detailed molecular interactions, complex regulations by extra- and intra-cellular proteins and cross talks with other pathways pose a serious challenge to get a coherent understanding of this signaling pathway for making therapeutic strategy. This motivated us to perform a computational study of the pathway and to identify probable drug targets. In this work, from available databases and literature, we reconstructed a complete hedgehog pathway which reports the largest number of molecules and interactions to date. Using recently developed computational techniques, we further performed structural and logical analysis of this pathway. In structural analysis, the connectivity and centrality parameters were calculated to identify the important proteins from the network. To capture the regulations of the molecules, we developed a master Boolean model of all the interactions between the proteins and created different cancer scenarios, such as Glioma, Colon and Pancreatic. We performed perturbation analysis on these cancer conditions to identify the important and minimal combinations of proteins that can be used as drug targets. From our study we observed the under expressions of various oncoproteins in Hedgehog pathway while perturbing at a time the combinations of the proteins GLI1, GLI2 and SMO in Glioma; SMO, HFU, ULK3 and RAS in Colon cancer; SMO, HFU, ULK3, RAS and ERK12 in Pancreatic cancer. This reconstructed Hedgehog signaling pathway and the computational analysis for identifying new combinatory drug targets will be useful for

  2. Structural and Logical Analysis of a Comprehensive Hedgehog Signaling Pathway to Identify Alternative Drug Targets for Glioma, Colon and Pancreatic Cancer

    PubMed Central

    Chowdhury, Saikat; Pradhan, Rachana N.; Sarkar, Ram Rup

    2013-01-01

    Hedgehog is an evolutionarily conserved developmental pathway, widely implicated in controlling various cellular responses such as cellular proliferation and stem cell renewal in human and other organisms, through external stimuli. Aberrant activation of this pathway in human adult stem cell line may cause different types of cancers. Hence, targeting this pathway in cancer therapy has become indispensable, but the non availability of detailed molecular interactions, complex regulations by extra- and intra-cellular proteins and cross talks with other pathways pose a serious challenge to get a coherent understanding of this signaling pathway for making therapeutic strategy. This motivated us to perform a computational study of the pathway and to identify probable drug targets. In this work, from available databases and literature, we reconstructed a complete hedgehog pathway which reports the largest number of molecules and interactions to date. Using recently developed computational techniques, we further performed structural and logical analysis of this pathway. In structural analysis, the connectivity and centrality parameters were calculated to identify the important proteins from the network. To capture the regulations of the molecules, we developed a master Boolean model of all the interactions between the proteins and created different cancer scenarios, such as Glioma, Colon and Pancreatic. We performed perturbation analysis on these cancer conditions to identify the important and minimal combinations of proteins that can be used as drug targets. From our study we observed the under expressions of various oncoproteins in Hedgehog pathway while perturbing at a time the combinations of the proteins GLI1, GLI2 and SMO in Glioma; SMO, HFU, ULK3 and RAS in Colon cancer; SMO, HFU, ULK3, RAS and ERK12 in Pancreatic cancer. This reconstructed Hedgehog signaling pathway and the computational analysis for identifying new combinatory drug targets will be useful for

  3. Prediction of Protein-Peptide Interactions: Application of the XPairIT to Anthrax Lethal Factor and Substrates

    DTIC Science & Technology

    2013-09-01

    Prediction of Protein-Peptide Interactions: Application of the XPairIt API to Anthrax Lethal Factor and Substrates by Margaret M. Hurley and...Peptide Interactions: Application of the XPairIt API to Anthrax Lethal Factor and Substrates Margaret M. Hurley and Michael S. Sellers Weapons and...Prediction of Protein-Peptide Interactions: Application of the XPairIt API to Anthrax Lethal Factor and Substrates 5a. CONTRACT NUMBER ORAUW911QX-04-C

  4. Link prediction measures considering different neighbors’ effects and application in social networks

    NASA Astrophysics Data System (ADS)

    Luo, Peng; Wu, Chong; Li, Yongli

    Link prediction measures have been attracted particular attention in the field of mathematical physics. In this paper, we consider the different effects of neighbors in link prediction and focus on four different situations: only consider the individual’s own effects; consider the effects of individual, neighbors and neighbors’ neighbors; consider the effects of individual, neighbors, neighbors’ neighbors, neighbors’ neighbors’ neighbors and neighbors’ neighbors’ neighbors’ neighbors; consider the whole network participants’ effects. Then, according to the four situations, we present our link prediction models which also take the effects of social characteristics into consideration. An artificial network is adopted to illustrate the parameter estimation based on logistic regression. Furthermore, we compare our methods with the some other link prediction methods (LPMs) to examine the validity of our proposed model in online social networks. The results show the superior of our proposed link prediction methods compared with others. In the application part, our models are applied to study the social network evolution and used to recommend friends and cooperators in social networks.

  5. Statistical Learning Theory for High Dimensional Prediction: Application to Criterion-Keyed Scale Development

    PubMed Central

    Chapman, Benjamin P.; Weiss, Alexander; Duberstein, Paul

    2016-01-01

    Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in “big data” problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how three common SLT algorithms–Supervised Principal Components, Regularization, and Boosting—can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach—or perhaps because of them–SLT methods may hold value as a statistically rigorous approach to exploratory regression. PMID:27454257

  6. Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.

    PubMed

    Chapman, Benjamin P; Weiss, Alexander; Duberstein, Paul R

    2016-12-01

    Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in "big data" problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how 3 common SLT algorithms-supervised principal components, regularization, and boosting-can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach-or perhaps because of them-SLT methods may hold value as a statistically rigorous approach to exploratory regression. (PsycINFO Database Record

  7. Introducing a Novel Applicant Ranking Tool to Predict Future Resident Performance: A Pilot Study.

    PubMed

    Bowe, Sarah N; Weitzel, Erik K; Hannah, William N; Fitzgerald, Brian M; Kraus, Gregory P; Nagy, Christopher J; Harrison, Stephen A

    2017-01-01

    The purposes of this study are to (1) introduce our novel Applicant Ranking Tool that aligns with the Accreditation Council for Graduate Medical Education competencies and (2) share our preliminary results comparing applicant rank to current performance. After a thorough literature review and multiple roundtable discussions, an Applicant Ranking Tool was created. Feasibility, satisfaction, and critiques were discussed via open feedback session. Inter-rater reliability was assessed using weighted kappa statistic (κ) and Kendall coefficient of concordance (W). Fisher's exact tests evaluated the ability of the tool to stratify performance into the top or bottom half of their class. Internal medicine and anesthesiology residents served as the pilot cohorts. The tool was considered user-friendly for both data input and analysis. Inter-rater reliability was strongest with intradisciplinary evaluation (W = 0.8-0.975). Resident performance was successfully stratified into those functioning in the upper vs. lower half of their class within the Clinical Anesthesia-3 grouping (p = 0.008). This novel Applicant Ranking Tool lends support for the use of both cognitive and noncognitive traits in predicting resident performance. While the ability of this instrument to accurately predict future resident performance will take years to answer, this pilot study suggests the instrument is worthy of ongoing investigation.

  8. Cytochrome P450 2W1 (CYP2W1) - ready for use as the biomarker and drug target for cancer?

    PubMed

    Yan, Pan; Ong, Chin Eng

    2016-10-03

    1. This article aims to evaluate the potentials of using cytochrome P450 2W1 (CYP2W1) as a biomarker as well as a drug target of cancer because of its characteristic cancer-specific expression. 2. Discrepant findings comparing the expression levels of CYP2W1 in cancer and non-cancer samples were reported. In general, the expression followed a developmental pattern. The demethylation status of CpG island and the expression levels of CYP2W1 genes was positively correlated. 3. CYP2W1 was able to activate several procarcinogens, anti-cancer pro-drugs, and to metabolise many endogenous substances including fatty acids and lysophospholipids. 4. CYP2W1 expression level was suggested to serve as an independent prognostic biomarker in colorectal cancer and hepatocellular carcinoma. The correlation of genetic polymorphisms of CYP2W1 and cancer risk was uncertain. 5. Further characterisation of CYP2W1 structure is suggested to link to its functions. More studies are warranted to reveal the true status and the regulation of CYP2W1 expression across normal and cancer tissues. Catalytic activity of CYP2W1 should be tested on a wider spectrum of endogenous and exogenous substances before its use as the drug target. Larger size of clinical samples can be included to verify the potential of CYP2W1 as the prognostic or cancer risk biomarker.

  9. Comparison of FDA Approved Kinase Targets to Clinical Trial Ones: Insights from Their System Profiles and Drug-Target Interaction Networks

    PubMed Central

    Xu, Jingyu; Wang, Panpan; Yang, Hong; Li, Yinghong; Yu, Chunyan; Tian, Yubin

    2016-01-01

    Kinase is one of the most productive classes of established targets, but the majority of approved drugs against kinase were developed only for cancer. Intensive efforts were therefore exerted for releasing its therapeutic potential by discovering new therapeutic area. Kinases in clinical trial could provide great opportunities for treating various diseases. However, no systematic comparison between system profiles of established targets and those of clinical trial ones was conducted. The reveal of probable difference or shift of trend would help to identify key factors defining druggability of established targets. In this study, a comparative analysis of system profiles of both types of targets was conducted. Consequently, the systems profiles of the majority of clinical trial kinases were identified to be very similar to those of established ones, but percentages of established targets obeying the system profiles appeared to be slightly but consistently higher than those of clinical trial targets. Moreover, a shift of trend in the system profiles from the clinical trial to the established targets was identified, and popular kinase targets were discovered. In sum, this comparative study may help to facilitate the identification of the druggability of established drug targets by their system profiles and drug-target interaction networks. PMID:27547755

  10. Bioactive lipid profiling reveals drug target engagement of a soluble epoxide hydrolase inhibitor in a murine model of tobacco smoke exposure.

    PubMed

    Nording, Malin L; Yang, Jun; Hoang, Laura; Zamora, Vanessa; Uyeminami, Dale; Espiritu, Imelda; Pinkerton, Kent E; Hammock, Bruce D; Luria, Ayala

    2015-04-01

    The inflammatory process underlying chronic obstructive pulmonary disease (COPD) may be caused by tobacco smoke (TS) exposure. Previous studies show that epoxyeicosatrienoic acids (EETs) possess promising anti-inflammatory properties, therefore stabilization of EETs and other fatty acid epoxides through inhibition of soluble epoxide hydrolase (sEH) was investigated in mouse models of acute and sub-chronic inflammation caused by TS exposure. During the entire TS exposure, the potent sEH inhibitor 1-(1-methylsulfonyl-piperidin-4-yl)-3-(4-trifluoromethoxy-phenyl)-urea (TUPS) was given via drinking water. To assess drug target engagement of TUPS, a tandem mass spectrometry method was used for bioactive lipid profiling of a broad range of fatty acid metabolites, including EETs, and their corresponding diols (DHETs) derived from arachidonic acid, as well as epoxides and diols derived from other fatty acids. Several, but not all, plasma epoxide/diol ratios increased in mice treated with sEH inhibitor, compared to non-treated mice suggesting a wider role for sEH involving more fatty acid precursors besides arachidonic acid. This study supports qualitative use of epoxide/diol ratios explored by bioactive lipid profiling to indicate drug target engagement in mouse models of TS exposure relevant to COPD, which may have ramifications for future therapeutic interventions of sEH.

  11. AMP-activated protein kinase: an emerging drug target to regulate imbalances in lipid and carbohydrate metabolism to treat cardio-metabolic diseases

    PubMed Central

    Srivastava, Rai Ajit K.; Pinkosky, Stephen L.; Filippov, Sergey; Hanselman, Jeffrey C.; Cramer, Clay T.; Newton, Roger S.

    2012-01-01

    The adenosine monophosphate-activated protein kinase (AMPK) is a metabolic sensor of energy metabolism at the cellular as well as whole-body level. It is activated by low energy status that triggers a switch from ATP-consuming anabolic pathways to ATP-producing catabolic pathways. AMPK is involved in a wide range of biological activities that normalizes lipid, glucose, and energy imbalances. These pathways are dysregulated in patients with metabolic syndrome (MetS), which represents a clustering of major cardiovascular risk factors including diabetes, lipid abnormalities, and energy imbalances. Clearly, there is an unmet medical need to find a molecule to treat alarming number of patients with MetS. AMPK, with multifaceted activities in various tissues, has emerged as an attractive drug target to manage lipid and glucose abnormalities and maintain energy homeostasis. A number of AMPK activators have been tested in preclinical models, but many of them have yet to reach to the clinic. This review focuses on the structure-function and role of AMPK in lipid, carbohydrate, and energy metabolism. The mode of action of AMPK activators, mechanism of anti-inflammatory activities, and preclinical and clinical findings as well as future prospects of AMPK as a drug target in treating cardio-metabolic disease are discussed. PMID:22798688

  12. Essential proteins and possible therapeutic targets of Wolbachia endosymbiont and development of FiloBase-a comprehensive drug target database for Lymphatic filariasis

    NASA Astrophysics Data System (ADS)

    Sharma, Om Prakash; Kumar, Muthuvel Suresh

    2016-01-01

    Lymphatic filariasis (Lf) is one of the oldest and most debilitating tropical diseases. Millions of people are suffering from this prevalent disease. It is estimated to infect over 120 million people in at least 80 nations of the world through the tropical and subtropical regions. More than one billion people are in danger of getting affected with this life-threatening disease. Several studies were suggested its emerging limitations and resistance towards the available drugs and therapeutic targets for Lf. Therefore, better medicine and drug targets are in demand. We took an initiative to identify the essential proteins of Wolbachia endosymbiont of Brugia malayi, which are indispensable for their survival and non-homologous to human host proteins. In this current study, we have used proteome subtractive approach to screen the possible therapeutic targets for wBm. In addition, numerous literatures were mined in the hunt for potential drug targets, drugs, epitopes, crystal structures, and expressed sequence tag (EST) sequences for filarial causing nematodes. Data obtained from our study were presented in a user friendly database named FiloBase. We hope that information stored in this database may be used for further research and drug development process against filariasis. URL: http://filobase.bicpu.edu.in.

  13. Essential proteins and possible therapeutic targets of Wolbachia endosymbiont and development of FiloBase-a comprehensive drug target database for Lymphatic filariasis

    PubMed Central

    Sharma, Om Prakash; Kumar, Muthuvel Suresh

    2016-01-01

    Lymphatic filariasis (Lf) is one of the oldest and most debilitating tropical diseases. Millions of people are suffering from this prevalent disease. It is estimated to infect over 120 million people in at least 80 nations of the world through the tropical and subtropical regions. More than one billion people are in danger of getting affected with this life-threatening disease. Several studies were suggested its emerging limitations and resistance towards the available drugs and therapeutic targets for Lf. Therefore, better medicine and drug targets are in demand. We took an initiative to identify the essential proteins of Wolbachia endosymbiont of Brugia malayi, which are indispensable for their survival and non-homologous to human host proteins. In this current study, we have used proteome subtractive approach to screen the possible therapeutic targets for wBm. In addition, numerous literatures were mined in the hunt for potential drug targets, drugs, epitopes, crystal structures, and expressed sequence tag (EST) sequences for filarial causing nematodes. Data obtained from our study were presented in a user friendly database named FiloBase. We hope that information stored in this database may be used for further research and drug development process against filariasis. URL: http://filobase.bicpu.edu.in. PMID:26806463

  14. COBRA: A Computational Brewing Application for Predicting the Molecular Composition of Organic Aerosols

    SciTech Connect

    Fooshee, David R.; Nguyen, Tran B.; Nizkorodov, Sergey A.; Laskin, Julia; Laskin, Alexander; Baldi, Pierre

    2012-05-08

    Atmospheric organic aerosols (OA) represent a significant fraction of airborne particulate matter and can impact climate, visibility, and human health. These mixtures are difficult to characterize experimentally due to the enormous complexity and dynamic nature of their chemical composition. We introduce a novel Computational Brewing Application (COBRA) and apply it to modeling oligomerization chemistry stemming from condensation and addition reactions of monomers pertinent to secondary organic aerosol (SOA) formed by photooxidation of isoprene. COBRA uses two lists as input: a list of chemical structures comprising the molecular starting pool, and a list of rules defining potential reactions between molecules. Reactions are performed iteratively, with products of all previous iterations serving as reactants for the next one. The simulation generated thousands of molecular structures in the mass range of 120-500 Da, and correctly predicted ~70% of the individual SOA constituents observed by high-resolution mass spectrometry (HR-MS). Selected predicted structures were confirmed with tandem mass spectrometry. Esterification and hemiacetal formation reactions were shown to play the most significant role in oligomer formation, whereas aldol condensation was shown to be insignificant. COBRA is not limited to atmospheric aerosol chemistry, but is broadly applicable to the prediction of reaction products in other complex mixtures for which reasonable reaction mechanisms and seed molecules can be supplied by experimental or theoretical methods.

  15. Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT.

    PubMed

    Shouval, R; Bondi, O; Mishan, H; Shimoni, A; Unger, R; Nagler, A

    2014-03-01

    Data collected from hematopoietic SCT (HSCT) centers are becoming more abundant and complex owing to the formation of organized registries and incorporation of biological data. Typically, conventional statistical methods are used for the development of outcome prediction models and risk scores. However, these analyses carry inherent properties limiting their ability to cope with large data sets with multiple variables and samples. Machine learning (ML), a field stemming from artificial intelligence, is part of a wider approach for data analysis termed data mining (DM). It enables prediction in complex data scenarios, familiar to practitioners and researchers. Technological and commercial applications are all around us, gradually entering clinical research. In the following review, we would like to expose hematologists and stem cell transplanters to the concepts, clinical applications, strengths and limitations of such methods and discuss current research in HSCT. The aim of this review is to encourage utilization of the ML and DM techniques in the field of HSCT, including prediction of transplantation outcome and donor selection.

  16. Application of infinite model predictive control methodology to other advanced controllers.

    PubMed

    Abu-Ayyad, M; Dubay, R; Hernandez, J M

    2009-01-01

    This paper presents an application of most recent developed predictive control algorithm an infinite model predictive control (IMPC) to other advanced control schemes. The IMPC strategy was derived for systems with different degrees of nonlinearity on the process gain and time constant. Also, it was shown that IMPC structure uses nonlinear open-loop modeling which is conducted while closed-loop control is executed every sampling instant. The main objective of this work is to demonstrate that the methodology of IMPC can be applied to other advanced control strategies making the methodology generic. The IMPC strategy was implemented on several advanced controllers such as PI controller using Smith-Predictor, Dahlin controller, simplified predictive control (SPC), dynamic matrix control (DMC), and shifted dynamic matrix (m-DMC). Experimental work using these approaches combined with IMPC was conducted on both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems and compared with the original forms of these advanced controllers. Computer simulations were performed on nonlinear plants demonstrating that the IMPC strategy can be readily implemented on other advanced control schemes providing improved control performance. Practical work included real-time control applications on a DC motor, plastic injection molding machine and a MIMO three zone thermal system.

  17. Application of cyclic damage accumulation life prediction model to high temperature components

    NASA Technical Reports Server (NTRS)

    Nelson, Richard S.

    1989-01-01

    A high temperature, low cycle fatigue life prediction method was developed. This method, Cyclic Damage Accumulation (CDA), was developed for use in predicting the crack initiation lifetime of gas turbine engine materials, but it can be applied to other materials as well. The method is designed to account for the effects on creep-fatigue life of complex loading such as thermomechanical fatigue, hold periods, waveshapes, mean stresses, multiaxiality, cumulative damage, coatings, and environmental attack. Several features of this model were developed to make it practical for application to actual component analysis, such as the ability to handle nonisothermal loading (including TMF), arbitrary cycle paths, and multiple damage modes. The CDA life prediction model was derived from extensive specimen tests conducted on cast nickel-base superalloy B1900 + Hf. These included both monotonic tests (tensile and creep) and strain-controlled fatigue experiments (uniaxial, biaxial, TMF, mixed creep-fatigue, and controlled mean stress). Additional specimen tests were conducted on wrought INCO 718 to verify the applicability of the final CDA model to other high-temperature alloys. The model will be available to potential users in the near future in the form of a FORTRAN-77 computer program.

  18. From Earthquake Prediction Research to Time-Variable Seismic Hazard Assessment Applications

    NASA Astrophysics Data System (ADS)

    Bormann, Peter

    2011-01-01

    The first part of the paper defines the terms and classifications common in earthquake prediction research and applications. This is followed by short reviews of major earthquake prediction programs initiated since World War II in several countries, for example the former USSR, China, Japan, the United States, and several European countries. It outlines the underlying expectations, concepts, and hypotheses, introduces the technologies and methodologies applied and some of the results obtained, which include both partial successes and failures. Emphasis is laid on discussing the scientific reasons why earthquake prediction research is so difficult and demanding and why the prospects are still so vague, at least as far as short-term and imminent predictions are concerned. However, classical probabilistic seismic hazard assessments, widely applied during the last few decades, have also clearly revealed their limitations. In their simple form, they are time-independent earthquake rupture forecasts based on the assumption of stable long-term recurrence of earthquakes in the seismotectonic areas under consideration. Therefore, during the last decade, earthquake prediction research and pilot applications have focused mainly on the development and rigorous testing of long and medium-term rupture forecast models in which event probabilities are conditioned by the occurrence of previous earthquakes, and on their integration into neo-deterministic approaches for improved time-variable seismic hazard assessment. The latter uses stress-renewal models that are calibrated for variations in the earthquake cycle as assessed on the basis of historical, paleoseismic, and other data, often complemented by multi-scale seismicity models, the use of pattern-recognition algorithms, and site-dependent strong-motion scenario modeling. International partnerships and a global infrastructure for comparative testing have recently been developed, for example the Collaboratory for the Study of

  19. Measuring predictability in ultrasonic signals: an application to scattering material characterization.

    PubMed

    Carrión, Alicia; Miralles, Ramón; Lara, Guillermo

    2014-09-01

    In this paper, we present a novel and completely different approach to the problem of scattering material characterization: measuring the degree of predictability of the time series. Measuring predictability can provide information of the signal strength of the deterministic component of the time series in relation to the whole time series acquired. This relationship can provide information about coherent reflections in material grains with respect to the rest of incoherent noises that typically appear in non-destructive testing using ultrasonics. This is a non-parametric technique commonly used in chaos theory that does not require making any kind of assumptions about attenuation profiles. In highly scattering media (low SNR), it has been shown theoretically that the degree of predictability allows material characterization. The experimental results obtained in this work with 32 cement probes of 4 different porosities demonstrate the ability of this technique to do classification. It has also been shown that, in this particular application, the measurement of predictability can be used as an indicator of the percentages of porosity of the test samples with great accuracy.

  20. Chemometrics applications in biotechnology processes: predicting column integrity and impurity clearance during reuse of chromatography resin.

    PubMed

    Rathore, Anurag S; Mittal, Shachi; Lute, Scott; Brorson, Kurt

    2012-01-01

    Separation media, in particular chromatography media, is typically one of the major contributors to the cost of goods for production of a biotechnology therapeutic. To be cost-effective, it is industry practice that media be reused over several cycles before being discarded. The traditional approach for estimating the number of cycles a particular media can be reused for involves performing laboratory scale experiments that monitor column performance and carryover. This dataset is then used to predict the number of cycles the media can be used at manufacturing scale (concurrent validation). Although, well accepted and widely practiced, there are challenges associated with extrapolating the laboratory scale data to manufacturing scale due to differences that may exist across scales. Factors that may be different include: level of impurities in the feed material, lot to lot variability in feedstock impurities, design of the column housing unit with respect to cleanability, and homogeneity of the column packing. In view of these challenges, there is a need for approaches that may be able to predict column underperformance at the manufacturing scale over the product lifecycle. In case such an underperformance is predicted, the operators can unpack and repack the chromatography column beforehand and thus avoid batch loss. Chemometrics offers one such solution. In this article, we present an application of chemometrics toward the analysis of a set of chromatography profiles with the intention of predicting the various events of column underperformance including the backpressure buildup and inefficient deoxyribonucleic acid clearance.

  1. Application of a pattern recognition technique to the prediction of tire noise

    NASA Astrophysics Data System (ADS)

    Chiu, Jinn-Tong; Tu, Fu-Yuan

    2015-08-01

    Tire treads are one of the main sources of car noise. To meet the EU's tire noise regulation ECE-R117, a new method using a pattern recognition technique is adopted in this paper to predict noise from tire tread patterns, thus facilitating the design of low-noise tires. When tires come into contact with the road surface, air pumping may occur in the grooves of tire tread patterns. Using the image of a tread pattern, a matrix is constructed by setting the patterns of tire grooves and tread blocks. The length and width of the contact patch are multiplied by weight functions. The resulting sound pressure as a function of time is subjected to a Fourier transform to simulate a 1/3-octave-band sound pressure level. A particle swarm algorithm is adopted to optimize the weighting parameters for the sound pressure in the frequency domain so that simulated values approach the measured noise level. Two sets of optimal weighting parameters associated with the length and width of the contact patch are obtained. Finally, the weight function is used to predict the tread pattern noise of tires in the same series. A comparison of the prediction and experimental results reveals that, in the 1/3-octave band of frequency (800-2000 Hz), average errors in sound pressure are within 2.5 dB. The feasibility of the proposed application of the pattern recognition technique in predicting noise from tire treads is verified.

  2. Predicting drugs and proteins in parasite infections with topological indices of complex networks: theoretical backgrounds, applications, and legal issues.

    PubMed

    González-Díaz, Humberto; Romaris, Fernanda; Duardo-Sanchez, Aliuska; Pérez-Montoto, Lázaro G; Prado-Prado, Francisco; Patlewicz, Grace; Ubeira, Florencio M

    2010-01-01

    Quantitative Structure-Activity Relationship (QSAR) models have been used in Pharmaceutical design and Medicinal Chemistry for the discovery of anti-parasite drugs. QSAR models predict biological activity using as input different types of structural parameters of molecules. Topological Indices (TIs) are a very interesting class of these parameters. We can derive TIs from graph representations based on only nodes (atoms) and edges (chemical bonds). TIs are not time-consuming in terms of computational resources because they depend only on atom-atom connectivity information. This information expressed in the molecular graphs can be tabulated in the form of adjacency matrices easy to manipulate with computers. Consequently, TIs allow the rapid collection, annotation, retrieval, comparison and mining of molecular structures within large databases. The interest in TIs has exploded because we can use them to describe also macromolecular and macroscopic systems represented by complex networks of interactions (links) between the different parts of a system (nodes) such as: drug-target, protein-protein, metabolic, host-parasite, brain cortex, parasite disease spreading, Internet, or social networks. In this work, we review and comment on the following topics related to the use of TIs in anti-parasite drugs and target discovery. The first topic reviewed was: Topological Indices and QSAR for antiparasitic drugs. This topic included: Theoretical Background, QSAR for anti-malaria drugs, QSAR for anti-Toxoplasma drugs. The second topic was: TOMO-COMD approach to QSAR of antiparasitic drugs. We included in this topic: TOMO-COMD theoretical background and TOMO-COMD models for antihelmintic activity, Trichomonas, anti-malarials, anti-trypanosome compounds. The third section was inserted to discuss Topological Indices in the context of Complex Networks. The last section is devoted to the MARCH-INSIDE approach to QSAR of antiparasitic drugs and targets. This begins with a theoretical

  3. MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development | Office of Cancer Genomics

    Cancer.gov

    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.

  4. On the applicability of brain reading for predictive human-machine interfaces in robotics.

    PubMed

    Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

    2013-01-01

    The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

  5. On the Applicability of Brain Reading for Predictive Human-Machine Interfaces in Robotics

    PubMed Central

    Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

    2013-01-01

    The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors. PMID:24358125

  6. Use of Microarray Test Data for Toxicogenomic Prediction-Multi-Intelligent Systems for Toxicogenomic Applications (MISTA)

    SciTech Connect

    Wasson, J.S.; Lu, P.-Y.

    2005-09-12

    The YAHSGS LLC and Oak Ridge National Laboratory established a CRADA to develop a computational neural network and wavelets software to facilitate providing national needs for toxicity prediction and overcome the voracious drain of resources (money and time) being directed to the development of pharmaceutical agents. The research project was supported through a STTR Phase I task by NIEHS in 2004. The research deploys state-of-the-art computational neural networks and wavelets to make toxicity prediction on three independent bases: (1) quantitative structure-activity relationships, (2) microarray data, and (3) Massively Parallel Signature Sequencing technology. Upon completion of Phase I, a prototype software Multi-Intelligent System for Toxicogenomic and Applications (MISTA) was developed, the utility's feasibility was demonstrated, and a Phase II proposal was jointly prepared and submitted to NIEHS for funding evaluation. The goals and objectives of the program have been achieved.

  7. Validation of a method for prediction of isotopic concentrations in burnup credit applications

    SciTech Connect

    DeHart, M.D.; Hermann, O.W.; Parks, C.V.

    1995-09-01

    Unlike fresh fuel assumptions typically employed in the criticality safety analysis of spent fuel configurations, burnup credit applications rely on depletion and decay calculations to predict the isotopic composition of spent fuel. These isotopics are used in subsequent criticality calculations to assess the reduced worth of the spent fuel. To validate the codes and data used in depletion approaches, experimental measurements are compared with numerical predictions for relevant spent fuel samples. This paper describes a set of experimentally characterized pressurized-water-reactor (PWR) fuel samples and provides a comparison to results of SCALE-4 depletion calculations. An approach to determine biases and uncertainties between calculated and measured isotopic concentrations is discussed, together with a method to statistically combine these terms to obtain a conservative estimate of spent fuel isotopic concentrations.

  8. Noise and randomlike behavior in perceptrons: theory and application to protein structure prediction

    NASA Astrophysics Data System (ADS)

    Compiani, Mario; Fariselli, Piero; Casadio, Rita

    1996-03-01

    In this paper we study the effective behavior of a single-layer perceptron that is forced to learn a noisy mapping (e.g. associations of patterns with classes). The effect of different kinds of noise on the output of the network is discussed as a function of the noise intensity. It is argued that noise induces a random-like component in the overall behavior of the perceptron which we describe in terms of independent biased random flights in the space of the weights. These random processes (one for each class) are ruled by probability distributions specified by the weights themselves. Our model is applied to the real world application of the prediction of protein secondary structures. Several observations made in this task domain are rationalized in terms of the present model that, among others, provides a link between the seeming existence of an upper bound for the prediction efficiency and the amount of noise in the mapping.

  9. Using data mining to predict soil quality after application of biosolids in agriculture.

    PubMed

    Cortet, Jérôme; Kocev, Dragi; Ducobu, Caroline; Džeroski, Sašo; Debeljak, Marko; Schwartz, Christophe

    2011-01-01

    The amount of biosolids recycled in agriculture has steadily increased during the last decades. However, few models are available to predict the accompanying risks, mainly due to the presence of trace element and organic contaminants, and benefits for soil fertility of their application. This paper deals with using data mining to assess the benefits and risks of biosolids application in agriculture. The analyzed data come from a 10-yr field experiment in northeast France focusing on the effects of biosolid application and mineral fertilization on soil fertility and contamination. Biosolids were applied at agriculturally recommended rates. Biosolids had a significant effect on soil fertility, causing in particular a persistent increase in plant-available phosphorus (P) relative to plots receiving mineral fertilizer. However, soil fertility at seeding and crop management method had greater effects than biosolid application on soil fertility at harvest, especially soil nitrogen (N) content. Levels of trace elements and organic contaminants in soils remained below legal threshold values. Levels of extractable metals correlated more strongly than total metal levels with other factors. Levels of organic contaminants, particularly polycyclic aromatic hydrocarbons, were linked to total metal levels in biosolids and treated soil. This study confirmed that biosolid application at rates recommended for agriculture is a safe option for increasing soil fertility. However, the quality of the biosolids selected has to be taken into account. The results also indicate the power of data mining in examining links between parameters in complex data sets.

  10. A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer.

    PubMed

    Randhawa, Vinay; Kumar Singh, Anil; Acharya, Vishal

    2015-12-01

    Systems-biology inspired identification of drug targets and machine learning-based screening of small molecules which modulate their activity have the potential to revolutionize modern drug discovery by complementing conventional methods. To utilize the effectiveness of such pipelines, we first analyzed the dysregulated gene pairs between control and tumor samples and then implemented an ensemble-based feature selection approach to prioritize targets in oral squamous cell carcinoma (OSCC) for therapeutic exploration. Based on the structural information of known inhibitors of CXCR4-one of the best targets identified in this study-a feature selection was implemented for the identification of optimal structural features (molecular descriptor) based on which a classification model was generated. Furthermore, the CXCR4-centered descriptor-based classification model was finally utilized to screen a repository of plant derived small-molecules to obtain potential inhibitors. The application of our methodology may assist effective selection of the best targets which may have previously been overlooked, that in turn will lead to the development of new oral cancer medications. The small molecules identified in this study can be ideal candidates for trials as potential novel anti-oral cancer agents. Importantly, distinct steps of this whole study may provide reference for the analysis of other complex human diseases.

  11. Application of the cracked pipe element to creep crack growth prediction

    SciTech Connect

    Brochard, J.; Charras, T.

    1997-04-01

    Modifications to a computer code for ductile fracture assessment of piping systems with postulated circumferential through-wall cracks under static or dynamic loading are very briefly described. The modifications extend the capabilities of the CASTEM2000 code to the determination of fracture parameters under creep conditions. The main advantage of the approach is that thermal loads can be evaluated as secondary stresses. The code is applicable to piping systems for which crack propagation predictions differ significantly depending on whether thermal stresses are considered as primary or secondary stresses.

  12. Big Data and Predictive Analytics: Applications in the Care of Children.

    PubMed

    Suresh, Srinivasan

    2016-04-01

    Emerging changes in the United States' healthcare delivery model have led to renewed interest in data-driven methods for managing quality of care. Analytics (Data plus Information) plays a key role in predictive risk assessment, clinical decision support, and various patient throughput measures. This article reviews the application of a pediatric risk score, which is integrated into our hospital's electronic medical record, and provides an early warning sign for clinical deterioration. Dashboards that are a part of disease management systems, are a vital tool in peer benchmarking, and can help in reducing unnecessary variations in care.

  13. Interactions of dendrimers with biological drug targets: reality or mystery - a gap in drug delivery and development research.

    PubMed

    Ahmed, Shaimaa; Vepuri, Suresh B; Kalhapure, Rahul S; Govender, Thirumala

    2016-07-21

    Dendrimers have emerged as novel and efficient materials that can be used as therapeutic agents/drugs or as drug delivery carriers to enhance therapeutic outcomes. Molecular dendrimer interactions are central to their applications and realising their potential. The molecular interactions of dendrimers with drugs or other materials in drug delivery systems or drug conjugates have been extensively reported in the literature. However, despite the growing application of dendrimers as biologically active materials, research focusing on the mechanistic analysis of dendrimer interactions with therapeutic biological targets is currently lacking in the literature. This comprehensive review on dendrimers over the last 15 years therefore attempts to identify the reasons behind the apparent lack of dendrimer-receptor research and proposes approaches to address this issue. The structure, hierarchy and applications of dendrimers are briefly highlighted, followed by a review of their various applications, specifically as biologically active materials, with a focus on their interactions at the target site. It concludes with a technical guide to assist researchers on how to employ various molecular modelling and computational approaches for research on dendrimer interactions with biological targets at a molecular level. This review highlights the impact of a mechanistic analysis of dendrimer interactions on a molecular level, serves to guide and optimise their discovery as medicinal agents, and hopes to stimulate multidisciplinary research between scientific, experimental and molecular modelling research teams.

  14. Molecular docking and structure-based virtual screening studies of potential drug target, CAAX prenyl proteases, of Leishmania donovani.

    PubMed

    Singh, Shalini; Vijaya Prabhu, Sitrarasu; Suryanarayanan, Venkatesan; Bhardwaj, Ruchika; Singh, Sanjeev Kumar; Dubey, Vikash Kumar

    2016-11-01

    Targeting CAAX prenyl proteases of Leishmania donovani can be a good approach towards developing a drug molecule against Leishmaniasis. We have modeled the structure of CAAX prenyl protease I and II of L. donovani, using homology modeling approach. The structures were further validated using Ramachandran plot and ProSA. Active site prediction has shown difference in the amino acid residues present at the active site of CAAX prenyl protease I and CAAX prenyl protease II. The electrostatic potential surface of the CAAX prenyl protease I and II has revealed that CAAX prenyl protease I has more electropositive and electronegative potentials as compared CAAX prenyl protease II suggesting significant difference in their activity. Molecular docking with known bisubstrate analog inhibitors of protein farnesyl transferase and peptidyl (acyloxy) methyl ketones reveals significant binding of these molecules with CAAX prenyl protease I, but comparatively less binding with CAAX prenyl protease II. New and potent inhibitors were also found using structure-based virtual screening. The best docked compounds obtained from virtual screening were subjected to induced fit docking to get best docked configurations. Prediction of drug-like characteristics has revealed that the best docked compounds are in line with Lipinski's rule. Moreover, best docked protein-ligand complexes of CAAX prenyl protease I and II are found to be stable throughout 20 ns simulation. Overall, the study has identified potent drug molecules targeting CAAX prenyl protease I and II of L. donovani whose drug candidature can be verified further using biochemical and cellular studies.

  15. Cytochrome P450 monooxygenase CYP53 family in fungi: comparative structural and evolutionary analysis and its role as a common alternative anti-fungal drug target.

    PubMed

    Jawallapersand, Poojah; Mashele, Samson Sitheni; Kovačič, Lidija; Stojan, Jure; Komel, Radovan; Pakala, Suresh Babu; Kraševec, Nada; Syed, Khajamohiddin

    2014-01-01

    Cytochrome P450 monooxygenases (CYPs/P450s) are heme-thiolate proteins whose role as a drug target against pathogenic microbes has been explored because of their stereo- and regio-specific oxidation activity. We aimed to assess the CYP53 family's role as a common alternative drug target against animal (including human) and plant pathogenic fungi and its role in fungal-mediated wood degradation. Genome-wide analysis of fungal species revealed the presence of CYP53 members in ascomycetes and basidiomycetes. Basidiomycetes had a higher number of CYP53 members in their genomes than ascomycetes. Only two CYP53 subfamilies were found in ascomycetes and six subfamilies in basidiomycetes, suggesting that during the divergence of phyla ascomycetes lost CYP53 P450s. According to phylogenetic and gene-structure analysis, enrichment of CYP53 P450s in basidiomycetes occurred due to the extensive duplication of CYP53 P450s in their genomes. Numerous amino acids (103) were found to be conserved in the ascomycetes CYP53 P450s, against only seven in basidiomycetes CYP53 P450s. 3D-modelling and active-site cavity mapping data revealed that the ascomycetes CYP53 P450s have a highly conserved protein structure whereby 78% amino acids in the active-site cavity were found to be conserved. Because of this rigid nature of ascomycetes CYP53 P450s' active site cavity, any inhibitor directed against this P450 family can serve as a common anti-fungal drug target, particularly toward pathogenic ascomycetes. The dynamic nature of basidiomycetes CYP53 P450s at a gene and protein level indicates that these P450s are destined to acquire novel functions. Functional analysis of CYP53 P450s strongly supported our hypothesis that the ascomycetes CYP53 P450s ability is limited for detoxification of toxic molecules, whereas basidiomycetes CYP53 P450s play an additional role, i.e. involvement in degradation of wood and its derived components. This study is the first report on genome-wide comparative

  16. Cytochrome P450 Monooxygenase CYP53 Family in Fungi: Comparative Structural and Evolutionary Analysis and Its Role as a Common Alternative Anti-Fungal Drug Target

    PubMed Central

    Jawallapersand, Poojah; Mashele, Samson Sitheni; Kovačič, Lidija; Stojan, Jure; Komel, Radovan; Pakala, Suresh Babu; Kraševec, Nada; Syed, Khajamohiddin

    2014-01-01

    Cytochrome P450 monooxygenases (CYPs/P450s) are heme-thiolate proteins whose role as a drug target against pathogenic microbes has been explored because of their stereo- and regio-specific oxidation activity. We aimed to assess the CYP53 family's role as a common alternative drug target against animal (including human) and plant pathogenic fungi and its role in fungal-mediated wood degradation. Genome-wide analysis of fungal species revealed the presence of CYP53 members in ascomycetes and basidiomycetes. Basidiomycetes had a higher number of CYP53 members in their genomes than ascomycetes. Only two CYP53 subfamilies were found in ascomycetes and six subfamilies in basidiomycetes, suggesting that during the divergence of phyla ascomycetes lost CYP53 P450s. According to phylogenetic and gene-structure analysis, enrichment of CYP53 P450s in basidiomycetes occurred due to the extensive duplication of CYP53 P450s in their genomes. Numerous amino acids (103) were found to be conserved in the ascomycetes CYP53 P450s, against only seven in basidiomycetes CYP53 P450s. 3D-modelling and active-site cavity mapping data revealed that the ascomycetes CYP53 P450s have a highly conserved protein structure whereby 78% amino acids in the active-site cavity were found to be conserved. Because of this rigid nature of ascomycetes CYP53 P450s' active site cavity, any inhibitor directed against this P450 family can serve as a common anti-fungal drug target, particularly toward pathogenic ascomycetes. The dynamic nature of basidiomycetes CYP53 P450s at a gene and protein level indicates that these P450s are destined to acquire novel functions. Functional analysis of CYP53 P450s strongly supported our hypothesis that the ascomycetes CYP53 P450s ability is limited for detoxification of toxic molecules, whereas basidiomycetes CYP53 P450s play an additional role, i.e. involvement in degradation of wood and its derived components. This study is the first report on genome-wide comparative

  17. Investigation of the Jet Noise Prediction Theory and Application Utilizing the PAO Formulation. [mathematical model for calculating noise radiation

    NASA Technical Reports Server (NTRS)

    1973-01-01

    Application of the Phillips theory to engineering calculations of rocket and high speed jet noise radiation is reported. Presented are a detailed derivation of the theory, the composition of the numerical scheme, and discussions of the practical problems arising in the application of the present noise prediction method. The present method still contains some empirical elements, yet it provides a unified approach in the prediction of sound power, spectrum, and directivity.

  18. Local structure based method for prediction of the biochemical function of proteins: Applications to glycoside hydrolases.

    PubMed

    Parasuram, Ramya; Mills, Caitlyn L; Wang, Zhouxi; Somasundaram, Saroja; Beuning, Penny J; Ondrechen, Mary Jo

    2016-01-15

    similarity at the predicted active site with the known members of the GH16 family, with the closest match to the endoglucanase subfamily. The method discussed herein can predict whether an SG protein is correctly or incorrectly annotated and can sometimes provide a reliable functional annotation. Examples of application of the method across folds, comparing active sites between two proteins of different structural folds, are also given.

  19. Computational drug repositioning for peripheral arterial disease: prediction of anti-inflammatory and pro-angiogenic therapeutics

    PubMed Central

    Chu, Liang-Hui; Annex, Brian H.; Popel, Aleksander S.

    2015-01-01

    Peripheral arterial disease (PAD) results from atherosclerosis that leads to blocked arteries and reduced blood flow, most commonly in the arteries of the legs. PAD clinical trials to induce angiogenesis to improve blood flow conducted in the last decade have not succeeded. We have recently constructed PADPIN, protein-protein interaction network (PIN) of PAD, and here we combine it with the drug-target relations to identify potential drug targets for PAD. Specifically, the proteins in the PADPIN were classified as belonging to the angiome, immunome, and arteriome, characterizing the processes of angiogenesis, immune response/inflammation, and arteriogenesis, respectively. Using the network-based approach we predict the candidate drugs for repositioning that have potential applications to PAD. By compiling the drug information in two drug databases DrugBank and PharmGKB, we predict FDA-approved drugs whose targets are the proteins annotated as anti-angiogenic and pro-inflammatory, respectively. Examples of pro-angiogenic drugs are carvedilol and urokinase. Examples of anti-inflammatory drugs are ACE inhibitors and maraviroc. This is the first computational drug repositioning study for PAD. PMID:26379552

  20. Computational drug repositioning for peripheral arterial disease: prediction of anti-inflammatory and pro-angiogenic therapeutics.

    PubMed

    Chu, Liang-Hui; Annex, Brian H; Popel, Aleksander S

    2015-01-01

    Peripheral arterial disease (PAD) results from atherosclerosis that leads to blocked arteries and reduced blood flow, most commonly in the arteries of the legs. PAD clinical trials to induce angiogenesis to improve blood flow conducted in the last decade have not succeeded. We have recently constructed PADPIN, protein-protein interaction network (PIN) of PAD, and here we combine it with the drug-target relations to identify potential drug targets for PAD. Specifically, the proteins in the PADPIN were classified as belonging to the angiome, immunome, and arteriome, characterizing the processes of angiogenesis, immune response/inflammation, and arteriogenesis, respectively. Using the network-based approach we predict the candidate drugs for repositioning that have potential applications to PAD. By compiling the drug information in two drug databases DrugBank and PharmGKB, we predict FDA-approved drugs whose targets are the proteins annotated as anti-angiogenic and pro-inflammatory, respectively. Examples of pro-angiogenic drugs are carvedilol and urokinase. Examples of anti-inflammatory drugs are ACE inhibitors and maraviroc. This is the first computational drug repositioning study for PAD.

  1. An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems

    PubMed Central

    Nandola, Naresh N.; Rivera, Daniel E.

    2013-01-01

    We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of non-traditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty. PMID:24348004

  2. 2D MI-DRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins.

    PubMed

    Prado-Prado, Francisco; García-Mera, Xerardo; Escobar, Manuel; Sobarzo-Sánchez, Eduardo; Yañez, Matilde; Riera-Fernandez, Pablo; González-Díaz, Humberto

    2011-12-01

    There are many pairs of possible Drug-Proteins Interactions that may take place or not (DPIs/nDPIs) between drugs with high affinity/non-affinity for different proteins. This fact makes expensive in terms of time and resources, for instance, the determination of all possible ligands-protein interactions for a single drug. In this sense, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out rational DPIs prediction. Unfortunately, almost all QSAR models predict activity against only one target. To solve this problem we can develop multi-target QSAR (mt-QSAR) models. In this work, we introduce the technique 2D MI-DRAGON a new predictor for DPIs based on two different well-known software. We use the software MARCH-INSIDE (MI) to calculate 3D structural parameters for targets and the software DRAGON was used to calculated 2D molecular descriptors all drugs showing known DPIs present in the Drug Bank (US FDA benchmark dataset). Both classes of parameters were used as input of different Artificial Neural Network (ANN) algorithms to seek an accurate non-linear mt-QSAR predictor. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 21:21-31-1:1. This MLP classifies correctly 303 out of 339 DPIs (Sensitivity = 89.38%) and 480 out of 510 nDPIs (Specificity = 94.12%), corresponding to training Accuracy = 92.23%. The validation of the model was carried out by means of external predicting series with Sensitivity = 92.18% (625/678 DPIs; Specificity = 90.12% (730/780 nDPIs) and Accuracy = 91.06%. 2D MI-DRAGON offers a good opportunity for fast-track calculation of all possible DPIs of one drug enabling us to re-construct large drug-target or DPIs Complex Networks (CNs). For instance, we reconstructed the CN of the US FDA benchmark dataset with 855 nodes 519 drugs+336 targets). We predicted CN with similar topology (observed and predicted values of average distance are equal to 6.7 vs. 6.6). These CNs can be used to explore

  3. Prediction of Charge Mobility in Amorphous Organic Materials through the Application of Hopping Theory.

    PubMed

    Lee, Choongkeun; Waterland, Robert; Sohlberg, Karl

    2011-08-09

    The application of hopping theory to predict charge (hole) mobility in amorphous organic molecular materials is studied in detail. Application is made to amorphous cells of N,N'-diphenyl-N,N'-bis-(3-methylphenylene)-1,1'-diphenyl-4,4'-diamine (TPD), 1,1-bis-(4,4'-diethylaminophenyl)-4,4-diphenyl-1,3,butadinene (DEPB), N4,N4'-di(biphenyl-3-yl)-N4,N4'-diphenylbiphenyl-4,4'-diamine (mBPD), N1,N4-di(naphthalen-1-yl)-N1,N4-diphenylbenzene-1,4-diamine (NNP), and N,N'-bis[9,9-dimethyl-2-fluorenyl]-N,N'-diphenyl-9,9-dimethylfluorene-2,7-diamine (pFFA). Detailed analysis of the computation of each of the parameters in the equations for hopping rate is presented, including studies of their convergence with respect to various numerical approximations. Based on these convergence studies, the most robust methodology is then applied to investigate the dependence of mobility on such parameters as the monomer reorganization energy, the monomer-monomer coupling, and the material density. The results give insight into what will be required to improve the accuracy of predictions of mobility in amorphous organic materials, and what factors should be controlled to develop materials with higher (or lower) charge (hole) mobility.

  4. Prediction of Druggable Proteins Using Machine Learning and Systems Biology: A Mini-Review.

    PubMed

    Kandoi, Gaurav; Acencio, Marcio L; Lemke, Ney

    2015-01-01

    The emergence of -omics technologies has allowed the collection of vast amounts of data on biological systems. Although, the pace of such collection has been exponential, the impact of these data remains small on many critical biomedical applications such as drug development. Limited resources, high costs, and low hit-to-lead ratio have led researchers to search for more cost effective methodologies. A possible alternative is to incorporate computational methods of potential drug target prediction early during drug discovery workflow. Computational methods based on systems approaches have the advantage of taking into account the global properties of a molecule not limited to its sequence, structure or function. Machine learning techniques are powerful tools that can extract relevant information from massive and noisy data sets. In recent years the scientific community has explored the combined power of these fields to propose increasingly accurate and low cost methods to propose interesting drug targets. In this mini-review, we describe promising approaches based on the simultaneous use of systems biology and machine learning to access gene and protein druggability. Moreover, we discuss the state-of-the-art of this emerging and interdisciplinary field, discussing data sources, algorithms and the performance of the different methodologies. Finally, we indicate interesting avenues of research and some remaining open challenges.

  5. TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples

    PubMed Central

    He, Liye; Wennerberg, Krister; Aittokallio, Tero; Tang, Jing

    2015-01-01

    Summary: Network pharmacology-based prediction of multi-targeted drug combinations is becoming a promising strategy to improve anticancer efficacy and safety. We developed a logic-based network algorithm, called Target Inhibition Interaction using Maximization and Minimization Averaging (TIMMA), which predicts the effects of drug combinations based on their binary drug-target interactions and single-drug sensitivity profiles in a given cancer sample. Here, we report the R implementation of the algorithm (TIMMA-R), which is much faster than the original MATLAB code. The major extensions include modeling of multiclass drug-target profiles and network visualization. We also show that the TIMMA-R predictions are robust to the intrinsic noise in the experimental data, thus making it a promising high-throughput tool to prioritize drug combinations in various cancer types for follow-up experimentation or clinical applications. Availability and implementation: TIMMA-R source code is freely available at http://cran.r-project.org/web/packages/timma/. Contact: jing.tang@helsinki.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25638808

  6. Novel application of species richness estimators to predict the host range of parasites.

    PubMed

    Watson, David M; Milner, Kirsty V; Leigh, Andrea

    2017-01-01

    Host range is a critical life history trait of parasites, influencing prevalence, virulence and ultimately determining their distributional extent. Current approaches to measure host range are sensitive to sampling effort, the number of known hosts increasing with more records. Here, we develop a novel application of results-based stopping rules to determine how many hosts should be sampled to yield stable estimates of the number of primary hosts within regions, then use species richness estimation to predict host ranges of parasites across their distributional ranges. We selected three mistletoe species (hemiparasitic plants in the Loranthaceae) to evaluate our approach: a strict host specialist (Amyema lucasii, dependent on a single host species), an intermediate species (Amyema quandang, dependent on hosts in one genus) and a generalist (Lysiana exocarpi, dependent on many genera across multiple families), comparing results from geographically-stratified surveys against known host lists derived from herbarium specimens. The results-based stopping rule (stop sampling bioregion once observed host richness exceeds 80% of the host richness predicted using the Abundance-based Coverage Estimator) worked well for most bioregions studied, being satisfied after three to six sampling plots (each representing 25 host trees) but was unreliable in those bioregions with high host richness or high proportions of rare hosts. Although generating stable predictions of host range with minimal variation among six estimators trialled, distribution-wide estimates fell well short of the number of hosts known from herbarium records. This mismatch, coupled with the discovery of nine previously unrecorded mistletoe-host combinations, further demonstrates the limited ecological relevance of simple host-parasite lists. By collecting estimates of host range of constrained completeness, our approach maximises sampling efficiency while generating comparable estimates of the number of primary

  7. A QSAR for the hydroxyl radical reaction rate constant: validation, domain of application, and prediction

    NASA Astrophysics Data System (ADS)

    Öberg, Tomas

    A large number of anthropogenic organic chemicals are emitted into the troposphere. Reactions with the hydroxyl radical are a dominant removal pathway for most organic compounds, but experimentally determined gas-phase reaction rate constants are only available for about 750 compounds. The lack of experimental data increases the importance of applying quantitative structure-activity relationships (QSAR) to evaluate and predict reactivities. It is generally acknowledged that these empirical relationships are valid only within the same domain for which they were developed. However, model validation is sometimes neglected and the application domain is not always well defined. The purpose of this paper is to outline how validation and domain definition can facilitate the modeling and prediction of the hydroxyl radical reaction rates for a large database. A substantial number of theoretical descriptors (867) were generated from 2D molecular structures for compounds present in the Syracuse Research Corporation's PhysProp Database. A QSAR model was developed for the hydroxyl radical reaction rate constant using a projection-based regression technique, partial least squares regression (PLSR). The PLSR model was subsequently validated with an external test set. The main factors of variation could be attributed to two reaction pathways, hydrogen atom abstraction and addition to double bonds or aromatic systems. A set of 17 293 compounds, drawn from the PhysProp Database, was projected onto the PLSR model and 74% were inside the applicability domain. The predicted hydroxyl reaction rates for 25% of these compounds were slow or negligible, with atmospheric half-lives in the range from days to years. Finally, the list of persistent organic compounds was matched against the OECD list of high production volume chemicals (HPVC). Together with the experimental data, nearly three hundred compounds were identified as both persistent and in high volume production.

  8. Bioinformatics and Molecular Biological Characterization of a Hypothetical Protein SAV1226 as a Potential Drug Target for Methicillin/Vancomycin-Staphylococcus aureus Infections

    PubMed Central

    Haag, Nichole; Velk, Kimberly; McCune, Tyler; Wu, Chun

    2015-01-01

    Methicillin/multiple-resistant Staphylococcus aureus (MRSA) are infectious bacteria that are resistant to common antibiotics. A previous in silico study in our group has identified a hypothetical protein SAV1226 as one of the potential drug targets. In this study, we reported the bioinformatics characterization, as well as cloning, expression, purification and kinetic assays of hypothetical protein SAV1226 from methicillin/vancomycin-resistant Staphylococcus aureus Mu50 strain. MALDI-TOF/MS analysis revealed a low degree of structural similarity with known proteins. Kinetic assays demonstrated that hypothetical protein SAV1226 is neither a domain of an ATP dependent dihydroxyacetone kinase nor of a phosphotransferase system (PTS) dihydroxyacetone kinase, suggesting that the function of hypothetical protein SAV1226 might be misannotated on public databases such as UniProt and InterProScan 5. PMID:26388980

  9. Design and characterization of a device to quantify the magnetic drug targeting efficiency of magnetic nanoparticles in a tube flow phantom by magnetic particle spectroscopy

    NASA Astrophysics Data System (ADS)

    Radon, Patricia; Löwa, Norbert; Gutkelch, Dirk; Wiekhorst, Frank

    2017-04-01

    The aim of magnetic drug targeting (MDT) is to transfer a therapeutic drug coupled to magnetic nanoparticles (MNP) to desired disease locations (e.g. tumor region) with the help of magnetic field gradients. To transfer the MDT approach into clinical practice a number of important issues remain to be solved. We developed and characterized an in-vitro flow phantom to provide a defined and reproducible MDT environment. The tube system of the flow phantom is directed through the detection coil of a magnetic particle spectroscopy (MPS) device to determine the targeting efficiency. MPS offers an excellent temporal resolution of seconds and an outstanding specific sensitivity of some nanograms of iron. In the flow phantom different MNP types, magnet geometries and tube materials can be employed to vary physical parameters like diameter, flow rate, magnetic targeting gradient, and MNP properties.

  10. New drugs targeting the cardiac ultra-rapid delayed-rectifier current (I Kur): rationale, pharmacology and evidence for potential therapeutic value.

    PubMed

    Ford, John W; Milnes, James T

    2008-08-01

    There is a clear unmet medical need for new pharmacologic therapies for the treatment of atrial fibrillation (AF) with improved efficacy and safety. This article reviews the development of new and novel Kv1.5/ultra-rapid delayed-rectifier current (I Kur) inhibitors and presents evidence that Kv1.5 modulation provides an atrial-selective mechanism for treating AF. Academia and industry have invested heavily in Kv1.5 (>500 scientific publications and >50 patents published since 1993); however, to realize the full value of this therapeutic drug target, clinical efficacy and safety data are required for a selective Kv1.5 modulator. The reward for demonstrating clinical efficacy and safety in a pivotal Phase 3 trial, on regulatory approval, is "first in class" status.

  11. Memo to the FDA and ICH: appeal for in vivo drug target identification and target pharmacokinetics Recommendations for improved procedures and requirements.

    PubMed

    Stumpf, Walter E

    2007-08-01

    This memorandum is addressed to members of regulatory agencies, as well as managers of pharmaceutical companies. Pharmacokineticists and toxicologists may consider this proposal, weigh its merits, and provide input for implementation. Experience from academic research and ADME experiments during drug development has prompted this appeal for improved drug target recognition. Similar demands have been made repeatedly in the literature. Such efforts are not new, but a renewed urgency has come from comparing results obtained with methods of different resolution and sensitivity, namely high-resolution receptor microscopic autoradiography compared and viewed in parallel to conventional low-resolution 'cut-and-count' radioassays and whole body autoradiography. Conflicting results reveal astounding deficiencies of current ADME approaches. False negatives and false positives of favored 'expedient' procedures allow drugs to reach the market with misleading and inaccurate information about the total drug effect.

  12. Homology modeling of NAD+-dependent DNA ligase of the Wolbachia endosymbiont of Brugia malayi and its drug target potential using dispiro-cycloalkanones.

    PubMed

    Shrivastava, Nidhi; Nag, Jeetendra K; Pandey, Jyoti; Tripathi, Rama Pati; Shah, Priyanka; Siddiqi, Mohammad Imran; Misra-Bhattacharya, Shailja

    2015-07-01

    Lymphatic filarial nematodes maintain a mutualistic relationship with the endosymbiont Wolbachia. Depletion of Wolbachia produces profound defects in nematode development, fertility, and viability and thus has great promise as a novel approach for treating filarial diseases. NAD(+)-dependent DNA ligase is an essential enzyme of DNA replication, repair, and recombination. Therefore, in the present study, the antifilarial drug target potential of the NAD(+)-dependent DNA ligase of the Wolbachia symbiont of Brugia malayi (wBm-LigA) was investigated using dispiro-cycloalkanone compounds. Dispiro-cycloalkanone specifically inhibited the nick-closing and cohesive-end ligation activities of the enzyme without inhibiting human or T4 DNA ligase. The mode of inhibition was competitive with the NAD(+) cofactor. Docking studies also revealed the interaction of these compounds with the active site of the target enzyme. The adverse effects of these inhibitors were observed on adult and microfilarial stages of B. malayi in vitro, and the most active compounds were further monitored in vivo in jirds and mastomys rodent models. Compounds 1, 2, and 5 had severe adverse effects in vitro on the motility of both adult worms and microfilariae at low concentrations. Compound 2 was the best inhibitor, with the lowest 50% inhibitory concentration (IC50) (1.02 μM), followed by compound 5 (IC50, 2.3 μM) and compound 1 (IC50, 2.9 μM). These compounds also exhibited the same adverse effect on adult worms and microfilariae in vivo (P < 0.05). These compounds also tremendously reduced the wolbachial load, as evident by quantitative real-time PCR (P < 0.05). wBm-LigA thus shows great promise as an antifilarial drug target, and dispiro-cycloalkanone compounds show great promise as antifilarial lead candidates.

  13. Cellular Signaling Pathways in Insulin Resistance-Systems Biology Analyses of Microarray Dataset Reveals New Drug Target Gene Signatures of Type 2 Diabetes Mellitus

    PubMed Central

    Muhammad, Syed Aun; Raza, Waseem; Nguyen, Thanh; Bai, Baogang; Wu, Xiaogang; Chen, Jake

    2017-01-01

    Purpose: Type 2 diabetes mellitus (T2DM) is a chronic and metabolic disorder affecting large set of population of the world. To widen the scope of understanding of genetic causes of this disease, we performed interactive and toxicogenomic based systems biology study to find potential T2DM related genes after cDNA differential analysis. Methods: From the list of 50-differential expressed genes (p < 0.05), we found 9-T2DM related genes using extensive data mapping. In our constructed gene-network, T2DM-related differentially expressed seeder genes (9-genes) are found to interact with functionally related gene signatures (31-genes). The genetic interaction network of both T2DM-associated seeder as well as signature genes generally relates well with the disease condition based on toxicogenomic and data curation. Results: These networks showed significant enrichment of insulin signaling, insulin secretion and other T2DM-related pathways including JAK-STAT, MAPK, TGF, Toll-like receptor, p53 and mTOR, adipocytokine, FOXO, PPAR, P13-AKT, and triglyceride metabolic pathways. We found some enriched pathways that are common in different conditions. We recognized 11-signaling pathways as a connecting link between gene signatures in insulin resistance and T2DM. Notably, in the drug-gene network, the interacting genes showed significant overlap with 13-FDA approved and few non-approved drugs. This study demonstrates the value of systems genetics for identifying 18 potential genes associated with T2DM that are probable drug targets. Conclusions: This integrative and network based approaches for finding variants in genomic data expect to accelerate identification of new drug target molecules for different diseases and can speed up drug discovery outcomes. PMID:28179884

  14. Molecular interaction of a kinase inhibitor midostaurin with anticancer drug targets, S100A8 and EGFR: transcriptional profiling and molecular docking study for kidney cancer therapeutics.

    PubMed

    Mirza, Zeenat; Schulten, Hans-Juergen; Farsi, Hasan Ma; Al-Maghrabi, Jaudah A; Gari, Mamdooh A; Chaudhary, Adeel Ga; Abuzenadah, Adel M; Al-Qahtani, Mohammed H; Karim, Sajjad

    2015-01-01

    The S100A8 and epidermal growth factor receptor (EGFR) proteins are proto-oncogenes that are strongly expressed in a number of cancer types. EGFR promotes cellular proliferation, differentiation, migration and survival by activating molecular pathways. Involvement of proinflammatory S100A8 in tumor cell differentiation and progression is largely unclear and not studied in kidney cancer (KC). S100A8 and EGFR are potential therapeutic biomarkers and anticancer drug targets for KC. In this study, we explored molecular mechanisms of interaction profiles of both molecules with potential anticancer drugs. We undertook transcriptional profiling in Saudi KCs using Affymetrix HuGene 1.0 ST arrays. We identified 1478 significantly expressed genes, including S100A8 and EGFR overexpression, using cut-off p value <0.05 and fold change ≥2. Additionally, we compared and confirmed our findings with expression data available at NCBI's GEO database. A significant number of genes associated with cancer showed involvement in cell cycle progression, DNA repair, tumor morphology, tissue development, and cell survival. Atherosclerosis signaling, leukocyte extravasation signaling, notch signaling, and IL-12 signaling were the most significantly disrupted signaling pathways. The present study provides an initial transcriptional profiling of Saudi KC patients. Our analysis suggests distinct transcriptomic signatures and pathways underlying molecular mechanisms of KC progression. Molecular docking analysis revealed that the kinase inhibitor "midostaurin" has amongst the selected drug targets, the best ligand properties to S100A8 and EGFR, with the implication that its binding inhibits downstream signaling in KC. This is the first structure-based docking study for the selected protein targets and anticancer drug, and the results indicate S100A8 and EGFR as attractive anticancer targets and midostaurin with effective drug properties for therapeutic intervention in KC.

  15. Cellular Signaling Pathways in Insulin Resistance-Systems Biology Analyses of Microarray Dataset Reveals New Drug Target Gene Signatures of Type 2 Diabetes Mellitus.

    PubMed

    Muhammad, Syed Aun; Raza, Waseem; Nguyen, Thanh; Bai, Baogang; Wu, Xiaogang; Chen, Jake

    2017-01-01

    Purpose: Type 2 diabetes mellitus (T2DM) is a chronic and metabolic disorder affecting large set of population of the world. To widen the scope of understanding of genetic causes of this disease, we performed interactive and toxicogenomic based systems biology study to find potential T2DM related genes after cDNA differential analysis. Methods: From the list of 50-differential expressed genes (p < 0.05), we found 9-T2DM related genes using extensive data mapping. In our constructed gene-network, T2DM-related differentially expressed seeder genes (9-genes) are found to interact with functionally related gene signatures (31-genes). The genetic interaction network of both T2DM-associated seeder as well as signature genes generally relates well with the disease condition based on toxicogenomic and data curation. Results: These networks showed significant enrichment of insulin signaling, insulin secretion and other T2DM-related pathways including JAK-STAT, MAPK, TGF, Toll-like receptor, p53 and mTOR, adipocytokine, FOXO, PPAR, P13-AKT, and triglyceride metabolic pathways. We found some enriched pathways that are common in different conditions. We recognized 11-signaling pathways as a connecting link between gene signatures in insulin resistance and T2DM. Notably, in the drug-gene network, the interacting genes showed significant overlap with 13-FDA approved and few non-approved drugs. This study demonstrates the value of systems genetics for identifying 18 potential genes associated with T2DM that are probable drug targets. Conclusions: This integrative and network based approaches for finding variants in genomic data expect to accelerate identification of new drug target molecules for different diseases and can speed up drug discovery outcomes.

  16. Homology Modeling of NAD+-Dependent DNA Ligase of the Wolbachia Endosymbiont of Brugia malayi and Its Drug Target Potential Using Dispiro-Cycloalkanones

    PubMed Central

    Shrivastava, Nidhi; Nag, Jeetendra K.; Pandey, Jyoti; Tripathi, Rama Pati; Shah, Priyanka; Siddiqi, Mohammad Imran

    2015-01-01

    Lymphatic filarial nematodes maintain a mutualistic relationship with the endosymbiont Wolbachia. Depletion of Wolbachia produces profound defects in nematode development, fertility, and viability and thus has great promise as a novel approach for treating filarial diseases. NAD+-dependent DNA ligase is an essential enzyme of DNA replication, repair, and recombination. Therefore, in the present study, the antifilarial drug target potential of the NAD+-dependent DNA ligase of the Wolbachia symbiont of Brugia malayi (wBm-LigA) was investigated using dispiro-cycloalkanone compounds. Dispiro-cycloalkanone specifically inhibited the nick-closing and cohesive-end ligation activities of the enzyme without inhibiting human or T4 DNA ligase. The mode of inhibition was competitive with the NAD+ cofactor. Docking studies also revealed the interaction of these compounds with the active site of the target enzyme. The adverse effects of these inhibitors were observed on adult and microfilarial stages of B. malayi in vitro, and the most active compounds were further monitored in vivo in jirds and mastomys rodent models. Compounds 1, 2, and 5 had severe adverse effects in vitro on the motility of both adult worms and microfilariae at low concentrations. Compound 2 was the best inhibitor, with the lowest 50% inhibitory concentration (IC50) (1.02 μM), followed by compound 5 (IC50, 2.3 μM) and compound 1 (IC50, 2.9 μM). These compounds also exhibited the same adverse effect on adult worms and microfilariae in vivo (P < 0.05). These compounds also tremendously reduced the wolbachial load, as evident by quantitative real-time PCR (P < 0.05). wBm-LigA thus shows great promise as an antifilarial drug target, and dispiro-cycloalkanone compounds show great promise as antifilarial lead candidates. PMID:25845868

  17. A prediction tool for real-time application in the disruption protection system at JET

    NASA Astrophysics Data System (ADS)

    Cannas, B.; Fanni, A.; Sonato, P.; Zedda, M. K.; contributors, JET-EFDA

    2007-11-01

    A disruption prediction system, based on neural networks, is presented in this paper. The system is ideally suitable for on-line application in the disruption avoidance and/or mitigation scheme at the JET tokamak. A multi-layer perceptron (MLP) predictor module has been trained on nine plasma diagnostic signals extracted from 86 disruptive pulses, selected from four years of JET experiments in the pulse range 47830-57346 (from 1999 to 2002). The disruption class of the disruptive pulses is available. In particular, the selected pulses belong to four classes (density limit/high radiated power, internal transport barrier, mode lock and h-mode/l-mode). A self-organizing map has been used to select the samples of the pulses to train the MLP predictor module and to determine its target, increasing the prediction capability of the system. The prediction performance has been tested over 86 disruptive and 102 non-disruptive pulses. The test has been performed presenting to the network all the samples of each pulse sampled every 20 ms. The missed alarm rate and the false alarm rate of the predictor, up to 100 ms prior to the disruption time, are 23% and 1%, respectively. Recent plasma configurations might present features different from those observed in the experiments used in the training set. This 'novelty' can lead to incorrect behaviour of the predictor. To improve the robustness and reliability of the system, a novelty detection module has been integrated in the prediction system, increasing the system performance and resulting in a missed alarm rate reduced to 7% and a false alarm rate reduced to 0%.

  18. A Human Pluripotent Stem Cell Surface N-Glycoproteome Resource Reveals Markers, Extracellular Epitopes, and Drug Targets

    PubMed Central

    Boheler, Kenneth R.; Bhattacharya, Subarna; Kropp, Erin M.; Chuppa, Sandra; Riordon, Daniel R.; Bausch-Fluck, Damaris; Burridge, Paul W.; Wu, Joseph C.; Wersto, Robert P.; Chan, Godfrey Chi Fung; Rao, Sridhar; Wollscheid, Bernd; Gundry, Rebekah L.

    2014-01-01

    Summary Detailed knowledge of cell-surface proteins for isolating well-defined populations of human pluripotent stem cells (hPSCs) would significantly enhance their characterization and translational potential. Through a chemoproteomic approach, we developed a cell-surface proteome inventory containing 496 N-linked glycoproteins on human embryonic (hESCs) and induced PSCs (hiPSCs). Against a backdrop of human fibroblasts and 50 other cell types, >100 surface proteins of interest for hPSCs were revealed. The >30 positive and negative markers verified here by orthogonal approaches provide experimental justification for the rational selection of pluripotency and lineage markers, epitopes for cell isolation, and reagents for the characterization of putative hiPSC lines. Comparative differences between the chemoproteomic-defined surfaceome and the transcriptome-predicted surfaceome directly led to the discovery that STF-31, a reported GLUT-1 inhibitor, is toxic to hPSCs and efficient for selective elimination of hPSCs from mixed cultures. PMID:25068131

  19. In silico design of fragment-based drug targeting host processing α-glucosidase i for dengue fever

    NASA Astrophysics Data System (ADS)

    Toepak, E. P.; Tambunan, U. S. F.

    2017-02-01

    Dengue is a major health problem in the tropical and sub-tropical regions. The development of antiviral that targeting dengue’s host enzyme can be more effective and efficient treatment than the viral enzyme. Host enzyme processing α-glucosidase I has an important role in the maturation process of dengue virus envelope glycoprotein. The inhibition of processing α-glucosidase I can become a promising target for dengue fever treatment. The antiviral approach using in silico fragment-based drug design can generate drug candidates with high binding affinity. In this research, 198.621 compounds were obtained from ZINC15 Biogenic Database. These compounds were screened to find the favorable fragments according to Rules of Three and pharmacological properties. The screening fragments were docked into the active site of processing α-glucosidase I. The potential fragment candidates from the molecular docking simulation were linked with castanospermine (CAST) to generate ligands with a better binding affinity. The Analysis of ligand - enzyme interaction showed ligands with code LRS 22, 28, and 47 have the better binding free energy than the standard ligand. Ligand LRS 28 (N-2-4-methyl-5-((1S,3S,6S,7R,8R,8aR)-1,6,7,8-tetrahydroxyoctahydroindolizin-3-yl) pentyl) indolin-1-yl) propionamide) itself among the other ligands has the lowest binding free energy. Pharmacological properties prediction also showed the ligands LRS 22, 28, and 47 can be promising as the dengue fever drug candidates.

  20. Comparative Proteomic Analysis of Aminoglycosides Resistant and Susceptible Mycobacterium tuberculosis Clinical Isolates for Exploring Potential Drug Targets

    PubMed Central

    Sharma, Divakar; Kumar, Bhavnesh; Lata, Manju; Joshi, Beenu; Venkatesan, Krishnamurthy; Shukla, Sangeeta; Bisht, Deepa

    2015-01-01

    Aminoglycosides, amikacin (AK) and kanamycin (KM) are second line anti-tuberculosis drugs used to treat tuberculosis (TB) and resistance to them affects the treatment. Membrane and membrane associated proteins have an anticipated role in biological processes and pathogenesis and are potential targets for the development of new diagnostics/vaccine/therapeutics. In this study we compared membrane and membrane associated proteins of AK and KM resistant and susceptible Mycobacterium tuberculosis isolates by 2DE coupled with MALDI-TOF/TOF-MS and bioinformatic tools. Twelve proteins were found to have increased intensities (PDQuest Advanced Software) in resistant isolates and were identified as ATP synthase subunit alpha (Rv1308), Trigger factor (Rv2462c), Dihydrolipoyl dehydrogenase (Rv0462), Elongation factor Tu (Rv0685), Transcriptional regulator MoxR1(Rv1479), Universal stress protein (Rv2005c), 35kDa hypothetical protein (Rv2744c), Proteasome subunit alpha (Rv2109c), Putative short-chain type dehydrogenase/reductase (Rv0148), Bacterioferritin (Rv1876), Ferritin (Rv3841) and Alpha-crystallin/HspX (Rv2031c). Among these Rv2005c, Rv2744c and Rv0148 are proteins with unknown functions. Docking showed that both drugs bind to the conserved domain (Usp, PspA and SDR domain) of these hypothetical proteins and GPS-PUP predicted potential pupylation sites within them. Increased intensities of these proteins and proteasome subunit alpha might not only be neutralized/modulated the drug molecules but also involved in protein turnover to overcome the AK and KM resistance. Besides that Rv1876, Rv3841 and Rv0685 were found to be associated with iron regulation signifying the role of iron in resistance. Further research is needed to explore how these potential protein targets contribute to resistance of AK and KM. PMID:26436944

  1. Chemical Genetic Analysis and Functional Characterization of Staphylococcal Wall Teichoic Acid 2-Epimerases Reveals Unconventional Antibiotic Drug Targets.

    PubMed

    Mann, Paul A; Müller, Anna; Wolff, Kerstin A; Fischmann, Thierry; Wang, Hao; Reed, Patricia; Hou, Yan; Li, Wenjin; Müller, Christa E; Xiao, Jianying; Murgolo, Nicholas; Sher, Xinwei; Mayhood, Todd; Sheth, Payal R; Mirza, Asra; Labroli, Marc; Xiao, Li; McCoy, Mark; Gill, Charles J; Pinho, Mariana G; Schneider, Tanja; Roemer, Terry

    2016-05-01

    Here we describe a chemical biology strategy performed in Staphylococcus aureus and Staphylococcus epidermidis to identify MnaA, a 2-epimerase that we demonstrate interconverts UDP-GlcNAc and UDP-ManNAc to modulate substrate levels of TarO and TarA wall teichoic acid (WTA) biosynthesis enzymes. Genetic inactivation of mnaA results in complete loss of WTA and dramatic in vitro β-lactam hypersensitivity in methicillin-resistant S. aureus (MRSA) and S. epidermidis (MRSE). Likewise, the β-lactam antibiotic imipenem exhibits restored bactericidal activity against mnaA mutants in vitro and concomitant efficacy against 2-epimerase defective strains in a mouse thigh model of MRSA and MRSE infection. Interestingly, whereas MnaA serves as the sole 2-epimerase required for WTA biosynthesis in S. epidermidis, MnaA and Cap5P provide compensatory WTA functional roles in S. aureus. We also demonstrate that MnaA and other enzymes of WTA biosynthesis are required for biofilm formation in MRSA and MRSE. We further determine the 1.9Å crystal structure of S. aureus MnaA and identify critical residues for enzymatic dimerization, stability, and substrate binding. Finally, the natural product antibiotic tunicamycin is shown to physically bind MnaA and Cap5P and inhibit 2-epimerase activity, demonstrating that it inhibits a previously unanticipated step in WTA biosynthesis. In summary, MnaA serves as a new Staphylococcal antibiotic target with cognate inhibitors predicted to possess dual therapeutic benefit: as combination agents to restore β-lactam efficacy against MRSA and MRSE and as non-bioactive prophylactic agents to prevent Staphylococcal biofilm formation.

  2. Comparative Proteomic Analysis of Aminoglycosides Resistant and Susceptible Mycobacterium tuberculosis Clinical Isolates for Exploring Potential Drug Targets.

    PubMed

    Sharma, Divakar; Kumar, Bhavnesh; Lata, Manju; Joshi, Beenu; Venkatesan, Krishnamurthy; Shukla, Sangeeta; Bisht, Deepa

    2015-01-01

    Aminoglycosides, amikacin (AK) and kanamycin (KM) are second line anti-tuberculosis drugs used to treat tuberculosis (TB) and resistance to them affects the treatment. Membrane and membrane associated proteins have an anticipated role in biological processes and pathogenesis and are potential targets for the development of new diagnostics/vaccine/therapeutics. In this study we compared membrane and membrane associated proteins of AK and KM resistant and susceptible Mycobacterium tuberculosis isolates by 2DE coupled with MALDI-TOF/TOF-MS and bioinformatic tools. Twelve proteins were found to have increased intensities (PDQuest Advanced Software) in resistant isolates and were identified as ATP synthase subunit alpha (Rv1308), Trigger factor (Rv2462c), Dihydrolipoyl dehydrogenase (Rv0462), Elongation factor Tu (Rv0685), Transcriptional regulator MoxR1(Rv1479), Universal stress protein (Rv2005c), 35kDa hypothetical protein (Rv2744c), Proteasome subunit alpha (Rv2109c), Putative short-chain type dehydrogenase/reductase (Rv0148), Bacterioferritin (Rv1876), Ferritin (Rv3841) and Alpha-crystallin/HspX (Rv2031c). Among these Rv2005c, Rv2744c and Rv0148 are proteins with unknown functions. Docking showed that both drugs bind to the conserved domain (Usp, PspA and SDR domain) of these hypothetical proteins and GPS-PUP predicted potential pupylation sites within them. Increased intensities of these proteins and proteasome subunit alpha might not only be neutralized/modulated the drug molecules but also involved in protein turnover to overcome the AK and KM resistance. Besides that Rv1876, Rv3841 and Rv0685 were found to be associated with iron regulation signifying the role of iron in resistance. Further research is needed to explore how these potential protein targets contribute to resistance of AK and KM.

  3. Chemical Genetic Analysis and Functional Characterization of Staphylococcal Wall Teichoic Acid 2-Epimerases Reveals Unconventional Antibiotic Drug Targets

    PubMed Central

    Mann, Paul A.; Müller, Anna; Wolff, Kerstin A.; Fischmann, Thierry; Wang, Hao; Reed, Patricia; Hou, Yan; Li, Wenjin; Müller, Christa E.; Xiao, Jianying; Murgolo, Nicholas; Sher, Xinwei; Mayhood, Todd; Sheth, Payal R.; Mirza, Asra; Labroli, Marc; Xiao, Li; McCoy, Mark; Gill, Charles J.; Pinho, Mariana G.; Schneider, Tanja; Roemer, Terry

    2016-01-01

    Here we describe a chemical biology strategy performed in Staphylococcus aureus and Staphylococcus epidermidis to identify MnaA, a 2-epimerase that we demonstrate interconverts UDP-GlcNAc and UDP-ManNAc to modulate substrate levels of TarO and TarA wall teichoic acid (WTA) biosynthesis enzymes. Genetic inactivation of mnaA results in complete loss of WTA and dramatic in vitro β-lactam hypersensitivity in methicillin-resistant S. aureus (MRSA) and S. epidermidis (MRSE). Likewise, the β-lactam antibiotic imipenem exhibits restored bactericidal activity against mnaA mutants in vitro and concomitant efficacy against 2-epimerase defective strains in a mouse thigh model of MRSA and MRSE infection. Interestingly, whereas MnaA serves as the sole 2-epimerase required for WTA biosynthesis in S. epidermidis, MnaA and Cap5P provide compensatory WTA functional roles in S. aureus. We also demonstrate that MnaA and other enzymes of WTA biosynthesis are required for biofilm formation in MRSA and MRSE. We further determine the 1.9Å crystal structure of S. aureus MnaA and identify critical residues for enzymatic dimerization, stability, and substrate binding. Finally, the natural product antibiotic tunicamycin is shown to physically bind MnaA and Cap5P and inhibit 2-epimerase activity, demonstrating that it inhibits a previously unanticipated step in WTA biosynthesis. In summary, MnaA serves as a new Staphylococcal antibiotic target with cognate inhibitors predicted to possess dual therapeutic benefit: as combination agents to restore β-lactam efficacy against MRSA and MRSE and as non-bioactive prophylactic agents to prevent Staphylococcal biofilm formation. PMID:27144276

  4. Analyses of the Binding between Water Soluble C60 Derivatives and Potential Drug Targets through a Molecular Docking Approach

    PubMed Central

    Liu, Junjun; Zhang, Houjin

    2016-01-01

    Fullerene C60, a unique sphere-shaped molecule consisting of carbon, has been proved to have inhibitory effects on many diseases. However, the applications of C60 in medicine have been severely hindered by its complete insolubility in water and low solubility in almost all organic solvents. In this study, the water-soluble C60 derivatives and the C60 binding protein’s structures were collected from the literature. The selected proteins fall into several groups, including acetylcholinesterase, glutamate racemase, inosine monophosphate dehydrogenase, lumazine synthase, human estrogen receptor alpha, dihydrofolate reductase and N-myristoyltransferase. The C60 derivatives were docked into the binding sites in the proteins. The binding affinities of the C60 derivatives were calculated. The bindings between proteins and their known inhibitors or native ligands were also characterized in the same way. The results show that C60 derivatives form good interactions with the binding sites of different protein targets. In many cases, the binding affinities of C60 derivatives are better than those of known inhibitors and native ligands. This study demonstrates the interaction patterns of C60 derivatives and their binding partners, which will have good impact on the fullerene-based drug discovery. PMID:26829126

  5. Antiarrhythmic potential of drugs targeting the cardiac ryanodine receptor Ca2+ release channel: case study of dantrolene.

    PubMed

    Acsai, Karoly; Nagy, Norbert; Marton, Zoltan; Oravecz, Kinga; Varro, Andras

    2015-01-01

    Driven by the limitations of the traditional antiarrhythmic pharmacology, current antiarrhythmic research is trying to identify new avenues for the development of specific and safe antiarrhythmic drugs. One of the most promising approaches in this field is the amelioration of the abnormal events in cellular Ca(2+) handling originating from the dysfunction of ryanodine receptor Ca(2+) release complex (RyR), which is an inevitable key factor in the pathology of myocardial dysfunction, remodeling and arrhythmogenesis. Accordingly, both in experimental and clinical situations, inhibition of abnormal activity of RyR, regardless of being the primary cause or a consequence during the pathogenesis appears to exert beneficial effect on disease outcome, including a marked antiarrhythmic defense. Considerable amount of our knowledge in this field originates from studies using dantrolene, a human drug with RyR stabilizing effect. Our review summarizes the cardiovascular pharmacology of dantrolene and the results of its use in experimental models of cardiac diseases, which emphasize a promising perspective for the possible antiarrhythmic application of RyR inhibition in the future.

  6. Neural networks for learning and prediction with applications to remote sensing and speech perception

    NASA Astrophysics Data System (ADS)

    Gjaja, Marin N.

    1997-11-01

    Neural networks for supervised and unsupervised learning are developed and applied to problems in remote sensing, continuous map learning, and speech perception. Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART networks synthesize fuzzy logic and neural networks, and supervised ARTMAP networks incorporate ART modules for prediction and classification. New ART and ARTMAP methods resulting from analyses of data structure, parameter specification, and category selection are developed. Architectural modifications providing flexibility for a variety of applications are also introduced and explored. A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on fuzzy ARTMAP, is developed. System capabilities are tested on a challenging remote sensing problem, prediction of vegetation classes in the Cleveland National Forest from spectral and terrain features. After training at the pixel level, performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, back propagation neural networks, and K-nearest neighbor algorithms. Best performance is obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. This work forms the foundation for additional studies exploring fuzzy ARTMAP's capability to estimate class mixture composition for non-homogeneous sites. Exploratory simulations apply ARTMAP to the problem of learning continuous multidimensional mappings. A novel system architecture retains basic ARTMAP properties of incremental and fast learning in an on-line setting while adding components to solve this class of problems. The perceptual magnet effect is a language-specific phenomenon arising early in infant speech development that is characterized by a warping of speech sound perception. An

  7. Prediction of Critical Power and W′ in Hypoxia: Application to Work-Balance Modelling

    PubMed Central

    Townsend, Nathan E.; Nichols, David S.; Skiba, Philip F.; Racinais, Sebastien; Périard, Julien D.

    2017-01-01

    Purpose: Develop a prediction equation for critical power (CP) and work above CP (W′) in hypoxia for use in the work-balance (WBAL′) model. Methods: Nine trained male cyclists completed cycling time trials (TT; 12, 7, and 3 min) to determine CP and W′ at five altitudes (250, 1,250, 2,250, 3,250, and 4,250 m). Least squares regression was used to predict CP and W′ at altitude. A high-intensity intermittent test (HIIT) was performed at 250 and 2,250 m. Actual and predicted CP and W′ were used to compute W′ during HIIT using differential (WBALdiff′) and integral (WBALint′) forms of the WBAL′ model. Results: CP decreased at altitude (P < 0.001) as described by 3rd order polynomial function (R2 = 0.99). W′ decreased at 4,250 m only (P < 0.001). A double-linear function characterized the effect of altitude on W′ (R2 = 0.99). There was no significant effect of parameter input (actual vs. predicted CP and W′) on modelled WBAL′ at 2,250 m (P = 0.24). WBALdiff′ returned higher values than WBALint′ throughout HIIT (P < 0.001). During HIIT, WBALdiff′ was not different to 0 kJ at completion, at 250 m (0.7 ± 2.0 kJ; P = 0.33) and 2,250 m (−1.3 ± 3.5 kJ; P = 0.30). However, WBALint′ was lower than 0 kJ at 250 m (−0.9 ± 1.3 kJ; P = 0.058) and 2,250 m (−2.8 ± 2.8 kJ; P = 0.02). Conclusion: The altitude prediction equations for CP and W′ developed in this study are suitable for use with the WBAL′ model in acute hypoxia. This enables the application of WBAL′ modelling to training prescription and competition analysis at altitude. PMID:28386237

  8. Prediction of Critical Power and W' in Hypoxia: Application to Work-Balance Modelling.

    PubMed

    Townsend, Nathan E; Nichols, David S; Skiba, Philip F; Racinais, Sebastien; Périard, Julien D

    2017-01-01

    Purpose: Develop a prediction equation for critical power (CP) and work above CP (W') in hypoxia for use in the work-balance ([Formula: see text]) model. Methods: Nine trained male cyclists completed cycling time trials (TT; 12, 7, and 3 min) to determine CP and W' at five altitudes (250, 1,250, 2,250, 3,250, and 4,250 m). Least squares regression was used to predict CP and W' at altitude. A high-intensity intermittent test (HIIT) was performed at 250 and 2,250 m. Actual and predicted CP and W' were used to compute W' during HIIT using differential ([Formula: see text]) and integral ([Formula: see text]) forms of the [Formula: see text] model. Results: CP decreased at altitude (P < 0.001) as described by 3rd order polynomial function (R(2) = 0.99). W' decreased at 4,250 m only (P < 0.001). A double-linear function characterized the effect of altitude on W' (R(2) = 0.99). There was no significant effect of parameter input (actual vs. predicted CP and W') on modelled [Formula: see text] at 2,250 m (P = 0.24). [Formula: see text] returned higher values than [Formula: see text] throughout HIIT (P < 0.001). During HIIT, [Formula: see text] was not different to 0 kJ at completion, at 250 m (0.7 ± 2.0 kJ; P = 0.33) and 2,250 m (-1.3 ± 3.5 kJ; P = 0.30). However, [Formula: see text] was lower than 0 kJ at 250 m (-0.9 ± 1.3 kJ; P = 0.058) and 2,250 m (-2.8 ± 2.8 kJ; P = 0.02). Conclusion: The altitude prediction equations for CP and W' developed in this study are suitable for use with the [Formula: see text] model in acute hypoxia. This enables the application of [Formula: see text] modelling to training prescription and competition analysis at altitude.

  9. Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications.

    PubMed

    Wu, Xiao-Lin; Xu, Jiaqi; Feng, Guofei; Wiggans, George R; Taylor, Jeremy F; He, Jun; Qian, Changsong; Qiu, Jiansheng; Simpson, Barry; Walker, Jeremy; Bauck, Stewart

    2016-01-01

    utility of this MOLO algorithm was also demonstrated in a real application, in which a 6K SNP panel was optimized conditional on 5,260 obligatory SNP selected based on SNP-trait association in U.S. Holstein animals. With this MOLO algorithm, both imputation error rate and genomic prediction error rate were minimal.

  10. Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications

    PubMed Central

    Wu, Xiao-Lin; Xu, Jiaqi; Feng, Guofei; Wiggans, George R.; Taylor, Jeremy F.; He, Jun; Qian, Changsong; Qiu, Jiansheng; Simpson, Barry; Walker, Jeremy; Bauck, Stewart

    2016-01-01

    utility of this MOLO algorithm was also demonstrated in a real application, in which a 6K SNP panel was optimized conditional on 5,260 obligatory SNP selected based on SNP-trait association in U.S. Holstein animals. With this MOLO algorithm, both imputation error rate and genomic prediction error rate were minimal. PMID:27583971

  11. Modeling, molecular dynamics, and docking assessment of transcription factor rho: a potential drug target in Brucella melitensis 16M

    PubMed Central

    Pradeepkiran, Jangampalli Adi; Kumar, Konidala Kranthi; Kumar, Yellapu Nanda; Bhaskar, Matcha

    2015-01-01

    The zoonotic disease brucellosis, a chronic condition in humans affecting renal and cardiac systems and causing osteoarthritis, is caused by Brucella, a genus of Gram-negative, facultative, intracellular pathogens. The mode of transmission and the virulence of the pathogens are still enigmatic. Transcription regulatory elements, such as rho proteins, play an important role in the termination of transcription and/or the selection of genes in Brucella. Adverse effects of the transcription inhibitors play a key role in the non-successive transcription challenges faced by the pathogens. In the investigation presented here, we computationally predicted the transcription termination factor rho (TtFRho) inhibitors against Brucella melitensis 16M via a structure-based method. In view the unknown nature of its crystal structure, we constructed a robust three-dimensional homology model of TtFRho’s structure by comparative modeling with the crystal structure of the Escherichia coli TtFRho (Protein Data Bank ID: 1PVO) as a template in MODELLER (v 9.10). The modeled structure was optimized by applying a molecular dynamics simulation for 2 ns with the CHARMM (Chemistry at HARvard Macromolecular Mechanics) 27 force field in NAMD (NAnoscale Molecular Dynamics program; v 2.9) and then evaluated by calculating the stereochemical quality of the protein. The flexible docking for the interaction phenomenon of the template consists of ligand-related inhibitor molecules from the ZINC (ZINC Is Not Commercial) database using a structure-based virtual screening strategy against minimized TtFRho. Docking simulations revealed two inhibitors compounds – ZINC24934545 and ZINC72319544 – that showed high binding affinity among 2,829 drug analogs that bind with key active-site residues; these residues are considered for protein-ligand binding and unbinding pathways via steered molecular dynamics simulations. Arg215 in the model plays an important role in the stability of the protein

  12. Modeling, molecular dynamics, and docking assessment of transcription factor rho: a potential drug target in Brucella melitensis 16M.

    PubMed

    Pradeepkiran, Jangampalli Adi; Kumar, Konidala Kranthi; Kumar, Yellapu Nanda; Bhaskar, Matcha

    2015-01-01

    The zoonotic disease brucellosis, a chronic condition in humans affecting renal and cardiac systems and causing osteoarthritis, is caused by Brucella, a genus of Gram-negative, facultative, intracellular pathogens. The mode of transmission and the virulence of the pathogens are still enigmatic. Transcription regulatory elements, such as rho proteins, play an important role in the termination of transcription and/or the selection of genes in Brucella. Adverse effects of the transcription inhibitors play a key role in the non-successive transcription challenges faced by the pathogens. In the investigation presented here, we computationally predicted the transcription termination factor rho (TtFRho) inhibitors against Brucella melitensis 16M via a structure-based method. In view the unknown nature of its crystal structure, we constructed a robust three-dimensional homology model of TtFRho's structure by comparative modeling with the crystal structure of the Escherichia coli TtFRho (Protein Data Bank ID: 1PVO) as a template in MODELLER (v 9.10). The modeled structure was optimized by applying a molecular dynamics simulation for 2 ns with the CHARMM (Chemistry at HARvard Macromolecular Mechanics) 27 force field in NAMD (NAnoscale Molecular Dynamics program; v 2.9) and then evaluated by calculating the stereochemical quality of the protein. The flexible docking for the interaction phenomenon of the template consists of ligand-related inhibitor molecules from the ZINC (ZINC Is Not Commercial) database using a structure-based virtual screening strategy against minimized TtFRho. Docking simulations revealed two inhibitors compounds - ZINC24934545 and ZINC72319544 - that showed high binding affinity among 2,829 drug analogs that bind with key active-site residues; these residues are considered for protein-ligand binding and unbinding pathways via steered molecular dynamics simulations. Arg215 in the model plays an important role in the stability of the protein-ligand complex

  13. Magnetic nanoparticles for drug targeting: from design to insights into systemic toxicity. Preclinical evaluation of hematological, vascular and neurobehavioral toxicology.

    PubMed

    Agotegaray, Mariela A; Campelo, Adrián E; Zysler, Roberto D; Gumilar, Fernanda; Bras, Cristina; Gandini, Ariel; Minetti, Alejandra; Massheimer, Virginia L; Lassalle, Verónica L

    2017-03-28

    A simple two-step drug encapsulation method was developed to obtain biocompatible magnetic nanocarriers for the potential targeted treatment of diverse diseases. The nanodevice consists of a magnetite core coated with chitosan (Chit@MNPs) as a platform for diclofenac (Dic) loading as a model drug (Dic-Chit@MNPs). Mechanistic and experimental conditions related to drug incorporation and quantification are further addressed. This multi-disciplinary study aims to elucidate the toxicological impact of the MNPs at hematological, vascular, neurological and behavioral levels. Blood compatibility assays revealed that MNPs did not affect either erythrosedimentation rates or erythrocyte integrity at the evaluated doses (1, 10 and 100 μg mL(-1)). A microscopic evaluation of blood smears indicated that MNPs did not induce morphological changes in blood cells. Platelet aggregation was not affected by MNPs either and just a slight diminution was observed with Dic-Chit@MNPs, an effect possibly due to diclofenac. The examined formulations did not exert cytotoxicity on rat aortic endothelial cells and no changes in cell viability or their capacity to synthesize NO were observed. Behavioral and functional nervous system parameters in a functional observational battery were assessed after a subacute treatment of mice with Chit@MNPs. The urine pools of the exposed group were decreased. Nephritis and an increased number of megakaryocytes in the spleen were observed in the histopathological studies. Sub-acute exposure to Chit@MNPs did not produce significant changes in the parameters used to evaluate neurobehavioral toxicity. The aspects focused on within this manuscript are relevant at the pre-clinical level providing new and novel knowledge concerning the biocompatibility of magnetic nanodevices for biomedical applications.

  14. Prediction of the mechanical properties of zeolite pellets for aerospace molecular decontamination applications

    PubMed Central

    Rioland, Guillaume; Faye, Delphine; Patarin, Joël

    2016-01-01

    Zeolite pellets containing 5 wt % of binder (methylcellulose or sodium metasilicate) were formed with a hydraulic press. This paper describes a mathematical model to predict the mechanical properties (uniaxial and diametric compression) of these pellets for arbitrary dimensions (height and diameter) using a design of experiments (DOE) methodology. A second-degree polynomial equation including interactions was used to approximate the experimental results. This leads to an empirical model for the estimation of the mechanical properties of zeolite pellets with 5 wt % of binder. The model was verified by additional experimental tests including pellets of different dimensions created with different applied pressures. The optimum dimensions were found to be a diameter of 10–23 mm, a height of 1–3.5 mm and an applied pressure higher than 200 MPa. These pellets are promising for technological uses in molecular decontamination for aerospace-based applications. PMID:28144526

  15. Prediction of the mechanical properties of zeolite pellets for aerospace molecular decontamination applications.

    PubMed

    Rioland, Guillaume; Dutournié, Patrick; Faye, Delphine; Daou, T Jean; Patarin, Joël

    2016-01-01

    Zeolite pellets containing 5 wt % of binder (methylcellulose or sodium metasilicate) were formed with a hydraulic press. This paper describes a mathematical model to predict the mechanical properties (uniaxial and diametric compression) of these pellets for arbitrary dimensions (height and diameter) using a design of experiments (DOE) methodology. A second-degree polynomial equation including interactions was used to approximate the experimental results. This leads to an empirical model for the estimation of the mechanical properties of zeolite pellets with 5 wt % of binder. The model was verified by additional experimental tests including pellets of different dimensions created with different applied pressures. The optimum dimensions were found to be a diameter of 10-23 mm, a height of 1-3.5 mm and an applied pressure higher than 200 MPa. These pellets are promising for technological uses in molecular decontamination for aerospace-based applications.

  16. Applying predictive analytics to develop an intelligent risk detection application for healthcare contexts.

    PubMed

    Moghimi, Fatemeh Hoda; Cheung, Michael; Wickramasinghe, Nilmini

    2013-01-01

    Healthcare is an information rich industry where successful outcomes require the processing of multi-spectral data and sound decision making. The exponential growth of data and big data issues coupled with a rapid increase of service demands in healthcare contexts today, requires a robust framework enabled by IT (information technology) solutions as well as real-time service handling in order to ensure superior decision making and successful healthcare outcomes. Such a context is appropriate for the application of real time intelligent risk detection decision support systems using predictive analytic techniques such as data mining. To illustrate the power and potential of data science technologies in healthcare decision making scenarios, the use of an intelligent risk detection (IRD) model is proffered for the context of Congenital Heart Disease (CHD) in children, an area which requires complex high risk decisions that need to be made expeditiously and accurately in order to ensure successful healthcare outcomes.

  17. Voxelwise spectral diffusional connectivity and its applications to Alzheimer's disease and intelligence prediction.

    PubMed

    Li, Junning; Jin, Yan; Shi, Yonggang; Dinov, Ivo D; Wang, Danny J; Toga, Arthur W; Thompson, Paul M

    2013-01-01

    Human brain connectivity can be studied using graph theory. Many connectivity studies parcellate the brain into regions and count fibres extracted between them. The resulting network analyses require validation of the tractography, as well as region and parameter selection. Here we investigate whole brain connectivity from a different perspective. We propose a mathematical formulation based on studying the eigenvalues of the Laplacian matrix of the diffusion tensor field at the voxel level. This voxelwise matrix has over a million parameters, but we derive the Kirchhoff complexity and eigen-spectrum through elegant mathematical theorems, without heavy computation. We use these novel measures to accurately estimate the voxelwise connectivity in multiple biomedical applications such as Alzheimer's disease and intelligence prediction.

  18. Application of Modular Modeling System to Predict Evaporation, Infiltration, Air Temperature, and Soil Moisture

    NASA Technical Reports Server (NTRS)

    Boggs, Johnny; Birgan, Latricia J.; Tsegaye, Teferi; Coleman, Tommy; Soman, Vishwas

    1997-01-01

    Models are used for numerous application including hydrology. The Modular Modeling System (MMS) is one of the few that can simulate a hydrology process. MMS was tested and used to compare infiltration, soil moisture, daily temperature, and potential and actual evaporation for the Elinsboro sandy loam soil and the Mattapex silty loam soil in the Microwave Radiometer Experiment of Soil Moisture Sensing at Beltsville Agriculture Research Test Site in Maryland. An input file for each location was created to nut the model. Graphs were plotted, and it was observed that the model gave a good representation for evaporation for both plots. In comparing the two plots, it was noted that infiltration and soil moisture tend to peak around the same time, temperature peaks in July and August and the peak evaporation was observed on September 15 and July 4 for the Elinsboro Mattapex plot respectively. MMS can be used successfully to predict hydrological processes as long as the proper input parameters are available.

  19. An Application in the Ultra Short-term Prediction of UT1--UTC Based on Grey System Model

    NASA Astrophysics Data System (ADS)

    Lei, Y.; Zhao, D. N.; Cai, H. B.

    2016-05-01

    This work presents an application of the grey system model in the prediction of UT1--UTC. The short-term prediction of UT1--UTC is studied up to 30 days by means of the grey system model. The EOP (Earth orientation parameter) C04 time series with daily values from the International Earth Rotation and Reference Systems Service (IERS) serve as the data base. The results of the prediction are analyzed and compared with those obtained by the artificial neural network (ANN), the combination of least squares (LS) and autoregressive (AR) model (LS+AR), and the Earth Orientation Parameters Prediction Comparison Campaign (EOP PCC). The accuracies of the ultra short-term (1--10 d) prediction are comparable to those obtained by the other prediction methods. The presented method is easy to use.

  20. Application of infrared portable sensor technology for predicting perceived astringency of acidic whey protein beverages.

    PubMed

    Wang, Ting; Tan, Siow-Ying; Mutilangi, William; Plans, Marcal; Rodriguez-Saona, Luis

    2016-12-01

    Formulating whey protein beverages at acidic pH provides better clarity but the beverages typically develop an unpleasant and astringent flavor. Our aim was to evaluate the application of infrared spectroscopy and chemometrics in predicting astringency of acidic whey protein beverages. Whey protein isolate (WPI), whey protein concentrate (WPC), and whey protein hydrolysate (WPH) from different manufacturers were used to formulate beverages at pH ranging from 2.2 to 3.9. Trained panelists using the spectrum method of descriptive analysis tested the beverages providing astringency scores. A portable Fourier transform infrared spectroscopy attenuated total reflectance spectrometer was used for spectra collection that was analyzed by multivariate regression analysis (partial least squares regression) to build calibration models with the sensory astringency scores. Beverage astringency scores fluctuated from 1.9 to 5.2 units and were explained by pH, protein type (WPC, WPI, or WPH), source (manufacturer), and their interactions, revealing the complexity of astringency development in acidic whey protein beverages. The WPC and WPH beverages showed an increase in astringency as the pH of the solution was lowered, but no relationship was found for WPI beverages. The partial least squares regression analysis showed strong relationship between the reference astringency scores and the infrared predicted values (correlation coefficient >0.94), giving standard error of cross-validation ranging from 0.08 to 0.12 units, depending on whey protein type. Major absorption bands explaining astringency scores were associated with carboxylic groups and amide regions of proteins. The portable infrared technique allowed rapid prediction of astringency of acidic whey protein beverages, providing the industry a novel tool for monitoring sensory characteristics of whey-containing beverages.

  1. Mathematical model to predict skin concentration after topical application of drugs.

    PubMed

    Todo, Hiroaki; Oshizaka, Takeshi; Kadhum, Wesam R; Sugibayashi, Kenji

    2013-12-16

    Skin permeation experiments have been broadly done since 1970s to 1980s as an evaluation method for transdermal drug delivery systems. In topically applied drug and cosmetic formulations, skin concentration of chemical compounds is more important than their skin permeations, because primary target site of the chemical compounds is skin surface or skin tissues. Furthermore, the direct pharmacological reaction of a metabolically stable drug that binds with specific receptors of known expression levels in an organ can be determined by Hill's equation. Nevertheless, little investigation was carried out on the test method of skin concentration after topically application of chemical compounds. Recently we investigated an estimating method of skin concentration of the chemical compounds from their skin permeation profiles. In the study, we took care of "3Rs" issues for animal experiments. We have proposed an equation which was capable to estimate animal skin concentration from permeation profile through the artificial membrane (silicone membrane) and animal skin. This new approach may allow the skin concentration of a drug to be predicted using Fick's second law of diffusion. The silicone membrane was found to be useful as an alternative membrane to animal skin for predicting skin concentration of chemical compounds, because an extremely excellent extrapolation to animal skin concentration was attained by calculation using the silicone membrane permeation data. In this chapter, we aimed to establish an accurate and convenient method for predicting the concentration profiles of drugs in the skin based on the skin permeation parameters of topically active drugs derived from steady-state skin permeation experiments.

  2. Applications and limits of theoretical adsorption models for predicting the adsorption properties of adsorbents.

    PubMed

    Park, Hyun Ju; Nguyen, Duc Canh; Na, Choon-Ki; Kim, Chung-il

    2015-01-01

    The objective of this study is to evaluate the applicability of adsorption models for predicting the properties of adsorbents. The kinetics of the adsorption of NO3- ions on a PP-g-AA-Am non-woven fabric have been investigated under equilibrium conditions in both batch and fixed bed column processes. The adsorption equilibrium experiments in the batch process were carried out under different adsorbate concentration and adsorbent dosage conditions and the results were analyzed using adsorption isotherm models, energy models, and kinetic models. The results of the analysis indicate that the adsorption occurring at a fixed adsorbate concentration with a varying adsorbent dosage occur more easily compared to those under a fixed adsorbent dosage with a varying adsorbate concentration. In the second part of the study, the experimental data obtained using fixed bed columns were fit to Bed Depth Service Time, Bohart-Adams, Clark, and Wolborska models, to predict the breakthrough curves and determine the column kinetic parameters. The adsorption properties of the NO3- ions on the PP-g-AA-Am non-woven fabric were differently described by different models for both the batch and fixed bed column process. Therefore, it appears reasonable to assume that the adsorption properties were dominated by multiple mechanisms, depending on the experimental conditions.

  3. T-CREST: A Time-Predictable Multi-Core Platform for Aerospace Applications

    NASA Astrophysics Data System (ADS)

    Schoeberl, Martin; Silva, Claudio; Rocha, Andre

    2014-08-01

    Space systems are hard real-time systems, where the worst-case execution time (WCET) of tasks needs to be known to prove absence of deadline misses. For simple processor and memory architectures it is possible to statically derive a safe upper bound of the WCET. However, future requirements in more autonomous missions require more processing power. This increase in processing power is approached by multi-core processors. However, current multi-core processors are not WCET analyzable.The mission of T-CREST is to develop tools and build a multi-core system that provides high performance, but be WCET analyzable. The T-CREST time-predictable system will simplify the safety argument with respect to the maximum execution time and increase the performance with multi-core technology. Thus the T-CREST system will result in lower costs for safety-relevant applications, reducing system complexity, simultaneously providing faster time-predictable execution. Most of the T-CREST technology is available in open-source.

  4. An analysis for high speed propeller-nacelle aerodynamic performance prediction. Volume 1: Theory and application

    NASA Technical Reports Server (NTRS)

    Egolf, T. Alan; Anderson, Olof L.; Edwards, David E.; Landgrebe, Anton J.

    1988-01-01

    A computer program, the Propeller Nacelle Aerodynamic Performance Prediction Analysis (PANPER), was developed for the prediction and analysis of the performance and airflow of propeller-nacelle configurations operating over a forward speed range inclusive of high speed flight typical of recent propfan designs. A propeller lifting line, wake program was combined with a compressible, viscous center body interaction program, originally developed for diffusers, to compute the propeller-nacelle flow field, blade loading distribution, p