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Sample records for applications predicting drug-target

  1. [Research advance in the drug target prediction based on chemoinformatics].

    PubMed

    Fang, Jian-song; Liu, Ai-lin; Du, Guan-hua

    2014-10-01

    The emerging of network pharmacology and polypharmacology forces the scientists to recognize and explore new mechanisms of existing drugs. The drug target prediction can play a key significance on the elucidation of the molecular mechanism of drugs and drug reposition. In this paper, we systematically review the existing approaches to the prediction of biological targets of small molecule based on chemoinformatics, including ligand-based prediction, receptor-based prediction and data mining-based prediction. We also depict the strength of these methods as well as their applications, and put forward their developing direction. PMID:25577863

  2. In silico prediction of drug targets in Vibrio cholerae.

    PubMed

    Katara, Pramod; Grover, Atul; Kuntal, Himani; Sharma, Vinay

    2011-10-01

    Identification of potential drug targets is the first step in the process of modern drug discovery, subjected to their validation and drug development. Whole genome sequences of a number of organisms allow prediction of potential drug targets using sequence comparison approaches. Here, we present a subtractive approach exploiting the knowledge of global gene expression along with sequence comparisons to predict the potential drug targets more efficiently. Based on the knowledge of 155 known virulence and their coexpressed genes mined from microarray database in the public domain, 357 coexpressed probable virulence genes for Vibrio cholerae were predicted. Based on screening of Database of Essential Genes using blastn, a total of 102 genes out of these 357 were enlisted as vitally essential genes, and hence good putative drug targets. As the effective drug target is a protein which is only present in the pathogen, similarity search of these 102 essential genes against human genome sequence led to subtraction of 66 genes, thus leaving behind a subset of 36 genes whose products have been called as potential drug targets. The gene ontology analysis using Blast2GO of these 36 genes revealed their roles in important metabolic pathways of V. cholerae or on the surface of the pathogen. Thus, we propose that the products of these genes be evaluated as target sites of drugs against V. cholerae in future investigations. PMID:21174131

  3. GESSE: Predicting Drug Side Effects from Drug-Target Relationships.

    PubMed

    Pérez-Nueno, Violeta I; Souchet, Michel; Karaboga, Arnaud S; Ritchie, David W

    2015-09-28

    The in silico prediction of unwanted side effects (SEs) caused by the promiscuous behavior of drugs and their targets is highly relevant to the pharmaceutical industry. Considerable effort is now being put into computational and experimental screening of several suspected off-target proteins in the hope that SEs might be identified early, before the cost associated with developing a drug candidate rises steeply. Following this need, we present a new method called GESSE to predict potential SEs of drugs from their physicochemical properties (three-dimensional shape plus chemistry) and to target protein data extracted from predicted drug-target relationships. The GESSE approach uses a canonical correlation analysis of the full drug-target and drug-SE matrices, and it then calculates a probability that each drug in the resulting drug-target matrix will have a given SE using a Bayesian discriminant analysis (DA) technique. The performance of GESSE is quantified using retrospective (external database) analysis and literature examples by means of area under the ROC curve analysis, "top hit rates", misclassification rates, and a χ(2) independence test. Overall, the robust and very promising retrospective statistics obtained and the many SE predictions that have experimental corroboration demonstrate that GESSE can successfully predict potential drug-SE profiles of candidate drug compounds from their predicted drug-target relationships. PMID:26251970

  4. Image-based prediction of drug target in yeast.

    PubMed

    Ohnuki, Shinsuke; Okada, Hiroki; Ohya, Yoshikazu

    2015-01-01

    Discovering the intracellular target of drugs is a fundamental challenge in biomedical research. We developed an image-based technique with which we were able to identify intracellular target of the compounds in the yeast Saccharomyces cerevisiae. Here, we describe the rationale of the technique, staining of yeast cells, image acquisition, data processing, and statistical analysis required for prediction of drug targets. PMID:25618355

  5. 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

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

    PubMed Central

    2015-01-01

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

  7. Mining Predicted Essential Genes of Brugia malayi for Nematode Drug Targets

    PubMed Central

    Kumar, Sanjay; Chaudhary, Kshitiz; Foster, Jeremy M.; Novelli, Jacopo F.; Zhang, Yinhua; Wang, Shiliang; Spiro, David; Ghedin, Elodie; Carlow, Clotilde K. S.

    2007-01-01

    We report results from the first genome-wide application of a rational drug target selection methodology to a metazoan pathogen genome, the completed draft sequence of Brugia malayi, a parasitic nematode responsible for human lymphatic filariasis. More than 1.5 billion people worldwide are at risk of contracting lymphatic filariasis and onchocerciasis, a related filarial disease. Drug treatments for filariasis have not changed significantly in over 20 years, and with the risk of resistance rising, there is an urgent need for the development of new anti-filarial drug therapies. The recent publication of the draft genomic sequence for B. malayi enables a genome-wide search for new drug targets. However, there is no functional genomics data in B. malayi to guide the selection of potential drug targets. To circumvent this problem, we have utilized the free-living model nematode Caenorhabditis elegans as a surrogate for B. malayi. Sequence comparisons between the two genomes allow us to map C. elegans orthologs to B. malayi genes. Using these orthology mappings and by incorporating the extensive genomic and functional genomic data, including genome-wide RNAi screens, that already exist for C. elegans, we identify potentially essential genes in B. malayi. Further incorporation of human host genome sequence data and a custom algorithm for prioritization enables us to collect and rank nearly 600 drug target candidates. Previously identified potential drug targets cluster near the top of our prioritized list, lending credibility to our methodology. Over-represented Gene Ontology terms, predicted InterPro domains, and RNAi phenotypes of C. elegans orthologs associated with the potential target pool are identified. By virtue of the selection procedure, the potential B. malayi drug targets highlight components of key processes in nematode biology such as central metabolism, molting and regulation of gene expression. PMID:18000556

  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-targeting methodologies with applications: A review.

    PubMed

    Kleinstreuer, Clement; Feng, Yu; Childress, Emily

    2014-12-16

    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

  10. 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. PMID:26283676

  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 Central

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

    2016-01-01

    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. PMID:26872142

  13. An improved approach for predicting drug-target interaction: proteochemometrics to molecular docking.

    PubMed

    Shaikh, Naeem; Sharma, Mahesh; Garg, Prabha

    2016-02-23

    Proteochemometric (PCM) methods, which use descriptors of both the interacting species, i.e. drug and the target, are being successfully employed for the prediction of drug-target interactions (DTI). However, unavailability of non-interacting dataset and determining the applicability domain (AD) of model are a main concern in PCM modeling. In the present study, traditional PCM modeling was improved by devising novel methodologies for reliable negative dataset generation and fingerprint based AD analysis. In addition, various types of descriptors and classifiers were evaluated for their performance. The Random Forest and Support Vector Machine models outperformed the other classifiers (accuracies >98% and >89% for 10-fold cross validation and external validation, respectively). The type of protein descriptors had negligible effect on the developed models, encouraging the use of sequence-based descriptors over the structure-based descriptors. To establish the practical utility of built models, targets were predicted for approved anticancer drugs of natural origin. The molecular recognition interactions between the predicted drug-target pair were quantified with the help of a reverse molecular docking approach. The majority of predicted targets are known for anticancer therapy. These results thus correlate well with anticancer potential of the selected drugs. Interestingly, out of all predicted DTIs, thirty were found to be reported in the ChEMBL database, further validating the adopted methodology. The outcome of this study suggests that the proposed approach, involving use of the improved PCM methodology and molecular docking, can be successfully employed to elucidate the intricate mode of action for drug molecules as well as repositioning them for new therapeutic applications. PMID:26822863

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

    PubMed

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

    2015-05-01

    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

  17. Prediction of drug-target interactions and drug repositioning via network-based inference.

    PubMed

    Cheng, Feixiong; Liu, Chuang; Jiang, Jing; Lu, Weiqiang; Li, Weihua; Liu, Guixia; Zhou, Weixing; Huang, Jin; Tang, Yun

    2012-01-01

    Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning. PMID:22589709

  18. Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

    PubMed Central

    Jiang, Jing; Lu, Weiqiang; Li, Weihua; Liu, Guixia; Zhou, Weixing; Huang, Jin; Tang, Yun

    2012-01-01

    Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning. PMID:22589709

  19. Similarity-based machine learning methods for predicting drug-target interactions: a brief review.

    PubMed

    Ding, Hao; Takigawa, Ichigaku; Mamitsuka, Hiroshi; Zhu, Shanfeng

    2014-09-01

    Computationally predicting drug-target interactions is useful to select possible drug (or target) candidates for further biochemical verification. We focus on machine learning-based approaches, particularly similarity-based methods that use drug and target similarities, which show relationships among drugs and those among targets, respectively. These two similarities represent two emerging concepts, the chemical space and the genomic space. Typically, the methods combine these two types of similarities to generate models for predicting new drug-target interactions. This process is also closely related to a lot of work in pharmacogenomics or chemical biology that attempt to understand the relationships between the chemical and genomic spaces. This background makes the similarity-based approaches attractive and promising. This article reviews the similarity-based machine learning methods for predicting drug-target interactions, which are state-of-the-art and have aroused great interest in bioinformatics. We describe each of these methods briefly, and empirically compare these methods under a uniform experimental setting to explore their advantages and limitations. PMID:23933754

  20. 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. PMID:26851083

  1. Drug-target interaction prediction by integrating chemical, genomic, functional and pharmacological data.

    PubMed

    Yang, Fan; Xu, Jinbo; Zeng, Jianyang

    2014-01-01

    In silico prediction of unknown drug-target interactions (DTIs) has become a popular tool for drug repositioning and drug development. A key challenge in DTI prediction lies in integrating multiple types of data for accurate DTI prediction. Although recent studies have demonstrated that genomic, chemical and pharmacological data can provide reliable information for DTI prediction, it remains unclear whether functional information on proteins can also contribute to this task. Little work has been developed to combine such information with other data to identify new interactions between drugs and targets. In this paper, we introduce functional data into DTI prediction and construct biological space for targets using the functional similarity measure. We present a probabilistic graphical model, called conditional random field (CRF), to systematically integrate genomic, chemical, functional and pharmacological data plus the topology of DTI networks into a unified framework to predict missing DTIs. Tests on two benchmark datasets show that our method can achieve excellent prediction performance with the area under the precision-recall curve (AUPR) up to 94.9. These results demonstrate that our CRF model can successfully exploit heterogeneous data to capture the latent correlations of DTIs, and thus will be practically useful for drug repositioning. Supplementary Material is available at http://iiis.tsinghua.edu.cn/~compbio/papers/psb2014/psb2014_sm.pdf. PMID:24297542

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

    PubMed Central

    2010-01-01

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

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

    PubMed

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

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com . PMID:27167132

  4. Application of high gradient magnetic separation principles to magnetic drug targeting

    NASA Astrophysics Data System (ADS)

    Ritter, James A.; Ebner, Armin D.; Daniel, Karen D.; Stewart, Krystle L.

    2004-09-01

    A hypothetical magnetic drug targeting system, utilizing high gradient magnetic separation (HGMS) principles, was studied theoretically using FEMLAB simulations. This new approach uses a ferromagnetic wire placed at a bifurcation point inside a blood vessel and an externally applied magnetic field, to magnetically guide magnetic drug carrier particles (MDCP) through the circulatory system and then to magnetically retain them at a target site. Wire collection (CE) and diversion (DE) efficiencies were defined and used to evaluate the system performance. CE and DE both increase as the strength of the applied magnetic field (0.3-2.0 T), the amount of ferromagnetic material (iron) in the MDCP (20-100%) and the size of the MDCP (1-10 μm radius) increase, and as the average inlet velocity (0.1-0.8 m s-1), the size of the wire (50-250 μm radius) and the ratio (4-10) of the parent vessel radius (0.25-1.25 mm radius) to wire radius decrease. The effect of the applied magnetic field direction (0° and 90°) on CE and DE was minimal. Under these plausible conditions, CEs as high as 70% were obtained, with DEs reaching only 30%; however, when the MDCPs were allowed to agglomerate (4-10 μm radius), CEs and DEs of 100% were indeed achieved. These results reveal that this new magnetic drug targeting approach for magnetically collecting MDCPs at a target site, even in arteries with very high velocities, is feasible and very promising; this new approach for magnetically guiding MDCPs through the circulatory system is also feasible but more limited. Overall, this study shows that magnetic drug targeting, based on HGMS principles, has considerable promise as an effective drug targeting tool with many potential applications.

  5. RepurposeVS: A Drug Repurposing-Focused Computational Method for Accurate Drug-Target Signature Predictions.

    PubMed

    Issa, Naiem T; Peters, Oakland J; Byers, Stephen W; Dakshanamurthy, Sivanesan

    2015-01-01

    We describe here RepurposeVS for the reliable prediction of drug-target signatures using X-ray protein crystal structures. RepurposeVS is a virtual screening method that incorporates docking, drug-centric and protein-centric 2D/3D fingerprints with a rigorous mathematical normalization procedure to account for the variability in units and provide high-resolution contextual information for drug-target binding. Validity was confirmed by the following: (1) providing the greatest enrichment of known drug binders for multiple protein targets in virtual screening experiments, (2) determining that similarly shaped protein target pockets are predicted to bind drugs of similar 3D shapes when RepurposeVS is applied to 2,335 human protein targets, and (3) determining true biological associations in vitro for mebendazole (MBZ) across many predicted kinase targets for potential cancer repurposing. Since RepurposeVS is a drug repurposing-focused method, benchmarking was conducted on a set of 3,671 FDA approved and experimental drugs rather than the Database of Useful Decoys (DUDE) so as to streamline downstream repurposing experiments. We further apply RepurposeVS to explore the overall potential drug repurposing space for currently approved drugs. RepurposeVS is not computationally intensive and increases performance accuracy, thus serving as an efficient and powerful in silico tool to predict drug-target associations in drug repurposing. PMID:26234515

  6. 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.

  7. A new look at drugs targeting malignant melanoma--an application for mass spectrometry imaging.

    PubMed

    Sugihara, Yutaka; Végvári, Akos; Welinder, Charlotte; Jönsson, Göran; Ingvar, Christian; Lundgren, Lotta; Olsson, Håkan; Breslin, Thomas; Wieslander, Elisabet; Laurell, Thomas; Rezeli, Melinda; Jansson, Bo; Nishimura, Toshihide; Fehniger, Thomas E; Baldetorp, Bo; Marko-Varga, György

    2014-09-01

    Malignant melanoma (MM) patients are being treated with an increasing number of personalized medicine (PM) drugs, several of which are small molecule drugs developed to treat patients with specific disease genotypes and phenotypes. In particular, the clinical application of protein kinase inhibitors has been highly effective for certain subsets of MM patients. Vemurafenib, a protein kinase inhibitor targeting BRAF-mutated protein, has shown significant efficacy in slowing disease progression. In this paper, we provide an overview of this new generation of targeted drugs, and demonstrate the first data on localization of PM drugs within tumor compartments. In this study, we have introduced MALDI-MS imaging to provide new information on one of the drugs currently used in the PM treatment of MM, vemurafenib. In a proof-of-concept in vitro study, MALDI-MS imaging was used to identify vemurafenib applied to metastatic lymph nodes tumors of subjects attending the regional hospital network of Southern Sweden. The paper provides evidence of BRAF overexpression in tumors isolated from MM patients and localization of the specific drug targeting BRAF, vemurafenib, using MS fragment ion signatures. Our ability to determine drug uptake at the target sites of directed therapy provides important opportunity for increasing our understanding about the mode of action of drug activity within the disease environment. PMID:25044963

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

    NASA Astrophysics Data System (ADS)

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

    2015-11-01

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

  9. In silico prediction of drug targets in phytopathogenic Pseudomonas syringae pv. phaseolicola: charting a course for agrigenomics translation research.

    PubMed

    Katara, Pramod; Grover, Atul; Sharma, Vinay

    2012-12-01

    Pseudomonas syringae pv. phaseolicola is a major plant pathogen causing halo blight disease and has world-wide importance. The emerging post-genomics field of agrigenomics, together with the availability of whole genome sequences of a number of pathogens and host organisms, offer the promise for identification of potential drug targets using sequence comparison approaches. On the other hand, lack of gene expression data for most of the phytopathogenic microbes still remains a formidable barrier. The present study aimed at the prediction of drug targets in Pseudomonas syringae pv. phaseolicola by exploiting the knowledge of Codon Usage bias for gene expression subtractively, supported by gene expression analysis and sequence comparisons. Based on screening of the Database of Essential Genes using blastx, 158 of the total 5172 genes of P. syringae pv. phaseolicola were enlisted as vitally essential genes. Similarity search for these 158 essential genes against available host-plant sequences (Phaseolous vulgaris) led to the identification of homologues of 21 genes in the host genome, thus leaving behind a subset of 137 genes. Expression analysis of these 137 genes using RSCU(gene,) validated by microarray gene expression data suggested 22 genes had higher expression levels in the cell, and therefore their products have been identified as putative drug targets. The gene ontology analysis of these 22 genes revealed their indispensable roles in pivotal metabolic pathways of P. syringae pv. phaseolicola. Upon comparison of the sequences of these genes with other soil bacteria, we identified two genes that were unique to P. syringae pv. phaseolicola. The products of these genes can potentially be utilized for drug development so as to control the halo blight disease and thereby accelerate translation research in the nascent field of agrigenomics. PMID:23215808

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

    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

  12. 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

  13. 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. PMID:25957673

  14. 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

  15. Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica.

    PubMed

    Prado-Prado, Francisco; García-Mera, Xerardo; Abeijón, Paula; Alonso, Nerea; Caamaño, Olga; Yáñez, Matilde; Gárate, Teresa; Mezo, Mercedes; González-Warleta, Marta; Muiño, Laura; Ubeira, Florencio M; González-Díaz, Humberto

    2011-04-01

    There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets like proteins. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments. Unfortunately, most QSAR models predict activity against only one protein. To solve this problem, we developed here a multi-target QSAR (mt-QSAR) classifier using the MARCH-INSIDE technique to calculate structural parameters of drug and target plus one Artificial Neuronal Network (ANN) to seek the model. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 32:32-15-1:1. This MLP classifies correctly 623 out of 678 DTPs (Sensitivity = 91.89%) and 2995 out of 3234 nDTPs (Specificity = 92.61%), corresponding to training Accuracy = 92.48%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 313 out of 338 DTPs (Sensitivity = 92.60%) and 1411 out of 1534 nDTP (Specificity = 91.98%) in validation series, corresponding to total Accuracy = 92.09% for validation series (Predictability). This model favorably compares with other LDA and ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. These mt-QSARs offer also a good opportunity to construct drug-protein Complex Networks (CNs) that can be used to explore large and complex drug-protein receptors databases. Finally, we illustrated two practical uses of this model with two different experiments. In experiment 1, we report prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of 10 rasagiline derivatives promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, SEC and 1DE sample preparation, MALDI-TOF MS

  16. 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

  17. Using bioinformatics for drug target identification from the genome.

    PubMed

    Jiang, Zhenran; Zhou, Yanhong

    2005-01-01

    Genomics and proteomics technologies have created a paradigm shift in the drug discovery process, with bioinformatics having a key role in the exploitation of genomic, transcriptomic, and proteomic data to gain insights into the molecular mechanisms that underlie disease and to identify potential drug targets. We discuss the current state of the art for some of the bioinformatic approaches to identifying drug targets, including identifying new members of successful target classes and their functions, predicting disease relevant genes, and constructing gene networks and protein interaction networks. In addition, we introduce drug target discovery using the strategy of systems biology, and discuss some of the data resources for the identification of drug targets. Although bioinformatics tools and resources can be used to identify putative drug targets, validating targets is still a process that requires an understanding of the role of the gene or protein in the disease process and is heavily dependent on laboratory-based work. PMID:16336003

  18. Microarray: an approach for current drug targets.

    PubMed

    Gomase, Virendra S; Tagore, Somnath; Kale, Karbhari V

    2008-03-01

    Microarrays are a powerful tool has multiple applications both in clinical and cellular and molecular biology arenas. Early assessment of the probable biological importance of drug targets, pharmacogenomics, toxicogenomics and single nucleotide polymorphisms (SNPs). A list of new drug candidates along with proposed targets for intervention is described. Recent advances in the knowledge of microarrays analysis of organisms and the availability of the genomics sequences provide a wide range of novel targets for drug design. This review gives different process of microarray technologies; methods for comparative gene expression study, applications of microarrays in medicine and pharmacogenomics and current drug targets in research, which are relevant to common diseases as they relate to clinical and future perspectives. PMID:18336225

  19. 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. PMID:26415215

  20. Drug targeting to the brain.

    PubMed

    Pardridge, William M

    2007-09-01

    The goal of brain drug targeting technology is the delivery of therapeutics across the blood-brain barrier (BBB), including the human BBB. This is accomplished by re-engineering pharmaceuticals to cross the BBB via specific endogenous transporters localized within the brain capillary endothelium. Certain endogenous peptides, such as insulin or transferrin, undergo receptor-mediated transport (RMT) across the BBB in vivo. In addition, peptidomimetic monoclonal antibodies (MAb) may also cross the BBB via RMT on the endogenous transporters. The MAb may be used as a molecular Trojan horse to ferry across the BBB large molecule pharmaceuticals, including recombinant proteins, antibodies, RNA interference drugs, or non-viral gene medicines. Fusion proteins of the molecular Trojan horse and either neurotrophins or single chain Fv antibodies have been genetically engineered. The fusion proteins retain bi-functional properties, and both bind the BBB receptor, to trigger transport into brain, and bind the cognate receptor inside brain to induce the pharmacologic effect. Trojan horse liposome technology enables the brain targeting of non-viral plasmid DNA. Molecular Trojan horses may be formulated with fusion protein technology, avidin-biotin technology, or Trojan horse liposomes to target to brain virtually any large molecule pharmaceutical. PMID:17554607

  1. Helicases as Antiviral Drug Targets

    PubMed Central

    Frick, David N.

    2012-01-01

    Summary Helicases catalytically unwind duplex DNA or RNA using energy derived from the hydrolysis of nucleoside triphosphates and are attractive drug targets because they are required for viral replication. This review discusses methods for helicase identification, classification and analysis, and presents an overview of helicases that are necessary for the replication of human pathogenic viruses. Newly developed methods to analyze helicases, coupled with recently determined atomic structures, have led to a better understanding of their mechanisms of action. The majority of this research has concentrated on enzymes encoded by the herpes simplex virus (HSV) and the hepatitis C virus (HCV). Helicase inhibitors that target the HSV helicase–primase complex comprised of the UL5, UL8 and UL52 proteins have recently been shown to effectively control HSV infection in animal models. In addition, several groups have reported structures of the HCV NS3 helicase at atomic resolutions, and mechanistic studies have uncovered characteristics that distinguish the HCV helicase from related cellular proteins. These new developments should eventually lead to new antiviral medications. PMID:12973446

  2. 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. PMID:24717692

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

    PubMed Central

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

    2013-01-01

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

  4. 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. PMID:27284062

  5. 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.

  6. Histamine pharmacology and new CNS drug targets.

    PubMed

    Tiligada, Ekaterini; Kyriakidis, Konstantinos; Chazot, Paul L; Passani, M Beatrice

    2011-12-01

    During the last decade, the identification of a number of novel drug targets led to the development of promising new compounds which are currently under evaluation for their therapeutic prospective in CNS related disorders. Besides the established pleiotropic regulatory functions in the periphery, the interest in the potential homeostatic role of histamine in the brain was revived following the identification of H(3) and H(4) receptors some years ago. Complementing classical CNS pharmacology, the development of selective histamine receptor agonists, antagonists, and inverse agonists provides the lead for the potential exploitation of the histaminergic system in the treatment of brain pathologies. Although no CNS disease entity has been associated directly to brain histamine dysfunction until now, the H(3) receptor is recognized as a drug target for neuropathic pain, sleep-wake disorders, including narcolepsy, and cognitive impairment associated with attention deficit hyperactivity disorder, schizophrenia, Alzheimer's, or Parkinson's disease, while the first H(3) receptor ligands have already entered phase I-III clinical trials. Interestingly, the localization of the immunomodulatory H(4) receptor in the nervous system exposes attractive perspectives for the therapeutic exploitation of this new drug target in neuroimmunopharmacology. This review focuses on a concise presentation of the current "translational research" approach that exploits the latest advances in histamine pharmacology for the development of beneficial drug targets for the treatment of neuronal disorders, such as neuropathic pain, cognitive, and sleep-wake pathologies. Furthermore, the role of the brain histaminergic system(s) in neuroprotection and neuroimmunology/inflammation remains a challenging research area that is currently under consideration. PMID:22070192

  7. Open challenges in magnetic drug targeting.

    PubMed

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

    2015-01-01

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

  8. Open Challenges in Magnetic Drug Targeting

    PubMed Central

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

    2014-01-01

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

  9. 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

  10. Functional genomics and cancer drug target discovery.

    PubMed

    Moody, Susan E; Boehm, Jesse S; Barbie, David A; Hahn, William C

    2010-06-01

    The recent development of technologies for whole-genome sequencing, copy number analysis and expression profiling enables the generation of comprehensive descriptions of cancer genomes. However, although the structural analysis and expression profiling of tumors and cancer cell lines can allow the identification of candidate molecules that are altered in the malignant state, functional analyses are necessary to confirm such genes as oncogenes or tumor suppressors. Moreover, recent research suggests that tumor cells also depend on synthetic lethal targets, which are not mutated or amplified in cancer genomes; functional genomics screening can facilitate the discovery of such targets. This review provides an overview of the tools available for the study of functional genomics, and discusses recent research involving the use of these tools to identify potential novel drug targets in cancer. PMID:20521217

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

    PubMed

    Park, Junseong; Lee, Jungsul; Choi, Chulhee

    2015-01-01

    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. PMID:26336805

  12. Therapeutic approaches to drug targets in atherosclerosis.

    PubMed

    Jamkhande, Prasad G; Chandak, Prakash G; Dhawale, Shashikant C; Barde, Sonal R; Tidke, Priti S; Sakhare, Ram S

    2014-07-01

    Non-communicable diseases such as cancer, atherosclerosis and diabetes are responsible for major social and health burden as millions of people are dying every year. Out of which, atherosclerosis is the leading cause of deaths worldwide. The lipid abnormality is one of the major modifiable risk factors for atherosclerosis. Both genetic and environmental components are associated with the development of atherosclerotic plaques. Immune and inflammatory mediators have a complex role in the initiation and progression of atherosclerosis. Understanding of all these processes will help to invent a range of new biomarkers and novel treatment modalities targeting various cellular events in acute and chronic inflammation that are accountable for atherosclerosis. Several biochemical pathways, receptors and enzymes are involved in the development of atherosclerosis that would be possible targets for improving strategies for disease diagnosis and management. Earlier anti-inflammatory or lipid-lowering treatments could be useful for alleviating morbidity and mortality of atherosclerotic cardiovascular diseases. However, novel drug targets like endoglin receptor, PPARα, squalene synthase, thyroid hormone analogues, scavenger receptor and thyroid hormone analogues are more powerful to control the process of atherosclerosis. Therefore, the review briefly focuses on different novel targets that act at the starting stage of the plaque form to the thrombus formation in the atherosclerosis. PMID:25061401

  13. 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

  14. Mining metabolic networks for optimal drug targets.

    PubMed

    Sridhar, Padmavati; Song, Bin; Kahveci, Tamer; Ranka, Sanjay

    2008-01-01

    Recent advances in bioinformatics promote drug-design methods that aim to reduce side-effects. Efficient computational methods are required to identify the optimal enzyme-combination (i.e., drug targets) whose inhibition, will achieve the required effect of eliminating a given target set of compounds, while incurring minimal side-effects. We formulate the optimal enzyme-combination identification problem as an optimization problem on metabolic networks. We define a graph based computational damage model that encapsulates the impact of enzymes onto compounds in metabolic networks. We develop a branch-and-bound algorithm, named OPMET, to explore the search space dynamically. We also develop two filtering strategies to prune the search space while still guaranteeing an optimal solution. They compute an upper bound to the number of target compounds eliminated and a lower bound to the side-effect respectively. Our experiments on the human metabolic network demonstrate that the proposed algorithm can accurately identify the target enzymes for known successful drugs in the literature. Our experiments also show that OPMET can reduce the total search time by several orders of magnitude as compared to the exhaustive search. PMID:18229694

  15. Pin1 as an anticancer drug target.

    PubMed

    Xu, Guoyan G; Etzkorn, Felicia A

    2009-09-01

    Pin1 specifically catalyzes the cis/trans isomerization of phospho-Ser/Thr-Pro bonds and plays an important role in many cellular events through the effects of conformational change on the function of its biological substrates, including cell division cycle 25 C (Cdc25C), c-Jun and p53. Pin1 is overexpressed in many human cancer tissues, including breast, prostate and lung cancer. Its expression correlates with cyclin D1 levels, which contribute to cell transformation. Overexpression of Pin1 promotes tumor growth, while inhibition of Pin1 causes tumor cell apoptosis. Pin1 plays an important role in oncogenesis and therefore may serve as an effective anticancer target. Many inhibitors of Pin1 have been discovered, including several classes of designed inhibitors (alkene isosteres, reduced amides, indanyl ketones) and natural products (juglone, pepticinnamin E analogues, PiB and its derivatives obtained from a library screen). Pin1 inhibitors could be used as a novel type of anticancer drug by blocking cell cycle progression. Therefore, Pin1 represents a new diagnostic and therapeutic anticancer drug target. PMID:19890497

  16. Identification of potential drug targets in Helicobacter pylori strain HPAG1 by in silico genome analysis.

    PubMed

    Neelapu, Nageswara R R; Mutha, Naresh V R; Akula, Srinivas

    2015-01-01

    Helicobacter pylori colonizes the stomach, causing gastritis, peptic ulcers and gastric carcinoma. Drugs for treatment of H. pylori relieve from gastritis or pain but are not specific to H. pylori. Therefore, there is an immediate requirement for new therapeutic molecules to treat H. pylori. Current study investigates identification of drug targets in the strain HPAG1 of H. pylori by in silico genome analysis. Genome of HPAG1 was reconstructed for metabolic pathways and compared with Homosapien sapiens to identify genes which are unique to H. pylori. These unique genes were subjected to gene property analysis to identify the potentiality of the drug targets. Among the total number of genes analysed in H. pylori strain HPAG1, nearly 542 genes qualified as unique molecules and among them 29 were identified to be potential drug targets. Co/Zn/Cd efflux system membrane fusion protein, Ferric sidephore transport system and biopolymer transport protein EXbB were found to be critical drug targets to H. pylori HPAG1. Five genes (superoxide dismutase, HtrA protease/chaperone protein, Heatinducible transcription repressor HrcA, HspR, transcriptional repressor of DnaK operon, Cobalt-zinccadmium resistance protein CzcA) of the 29 predicted drug targets are already experimentally validated either genetically or biochemically lending credence to our unique approach. PMID:26205802

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

    PubMed

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

  18. The hydrogenosome as a drug target.

    PubMed

    Benchimol, Marlene

    2008-01-01

    Hydrogenosomes are spherical or slightly elongated organelles found in non-mitochondrial organisms. In Trichomonas hydrogenosomes measure between 200 to 500 nm, but under drug treatment they can reach 2 microm. Like mitochondria hydrogenosomes: (1) are surrounded by two closely apposed membranes and present a granular matrix: (2) divide in three different ways: segmentation, partition and the heart form; (3) they may divide at any phase of the cell cycle; (4) produce ATP; (5) participate in the metabolism of pyruvate formed during glycolysis; (6) are the site of molecular hydrogen formation; (7) present a relationship with the endoplasmic reticulum; (8) incorporate calcium; (9) import proteins post-translationally; (10) present cardiolipin. However, there are differences, such as: (1) absence of genetic material, at least in trichomonas; (2) lack a respiratory chain and cytochromes; (3) absence of the F(0)-F(1) ATPase; (4) absence of the tricarboxylic acid cycle; (5) lack of oxidative phosphorylation; (6) presence of peripheral vesicles. Hydrogenosomes are considered an excellent drug target since their metabolic pathway is distinct from those found in mitochondria and thus medicines directed to these organelles will probably not affect the host-cell. The main drug used against trichomonads is metronidazole, although other drugs such as beta-Lapachone, colchicine, Taxol, nocodazole, griseofulvin, cytochalasins, hydroxyurea, among others, have been used in trichomonad studies, showing: (1) flagella internalization forming pseudocyst; (2) dysfunctional hydrogenosomes; (3) hydrogenosomes with abnormal sizes and shapes and with an electron dense deposit called nucleoid; (4) intense autophagy in which hydrogenosomes are removed and further digested in lysosomes. PMID:18473836

  19. Targeting tuberculosis: a glimpse of promising drug targets.

    PubMed

    Arora, N; Banerjee, A K

    2012-03-01

    Tuberculosis caused by Mycobacterium tuberculosis has emerged as the biggest curse of our time causing significant morbidity and mortality. Increasing resistance in mycobacterium to existing drugs calls for exploration of metabolic pathways for finding novel drug targets and also for prioritization of known drug targets. Recent advances in molecular biology, bioinformatics and structural biology coupled with availability of M. tuberculosis genome sequence have provided much needed boost to drug discovery process. This review provides a glimpse of attractive drug targets for development of anti-mycobacterial drug development. PMID:22356190

  20. Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder.

    PubMed

    Fu, Yuanyuan; Guo, Yanzhi; Wang, Yuelong; Luo, Jiesi; Pu, Xuemei; Li, Menglong; Zhang, Zhihang

    2015-06-01

    Protein-protein interactions (PPIs) play essential roles in many biological processes. In protein-protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets. PMID:25854804

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

    PubMed

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

    2016-01-01

    Phosphorylation and proteolysis are among the most common post-translational modifications (PTMs), and play critical roles in various biological processes. More recent discoveries imply that the crosstalks between these two PTMs are involved in many diseases. In this work, we construct a post-translational regulatory network (PTRN) consists of phosphorylation and proteolysis processes, which enables us to investigate the regulatory interplays between these two PTMs. With the PTRN, we identify some functional network motifs that are significantly enriched with drug targets, some of which are further found to contain multiple proteins targeted by combinatorial drugs. These findings imply that the network motifs may be used to predict targets when designing new drugs. Inspired by this, we propose a novel computational approach called NetTar for predicting drug targets using the identified network motifs. Benchmarking results on real data indicate that our approach can be used for accurate prediction of novel proteins targeted by known drugs. PMID:26853265

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

    PubMed Central

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

    2016-01-01

    Phosphorylation and proteolysis are among the most common post-translational modifications (PTMs), and play critical roles in various biological processes. More recent discoveries imply that the crosstalks between these two PTMs are involved in many diseases. In this work, we construct a post-translational regulatory network (PTRN) consists of phosphorylation and proteolysis processes, which enables us to investigate the regulatory interplays between these two PTMs. With the PTRN, we identify some functional network motifs that are significantly enriched with drug targets, some of which are further found to contain multiple proteins targeted by combinatorial drugs. These findings imply that the network motifs may be used to predict targets when designing new drugs. Inspired by this, we propose a novel computational approach called NetTar for predicting drug targets using the identified network motifs. Benchmarking results on real data indicate that our approach can be used for accurate prediction of novel proteins targeted by known drugs. PMID:26853265

  3. 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

  4. Lipid A as a Drug Target and Therapeutic Molecule

    PubMed Central

    Joo, Sang Hoon

    2015-01-01

    In this review, lipid A, from its discovery to recent findings, is presented as a drug target and therapeutic molecule. First, the biosynthetic pathway for lipid A, the Raetz pathway, serves as a good drug target for antibiotic development. Several assay methods used to screen for inhibitors of lipid A synthesis will be presented, and some of the promising lead compounds will be described. Second, utilization of lipid A biosynthetic pathways by various bacterial species can generate modified lipid A molecules with therapeutic value. PMID:26535075

  5. 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. PMID:26555890

  6. 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...

  7. 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

  8. Magnetic drug targeting: biodistribution and dependency on magnetic field strength

    NASA Astrophysics Data System (ADS)

    Alexiou, Ch.; Schmidt, A.; Klein, R.; Hulin, P.; Bergemann, Ch.; Arnold, W.

    2002-11-01

    "Magnetic drug targeting," a model of locoregional chemotherapy showed encouraging results in treatment of VX2-squamous cell carcinoma in rabbits. In the present study we investigated the biokinetic behavior of Iod [123]-labelled ferrofluids in vivo and showed in vitro that the ferrofluid concentration is dependent on the magnetic field strength.

  9. A functional variomics tool for discovering drug resistance genes and drug targets

    PubMed Central

    Huang, Zhiwei; Chen, Kaifu; Zhang, Jianhuai; Li, Yongxiang; Wang, Hui; Cui, Dandan; Tang, Jiangwu; Liu, Yong; Shi, Xiaomin; Li, Wei; Liu, Dan; Chen, Rui; Sucgang, Richard S.; Pan, Xuewen

    2013-01-01

    Comprehensive discovery of genetic mechanisms of drug resistance and identification of in vivo drug targets represent significant challenges. Here we present a functional variomics technology in the model organism Saccharomyces cerevisiae. This tool analyzes numerous genetic variants and effectively tackles both problems simultaneously. Using this tool, we discovered almost all genes that, due to mutations or modest overexpression, confer resistance to rapamycin, cycloheximide, and amphotericin B. Most significant among the resistance genes were drug targets, including multiple targets of a given drug. With amphotericin B, we discovered the highly conserved membrane protein Pmp3 as a potent resistance factor and a possible novel target. Widespread application of this tool should allow rapid identification of conserved resistance mechanisms and targets of many more compounds. New genes and alleles that confer resistance to other stresses can also be discovered. Similar tools in other systems such as human cell lines will also be useful. PMID:23416056

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

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

    PubMed Central

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

    2012-01-01

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

  12. Drug Target Identification and Prioritization for Treatment of Ovine Foot Rot: An In Silico Approach

    PubMed Central

    2016-01-01

    Ovine foot rot is an infection of the feet of sheep, mainly caused by Dichelobacter nodosus. In its virulent form, it is highly contagious and debilitating, causing significant losses in the form of decline in wool growth and quality and poor fertility. Current methods of treatment are ineffective in complete eradication. Effective antibiotic treatment of foot rot is hence necessary to ensure better outcomes during control phases by reduction in culling count and the possibility of carriers of the infection. Using computational approaches, we have identified a set of 297 proteins that are essential to the D. nodosus and nonhomologous with sheep proteins. These proteins may be considered as potential vaccine candidates or drug targets for designing antibiotics against the bacterium. This core set of drug targets have been analyzed for pathway annotation to identify 67 proteins involved in unique bacterial pathways. Choke-point analysis on the drug targets identified 138 choke-point proteins, 29 involved in unique bacterial pathways. Subcellular localization was also predicted for each target to identify the ones that are membrane associated or secreted extracellularly. In addition, a total of 13 targets were identified that are common in at least 10 pathogenic bacterial species. PMID:27379247

  13. Biochemical pathway modeling tools for drug target detection in cancer and other complex diseases.

    PubMed

    Marin-Sanguino, Alberto; Gupta, Shailendra K; Voit, Eberhard O; Vera, Julio

    2011-01-01

    In the near future, computational tools and methods based on the mathematical modeling of biomedically relevant networks and pathways will be necessary for the design of therapeutic strategies that fight complex multifactorial diseases. Beyond the use of pharmacokinetic and pharmacodynamic approaches, we propose here the use of dynamic modeling as a tool for describing and analyzing the structure and responses of signaling, genetic and metabolic networks involved in such diseases. Specifically, we discuss the design and construction of meaningful models of biochemical networks, as well as tools, concepts, and strategies for using these models in the search of potential drug targets. We describe three different families of computational tools: predictive model simulations as tools for designing optimal drug profiles and doses; sensitivity analysis as a method to detect key interactions that affect critical outcomes and other characteristics of the network; and other tools integrating mathematical modeling with advanced computation and optimization for detecting potential drug targets. Furthermore, we show how potential drug targets detected with these approaches can be used in a computer-aided context to design or select new drug molecules. All concepts are illustrated with simplified examples and with actual case studies extracted from the recent literature. PMID:21187230

  14. Seizure prediction and its applications.

    PubMed

    Iasemidis, Leon D

    2011-10-01

    Epilepsy is characterized by intermittent, paroxysmal, hypersynchronous electrical activity that may remain localized and/or spread and severely disrupt the brain's normal multitask and multiprocessing function. Epileptic seizures are the hallmarks of such activity. The ability to issue warnings in real time of impending seizures may lead to novel diagnostic tools and treatments for epilepsy. Applications may range from a warning to the patient to avert seizure-associated injuries, to automatic timely administration of an appropriate stimulus. Seizure prediction could become an integral part of the treatment of epilepsy through neuromodulation, especially in the new generation of closed-loop seizure control systems. PMID:21939848

  15. Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives.

    PubMed

    Romero-Durán, Francisco J; Alonso, Nerea; Yañez, Matilde; Caamaño, Olga; García-Mera, Xerardo; González-Díaz, Humberto

    2016-04-01

    The use of Cheminformatics tools is gaining importance in the field of translational research from Medicinal Chemistry to Neuropharmacology. In particular, we need it for the analysis of chemical information on large datasets of bioactive compounds. These compounds form large multi-target complex networks (drug-target interactome network) resulting in a very challenging data analysis problem. Artificial Neural Network (ANN) algorithms may help us predict the interactions of drugs and targets in CNS interactome. In this work, we trained different ANN models able to predict a large number of drug-target interactions. These models predict a dataset of thousands of interactions of central nervous system (CNS) drugs characterized by > 30 different experimental measures in >400 different experimental protocols for >150 molecular and cellular targets present in 11 different organisms (including human). The model was able to classify cases of non-interacting vs. interacting drug-target pairs with satisfactory performance. A second aim focus on two main directions: the synthesis and assay of new derivatives of TVP1022 (S-analogues of rasagiline) and the comparison with other rasagiline derivatives recently reported. Finally, we used the best of our models to predict drug-target interactions for the best new synthesized compound against a large number of CNS protein targets. PMID:26721628

  16. Seizure Prediction and its Applications

    PubMed Central

    Iasemidis, Leon D.

    2011-01-01

    Epilepsy is characterized by intermittent, paroxysmal, hypersynchronous electrical activity, that may remain localized and/or spread and severely disrupt the brain’s normal multi-task and multi-processing function. Epileptic seizures are the hallmarks of such activity and had been considered unpredictable. It is only recently that research on the dynamics of seizure generation by analysis of the brain’s electrographic activity (EEG) has shed ample light on the predictability of seizures, and illuminated the way to automatic, prospective, long-term prediction of seizures. The ability to issue warnings in real time of impending seizures (e.g., tens of minutes prior to seizure occurrence in the case of focal epilepsy), may lead to novel diagnostic tools and treatments for epilepsy. Applications may range from a simple warning to the patient, in order to avert seizure-associated injuries, to intervention by automatic timely administration of an appropriate stimulus, for example of a chemical nature like an anti-epileptic drug (AED), electromagnetic nature like vagus nerve stimulation (VNS), deep brain stimulation (DBS), transcranial direct current (TDC) or transcranial magnetic stimulation (TMS), and/or of another nature (e.g., ultrasonic, cryogenic, biofeedback operant conditioning). It is thus expected that seizure prediction could readily become an integral part of the treatment of epilepsy through neuromodulation, especially in the new generation of closed-loop seizure control systems. PMID:21939848

  17. Pim-1 kinase as cancer drug target: An update

    PubMed Central

    TURSYNBAY, YERNAR; ZHANG, JINFU; LI, ZHI; TOKAY, TURSONJAN; ZHUMADILOV, ZHAXYBAY; WU, DENGLONG; XIE, YINGQIU

    2016-01-01

    Proviral integration site for Moloney murine leukemia virus-1 (Pim-1) is a serine/threonine kinase that regulates multiple cellular functions such as cell cycle, cell survival, drug resistance. Aberrant elevation of Pim-1 kinase is associated with numerous types of cancer. Two distinct isoforms of Pim-1 (Pim-1S and Pim-1L) show distinct cellular functions. Pim-1S predominately localizes to the nucleus and Pim-1L localizes to plasma membrane for drug resistance. Recent studies show that mitochondrial Pim-1 maintains mitochondrial integrity. Pim-1 is emerging as a cancer drug target, particularly in prostate cancer. Recently the potent new functions of Pim-1 in immunotherapy, senescence bypass, metastasis and epigenetic dynamics have been found. The aim of the present updated review is to provide brief information regarding networks of Pim-1 kinase and focus on its recent advances as a novel drug target. PMID:26893828

  18. 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

  19. 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

  20. DrugTargetSeqR: a genomics- and CRISPR-Cas9-based method to analyze drug targets.

    PubMed

    Kasap, Corynn; Elemento, Olivier; Kapoor, Tarun M

    2014-08-01

    To identify physiological targets of drugs and bioactive small molecules, we developed an approach, named DrugTargetSeqR, which combines high-throughput sequencing, computational mutation discovery and clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9-based genome editing. We applied this approach to ispinesib and YM155, drugs that have undergone clinical trials as anticancer agents, and uncovered mechanisms of action and identified genetic and epigenetic mechanisms likely to cause drug resistance in human cancer cells. PMID:24929528

  1. DrugTargetSeqR: a genomics- and CRISPR/Cas9-based method to analyze drug targets

    PubMed Central

    Kasap, Corynn; Elemento, Olivier; Kapoor, Tarun M.

    2014-01-01

    To identify the physiological targets of drugs and bioactive small molecules we have developed an approach, named DrugTargetSeqR, which combines high-throughput sequencing, computational mutation discovery and CRISPR/Cas9-based genome editing. We apply this approach to ispinesib and YM155, drugs that have undergone clinical trials as anti-cancer agents, and demonstrate target identification and uncover genetic and epigenetic mechanisms likely to cause drug resistance in human cancer cells. PMID:24929528

  2. 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

  3. Mitosis as an anti-cancer drug target.

    PubMed

    Salmela, Anna-Leena; Kallio, Marko J

    2013-10-01

    Suppression of cell proliferation by targeting mitosis is one potential cancer intervention. A number of existing chemotherapy drugs disrupt mitosis by targeting microtubule dynamics. While efficacious, these drugs have limitations, i.e. neuropathy, unpredictability and development of resistance. In order to overcome these issues, a great deal of effort has been spent exploring novel mitotic targets including Polo-like kinase 1, Aurora kinases, Mps1, Cenp-E and KSP/Eg5. Here we summarize the latest developments in the discovery and clinical evaluation of new mitotic drug targets. PMID:23775312

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

    PubMed Central

    Liu, Panpan; Luan, Meiwei; Zhu, Hongjie; Liu, Guiyou; Zhang, Mingming; Lv, Hongchao; Duan, Lian; Shang, Zhenwei; Li, Jin; Jiang, Yongshuai; Zhang, Ruijie

    2016-01-01

    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. PMID:26716901

  5. 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

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

    PubMed Central

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

    2015-01-01

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

  7. DrugTargetInspector: An assistance tool for patient treatment stratification.

    PubMed

    Schneider, Lara; Stöckel, Daniel; Kehl, Tim; Gerasch, Andreas; Ludwig, Nicole; Leidinger, Petra; Huwer, Hanno; Tenzer, Stefan; Kohlbacher, Oliver; Hildebrandt, Andreas; Kaufmann, Michael; Gessler, Manfred; Keller, Andreas; Meese, Eckart; Graf, Norbert; Lenhof, Hans-Peter

    2016-04-01

    Cancer is a large class of diseases that are characterized by a common set of features, known as the Hallmarks of cancer. One of these hallmarks is the acquisition of genome instability and mutations. This, combined with high proliferation rates and failure of repair mechanisms, leads to clonal evolution as well as a high genotypic and phenotypic diversity within the tumor. As a consequence, treatment and therapy of malignant tumors is still a grand challenge. Moreover, under selective pressure, e.g., caused by chemotherapy, resistant subpopulations can emerge that then may lead to relapse. In order to minimize the risk of developing multidrug-resistant tumor cell populations, optimal (combination) therapies have to be determined on the basis of an in-depth characterization of the tumor's genetic and phenotypic makeup, a process that is an important aspect of stratified medicine and precision medicine. We present DrugTargetInspector (DTI), an interactive assistance tool for treatment stratification. DTI analyzes genomic, transcriptomic, and proteomic datasets and provides information on deregulated drug targets, enriched biological pathways, and deregulated subnetworks, as well as mutations and their potential effects on putative drug targets and genes of interest. To demonstrate DTI's broad scope of applicability, we present case studies on several cancer types and different types of input -omics data. DTI's integrative approach allows users to characterize the tumor under investigation based on various -omics datasets and to elucidate putative treatment options based on clinical decision guidelines, but also proposing additional points of intervention that might be neglected otherwise. DTI can be freely accessed at http://dti.bioinf.uni-sb.de. PMID:26501925

  8. Targeting protein kinases in the malaria parasite: update of an antimalarial drug target.

    PubMed

    Zhang, Veronica M; Chavchich, Marina; Waters, Norman C

    2012-01-01

    Millions of deaths each year are attributed to malaria worldwide. Transmitted through the bite of an Anopheles mosquito, infection and subsequent death from the Plasmodium species, most notably P. falciparum, can readily spread through a susceptible population. A malaria vaccine does not exist and resistance to virtually every antimalarial drug predicts that mortality and morbidity associated with this disease will increase. With only a few antimalarial drugs currently in the pipeline, new therapeutic options and novel chemotypes are desperately needed. Hit-to-Lead diversity may successfully provide novel inhibitory scaffolds when essential enzymes are targeted, for example, the plasmodial protein kinases. Throughout the entire life cycle of the malaria parasite, protein kinases are essential for growth and development. Ongoing efforts continue to characterize these kinases, while simultaneously pursuing them as antimalarial drug targets. A collection of structural data, inhibitory profiles and target validation has set the foundation and support for targeting the malarial kinome. Pursuing protein kinases as cancer drug targets has generated a wealth of information on the inhibitory strategies that can be useful for antimalarial drug discovery. In this review, progress on selected protein kinases is described. As the search for novel antimalarials continues, an understanding of the phosphor-regulatory pathways will not only validate protein kinase targets, but also will identify novel chemotypes to thwart malaria drug resistance. PMID:22242850

  9. 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

  10. Reductionism and complexity in nanoparticle-vectored drug targeting.

    PubMed

    Florence, Alexander T

    2012-07-20

    This paper briefly discusses reductionism as a process for dissecting the complexities of drug targeting mediated by nanoparticulate carriers. While reductionism has been said to have been a drawback to enhanced appreciation and understanding of complex biological systems, it is concluded here that the dissection of the individual stages of the procession from injection to final destination in specific targets in a living complex organism is essential. It should allow a decrease in the empiricism from laudable and inventive efforts to achieve high levels of drug delivery to specific diseased targets such as tumours. At the stage of development of the field there have perhaps been fewer than desirable detailed experimental or theoretical investigations of these individual stages. However, there are frequently analogies in the literature from which to draw at least tentative conclusions about the physics, physical chemistry and biology which underpin the processes involved. PMID:22100439

  11. Toxoplasma histone acetylation remodelers as novel drug targets

    PubMed Central

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

    2013-01-01

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

  12. 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

  13. Melanocortin receptors as drug targets for disorders of energy balance.

    PubMed

    Adan, Roger A H; van Dijk, Gertjan

    2006-06-01

    There is overwhelming evidence that the brain melanocortin system is a key regulator of energy balance, and dysregulations in the brain melanocortin system can lead to obesity. The melanocortin system is one of the major downstream leptin signaling pathways in the brain. In contrast to leptin, preclinical studies indicate that diet-induced obese animals are still responsive to the anorectic effects of melanocortin receptor agonists, suggesting the melanocortin system is an interesting therapeutic opportunity. Besides regulating energy balance, melanocortins are involved in a variety of other neuroendocrine processes, including inflammation, blood pressure regulation, addictive and sexual behavior, and sensation of pain. This review evaluates the melanocortin system function from the perspective to use specific melanocortin (MC) receptors as drug targets, with a focus on the treatment of obesity and eating disorders in humans, and the implications this may have on mechanisms beyond the control of energy balance. PMID:16787227

  14. Drug Targets for Rational Design against Emerging Coronaviruses.

    PubMed

    Zhao, Qi; Weber, Erin; Yang, Haitao

    2013-07-26

    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. PMID:23885693

  15. 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. PMID:23895136

  16. Increasing the structural coverage of tuberculosis drug targets

    DOE PAGESBeta

    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.; et al

    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

  17. 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.

  18. Increasing the structural coverage of tuberculosis drug targets.

    PubMed

    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

    2015-03-01

    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 <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. PMID:25613812

  19. Phospholipid-Based Prodrugs for Drug Targeting in Inflammatory Bowel Disease: Computational Optimization and In-Vitro Correlation.

    PubMed

    Dahan, Arik; Ben-Shabat, Shimon; Cohen, Noa; Keinan, Shahar; Kurnikov, Igor; Aponick, Aaron; Zimmermann, Ellen M

    2016-01-01

    In inflammatory bowel disease (IBD) patients, the enzyme phospholipase A2 (PLA2) is overexpressed in the inflamed intestinal tissue, and hence may be exploited as a prodrug-activating enzyme allowing drug targeting to the site(s) of gut inflammation. The purpose of this work was to develop powerful modern computational approaches, to allow optimized a-priori design of phospholipid (PL) based prodrugs for IBD drug targeting. We performed simulations that predict the activation of PL-drug conjugates by PLA2 with both human and bee venom PLA2. The calculated results correlated well with in-vitro experimental data. In conclusion, a-priori drug design using a computational approach complements and extends experimentally derived data, and may improve resource utilization and speed drug development. PMID:27086789

  20. 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.

  1. 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

  2. Deep insights into Dictyocaulus viviparus transcriptomes provides unique prospects for new drug targets and disease intervention

    PubMed Central

    Cantacessi, Cinzia; Gasser, Robin B.; Strube, Christina; Schnieder, Thomas; Jex, Aaron R.; Hall, Ross S.; Campbell, Bronwyn E.; Young, Neil D.; Ranganathan, Shoba; Sternberg, Paul W.; Mitreva, Makedonka

    2013-01-01

    The lungworm, Dictyocaulus viviparus, causes parasitic bronchitis in cattle, and is responsible for substantial economic losses in temperate regions of the world. Here, we undertake the first large-scale exploration of available transcriptomic data for this lungworm, examine differences in transcription between different stages/both genders and identify and prioritize essential molecules linked to fundamental metabolic pathways, which could represent novel drug targets. Approximately 3 million expressed sequence tags (ESTs), generated by 454 sequencing from third-stage larvae (L3) as well as adult females and males of D. viviparus, were assembled and annotated. The assembly of these sequences yielded ~61,000 contigs, of which relatively large proportions encoded collagens (4.3%), ubiquitins (2.1%) and serine/threonine protein kinases (1.9%). Subtractive analysis in silico identified 6,928 nucleotide sequences as being uniquely transcribed in L3, and 5,203 and 7,889 transcripts as being exclusive to the adult female and male, respectively. Most peptides predicted from the conceptual translations were nucleoplasmins (L3), serine/threonine protein kinases (female) and major sperm proteins (male). Additional analyses allowed the prediction of three drug target candidates, whose Caenorhabditis elegans homologues were linked to a lethal RNA interference phenotype. This detailed exploration, combined with future transcriptomic sequencing of all developmental stages of D. viviparus, will facilitate future investigations of the molecular biology of this parasitic nematode as well as genomic sequencing. These advances will underpin the discovery of new drug and/or vaccine targets, focused on biotechnological outcomes. PMID:21182926

  3. Essential gene identification and drug target prioritization in Aspergillus fumigatus.

    PubMed

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

    2007-03-01

    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 (erg11BDelta, 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

  4. 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

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

    PubMed Central

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

    2014-01-01

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

  6. Metabolic Enzymes of Helminth Parasites: Potential as Drug Targets.

    PubMed

    Timson, David J

    2016-01-01

    Metabolic pathways that extract energy from carbon compounds are essential for an organism's survival. Therefore, inhibition of enzymes in these pathways represents a potential therapeutic strategy to combat parasitic infections. However, the high degree of similarity between host and parasite enzymes makes this strategy potentially difficult. Nevertheless, several existing drugs to treat infections by parasitic helminths (worms) target metabolic enzymes. These include the trivalent antimonials that target phosphofructokinase and Clorsulon that targets phosphoglycerate mutase and phosphoglycerate kinase. Glycolytic enzymes from a variety of helminths have been characterised biochemically, and some inhibitors identified. To date none of these inhibitors have been developed into therapies. Many of these enzymes are externalised from the parasite and so are also of interest in the development of potential vaccines. Less work has been done on tricarboxylic acid cycle enzymes and oxidative phosphorylation complexes. Again, while some inhibitors have been identified none have been developed into drug-like molecules. Barriers to the development of novel drugs targeting metabolic enzymes include the lack of experimentally determined structures of helminth enzymes, lack of direct proof that the enzymes are vital in the parasites and lack of cell culture systems for many helminth species. Nevertheless, the success of Clorsulon (which discriminates between highly similar host and parasite enzymes) should inspire us to consider making serious efforts to discover novel anthelminthics, which target metabolic enzymes. PMID:26983888

  7. Structures of Trypanosome Vacuolar Soluble Pyrophosphatases: Antiparasitic Drug Targets.

    PubMed

    Yang, Yunyun; Ko, Tzu-Ping; Chen, Chun-Chi; Huang, Guozhong; Zheng, Yingying; Liu, Weidong; Wang, Iren; Ho, Meng-Ru; Hsu, Shang-Te Danny; O'Dowd, Bing; Huff, Hannah C; Huang, Chun-Hsiang; Docampo, Roberto; Oldfield, Eric; Guo, Rey-Ting

    2016-05-20

    Trypanosomatid parasites are the causative agents of many neglected tropical diseases, including the leishmaniases, Chagas disease, and human African trypanosomiasis. They exploit unusual vacuolar soluble pyrophosphatases (VSPs), absent in humans, for cell growth and virulence and, as such, are drug targets. Here, we report the crystal structures of VSP1s from Trypanosoma cruzi and T. brucei, together with that of the T. cruzi protein bound to a bisphosphonate inhibitor. Both VSP1s form a hybrid structure containing an (N-terminal) EF-hand domain fused to a (C-terminal) pyrophosphatase domain. The two domains are connected via an extended loop of about 17 residues. Crystallographic analysis and size exclusion chromatography indicate that the VSP1s form tetramers containing head-to-tail dimers. Phosphate and diphosphate ligands bind in the PPase substrate-binding pocket and interact with several conserved residues, and a bisphosphonate inhibitor (BPH-1260) binds to the same site. On the basis of Cytoscape and other bioinformatics analyses, it is apparent that similar folds will be found in most if not all trypanosomatid VSP1s, including those found in insects (Angomonas deanei, Strigomonas culicis), plant pathogens (Phytomonas spp.), and Leishmania spp. Overall, the results are of general interest since they open the way to structure-based drug design for many of the neglected tropical diseases. PMID:26907161

  8. 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 ...

  9. 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. PMID:26222406

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

    PubMed

    Perilla, Juan R; Hadden, Jodi A; Goh, Boon Chong; Mayne, Christopher G; Schulten, Klaus

    2016-05-19

    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

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

    PubMed

    Zhang, Cong-hui; Zhu, Huai-min

    2016-02-01

    β-carbonic anhydrases (β-CAs) are ubiquitous metalloenzymes which active site contains a zinc ion (Zn²⁺), and they could catalyze the hydration of carbon dioxide to bicarbonate and protons efficiently and are involved in many biological processes, such as respiration, pH and CO₂ homeostasis, biosynthetic reactions, virulence regulation and so on, and may play a critical role in the life activity of many organisms which contain these enzymes. β-CAs are widely distributed in fungi, bacteria, algae, plants and a small number of protozoan and metazoan except vertebrates. Therefore, as potential drug targets for designing and developing antibacterial and anti-parasitic drugs, β-CAs promise a broad application prospect. This paper focuses on the distribution, physiological function and the progress of researches on β-CAs in parasites and their vectors. PMID:27356420

  12. 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

  13. Genetic Approaches To Identifying Novel Osteoporosis Drug Targets.

    PubMed

    Brommage, Robert

    2015-10-01

    During the past two decades effective drugs for treating osteoporosis have been developed, including anti-resorptives inhibiting bone resorption (estrogens, the SERM raloxifene, four bisphosphonates, RANKL inhibitor denosumab) and the anabolic bone forming daily injectable peptide teriparatide. Two potential drugs (odanacatib and romosozumab) are in late stage clinical development. The most pressing unmet need is for orally active anabolic drugs. This review describes the basic biological studies involved in developing these drugs, including the animal models employed for osteoporosis drug development. The genomics revolution continues to identify potential novel osteoporosis drug targets. Studies include human GWAS studies and identification of mutant genes in subjects having abnormal bone mass, mouse QTL and gene knockouts, and gene expression studies. Multiple lines of evidence indicate that Wnt signaling plays a major role in regulating bone formation and continued study of this complex pathway is likely to lead to key discoveries. In addition to the classic Wnt signaling targets DKK1 and sclerostin, LRP4, LRP5/LRP6, SFRP4, WNT16, and NOTUM can potentially be targeted to modulate Wnt signaling. Next-generation whole genome and exome sequencing, RNA-sequencing and CRISPR/CAS9 gene editing are new experimental techniques contributing to understanding the genome. The International Knockout Mouse Consortium efforts to knockout and phenotype all mouse genes are poised to accelerate. Accumulating knowledge will focus attention on readily accessible databases (Big Data). Efforts are underway by the International Bone and Mineral Society to develop an annotated Skeletome database providing information on all genes directly influencing bone mass, architecture, mineralization or strength. PMID:25833316

  14. Legionella pneumophila Carbonic Anhydrases: Underexplored Antibacterial Drug Targets.

    PubMed

    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

  15. 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

  16. 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. PMID:16912073

  17. New drugs targeting Th2 lymphocytes in asthma.

    PubMed

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

    2008-02-27

    Asthma represents a profound worldwide public health problem. The most effective anti-asthmatic drugs currently available include inhaled beta2-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

  18. 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

  19. 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...

  20. Prediction of cavitation erosion for marine applications

    NASA Astrophysics Data System (ADS)

    Maquil, T.; Yakubov, S.; Rung, T.

    2015-12-01

    The paper presents the development of a cavitation erosion prediction method. The approach is tailored to marine applications and embedded into a VoF-based procedure for the simulation of turbulent flows. Supplementary to the frequently employed Euler-Euler models, Euler-Lagrange approaches are employed to simulate cavitation. The study aims to convey the merits of an Euler-Lagrange approach for erosion simulations. Accordingly, the erosion model is able to separate different damage mechanisms, e.g. micro-jets, single and collective bubble collapse, and also quantifies their contribution to the total damage. Emphasis is devoted to the prediction of the cavitation extend, the influence of compressible effects and the performance of the material damage model in practical applications. Examples included refer to 2D validation test cases and reveal a fair predictive accuracy.

  1. Comparative analyses of the proteins from Mycobacterium tuberculosis and human genomes: Identification of potential tuberculosis drug targets.

    PubMed

    Sridhar, Settu; Dash, Pallabini; Guruprasad, Kunchur

    2016-03-15

    Tuberculosis, one of the major infectious diseases affecting human beings is caused by the bacillus Mycobacterium tuberculosis. Increased resistance to known drugs commonly used for the treatment of tuberculosis has created an urgent need to identify new targets for validation and to develop drugs. In this study, we have used various bioinformatics tools, to compare the protein sequences from twenty-three M. tuberculosis genome strains along with the known human protein sequences, in order to identify the 'conserved' M. tuberculosis proteins absent in human. Further, based on the analysis of protein interaction networks, we selected one-hundred and forty proteins that were predicted as potential M. tuberculosis drug targets and prioritized according to the ranking of 'clusters' of interacting proteins. Comparison of the predicted 140 TB targets with literature indicated that 46 of them were previously reported, thereby increasing the confidence in our predictions of the remaining 94 targets too. The analyses of the structures and functions corresponding to the predicted potential TB drug targets indicated a diverse range of proteins that included ten 'druggable' targets with some of the known drugs. PMID:26762852

  2. In silico analysis of Burkholderia pseudomallei genome sequence for potential drug targets.

    PubMed

    Chong, Chan-Eng; Lim, Boon-San; Nathan, Sheila; Mohamed, Rahmah

    2006-01-01

    Recent advances in DNA sequencing technology have enabled elucidation of whole genome information from a plethora of organisms. In parallel with this technology, various bioinformatics tools have driven the comparative analysis of the genome sequences between species and within isolates. While drawing meaningful conclusions from a large amount of raw material, computer-aided identification of suitable targets for further experimental analysis and characterization, has also led to the prediction of non-human homologous essential genes in bacteria as promising candidates for novel drug discovery. Here, we present a comparative genomic analysis to identify essential genes in Burkholderia pseudomallei. Our in silico prediction has identified 312 essential genes which could also be potential drug candidates. These genes encode essential proteins to support the survival of B. pseudomallei including outer-inner membrane and surface structures, regulators, proteins involved in pathogenenicity, adaptation, chaperones as well as degradation of small and macromolecules, energy metabolism, information transfer, central/intermediate/miscellaneous metabolism pathways and some conserved hypothetical proteins of unknown function. Therefore, our in silico approach has enabled rapid screening and identification of potential drug targets for further characterization in the laboratory. PMID:16922696

  3. Collaborative development of predictive toxicology applications.

    PubMed

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

    2010-01-01

    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

  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. 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

  6. 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.

  7. Design, synthesis, and pharmacokinetic evaluation of a chemical delivery system for drug targeting to lung tissue.

    PubMed

    Saah, M; Wu, W M; Eberst, K; Marvanyos, E; Bodor, N

    1996-05-01

    We espouse the application of a novel chemical delivery system (CDS) approach to a delivery mechanism for drug targeting to lung tissue using the 1,2-dithiolane-3-pentyl moiety of lipoic acid as the "targetor moiety". The synthesis and the physicochemical and pharmacokinetic evaluation of a CDS modeling the lipoyl and other ester derivatives of chlorambucil (an antineoplastic agent) and cromolyn (a bischromone used in antiasthma prophylaxis) as compared with their respective parent drugs are described. The chlorambucil CDS was synthesized by esterifying the alcohol derivative of lipoic acid with chlorambucil using dicyclohexylcarbodiimide as the coupling agent. The cromolyn CDS was prepared by a multistep synthetic procedure culminating in the reaction of the alkyl bromide derivative of lipoic acid with the disodium salt of the bischromone compound. All the esters were highly lipophilic unlike the parent compounds. The in-vitro kinetic and in-vivo pharmacokinetic studies showed that the respective CDSs were sufficiently stable in buffer and biological media, hydrolyzed rapidly into the respective active parent drugs, and significantly enhanced delivery and retention of the active compound to lung tissue in comparison with the underivatized parent compounds used in conventional therapy. PMID:8742941

  8. 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.

  9. 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. PMID:26691755

  10. 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

  11. 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. PMID:20688192

  12. 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

  13. Serine Proteases of Malaria Parasite Plasmodium falciparum: Potential as Antimalarial Drug Targets

    PubMed Central

    2014-01-01

    Malaria is a major global parasitic disease and a cause of enormous mortality and morbidity. Widespread drug resistance against currently available antimalarials warrants the identification of novel drug targets and development of new drugs. Malarial proteases are a group of molecules that serve as potential drug targets because of their essentiality for parasite life cycle stages and feasibility of designing specific inhibitors against them. Proteases belonging to various mechanistic classes are found in P. falciparum, of which serine proteases are of particular interest due to their involvement in parasite-specific processes of egress and invasion. In P. falciparum, a number of serine proteases belonging to chymotrypsin, subtilisin, and rhomboid clans are found. This review focuses on the potential of P. falciparum serine proteases as antimalarial drug targets. PMID:24799897

  14. 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

  15. 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

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

    PubMed Central

    Du, Qinglin; Wang, Honghai; Xie, Jianping

    2011-01-01

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

  17. Implant assisted-magnetic drug targeting: Comparison of in vitro experiments with theory

    NASA Astrophysics Data System (ADS)

    Avilés, Misael O.; Ebner, Armin D.; Ritter, James A.

    Implant assisted-magnetic drug targeting (IA-MDT) was studied both in vitro and theoretically, with extensive comparisons made between model and experiment. Magnetic drug carrier particles (MDCPs) comprised of magnetite encased in a polymer were collected magnetically using a ferromagnetic, coiled, wire stent as the implant and a NdFeB permanent magnet for the applied magnetic field. A 2-D mathematical model with no adjustable parameters was developed and compared to the 3-D experimental results. The effects of the fluid velocity, stent and MDCP properties, and magnetic field strength on the performance of the system were evaluated in terms of the capture efficiency (CE) of the MDCPs. In nearly all cases, the parametric trends predicted by the model were in good agreement with the experimental results: the CE always increased with decreasing velocity, increasing magnetic field strength, increasing MDCP size or magnetite content, or increasing wire size. The only exception was when experiments showed an increase in the CE with an increase in the number of loops in the wire, while the model showed no dependence. The discrepancies between experiment and theory were attributed to phenomena not accounted for by the model, such as 3-D to 2-D geometric and magnetic field orientation differences, and interparticle interactions between the MDCPs that lead to magnetic agglomeration and shearing force effects. Overall, this work showed the effectiveness of a stent-based IA-MDT system through both in vitro experimentation and corroborated theory, with the designs of the ferromagnetic wire and the MDCPs both being paramount to the CE.

  18. Implying Analytic Measures for Unravelling Rheumatoid Arthritis Significant Proteins Through Drug-Target Interaction.

    PubMed

    Singh, Sachidanand; Vennila, J Jannet; Snijesh, V P; George, Gincy; Sunny, Chinnu

    2016-06-01

    Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease that mainly alters the synovial joints and ultimately leads to their destruction. The involvement of the immune system and its related cells is a basic trademark of autoimmune-associated diseases. The present work focuses on network analysis and its functional characterization to predict novel targets for RA. The interactive model called as rheumatoid arthritis drug-target-protein (RA-DTP) is built of 1727 nodes and 7954 edges followed the power-law distribution. RA-DTP comprised of 20 islands, 55 modules and 123 submodules. Good interactome coverage of target-protein was detected in island 2 (Q-Score 0.875) which includes 673 molecules with 20 modules and 68 submodules. The biological landscape of these modules was examined based on the participation molecules in specific cellular localization, molecular function and biological pathway with favourable p value. Functional characterization and pathway analysis through KEGG, Biocarta and Reactome also showed their involvement in relation to the immune system and inflammatory processes and biological processes such as cell signalling and communication, glucosamine metabolic process, renin-angiotensin system, BCR signals, galactose metabolism, MAPK signalling, complement and coagulation system and NGF signalling pathways. Traffic values and centrality parameters were applied as the selection criteria for identifying potential targets from the important hubs which resulted into FOS, KNG1, PTGDS, HSP90AA1, REN, POMC, FCER1G, IL6, ICAM1, SGK1, NOS3 and PLA2G4A. This approach provides an insight into experimental validation of these associations of potential targets for clinical value to find their effect on animal studies. PMID:26286007

  19. Genetics of coronary heart disease: towards causal mechanisms, novel drug targets and more personalized prevention.

    PubMed

    Orho-Melander, M

    2015-11-01

    Coronary heart disease (CHD) is an archetypical multifactorial disorder that is influenced by genetic susceptibility as well as both modifiable and nonmodifiable risk factors, and their interactions. Advances during recent years in the field of multifactorial genetics, in particular genomewide association studies (GWASs) and their meta-analyses, have provided the statistical power to identify and replicate genetic variants in more than 50 risk loci for CHD and in several hundreds of loci for cardiometabolic risk factors for CHD such as blood lipids and lipoproteins. Although for a great majority of these loci both the causal variants and mechanisms remain unknown, progress in identifying the causal variants and underlying mechanisms has already been made for several genetic loci. Furthermore, identification of rare loss-of-function variants in genes such as PCSK9, NPC1L1, APOC3 and APOA5, which cause a markedly decreased risk of CHD and no adverse side effects, illustrates the power of translating genetic findings into novel mechanistic information and provides some optimism for the future of developing novel drugs, given the many genes associated with CHD in GWASs. Finally, Mendelian randomization can be used to reveal or exclude causal relationships between heritable biomarkers and CHD, and such approaches have already provided evidence of causal relationships between CHD and LDL cholesterol, triglycerides/remnant particles and lipoprotein(a), and indicated a lack of causality for HDL cholesterol, C-reactive protein and lipoprotein-associated phospholipase A2. Together, these genetic findings are beginning to lead to promising new drug targets and novel interventional strategies and thus have great potential to improve prevention, prediction and therapy of CHD. PMID:26477595

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

    PubMed Central

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

    2014-01-01

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

  1. UDP-galactopyranose mutase, a potential drug target against human pathogenic nematode Brugia malayi.

    PubMed

    Misra, Sweta; Valicherla, Guru R; Mohd Shahab; Gupta, Jyoti; Gayen, Jiaur R; Misra-Bhattacharya, Shailja

    2016-08-01

    Lymphatic filariasis, a vector-borne neglected tropical disease affects millions of population in tropical and subtropical countries. Vaccine unavailability and emerging drug resistance against standard antifilarial drugs necessitate search of novel drug targets for developing alternate drugs. Recently, UDP-galactopyranose mutases (UGM) have emerged as a promising drug target playing an important role in parasite virulence and survival. This study deals with the cloning and characterization of Brugia malayi UGM and further exploring its antifilarial drug target potential. The recombinant protein was actively involved in conversion of UDP-galactopyranose (substrate) to UDP-galactofuranose (product) revealing Km and Vmax to be ∼51.15 μM and ∼1.27 μM/min, respectively. The purified protein appeared to be decameric in native state and its 3D homology modeling using Aspergillus fumigatus UGM enzyme as template revealed conservation of active site residues. Two specific prokaryotic inhibitors (compounds A and B) of the enzyme inhibited B. malayi UGM enzymatic activity competitively depicting Ki values ∼22.68 and ∼23.0 μM, respectively. These compounds were also active in vitro and in vivo against B. malayi The findings suggest that B. malayi UGM could be a potential antifilarial therapeutic drug target. PMID:27465638

  2. Molecular Characterization of Legionellosis Drug Target Candidate Enzyme Phosphoglucosamine Mutase from Legionella pneumophila (strain Paris): An In Silico Approach

    PubMed Central

    Mazumder, Habibul Hasan; Khan, Arif; Hossain, Mohammad Uzzal; Chowdhury, Homaun Kabir

    2014-01-01

    The harshness of legionellosis differs from mild Pontiac fever to potentially fatal Legionnaire's disease. The increasing development of drug resistance against legionellosis has led to explore new novel drug targets. It has been found that phosphoglucosamine mutase, phosphomannomutase, and phosphoglyceromutase enzymes can be used as the most probable therapeutic drug targets through extensive data mining. Phosphoglucosamine mutase is involved in amino sugar and nucleotide sugar metabolism. The purpose of this study was to predict the potential target of that specific drug. For this, the 3D structure of phosphoglucosamine mutase of Legionella pneumophila (strain Paris) was determined by means of homology modeling through Phyre2 and refined by ModRefiner. Then, the designed model was evaluated with a structure validation program, for instance, PROCHECK, ERRAT, Verify3D, and QMEAN, for further structural analysis. Secondary structural features were determined through self-optimized prediction method with alignment (SOPMA) and interacting networks by STRING. Consequently, we performed molecular docking studies. The analytical result of PROCHECK showed that 95.0% of the residues are in the most favored region, 4.50% are in the additional allowed region and 0.50% are in the generously allowed region of the Ramachandran plot. Verify3D graph value indicates a score of 0.71 and 89.791, 1.11 for ERRAT and QMEAN respectively. Arg419, Thr414, Ser412, and Thr9 were found to dock the substrate for the most favorable binding of S-mercaptocysteine. However, these findings from this current study will pave the way for further extensive investigation of this enzyme in wet lab experiments and in that way assist drug design against legionellosis. PMID:25705169

  3. 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...

  4. DTome: a web-based tool for drug-target interactome construction

    PubMed Central

    2012-01-01

    Background Understanding drug bioactivities is crucial for early-stage drug discovery, toxicology studies and clinical trials. Network pharmacology is a promising approach to better understand the molecular mechanisms of drug bioactivities. With a dramatic increase of rich data sources that document drugs' structural, chemical, and biological activities, it is necessary to develop an automated tool to construct a drug-target network for candidate drugs, thus facilitating the drug discovery process. Results We designed a computational workflow to construct drug-target networks from different knowledge bases including DrugBank, PharmGKB, and the PINA database. To automatically implement the workflow, we created a web-based tool called DTome (Drug-Target interactome tool), which is comprised of a database schema and a user-friendly web interface. The DTome tool utilizes web-based queries to search candidate drugs and then construct a DTome network by extracting and integrating four types of interactions. The four types are adverse drug interactions, drug-target interactions, drug-gene associations, and target-/gene-protein interactions. Additionally, we provided a detailed network analysis and visualization process to illustrate how to analyze and interpret the DTome network. The DTome tool is publicly available at http://bioinfo.mc.vanderbilt.edu/DTome. Conclusions As demonstrated with the antipsychotic drug clozapine, the DTome tool was effective and promising for the investigation of relationships among drugs, adverse interaction drugs, drug primary targets, drug-associated genes, and proteins directly interacting with targets or genes. The resultant DTome network provides researchers with direct insights into their interest drug(s), such as the molecular mechanisms of drug actions. We believe such a tool can facilitate identification of drug targets and drug adverse interactions. PMID:22901092

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

    PubMed

    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

  6. 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

  7. Predicting Test Performance: A Content Valid Approach to Screening Applicants.

    ERIC Educational Resources Information Center

    Pannone, Ronald D.

    1984-01-01

    Examined the validity of a rationally developed biographical questionnaire for predicting content valid test performance for electrician applicants (N=221). Results showed that the utility of the questionnaire in screening applicant populations was both statistically and practically significant. (LLL)

  8. Undressing the fungal cell wall/cell membrane--the antifungal drug targets.

    PubMed

    Tada, Rui; Latgé, Jean-Paul; Aimanianda, Vishukumar

    2013-01-01

    Being external, the fungal cell wall plays a crucial role in the fungal life. By covering the underneath cell, it offers mechanical strength and acts as a barrier, thus protecting the fungus from the hostile environment. Chemically, this cell wall is composed of different polysaccharides. Because of their specific composition, the fungal cell wall and its underlying plasma membrane are unique targets for the development of drugs against pathogenic fungal species. The objective of this review is to consolidate the current knowledge on the antifungal drugs targeting the cell wall and plasma membrane, mainly of Aspergillus and Candida species - the most prevalent fungal pathogens, and also to present challenges and questions conditioning the development of new antifungal drugs targeting the cell wall. PMID:23278542

  9. HNF4α -- role in drug metabolism and potential drug target?

    PubMed Central

    Hwang-Verslues, Wendy W.; Sladek, Frances M.

    2010-01-01

    Hepatocyte nuclear factor 4α (HNF4α) is a highly conserved member of the nuclear receptor superfamily of ligand-dependent transcription factors. It is best known as a master regulator of liver-specific gene expression, especially those genes involved in lipid transport and glucose metabolism. However, there is also a growing body of work that indicates the importance of HNF4α in the regulation of genes involved in xenobiotic and drug metabolism. A recent study identifying the essential fatty acid linoleic acid (LA, C18:2) as the endogenous, reversible ligand for HNF4α suggests that HNF4α may also be a potential drug target and that its activity may be regulated by diet. This review will discuss the role of HNF4α in drug metabolism, including the genes it regulates, the factors that regulate its activity, and its potential as a drug target. PMID:20833107

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

    PubMed Central

    2014-01-01

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

  11. The microglial ATP-gated ion channel P2X7 as a CNS drug target.

    PubMed

    Bhattacharya, Anindya; Biber, Knut

    2016-10-01

    Based on promising preclinical evidence, microglial P2X7 has increasingly being recognized as a target for therapeutic intervention in neurological and psychiatric diseases. However, despite this knowledge no P2X7-related drug has yet entered clinical trials with respect to CNS diseases. We here discuss the current literature on P2X7 being a drug target and identify unsolved issues and still open questions that have hampered the development of P2X7 dependent therapeutic approaches for CNS diseases. It is concluded here that the lack of brain penetrating P2X7 antagonists is a major obstacle in the field and that central P2X7 is a yet untested clinical drug target. In the CNS, microglial P2X7 activation causes neuroinflammation, which in turn plays a role in various CNS disorders. This has resulted in a surge of brain penetrant P2X7 antagonists. P2X7 is a viable, clinically untested CNS drug target. GLIA 2016;64:1772-1787. PMID:27219534

  12. 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. PMID:25085183

  13. Hysteresis prediction inside magnetic shields and application

    NASA Astrophysics Data System (ADS)

    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.

  14. 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.

  15. Identification of novel drug targets in HpB38, HpP12, HpG27, Hpshi470, HpSJM180 strains of Helicobacter pylori : an in silico approach for therapeutic intervention.

    PubMed

    Neelapu, Nageswara Rao Reddy; Pavani, T

    2013-05-01

    Helicobacter species colonizes the stomach and are associated with the development of gastritis disease. Drugs for treatment of Helicobacter infection relieve pain or gastritis symptoms but they are not targeted specifically to Helicobacter pylori. Therefore, there is dire need for discovery of new drug targets and drugs for the treatment of H. pylori. The main objective of this study is to screen the potential drug targets by in silico analysis for the potent strains of H. pylori which include HpB38, HpP12, HpG27, Hpshi470 and HpSJM180. Genome and metabolic pathways of pathogen H. pylori and the host Homosapien sapiens are compared and genes which were unique to H. pylori were filtered and catalogued. These unique genes were subjected to gene property analysis to identify the potentiality of the drug targets. Among the total number of genes analysed in different strains of H. pylori nearly 558, 569, 539, 569, 567 number of genes in HpB38, HpP12, HpG27, Hpshi470 and HpSJM180 found qualified as unique molecules and among them 17 qualified as potential drug targets. Membrane fusion protein of hefABC efflux system, 50 S ribosomal protein L33, Hydrogenase expression protein/formation of HypD, Cag pathogenecity island protein X, Apolipoprotein N acyl transferase, DNA methyalse, Histone like binding protein, Peptidoglycan-associated lipoprotein OprL were found to be critical drug targets to H. pylori. Three (hefABC efflux system, Hydrogenase expression protein/formation of HypD, Cag pathogenecity island protein X) of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lending credence to our unique approach. PMID:23410125

  16. 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...

  17. Large-scale identification of potential drug targets based on the topological features of human protein-protein interaction network.

    PubMed

    Li, Zhan-Chao; Zhong, Wen-Qian; Liu, Zhi-Qing; Huang, Meng-Hua; Xie, Yun; Dai, Zong; Zou, Xiao-Yong

    2015-04-29

    Identifying potential drug target proteins is a crucial step in the process of drug discovery and plays a key role in the study of the molecular mechanisms of disease. Based on the fact that the majority of proteins exert their functions through interacting with each other, we propose a method to recognize target proteins by using the human protein-protein interaction network and graph theory. In the network, vertexes and edges are weighted by using the confidence scores of interactions and descriptors of protein primary structure, respectively. The novel network topological features are defined and employed to characterize protein using existing databases. A widely used minimum redundancy maximum relevance and random forests algorithm are utilized to select the optimal feature subset and construct model for the identification of potential drug target proteins at the proteome scale. The accuracies of training set and test set are 89.55% and 85.23%. Using the constructed model, 2127 potential drug target proteins have been recognized and 156 drug target proteins have been validated in the database of drug target. In addition, some new drug target proteins can be considered as targets for treating diseases of mucopolysaccharidosis, non-arteritic anterior ischemic optic neuropathy, Bernard-Soulier syndrome and pseudo-von Willebrand, etc. It is anticipated that the proposed method may became a powerful high-throughput virtual screening tool of drug target. PMID:25847157

  18. 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 {delta}-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.

  19. Predicting protein-ligand interactions based on chemical preference features with its application to new D-amino acid oxidase inhibitor discovery.

    PubMed

    Zhao, Mingzhu; Chang, Hao-Teng; Zhou, Qiang; Zeng, Tao; Shih, Chung-Shiuan; Liu, Zhi-Ping; Chen, Luonan; Wei, Dong-Qing

    2014-01-01

    In silico prediction of the new drug-target interactions from existing databases is of important value for the drug discovery process. Currently, the amount of protein targets that have been identified experimentally is still very small compared with the entire human proteins. In order to predict protein-ligand interactions in an accurate manner, we have developed a support vector machine (SVM) model based on the chemical-protein interactions from STITCH. New features from ligand chemical space and interaction networks have been selected and encoded as the feature vectors for SVM analysis. Both the 5-fold cross validation and independent test show high predictive accuracy that outperforms the state-of-the-art method based on ligand similarity. Moreover, 91 distinct pairs of features have been selected to rebuild a simplifier model, which still maintains the same performance as that based on all 332 features. Then, this refined model is used to search for the potential D-amino acid oxidase inhibitors from STITCH database and the predicted results are finally validated by our wet experiments. Out of 10 candidates obtained, seven D-amino acid oxidase inhibitors have been verified, in which four are newly found in the present study, and one may have a new application in therapy of psychiatric disorders other than being an antineoplastic agent. Clearly, our model is capable of predicting potential new drugs or targets on a large scale with high efficiency. PMID:24410568

  20. 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)

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

    PubMed Central

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

    2011-01-01

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

  2. Identification of Attractive Drug Targets in Neglected-Disease Pathogens Using an In Silico Approach

    PubMed Central

    Crowther, Gregory J.; Shanmugam, Dhanasekaran; Carmona, Santiago J.; Doyle, Maria A.; Hertz-Fowler, Christiane; Berriman, Matthew; Nwaka, Solomon; Ralph, Stuart A.; Roos, David S.; Van Voorhis, Wesley C.; Agüero, Fernán

    2010-01-01

    Background The increased sequencing of pathogen genomes and the subsequent availability of genome-scale functional datasets are expected to guide the experimental work necessary for target-based drug discovery. However, a major bottleneck in this has been the difficulty of capturing and integrating relevant information in an easily accessible format for identifying and prioritizing potential targets. The open-access resource TDRtargets.org facilitates drug target prioritization for major tropical disease pathogens such as the mycobacteria Mycobacterium leprae and Mycobacterium tuberculosis; the kinetoplastid protozoans Leishmania major, Trypanosoma brucei, and Trypanosoma cruzi; the apicomplexan protozoans Plasmodium falciparum, Plasmodium vivax, and Toxoplasma gondii; and the helminths Brugia malayi and Schistosoma mansoni. Methodology/Principal Findings Here we present strategies to prioritize pathogen proteins based on whether their properties meet criteria considered desirable in a drug target. These criteria are based upon both sequence-derived information (e.g., molecular mass) and functional data on expression, essentiality, phenotypes, metabolic pathways, assayability, and druggability. This approach also highlights the fact that data for many relevant criteria are lacking in less-studied pathogens (e.g., helminths), and we demonstrate how this can be partially overcome by mapping data from homologous genes in well-studied organisms. We also show how individual users can easily upload external datasets and integrate them with existing data in TDRtargets.org to generate highly customized ranked lists of potential targets. Conclusions/Significance Using the datasets and the tools available in TDRtargets.org, we have generated illustrative lists of potential drug targets in seven tropical disease pathogens. While these lists are broadly consistent with the research community's current interest in certain specific proteins, and suggest novel target candidates

  3. 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

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

    PubMed

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

    2015-01-01

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

  5. In vitro study of ferromagnetic stents for implant assisted-magnetic drug targeting

    NASA Astrophysics Data System (ADS)

    Avilés, Misael O.; Chen, Haitao; Ebner, Armin D.; Rosengart, Axel J.; Kaminski, Michael D.; Ritter, James A.

    2007-04-01

    Implant-assisted-magnetic drug targeting (IA-MDT) was studied in vitro using a coiled ferromagnetic wire stent made from stainless steel 430 or 304, and magnetic drug carrier particle (MDCP) surrogates composed of poly(styrene/divinylbenzene) embedded with 20 wt% magnetite. The fluid velocity, particle concentration, magnetic field strength, and stent material all proved to be important for capturing the MDCP surrogates. Overall, this in vitro study further confirmed the important role of the ferromagnetic implant for attracting and retaining MDCPs at the target zone.

  6. Neoadjuvant Window Studies of Metformin and Biomarker Development for Drugs Targeting Cancer Metabolism.

    PubMed

    Lord, Simon R; Patel, Neel; Liu, Dan; Fenwick, John; Gleeson, Fergus; Buffa, Francesca; Harris, Adrian L

    2015-05-01

    There has been growing interest in the potential of the altered metabolic state typical of cancer cells as a drug target. The antidiabetes drug, metformin, is now under intense investigation as a safe method to modify cancer metabolism. Several studies have used window of opportunity in breast cancer patients before neoadjuvant chemotherapy to correlate gene expression analysis, metabolomics, immunohistochemical markers, and metabolic serum markers with those likely to benefit. We review the role metabolite measurement, functional imaging and gene sequencing analysis play in elucidating the effects of metabolically targeted drugs in cancer treatment and determining patient selection. PMID:26063894

  7. 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

  8. Pediatric Malignant Bone Tumors: A Review and Update on Current Challenges, and Emerging Drug Targets.

    PubMed

    Jackson, Twana M; Bittman, Mark; Granowetter, Linda

    2016-07-01

    Osteosarcoma (OS) and the Ewing sarcoma family of tumors (ESFT) are the most common malignant bone tumors in children and adolescents. While significant improvements in survival have been seen in other pediatric malignancies the treatment and prognosis for pediatric bone tumors has remained unchanged for the past 3 decades. This review and update of pediatric malignant bone tumors will provide a general overview of osteosarcoma and the Ewing sarcoma family of tumors, discuss bone tumor genomics, current challenges, and emerging drug targets. PMID:27265835

  9. Emergence of zebrafish models in oncology for validating novel anticancer drug targets and nanomaterials

    PubMed Central

    Mimeault, Murielle; Batra, Surinder K.

    2013-01-01

    The in vivo zebrafish models have recently attracted great attention in molecular oncology to investigate multiple genetic alterations associated with the development of human cancers and validate novel anticancer drug targets. Particularly, the transparent zebrafish models can be used as a xenotransplantation system to rapidly assess the tumorigenicity and metastatic behavior of cancer stem and/or progenitor cells and their progenies. Moreover, the zebrafish models have emerged as powerful tools for an in vivo testing of novel anticancer agents and nanomaterials for counteracting tumor formation and metastases and improving the efficacy of current radiation and chemotherapeutic treatments against aggressive, metastatic and lethal cancers. PMID:22903142

  10. 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

  11. 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

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

    PubMed

    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

  13. Aminoacyl-tRNA synthetases as drug targets in eukaryotic parasites☆

    PubMed Central

    Pham, James S.; Dawson, Karen L.; Jackson, Katherine E.; Lim, Erin E.; Pasaje, Charisse Flerida A.; Turner, Kelsey E.C.; Ralph, Stuart A.

    2013-01-01

    Aminoacyl-tRNA synthetases are central enzymes in protein translation, providing the charged tRNAs needed for appropriate construction of peptide chains. These enzymes have long been pursued as drug targets in bacteria and fungi, but the past decade has seen considerable research on aminoacyl-tRNA synthetases in eukaryotic parasites. Existing inhibitors of bacterial tRNA synthetases have been adapted for parasite use, novel inhibitors have been developed against parasite enzymes, and tRNA synthetases have been identified as the targets for compounds in use or development as antiparasitic drugs. Crystal structures have now been solved for many parasite tRNA synthetases, and opportunities for selective inhibition are becoming apparent. For different biological reasons, tRNA synthetases appear to be promising drug targets against parasites as diverse as Plasmodium (causative agent of malaria), Brugia (causative agent of lymphatic filariasis), and Trypanosoma (causative agents of Chagas disease and human African trypanosomiasis). Here we review recent developments in drug discovery and target characterisation for parasite aminoacyl-tRNA synthetases. PMID:24596663

  14. Nanopore-Based Conformational Analysis of a Viral RNA Drug Target

    PubMed Central

    Stoloff, Daniel H.; Rynearson, Kevin D.; Hermann, Thomas; Wanunu, Meni

    2016-01-01

    Nanopores are single-molecule sensors that show exceptional promise as a biomolecular analysis tool by enabling label-free detection of small amounts of sample. In this paper, we demonstrate that nanopores are capable of detecting the conformation of an antiviral RNA drug target. The hepatitis C virus uses an internal ribosome entry site (IRES) motif in order to initiate translation by docking to ribosomes in its host cell. The IRES is therefore a viable and important drug target. Drug-induced changes to the conformation of the HCV IRES motif, from a bent to a straight conformation, have been shown to inhibit HCV replication. However, there is presently no straightforward method to analyze the effect of candidate small-molecule drugs on the RNA conformation. In this paper, we show that RNA translocation dynamics through a 3 nm diameter nanopore is conformation-sensitive by demonstrating a difference in transport times between bent and straight conformations of a short viral RNA motif. Detection is possible because bent RNA is stalled in the 3 nm pore, resulting in longer molecular dwell times than straight RNA. Control experiments show that binding of a weaker drug does not produce a conformational change, as consistent with independent fluorescence measurements. Nanopore measurements of RNA conformation can thus be useful for probing the structure of various RNA motifs, as well as structural changes to the RNA upon small-molecule binding. PMID:24861167

  15. In Search of Novel Drug Target Sites on Estrogen Receptors Using RNA Aptamers

    PubMed Central

    Xu, Daiying; Chatakonda, Vamsee-Krishna; Kourtidis, Antonis; Conklin, Douglas S.

    2014-01-01

    Estrogen receptor α (ERα) is a well-validated drug target for a majority of breast cancers. But the target sites on this receptor are far from exhaustively defined. Almost all ER antagonists in clinical use function by binding to the ligand-binding pocket to occlude agonist access. Resistance to this type of drugs may develop over time, not caused by the change of ERα itself, but by changes in ER associated proteins. This observation is fueling the development of reagents that downregulate ER activity through novel binding sites. However, it is challenging to find general ER antagonists that act independently from other known ER ligands. In this report, we describe the utility of RNA aptamers in the search for new drug target sites on ERα. We have identified three high affinity aptamers and characterized one of them in detail. This aptamer interacted with ERα in a way not affected by the presence or absence of either the steroidal ligands or the estrogen response DNA elements, and effectively inhibited ER-mediated transcriptional activation in a breast cancer cell line. Serving as a novel drug lead, it may also be used to guide the rational chemical synthesis of small molecule drugs or to perform screens of small molecule libraries for those that are able to displace the aptamer from its binding site. PMID:24588102

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

    PubMed

    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

    2016-05-01

    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 used parallel genome-wide high-coverage short hairpin RNA (shRNA) and clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 screens to identify the cellular target and mechanism of action of GSK983, a potent broad-spectrum antiviral with unexplained cytotoxicity. We found that GSK983 blocked 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 reduced GSK983 cytotoxicity but not antiviral activity, providing an attractive new approach to improve the therapeutic window of DHODH inhibitors against RNA viruses. 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 comprehensive probing of drug activity. PMID:27018887

  17. 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

  18. 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

    2015-01-01

    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

  19. 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

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

    PubMed Central

    Mardinoglu, Adil; Cregg, P. J.

    2015-01-01

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

  1. 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.

  2. Drug2Gene: an exhaustive resource to explore effectively the drug-target relation network

    PubMed Central

    2014-01-01

    Background Information about drug-target relations is at the heart of drug discovery. There are now dozens of databases providing drug-target interaction data with varying scope, and focus. Therefore, and due to the large chemical space, the overlap of the different data sets is surprisingly small. As searching through these sources manually is cumbersome, time-consuming and error-prone, integrating all the data is highly desirable. Despite a few attempts, integration has been hampered by the diversity of descriptions of compounds, and by the fact that the reported activity values, coming from different data sets, are not always directly comparable due to usage of different metrics or data formats. Description We have built Drug2Gene, a knowledge base, which combines the compound/drug-gene/protein information from 19 publicly available databases. A key feature is our rigorous unification and standardization process which makes the data truly comparable on a large scale, allowing for the first time effective data mining in such a large knowledge corpus. As of version 3.2, Drug2Gene contains 4,372,290 unified relations between compounds and their targets most of which include reported bioactivity data. We extend this set with putative (i.e. homology-inferred) relations where sufficient sequence homology between proteins suggests they may bind to similar compounds. Drug2Gene provides powerful search functionalities, very flexible export procedures, and a user-friendly web interface. Conclusions Drug2Gene v3.2 has become a mature and comprehensive knowledge base providing unified, standardized drug-target related information gathered from publicly available data sources. It can be used to integrate proprietary data sets with publicly available data sets. Its main goal is to be a ‘one-stop shop’ to identify tool compounds targeting a given gene product or for finding all known targets of a drug. Drug2Gene with its integrated data set of public compound

  3. Application of Hybrid Method for Aerodynamic Noise Prediction

    NASA Astrophysics Data System (ADS)

    Yu, L.; Song, W. P.

    2011-09-01

    A hybrid prediction method for aerodynamic noise is performed using high order accuracy method in this paper. The method combines a two-dimensional Unsteady Reynolds-Averaged Navier-Stokes(URANS) solver with the acoustic analogy method using Ffowcs Williams-Hawkings equation with penetrable data surface (FW-Hpds). Tandem cylinders are chosen to validate the prediction method. The computations are conducted at a Reynolds number of 1.66 × 105 based on the cylinder diameter. Both the aerodynamic and acoustic results show good agreement with the experimental data, showing a successful application of the hybrid prediction method using two-dimensional URANS simulation.

  4. 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

  5. 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

  6. 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.

  7. Host-bacterial coevolution and the search for new drug targets

    PubMed Central

    Zaneveld, Jesse; Turnbaugh, Peter J.; Lozupone, Catherine; Ley, Ruth E.; Hamady, Micah; Gordon, Jeffrey I.; Knight, Rob

    2008-01-01

    Understanding coevolution between humans and our microbial symbionts and pathogens requires complementary approaches, ranging from community analysis to in-depth analysis of individual genomes. Here we review the evidence for coevolution between symbionts and their hosts, the role of horizontal gene transfer in coevolution, and genomic and metagenomic approaches to identifying drug targets. Recent studies have shown that our symbiotic microbes confer many metabolic capabilities that our mammalian genomes lack, and that targeting mechanisms of horizontal gene transfer is a promising new direction for drug discovery. Gnotobiotic (“germ-free”) mice are an especially exciting new tool for unraveling the function of microbes, whether individually or in the context of complex communities. PMID:18280814

  8. 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

  9. Drug Target Optimization in Chronic Myeloid Leukemia Using Innovative Computational Platform

    PubMed Central

    Chuang, Ryan; Hall, Benjamin A.; Benque, David; Cook, Byron; Ishtiaq, Samin; Piterman, Nir; Taylor, Alex; Vardi, Moshe; Koschmieder, Steffen; Gottgens, Berthold; Fisher, Jasmin

    2015-01-01

    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. PMID:25644994

  10. 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. PMID:26854862

  11. 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

  12. Using mitochondrial sirtuins as drug targets: disease implications and available compounds.

    PubMed

    Gertz, Melanie; Steegborn, Clemens

    2016-08-01

    Sirtuins are an evolutionary conserved family of NAD(+)-dependent protein lysine deacylases. Mammals have seven Sirtuin isoforms, Sirt1-7. They contribute to regulation of metabolism, stress responses, and aging processes, and are considered therapeutic targets for metabolic and aging-related diseases. While initial studies were focused on Sirt1 and 2, recent progress on the mitochondrial Sirtuins Sirt3, 4, and 5 has stimulated research and drug development for these isoforms. Here we review the roles of Sirtuins in regulating mitochondrial functions, with a focus on the mitochondrially located isoforms, and on their contributions to disease pathologies. We further summarize the compounds available for modulating the activity of these Sirtuins, again with a focus on mitochondrial isoforms, and we describe recent results important for the further improvement of compounds. This overview illustrates the potential of mitochondrial Sirtuins as drug targets and summarizes the status, progress, and challenges in developing small molecule compounds modulating their activity. PMID:27007507

  13. Lipid biology of Apicomplexa: perspectives for new drug targets, particularly for Toxoplasma gondii.

    PubMed

    Sonda, Sabrina; Hehl, Adrian B

    2006-01-01

    Development of effective therapies for intracellular eukaryotic pathogens is a serious challenge, given the protected location of these pathogens and the similarity of their biology to that of the host. Identifying cellular processes that are unique to the parasite is therefore a crucial step towards defining appropriate drug targets. In the case of the apicomplexan parasite Toxoplasma gondii, the need to find alternative treatments is imperative because of the poor tolerability and frequent side-effects associated with existing therapeutic strategies. The discovery that the parasite uses lipid synthetic pathways which are different from, or absent in, the mammalian host is now driving a renewed interest in T. gondii lipid biology. Recent achievements in this field are promising and suggest that the elucidation of lipid pathways will provide new opportunities for designing potent antiparasitic strategies. PMID:16300997

  14. Genetic validation of aminoacyl-tRNA synthetases as drug targets in Trypanosoma brucei.

    PubMed

    Kalidas, Savitha; Cestari, Igor; Monnerat, Severine; Li, Qiong; Regmi, Sandesh; Hasle, Nicholas; Labaied, Mehdi; Parsons, Marilyn; Stuart, Kenneth; Phillips, Margaret A

    2014-04-01

    Human African trypanosomiasis (HAT) is an important public health threat in sub-Saharan Africa. Current drugs are unsatisfactory, and new drugs are being sought. Few validated enzyme targets are available to support drug discovery efforts, so our goal was to obtain essentiality data on genes with proven utility as drug targets. Aminoacyl-tRNA synthetases (aaRSs) are known drug targets for bacterial and fungal pathogens and are required for protein synthesis. Here we survey the essentiality of eight Trypanosoma brucei aaRSs by RNA interference (RNAi) gene expression knockdown, covering an enzyme from each major aaRS class: valyl-tRNA synthetase (ValRS) (class Ia), tryptophanyl-tRNA synthetase (TrpRS-1) (class Ib), arginyl-tRNA synthetase (ArgRS) (class Ic), glutamyl-tRNA synthetase (GluRS) (class 1c), threonyl-tRNA synthetase (ThrRS) (class IIa), asparaginyl-tRNA synthetase (AsnRS) (class IIb), and phenylalanyl-tRNA synthetase (α and β) (PheRS) (class IIc). Knockdown of mRNA encoding these enzymes in T. brucei mammalian stage parasites showed that all were essential for parasite growth and survival in vitro. The reduced expression resulted in growth, morphological, cell cycle, and DNA content abnormalities. ThrRS was characterized in greater detail, showing that the purified recombinant enzyme displayed ThrRS activity and that the protein localized to both the cytosol and mitochondrion. Borrelidin, a known inhibitor of ThrRS, was an inhibitor of T. brucei ThrRS and showed antitrypanosomal activity. The data show that aaRSs are essential for T. brucei survival and are likely to be excellent targets for drug discovery efforts. PMID:24562907

  15. 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. PMID:26983022

  16. Virus-encoded chemokine receptors--putative novel antiviral drug targets.

    PubMed

    Rosenkilde, Mette M

    2005-01-01

    Large DNA viruses, in particular herpes- and poxviruses, have evolved proteins that serve as mimics or decoys for endogenous proteins in the host. The chemokines and their receptors serve key functions in both innate and adaptive immunity through control of leukocyte trafficking, and have as such a paramount role in the antiviral immune responses. It is therefore not surprising that viruses have found ways to exploit and subvert the chemokine system by means of molecular mimicry. By ancient acts of molecular piracy and by induction and suppression of endogenous genes, viruses have utilized chemokines and their receptors to serve a variety of roles in viral life-cycle. This review focuses on the pharmacology of virus-encoded chemokine receptors, yet also the family of virus-encoded chemokines and chemokine-binding proteins will be touched upon. Key properties of the virus-encoded receptors, compared to their closest endogenous homologs, are interactions with a wider range of chemokines, which can act as agonists, antagonists and inverse agonists, and the exploitation of many signal transduction pathways. High constitutive activity is another key property of some--but not all--of these receptors. The chemokine receptors belong to the superfamily of G-protein coupled 7TM receptors that per se are excellent drug targets. At present, non-peptide antagonists have been developed against many chemokine receptors. The potentials of the virus-encoded chemokine receptors as drug targets--ie. as novel antiviral strategies--will be highlighted here together with the potentials of the virus-encoded chemokines and chemokine-binding proteins as novel anti-inflammatory biopharmaceutical strategies. PMID:15617722

  17. Phytochemical-mediated Protein Expression Profiling and the Potential Applications in Therapeutic Drug Target Identifications.

    PubMed

    Wong, Fai-Chu; Tan, Siok-Thing; Chai, Tsun-Thai

    2016-07-29

    Many phytochemicals derived from edible medicinal plants have been investigated intensively for their various bioactivities. However, the detailed mechanism and their corresponding molecular targets frequently remain elusive. In this review, we present a summary of the research works done on phytochemical-mediated molecular targets, identified via proteomic approach. Concurrently, we also highlighted some pharmaceutical drugs which could be traced back to their origins in phytochemicals. For ease of presentation, these identified protein targets were categorized into two important healthcare-related fields, namely anti-bacterial and anti-cancer research. Through this review, we hope to highlight the usefulness of comparative proteomic as a powerful tool in phytochemical-mediated protein target identifications. Likewise, we wish to inspire further investigations on some of these protein targets identified over the last few years. With contributions from all researchers, the accumulative efforts could eventually lead to the discovery of some target-specific, low-toxicity therapeutic agents. PMID:26193174

  18. PREDICT: a method for inferring novel drug indications with application to personalized medicine

    PubMed Central

    Gottlieb, Assaf; Stein, Gideon Y; Ruppin, Eytan; Sharan, Roded

    2011-01-01

    Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large-scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–drug and disease–disease similarity measures for the prediction task. On cross-validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue-specific expression information on the drug targets. We further show that disease-specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease-specific signatures. PMID:21654673

  19. 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. PMID:27088868

  20. 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...

  1. NASTRAN application for the prediction of aircraft interior noise

    NASA Astrophysics Data System (ADS)

    Marulo, Francesco; Beyer, Todd B.

    1987-08-01

    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.

  2. 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.

  3. 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. PMID:27162331

  4. 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

  5. A new tool for predicting drought: an application over India.

    PubMed

    Kulkarni, M N

    2015-01-01

    This is the first attempt of application of atmospheric electricity for rainfall prediction. The atmospheric electrical columnar resistance based on the model calculations involving satellite data has been proposed as a new predictor. It is physically sound, simple to calculate and not probabilistic like the standardized precipitation index. After applying this new predictor over India, it has been found that the data solely over the Bay of Bengal (BB) are sufficient to predict a drought over the country as a whole. Finally, two independent new methods to predict drought conditions and a preliminary forecast of the same for India for year 2014 have been given. Unlike the existing drought prediction techniques, the identification of drought conditions in a pre-drought year during 1981-1990 and 2001-2013 over India has been achieved 100% successfully using the suggested new methods. The association between rainfall and this new predictor has also been found on the sub-regional scale. So, the present predictor is expected to get global application and application in climate models. From the analysis, generally, a long period rising trend in aerosol concentration over the BB causes weak monsoon over India but that for a short time i.e. in pre-monsoon period strengthens it. PMID:25567244

  6. Mitoxantrone loaded superparamagnetic nanoparticles for drug targeting: a versatile and sensitive method for quantification of drug enrichment in rabbit tissues using HPLC-UV.

    PubMed

    Tietze, Rainer; Schreiber, Eveline; Lyer, Stefan; Alexiou, Christoph

    2010-01-01

    In medicine, superparamagnetic nanoparticles bound to chemotherapeutics are currently investigated for their feasibility in local tumor therapy. After intraarterial application, these particles can be accumulated in the targeted area by an external magnetic field to increase the drug concentration in the region of interest (Magnetic-Drug-Targeting). We here present an analytical method (HPLC-UV), to detect pure or ferrofluid-bound mitoxantrone in a complex matrix even in trace amounts in order to perform biodistribution studies. Mitoxantrone could be extracted in high yields from different tissues. Recovery of mitoxantrone in liver tissue (5000 ng/g) was 76 +/- 2%. The limit of quantification of mitoxantrone standard was 10 ng/mL +/-12%. Validation criteria such as linearity, precision, and stability were evaluated in ranges achieving the FDA requirements. As shown for pilot samples, biodistribution studies can easily be performed after application of pure or ferrofluid-bound mitoxantrone. PMID:20490266

  7. 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

  8. 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

  9. Carboxymethyl Chitosan-Modified Polyamidoamine Dendrimer Enables Progressive Drug Targeting of Tumors via pH-Sensitive Charge Inversion.

    PubMed

    Qi, Xiaole; Qin, Jiayi; Fan, Yuchao; Qin, Xiaoxue; Jiang, Yujie; Wu, Zhenghong

    2016-04-01

    Polyamidoamine dendrimers are potential candidates for drug delivery systems due to their remarkable cell-penetrating power that results from their strong positive surface charge. However, the positively charged surfaces always lead to serious cytotoxicity and the rapid clearance of polyamidoamine in vivo, which limit the application of these dendrimers. To overcome these drawbacks, we developed a carboxymethyl chitosan-modified polyamidoamine dendrimer to achieve progressive drug targeting of tumors via pH-sensitive charge inversion. With the shielding of carboxymethyl chitosan, the complex was negatively charged at physiological conditions (pH 7.4) and prone to enrich at tumor sites due to the enhanced permeation and retention effect; however, it regained a positive charge via the removal of the carboxymethyl chitosan coating under tumor-acidic conditions (pH 6.5) and achieved high intracellular uptake in tumor cells through electrostatic adsorptive endocytosis. In this study, these dendrimers exhibited 1.99- and 1.76-times higher cellular uptake efficiencies at pH 7.4 in MCF-7 or A549 cells, respectively, compared with efficiencies at pH 6.5, indicating an effective pH-dependent accumulation; the fluorescence intensities of these cells exposed to the dendrimers at pH 6.5 were also 16.45- and 9.27-fold greater, respectively, than those of free doxorubicin. After intravenous administration in mice bearing H22 tumors, doxorubicin-loaded dendrimers exhibited a 1.50-fold greater antitumor activity and presented no obvious systematic toxicity based on histological analysis compared with free drugs. Overall, a simple decoration of carboxymethyl chitosan demonstrated to be a promising way for cationic nanocarriers to achieve pH-sensitive drug release and charge conversion response to tumor microenvironment pH and enhance the antitumor therapy efficiency of anticancer drugs. PMID:27301193

  10. 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.

  11. Potential therapeutic drug target identification in Community Acquired-Methicillin Resistant Staphylococcus aureus (CA-MRSA) using computational analysis

    PubMed Central

    Yadav, Pramod Kumar; Singh, Gurmit; Singh, Satendra; Gautam, Budhayash; Saad, Esmaiel IF

    2012-01-01

    The emergence of multidrug-resistant strain of community-acquired methicillin resistant Staphylococcus aureus (CA-MRSA) strain has highlighted the urgent need for the alternative and effective therapeutic approach to combat the menace of this nosocomial pathogen. In the present work novel potential therapeutic drug targets have been identified through the metabolic pathways analysis. All the gene products involved in different metabolic pathways of CA-MRSA in KEGG database were searched against the proteome of Homo sapiens using the BLASTp program and the threshold of E-value was set to as 0.001. After database searching, 152 putative targets were identified. Among all 152 putative targets, 39 genes encoding for putative targets were identified as the essential genes from the DEG database which are indispensable for the survival of CA-MRSA. After extensive literature review, 7 targets were identified as potential therapeutic drug target. These targets are Fructose-bisphosphate aldolase, Phosphoglyceromutase, Purine nucleoside phosphorylase, Uridylate kinase, Tryptophan synthase subunit beta, Acetate kinase and UDP-N-acetylglucosamine 1-carboxyvinyltransferase. Except Uridylate kinase all the identified targets were involved in more than one metabolic pathways of CA-MRSA which underlines the importance of drug targets. These potential therapeutic drug targets can be exploited for the discovery of novel inhibitors for CA-MRSA using the structure based drug design (SBDD) strategy. PMID:23055607

  12. Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information.

    PubMed

    Yang, Hong; Qin, Chu; Li, Ying Hong; Tao, Lin; Zhou, Jin; Yu, Chun Yan; Xu, Feng; Chen, Zhe; Zhu, Feng; Chen, Yu Zong

    2016-01-01

    Extensive drug discovery efforts have yielded many approved and candidate drugs targeting various targets in different biological pathways. Several freely accessible databases provide the drug, target and drug-targeted pathway information for facilitating drug discovery efforts, but there is an insufficient coverage of the clinical trial drugs and the drug-targeted pathways. Here, we describe an update of the Therapeutic Target Database (TTD) previously featured in NAR. The updated contents include: (i) significantly increased coverage of the clinical trial targets and drugs (1.6 and 2.3 times of the previous release, respectively), (ii) cross-links of most TTD target and drug entries to the corresponding pathway entries of KEGG, MetaCyc/BioCyc, NetPath, PANTHER pathway, Pathway Interaction Database (PID), PathWhiz, Reactome and WikiPathways, (iii) the convenient access of the multiple targets and drugs cross-linked to each of these pathway entries and (iv) the recently emerged approved and investigative drugs. This update makes TTD a more useful resource to complement other databases for facilitating the drug discovery efforts. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp. PMID:26578601

  13. 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.

  14. Evaluation of Phosphatidylinositol-4-Kinase IIIα as a Hepatitis C Virus Drug Target

    PubMed Central

    Brault, Martine; Pilote, Louise; Uyttersprot, Nathalie; Gaillard, Elias T.; Stoltz, James H.; Knight, Brian L.; Pantages, Lynn; McFarland, Mary; Breitfelder, Steffen; Chiu, Tim T.; Mahrouche, Louiza; Faucher, Anne-Marie; Cartier, Mireille; Cordingley, Michael G.; Bethell, Richard C.; Jiang, Huiping; White, Peter W.

    2012-01-01

    Phosphatidylinositol-4-kinase IIIα (PI4KIIIα) is an essential host cell factor for hepatitis C virus (HCV) replication. An N-terminally truncated 130-kDa form was used to reconstitute an in vitro biochemical lipid kinase assay that was optimized for small-molecule compound screening and identified potent and specific inhibitors. Cell culture studies with PI4KIIIα inhibitors demonstrated that the kinase activity was essential for HCV RNA replication. Two PI4KIIIα inhibitors were used to select cell lines harboring HCV replicon mutants with a 20-fold loss in sensitivity to the compounds. Reverse genetic mapping isolated an NS4B-NS5A segment that rescued HCV RNA replication in PIK4IIIα-deficient cells. HCV RNA replication occurs on specialized membranous webs, and this study with PIK4IIIα inhibitor-resistant mutants provides a genetic link between NS4B/NS5A functions and PI4-phosphate lipid metabolism. A comprehensive assessment of PI4KIIIα as a drug target included its evaluation for pharmacologic intervention in vivo through conditional transgenic murine lines that mimic target-specific inhibition in adult mice. Homozygotes that induce a knockout of the kinase domain or knock in a single amino acid substitution, kinase-defective PI4KIIIα, displayed a lethal phenotype with a fairly widespread mucosal epithelial degeneration of the gastrointestinal tract. This essential host physiologic role raises doubt about the pursuit of PI4KIIIα inhibitors for treatment of chronic HCV infection. PMID:22896614

  15. 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.

  16. 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. PMID:22434495

  17. Metabonomic analysis of potential biomarkers and drug targets involved in diabetic nephropathy mice

    PubMed Central

    Wei, Tingting; Zhao, Liangcai; Jia, Jianmin; Xia, Huanhuan; Du, Yao; Lin, Qiuting; Lin, Xiaodong; Ye, Xinjian; Yan, Zhihan; Gao, Hongchang

    2015-01-01

    Diabetic nephropathy (DN) is one of the lethal manifestations of diabetic systemic microvascular disease. Elucidation of characteristic metabolic alterations during diabetic progression is critical to understand its pathogenesis and identify potential biomarkers and drug targets involved in the disease. In this study, 1H nuclear magnetic resonance (1H NMR)-based metabonomics with correlative analysis was performed to study the characteristic metabolites, as well as the related pathways in urine and kidney samples of db/db diabetic mice, compared with age-matched wildtype mice. The time trajectory plot of db/db mice revealed alterations, in an age-dependent manner, in urinary metabolic profiles along with progression of renal damage and dysfunction. Age-dependent and correlated metabolite analysis identified that cis-aconitate and allantoin could serve as biomarkers for the diagnosis of DN. Further correlative analysis revealed that the enzymes dimethylarginine dimethylaminohydrolase (DDAH), guanosine triphosphate cyclohydrolase I (GTPCH I), and 3-hydroxy-3-methylglutaryl-CoA lyase (HMG-CoA lyase) were involved in dimethylamine metabolism, ketogenesis and GTP metabolism pathways, respectively, and could be potential therapeutic targets for DN. Our results highlight that metabonomic analysis can be used as a tool to identify potential biomarkers and novel therapeutic targets to gain a better understanding of the mechanisms underlying the initiation and progression of diseases. PMID:26149603

  18. 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

  19. 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.

  20. The periplasmic protein TolB as a potential drug target in Pseudomonas aeruginosa.

    PubMed

    Lo Sciuto, Alessandra; Fernández-Piñar, Regina; Bertuccini, Lucia; Iosi, Francesca; Superti, Fabiana; Imperi, Francesco

    2014-01-01

    The Gram-negative bacterium Pseudomonas aeruginosa is one of the most dreaded pathogens in the hospital setting, and represents a prototype of multi-drug resistant "superbug" for which effective therapeutic options are very limited. The identification and characterization of new cellular functions that are essential for P. aeruginosa viability and/or virulence could drive the development of anti-Pseudomonas compounds with novel mechanisms of action. In this study we investigated whether TolB, the periplasmic component of the Tol-Pal trans-envelope protein complex of Gram-negative bacteria, represents a potential drug target in P. aeruginosa. By combining conditional mutagenesis with the analysis of specific pathogenicity-related phenotypes, we demonstrated that TolB is essential for P. aeruginosa growth, both in laboratory and clinical strains, and that TolB-depleted P. aeruginosa cells are strongly defective in cell-envelope integrity, resistance to human serum and several antibiotics, as well as in the ability to cause infection and persist in an insect model of P. aeruginosa infection. The essentiality of TolB for P. aeruginosa growth, resistance and pathogenicity highlights the potential of TolB as a novel molecular target for anti-P. aeruginosa drug discovery. PMID:25093328

  1. Drug-target interactions: only the first step in the commitment to a programmed cell death?

    PubMed Central

    Dive, C.; Hickman, J. A.

    1991-01-01

    The search for novel antitumour drugs has reached a plateau phase. The carcinomas remain almost as intractable as they did 40 years ago and the need for effective therapy is pressing. There is an argument that the current pharmacopoeia is sufficient but, to be effective, the biochemical mechanisms of drug resistance must be circumvented. In tackling the question of why certain cancer cells are resistant, the converse question of why others are sensitive still remains to be answered fully. Asking the fundamental question of why and how a cell dies may provide clues as to what avenues lie open for improved chemotherapy. In this review we survey the recent literature on cell death and we argue that it is possible that the outcome of chemotherapy may be determined by the response of the cell to the formation of the drug-target complex, and/or its sequellae, rather than to the biochemical changes brought about by the drug alone. One of these responses, determined by the phenotype of the cell, may be activation of a genetic programme for cell death. PMID:1854622

  2. LPTS: A Novel Tumor Suppressor Gene and a Promising Drug Target for Cancer Intervention.

    PubMed

    Baichuan, Li; Cao, Songshen; Liu, Yunlai

    2015-01-01

    Liver-related putative tumor suppressor (lpts) is a liver-related tumor suppressor candidate gene initially isolated by positional candidate cloning method. Three translation products of lpts gene are found, that are LPTS-L, LPTS-S and LPTS-M respectively. The gene highly expresses in normal tissues but lowly in cancer tissues. The LPTS proteins can suppress the activity of telomerase and trigger apoptosis for tumor cells in vivo and in vitro, despite that the detailed anti-cancer mechanism remains undefined. This review successively describes the lpts genomic assembly, transcriptional regulation and structure-activity evaluation of different LPTS isoforms; then it represents the LPTS binding partners, for example Pin2/TRF1 and MCRS2, which play important roles in decreasing telomerase activity, which benefits to reveal the anticancer mechanism; subsequently, it surveys several patents of recombinant LPTS proteins such as TAT-LPTS-LC, PinX1/C-G4S-9R-G4S-mBAFF and PinX1/C-9R-mBAF that can inhibit the growth of tumor cells. Lpts gene is becoming a promising drug target for cancer intervention owing to its powerful inhibition efficacy on telomerase activity, and recombinant LPTS proteins claimed by a couple of patents seem to be potential anti-cancer agents. PMID:25479038

  3. 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

  4. Functional alterations of astrocytes in mental disorders: pharmacological significance as a drug target

    PubMed Central

    Koyama, Yutaka

    2015-01-01

    Astrocytes play an essential role in supporting brain functions in physiological and pathological states. Modulation of their pathophysiological responses have beneficial actions on nerve tissue injured by brain insults and neurodegenerative diseases, therefore astrocytes are recognized as promising targets for neuroprotective drugs. Recent investigations have identified several astrocytic mechanisms for modulating synaptic transmission and neural plasticity. These include altered expression of transporters for neurotransmitters, release of gliotransmitters and neurotrophic factors, and intercellular communication through gap junctions. Investigation of patients with mental disorders shows morphological and functional alterations in astrocytes. According to these observations, manipulation of astrocytic function by gene mutation and pharmacological tools reproduce mental disorder-like behavior in experimental animals. Some drugs clinically used for mental disorders affect astrocyte function. As experimental evidence shows their role in the pathogenesis of mental disorders, astrocytes have gained much attention as drug targets for mental disorders. In this paper, I review functional alterations of astrocytes in several mental disorders including schizophrenia, mood disorder, drug dependence, and neurodevelopmental disorders. The pharmacological significance of astrocytes in mental disorders is also discussed. PMID:26217185

  5. Capture Efficiency of Biocompatible Magnetic Nanoparticles in Arterial Flow: A Computer Simulation for Magnetic Drug Targeting.

    PubMed

    Lunnoo, Thodsaphon; Puangmali, Theerapong

    2015-12-01

    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. PMID:26515074

  6. Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets.

    PubMed

    Vinayagam, Arunachalam; Gibson, Travis E; Lee, Ho-Joon; Yilmazel, Bahar; Roesel, Charles; Hu, Yanhui; Kwon, Young; Sharma, Amitabh; Liu, Yang-Yu; Perrimon, Norbert; Barabási, Albert-László

    2016-05-01

    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

  7. Metabonomic analysis of potential biomarkers and drug targets involved in diabetic nephropathy mice.

    PubMed

    Wei, Tingting; Zhao, Liangcai; Jia, Jianmin; Xia, Huanhuan; Du, Yao; Lin, Qiuting; Lin, Xiaodong; Ye, Xinjian; Yan, Zhihan; Gao, Hongchang

    2015-01-01

    Diabetic nephropathy (DN) is one of the lethal manifestations of diabetic systemic microvascular disease. Elucidation of characteristic metabolic alterations during diabetic progression is critical to understand its pathogenesis and identify potential biomarkers and drug targets involved in the disease. In this study, (1)H nuclear magnetic resonance ((1)H NMR)-based metabonomics with correlative analysis was performed to study the characteristic metabolites, as well as the related pathways in urine and kidney samples of db/db diabetic mice, compared with age-matched wildtype mice. The time trajectory plot of db/db mice revealed alterations, in an age-dependent manner, in urinary metabolic profiles along with progression of renal damage and dysfunction. Age-dependent and correlated metabolite analysis identified that cis-aconitate and allantoin could serve as biomarkers for the diagnosis of DN. Further correlative analysis revealed that the enzymes dimethylarginine dimethylaminohydrolase (DDAH), guanosine triphosphate cyclohydrolase I (GTPCH I), and 3-hydroxy-3-methylglutaryl-CoA lyase (HMG-CoA lyase) were involved in dimethylamine metabolism, ketogenesis and GTP metabolism pathways, respectively, and could be potential therapeutic targets for DN. Our results highlight that metabonomic analysis can be used as a tool to identify potential biomarkers and novel therapeutic targets to gain a better understanding of the mechanisms underlying the initiation and progression of diseases. PMID:26149603

  8. 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. PMID:21189126

  9. An integrative in silico approach for discovering candidates for drug-targetable protein-protein interactions in interactome data

    PubMed Central

    Sugaya, Nobuyoshi; Ikeda, Kazuyoshi; Tashiro, Toshiyuki; Takeda, Shizu; Otomo, Jun; Ishida, Yoshiko; Shiratori, Akiko; Toyoda, Atsushi; Noguchi, Hideki; Takeda, Tadayuki; Kuhara, Satoru; Sakaki, Yoshiyuki; Iwayanagi, Takao

    2007-01-01

    Background Protein-protein interactions (PPIs) are challenging but attractive targets for small chemical drugs. Whole PPIs, called the 'interactome', have been emerged in several organisms, including human, based on the recent development of high-throughput screening (HTS) technologies. Individual PPIs have been targeted by small drug-like chemicals (SDCs), however, interactome data have not been fully utilized for exploring drug targets due to the lack of comprehensive methodology for utilizing these data. Here we propose an integrative in silico approach for discovering candidates for drug-targetable PPIs in interactome data. Results Our novel in silico screening system comprises three independent assessment procedures: i) detection of protein domains responsible for PPIs, ii) finding SDC-binding pockets on protein surfaces, and iii) evaluating similarities in the assignment of Gene Ontology (GO) terms between specific partner proteins. We discovered six candidates for drug-targetable PPIs by applying our in silico approach to original human PPI data composed of 770 binary interactions produced by our HTS yeast two-hybrid (HTS-Y2H) assays. Among them, we further examined two candidates, RXRA/NRIP1 and CDK2/CDKN1A, with respect to their biological roles, PPI network around each candidate, and tertiary structures of the interacting domains. Conclusion An integrative in silico approach for discovering candidates for drug-targetable PPIs was applied to original human PPIs data. The system excludes false positive interactions and selects reliable PPIs as drug targets. Its effectiveness was demonstrated by the discovery of the six promising candidate target PPIs. Inhibition or stabilization of the two interactions may have potential therapeutic effects against human diseases. PMID:17705877

  10. Genomic profiling of murine mammary tumors identifies potential personalized drug targets for p53-deficient mammary cancers

    PubMed Central

    Agrawal, Yash N.; Koboldt, Daniel C.; Kanchi, Krishna L.; Herschkowitz, Jason I.; Mardis, Elaine R.; Rosen, Jeffrey M.; Perou, Charles M.

    2016-01-01

    ABSTRACT Targeted therapies against basal-like breast tumors, which are typically ‘triple-negative breast cancers (TNBCs)’, remain an important unmet clinical need. Somatic TP53 mutations are the most common genetic event in basal-like breast tumors and TNBC. To identify additional drivers and possible drug targets of this subtype, a comparative study between human and murine tumors was performed by utilizing a murine Trp53-null mammary transplant tumor model. We show that two subsets of murine Trp53-null mammary transplant tumors resemble aspects of the human basal-like subtype. DNA-microarray, whole-genome and exome-based sequencing approaches were used to interrogate the secondary genetic aberrations of these tumors, which were then compared to human basal-like tumors to identify conserved somatic genetic features. DNA copy-number variation produced the largest number of conserved candidate personalized drug targets. These candidates were filtered using a DNA-RNA Pearson correlation cut-off and a requirement that the gene was deemed essential in at least 5% of human breast cancer cell lines from an RNA-mediated interference screen database. Five potential personalized drug target genes, which were spontaneously amplified loci in both murine and human basal-like tumors, were identified: Cul4a, Lamp1, Met, Pnpla6 and Tubgcp3. As a proof of concept, inhibition of Met using crizotinib caused Met-amplified murine tumors to initially undergo complete regression. This study identifies Met as a promising drug target in a subset of murine Trp53-null tumors, thus identifying a potential shared driver with a subset of human basal-like breast cancers. Our results also highlight the importance of comparative genomic studies for discovering personalized drug targets and for providing a preclinical model for further investigations of key tumor signaling pathways. PMID:27149990

  11. In vitro study of magnetic particle seeding for implant assisted-magnetic drug targeting

    NASA Astrophysics Data System (ADS)

    Avilés, Misael O.; Ebner, Armin D.; Ritter, James A.

    The concept of using magnetic particles (seeds) as the implant for implant assisted-magnetic drug targeting (IA-MDT) was analyzed in vitro. Since this MDT system is being explored for use in capillaries, a highly porous ( ɛ˜70%), highly tortuous, cylindrical, polyethylene polymer was prepared to mimic capillary tissue, and the seeds (magnetite nanoparticles) were already fixed within. The well-dispersed seeds were used to enhance the capture of 0.87 μm diameter magnetic drug carrier particles (MDCPs) (polydivinylbenzene embedded with 24.8 wt% magnetite) under flow conditions typically found in capillary networks. The effects of the fluid velocity (0.015-0.15 cm/s), magnetic field strength (0.0-250 mT), porous polymer magnetite content (0-7 wt%) and MDCP concentration ( C=5 and 50 mg/L) on the capture efficiency (CE) of the MDCPs were studied. In all cases, when the magnetic field was applied, compared to when it was not, large increases in CE resulted; the CE increased even further when the magnetite seeds were present. The CE increased with increases in the magnetic field strength, porous polymer magnetite content and MDCP concentration. It decreased only with increases in the fluid velocity. Large magnetic field strengths were not necessary to induce MDCP capture by the seeds. A few hundred mT was sufficient. Overall, this first in vitro study of the magnetic seeding concept for IA-MDT was very encouraging, because it proved that magnetic particle seeds could serve as an effective implant for MDT systems, especially under conditions found in capillaries.

  12. Nucleoside transporter proteins as biomarkers of drug responsiveness and drug targets

    PubMed Central

    Pastor-Anglada, Marçal; Pérez-Torras, Sandra

    2015-01-01

    Nucleoside and nucleobase analogs are currently used in the treatment of solid tumors, lymphoproliferative diseases, viral infections such as hepatitis and AIDS, and some inflammatory diseases such as Crohn. Two gene families are implicated in the uptake of nucleosides and nucleoside analogs into cells, SCL28 and SLC29. The former encodes hCNT1, hCNT2, and hCNT3 proteins. They translocate nucleosides in a Na+ coupled manner with high affinity and some substrate selectivity, being hCNT1 and hCNT2 pyrimidine- and purine-preferring, respectively, and hCNT3 a broad selectivity transporter. SLC29 genes encode four members, being hENT1 and hENT2 the only two which are unequivocally implicated in the translocation of nucleosides and nucleobases (the latter mostly via hENT2) at the cell plasma membrane. Some nucleoside-derived drugs can also interact with and be translocated by members of the SLC22 gene family, particularly hOCT and hOAT proteins. Inter-individual differences in transporter function and perhaps, more importantly, altered expression associated with the disease itself might modulate the transporter profile of target cells, thereby determining drug bioavailability and action. Drug transporter pharmacology has been periodically reviewed. Thus, with this contribution we aim at providing a state-of-the-art overview of the clinical evidence generated so far supporting the concept that these membrane proteins can indeed be biomarkers suitable for diagnosis and/or prognosis. Last but not least, some of these transporter proteins can also be envisaged as drug targets, as long as they can show “transceptor” functions, in some cases related to their role as modulators of extracellular adenosine levels, thereby providing a functional link between P1 receptors and transporters. PMID:25713533

  13. 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

  14. In vitro study of magnetic nanoparticles as the implant for implant assisted magnetic drug targeting

    NASA Astrophysics Data System (ADS)

    Mangual, Jan O.; Avilés, Misael O.; Ebner, Armin D.; Ritter, James A.

    2011-07-01

    Magnetic nanoparticle (MNP) seeds were studied in vitro for use as an implant in implant assisted-magnetic drug targeting (IA-MDT). The magnetite seeds were captured in a porous polymer, mimicking capillary tissue, with an external magnetic field (70 mT) and then used subsequently to capture magnetic drug carrier particles (MDCPs) (0.87 μm diameter) with the same magnetic field. The effects of the MNP seed diameter (10, 50 and 100 nm), MNP seed concentration (0.25-2.0 mg/mL), and fluid velocity (0.03-0.15 cm/s) on the capture efficiency (CE) of both the MNP seeds and the MDCPs were studied. The CE of the 10 nm MNP seeds was never more than 30%, while those of the 50 and 100 nm MNP seeds was always greater than 80% and in many cases exceeded 90%. Only the MNP seed concentration affected its CE. The 10 nm MNP seeds did not increase the MDCP CE over that obtained in the absence of the MNP seeds, while the 50 and 100 nm MNP seeds increased significantly, typically by more than a factor of two. The 50 and 100 nm MNP seeds also exhibited similar abilities to capture the MDCPs, with the MDCP CE always increasing with decreasing fluid velocity and generally increasing with increasing MNP seed concentration. The MNP seed size, magnetic properties, and capacity to self-agglomerate and form clusters were key properties that make them a viable implant in IA-MDT.

  15. Characterization of parasite-specific indels and their proposed relevance for selective anthelminthic drug targeting.

    PubMed

    Wang, Qi; Heizer, Esley; Rosa, Bruce A; Wildman, Scott A; Janetka, James W; Mitreva, Makedonka

    2016-04-01

    Insertions and deletions (indels) are important sequence variants that are considered as phylogenetic markers that reflect evolutionary adaptations in different species. In an effort to systematically study indels specific to the phylum Nematoda and their structural impact on the proteins bearing them, we examined over 340,000 polypeptides from 21 nematode species spanning the phylum, compared them to non-nematodes and identified indels unique to nematode proteins in more than 3000 protein families. Examination of the amino acid composition revealed uneven usage of amino acids for insertions and deletions. The amino acid composition and cost, along with the secondary structure constitution of the indels, were analyzed in the context of their biological pathway associations. Species-specific indels could enable indel-based targeting for drug design in pathogens/parasites. Therefore, we screened the spatial locations of the indels in the parasite's protein 3D structures, determined the location of the indel and identified potential unique drug targeting sites. These indels could be confirmed by RNA-Seq data. Examples are presented illustrating the close proximity of some indels to established small-molecule binding pockets that can potentially facilitate selective targeting to the parasites and bypassing their host, thus reducing or eliminating the toxicity of the potential drugs. This study presents an approach for understanding the adaptation of pathogens/parasites at a molecular level, and outlines a strategy to identify such nematode-selective targets that remain essential to the organism. With further experimental characterization and validation, it opens a possible channel for the development of novel treatments with high target specificity, addressing both host toxicity and resistance concerns. PMID:26829384

  16. 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. PMID:27084466

  17. The clinicopathological significance and potential drug target of E-cadherin in NSCLC.

    PubMed

    Zhong, Kaize; Chen, Weiwen; Xiao, Ning; Zhao, Jian

    2015-08-01

    Human epithelial cadherin (E-cadherin), a member of transmembrane glycoprotein family, encoded by the E-cadherin gene, plays a key role in cell-cell adhesion, adherent junction in normal epithelial tissues, contributing to tissue differentiation and homeostasis. Although previous studies indicated that inactivation of the E-cadherin is mainly induced by hypermethylation of E-cadherin gene, evidence concerning E-cadherin hypermethylation in the carcinogenesis and development of non-small cell lung carcinoma (NSCLC) remains controversial. In this study, we conducted a meta-analysis to quantitatively evaluate the effects of E-cadherin hypermethylation on the incidence and clinicopathological characteristics of NSCLC. A comprehensive search of PubMed and Embase databases was performed up to October 2014. Analyses of pooled data were performed. Odds ratios (ORs) were calculated and summarized. Our meta-analysis combining 18 published articles demonstrated that the hypermethylation frequencies in NSCLC were significantly higher than those in normal control tissues, OR = 3.55, 95 % confidence interval (CI) = 1.98-6.36, p < 0.0001. Further analysis showed that E-cadherin hypermethylation was not strongly associated with the sex or smoking status in NSCLC patients. In addition, E-cadherin hypermethylation was also not strongly associated with pathological types, differentiated status, clinical stages, or metastatic status in NSCLC patients. The results from the current study indicate that the hypermethylation frequency of E-cadherin in NSCLC is strongly associated with NSCLC incidence and it may be an early event in carcinogenesis of NSCLC. We also discussed the potential value of E-cadherin as a drug target that may bring new direction and hope for cancer treatment through gene-targeted therapy. PMID:25758052

  18. The tuberculosis drug discovery and development pipeline and emerging drug targets.

    PubMed

    Mdluli, Khisimuzi; Kaneko, Takushi; Upton, Anna

    2015-06-01

    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. PMID:25635061

  19. Polyisoprenylated methylated protein methyl esterase as a putative drug target for androgen-insensitive prostate cancer

    PubMed Central

    Poku, Rosemary A; Amissah, Felix; Duverna, Randolph; Aguilar, Byron J; Kiros, Gebre-Egziabher; Lamango, Nazarius S

    2014-01-01

    Prostate cancer (CaP) is the most frequently diagnosed cancer in US men, with an estimated 236,590 new cases and 29,720 deaths in 2013. There exists the need to identify biomarkers/therapeutic targets for the early/companion diagnosis and development of novel therapies against the recalcitrant disease. Mutation and overexpression-induced abnormal activities of polyisoprenylated proteins have been implicated in CaP. Polyisoprenylated methylated protein methyl esterase (PMPMEase) catalyses the only reversible and terminal reaction of the polyisoprenylation pathway and may promote the effects of G proteins on cell viability. In this review, the potential role of PMPMEase to serve as a new drug target for androgen-insensitive CaP was determined. Specific PMPMEase activities were found to be 3.5- and 4.5-fold higher in androgen-sensitive 22Rv1 and androgen-dependent LNCaP and 1.5- and 9.8-fold higher in castration-resistant DU 145 and PC-3 CaP cells compared to normal WPE1-NA22 prostate cells. The PMPMEase inhibitor, L-28, induced apoptosis with EC50 values ranging from 1.8 to 4.6 μM. The PMPMEase activity in the cells following treatment with L-28 followed a similar profile, with IC50 ranging from 2.3 to 130 μM. L-28 disrupted F-actin filament organisation at 5 μM and inhibited cell migration 4-fold at 2 μM. Analysis of a CaP tissue microarray for PMPMEase expression revealed intermediate, strong, and very strong staining in 94.5% of the 92 adenocarcinoma cases compared to trace and weak staining in the normal and normal-adjacent tissue controls. The data are an indication that effective targeting of PMPMEase through the development of more potent agents may lead to the successful treatment of metastatic CaP. PMID:25228915

  20. 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

  1. 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

  2. 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

  3. 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).

  4. 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

  5. Predicting aquifer response time for application in catchment modeling.

    PubMed

    Walker, Glen R; Gilfedder, Mat; Dawes, Warrick R; Rassam, David W

    2015-01-01

    It is well established that changes in catchment land use can lead to significant impacts on water resources. Where land-use changes increase evapotranspiration there is a resultant decrease in groundwater recharge, which in turn decreases groundwater discharge to streams. The response time of changes in groundwater discharge to a change in recharge is a key aspect of predicting impacts of land-use change on catchment water yield. Predicting these impacts across the large catchments relevant to water resource planning can require the estimation of groundwater response times from hundreds of aquifers. At this scale, detailed site-specific measured data are often absent, and available spatial data are limited. While numerical models can be applied, there is little advantage if there are no detailed data to parameterize them. Simple analytical methods are useful in this situation, as they allow the variability in groundwater response to be incorporated into catchment hydrological models, with minimal modeling overhead. This paper describes an analytical model which has been developed to capture some of the features of real, sloping aquifer systems. The derived groundwater response timescale can be used to parameterize a groundwater discharge function, allowing groundwater response to be predicted in relation to different broad catchment characteristics at a level of complexity which matches the available data. The results from the analytical model are compared to published field data and numerical model results, and provide an approach with broad application to inform water resource planning in other large, data-scarce catchments. PMID:24842053

  6. 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

  7. 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. PMID:24035143

  8. 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. PMID:25750696

  9. Machine learning applications in cancer prognosis and prediction

    PubMed Central

    Kourou, Konstantina; Exarchos, Themis P.; Exarchos, Konstantinos P.; Karamouzis, Michalis V.; Fotiadis, Dimitrios I.

    2014-01-01

    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. PMID:25750696

  10. 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.

  11. Turbulent heat transfer prediction method for application to scramjet engines

    NASA Technical Reports Server (NTRS)

    Pinckney, S. Z.

    1974-01-01

    An integral method for predicting boundary layer development in turbulent flow regions on two-dimensional or axisymmetric bodies was developed. The method has the capability of approximating nonequilibrium velocity profiles as well as the local surface friction in the presence of a pressure gradient. An approach was developed for the problem of predicting the heat transfer in a turbulent boundary layer in the presence of a high pressure gradient. The solution was derived with particular emphasis on its applicability to supersonic combustion; thus, the effects of real gas flows were included. The resulting integrodifferential boundary layer method permits the estimation of cooling reguirements for scramjet engines. Theoretical heat transfer results are compared with experimental combustor and noncombustor heat transfer data. The heat transfer method was used in the development of engine design concepts which will produce an engine with reduced cooling requirements. The Langley scramjet engine module was designed by utilizing these design concepts and this engine design is discussed along with its corresponding cooling requirements. The heat transfer method was also used to develop a combustor cooling correlation for a combustor whose local properties are computed one dimensionally by assuming a linear area variation and a given heat release schedule.

  12. 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

  13. Novel Drug Targets for Food-Borne Pathogen Campylobacter jejuni: An Integrated Subtractive Genomics and Comparative Metabolic Pathway Study.

    PubMed

    Mehla, Kusum; Ramana, Jayashree

    2015-07-01

    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

  14. α6β2* and α4β2* Nicotinic Acetylcholine Receptors As Drug Targets for Parkinson's Disease

    PubMed Central

    Wonnacott, Susan

    2011-01-01

    Parkinson's disease is a debilitating movement disorder characterized by a generalized dysfunction of the nervous system, with a particularly prominent decline in the nigrostriatal dopaminergic pathway. Although there is currently no cure, drugs targeting the dopaminergic system provide major symptomatic relief. As well, agents directed to other neurotransmitter systems are of therapeutic benefit. Such drugs may act by directly improving functional deficits in these other systems, or they may restore aberrant motor activity that arises as a result of a dopaminergic imbalance. Recent research attention has focused on a role for drugs targeting the nicotinic cholinergic systems. The rationale for such work stems from basic research findings that there is an extensive overlap in the organization and function of the nicotinic cholinergic and dopaminergic systems in the basal ganglia. In addition, nicotinic acetylcholine receptor (nAChR) drugs could have clinical potential for Parkinson's disease. Evidence for this proposition stems from studies with experimental animal models showing that nicotine protects against neurotoxin-induced nigrostriatal damage and improves motor complications associated with l-DOPA, the “gold standard” for Parkinson's disease treatment. Nicotine interacts with multiple central nervous system receptors to generate therapeutic responses but also produces side effects. It is important therefore to identify the nAChR subtypes most beneficial for treating Parkinson's disease. Here we review nAChRs with particular emphasis on the subtypes that contribute to basal ganglia function. Accumulating evidence suggests that drugs targeting α6β2* and α4β2* nAChR may prove useful in the management of Parkinson's disease. PMID:21969327

  15. G protein-coupled receptors: from ligand identification to drug targets. 14-16 October 2002, San Diego, CA, USA.

    PubMed

    Chantry, David

    2003-05-01

    IBC advertised their seventh annual symposium on G protein-coupled receptors (GPCRs) under the heading 'GPCRs still the best drug targets' and, at the end of the 3-day meeting which took place at the Hilton San Diego Resort (October 14-16 2002), it seemed like an appropriate description. The meeting brought together researchers from a wide range of disciplines, and from both academia and industry, to discuss recent advances in GPCR biology, pharmacology and drug design. This review will cover the main themes that emerged during the meeting, with an emphasis on those areas that impact drug discovery. PMID:14610927

  16. Multiple myeloma-associated hDIS3 mutations cause perturbations in cellular RNA metabolism and suggest hDIS3 PIN domain as a potential drug target.

    PubMed

    Tomecki, Rafal; Drazkowska, Karolina; Kucinski, Iwo; Stodus, Krystian; Szczesny, Roman J; Gruchota, Jakub; Owczarek, Ewelina P; Kalisiak, Katarzyna; Dziembowski, Andrzej

    2014-01-01

    hDIS3 is a mainly nuclear, catalytic subunit of the human exosome complex, containing exonucleolytic (RNB) and endonucleolytic (PIN) active domains. Mutations in hDIS3 have been found in ∼10% of patients with multiple myeloma (MM). Here, we show that these mutations interfere with hDIS3 exonucleolytic activity. Yeast harboring corresponding mutations in DIS3 show growth inhibition and changes in nuclear RNA metabolism typical for exosome dysfunction. Construction of a conditional DIS3 knockout in the chicken DT40 cell line revealed that DIS3 is essential for cell survival, indicating that its function cannot be replaced by other exosome-associated nucleases: hDIS3L and hRRP6. Moreover, HEK293-derived cells, in which depletion of endogenous wild-type hDIS3 was complemented with exogenously expressed MM hDIS3 mutants, proliferate at a slower rate and exhibit aberrant RNA metabolism. Importantly, MM mutations are synthetically lethal with the hDIS3 PIN domain catalytic mutation both in yeast and human cells. Since mutations in PIN domain alone have little effect on cell physiology, our results predict the hDIS3 PIN domain as a potential drug target for MM patients with hDIS3 mutations. It is an interesting example of intramolecular synthetic lethality with putative therapeutic potential in humans. PMID:24150935

  17. 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

  18. A Review: The Current In Vivo Models for the Discovery and Utility of New Anti-leishmanial Drugs Targeting Cutaneous Leishmaniasis.

    PubMed

    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

  19. Treatment Efficiency of Free and Nanoparticle-Loaded Mitoxantrone for Magnetic Drug Targeting in Multicellular Tumor Spheroids.

    PubMed

    Hornung, Annkathrin; Poettler, Marina; Friedrich, Ralf P; Zaloga, Jan; Unterweger, Harald; Lyer, Stefan; Nowak, Johannes; Odenbach, Stefan; Alexiou, Christoph; Janko, Christina

    2015-01-01

    Major problems of cancer treatment using systemic chemotherapy are severe side effects. Magnetic drug targeting (MDT) employing superparamagnetic iron oxide nanoparticles (SPION) loaded with chemotherapeutic agents may overcome this dilemma by increasing drug accumulation in the tumor and reducing toxic side effects in the healthy tissue. For translation of nanomedicine from bench to bedside, nanoparticle-mediated effects have to be studied carefully. In this study, we compare the effect of SPION, unloaded or loaded with the cytotoxic drug mitoxantrone (MTO) with the effect of free MTO, on the viability and proliferation of HT-29 cells within three-dimensional multicellular tumor spheroids. Fluorescence microscopy and flow cytometry showed that both free MTO, as well as SPION-loaded MTO (SPION(MTO)) are able to penetrate into tumor spheroids and thereby kill tumor cells, whereas unloaded SPION did not affect cellular viability. Since SPION(MTO) has herewith proven its effectivity also in complex multicellular tumor structures with its surrounding microenvironment, we conclude that it is a promising candidate for further use in magnetic drug targeting in vivo. PMID:26437393

  20. 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.

  1. Hsp70s and J proteins of Plasmodium parasites infecting rodents and primates: structure, function, clinical relevance, and drug targets.

    PubMed

    Njunge, James M; Ludewig, Michael H; Boshoff, Aileen; Pesce, Eva-Rachele; Blatch, Gregory L

    2013-01-01

    Human malaria is an economically important disease caused by single-celled parasites of the Plasmodium genus whose biology displays great evolutionary adaptation to both its mammalian host and transmitting vectors. While the parasite has multiple life cycle stages, it is in the blood stage where clinical symptoms of the disease are manifested. Following erythrocyte entry, the parasite resides in the parasitophorous vacuole and actively transports its own proteins to the erythrocyte cytosol. This host-parasite "cross-talk" results in tremendous modifications of the infected erythrocyte imparting properties that allow it to adhere to the endothelium preventing splenic clearance. The Hsp70-J protein (DnaJ/Hsp40) molecular chaperone machinery, involved in cellular protein homeostasis, is being investigated as a novel drug target in various cellular systems including malaria. In Plasmodium the diverse chaperone complement is intimately involved in infected erythrocyte remodelling associated with the development and pathogenesis of malaria. In this review, we provide an overview of the Hsp70-J protein chaperone complement in Plasmodium falciparum and compare it with other Plasmodium species including the ones that serve as experimental study models for malaria. We propose that the unique traits possessed by this machinery not only provide avenues for drug targeting but also inform the evolutionary fitness of this parasite to its environment. PMID:22920898

  2. Characterizing and optimizing human anticancer drug targets based on topological properties in the context of biological pathways.

    PubMed

    Zhang, Jian; Wang, Yan; Shang, Desi; Yu, Fulong; Liu, Wei; Zhang, Yan; Feng, Chenchen; Wang, Qiuyu; Xu, Yanjun; Liu, Yuejuan; Bai, Xuefeng; Li, Xuecang; Li, Chunquan

    2015-04-01

    One of the challenging problems in drug discovery is to identify the novel targets for drugs. Most of the traditional methods for drug targets optimization focused on identifying the particular families of "druggable targets", but ignored their topological properties based on the biological pathways. In this study, we characterized the topological properties of human anticancer drug targets (ADTs) in the context of biological pathways. We found that the ADTs tended to present the following seven topological properties: influence the number of the pathways related to cancer, be localized at the start or end of the pathways, interact with cancer related genes, exhibit higher connectivity, vulnerability, betweenness, and closeness than other genes. We first ranked ADTs based on their topological property values respectively, then fused them into one global-rank using the joint cumulative distribution of an N-dimensional order statistic to optimize human ADTs. We applied the optimization method to 13 anticancer drugs, respectively. Results demonstrated that over 70% of known ADTs were ranked in the top 20%. Furthermore, the performance for mercaptopurine was significant: 6 known targets (ADSL, GMPR2, GMPR, HPRT1, AMPD3, AMPD2) were ranked in the top 15 and other four out of the top 15 (MAT2A, CDKN1A, AREG, JUN) have the potentialities to become new targets for cancer therapy. PMID:25724580

  3. 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

  4. Predicting Melting Points of Organic Molecules: Applications to Aqueous Solubility Prediction Using the General Solubility Equation.

    PubMed

    McDonagh, J L; van Mourik, T; Mitchell, J B O

    2015-11-01

    In this work we make predictions of several important molecular properties of academic and industrial importance to seek answers to two questions: 1) Can we apply efficient machine learning techniques, using inexpensive descriptors, to predict melting points to a reasonable level of accuracy? 2) Can values of this level of accuracy be usefully applied to predicting aqueous solubility? We present predictions of melting points made by several novel machine learning models, previously applied to solubility prediction. Additionally, we make predictions of solubility via the General Solubility Equation (GSE) and monitor the impact of varying the logP prediction model (AlogP and XlogP) on the GSE. We note that the machine learning models presented, using a modest number of 2D descriptors, can make melting point predictions in line with the current state of the art prediction methods (RMSE≥40 °C). We also find that predicted melting points, with an RMSE of tens of degrees Celsius, can be usefully applied to the GSE to yield accurate solubility predictions (log10 S RMSE<1) over a small dataset of drug-like molecules. PMID:27491032

  5. Echo state network prediction method and its application in flue gas turbine condition prediction

    NASA Astrophysics Data System (ADS)

    Wang, Shaohong; Chen, Tao; Xu, Xiaoli

    2010-12-01

    On the background of the complex production process of fluid catalytic cracking energy recovery system in large-scale petrochemical refineries, this paper introduced an improved echo state network (ESN) model prediction method which is used to address the condition trend prediction problem of the key power equipment--flue gas turbine. Singular value decomposition method was used to obtain the ESN output weight. Through selecting the appropriate parameters and discarding small singular value, this method overcame the defective solution problem in the prediction by using the linear regression algorithm, improved the prediction performance of echo state network, and gave the network prediction process. In order to solve the problem of noise contained in production data, the translation-invariant wavelet transform analysis method is combined to denoise the noisy time series before prediction. Condition trend prediction results show the effectiveness of the proposed method.

  6. Drug targeting to infectious diseases by nanoparticles surface functionalized with special biomolecules

    PubMed Central

    Sundar, Shyam; Prajapati, Vijay Kumar

    2012-01-01

    The potential to deliver nanoparticles directly into the targeted cells is important in the therapeutic applications for infectious diseases. The possibility of therapeutic agent being attached to the nanoparticles by chemical modification has provided a novel drug delivery option. Interestingly, the discovery of carbon nanotubes and graphene has given an excellent imaging and therapeutic agent for the biomedical applications. In spite of continuous advancement in pharmaceutical drug delivery viz. micelles, vesicles and liquid crystals etc. during the past decades, their prohibitive production has limited their use. Nanomaterials with their properties of biodegradation, equal biodistribution, mass production and long time storage make them attractive alternative for future biomedical applications. Nanoparticles surface functionalized with specific biomolecules based drug delivery has driven new direction for modulating the pharmacokinetics and pharmacodynamics, biorecognition; and increasing the efficacy of targeted drugs. These new strategies are likely to minimize drug degradation and loss, increase drug availability, and opens up new vistas for drug delivery. PMID:22612703

  7. 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...

  8. 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

  9. 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

    2016-01-01

    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

  10. Development and Application of Chronic Disease Risk Prediction Models

    PubMed Central

    Oh, Sun Min; Stefani, Katherine M.

    2014-01-01

    Currently, non-communicable chronic diseases are a major cause of morbidity and mortality worldwide, and a large proportion of chronic diseases are preventable through risk factor management. However, the prevention efficacy at the individual level is not yet satisfactory. Chronic disease prediction models have been developed to assist physicians and individuals in clinical decision-making. A chronic disease prediction model assesses multiple risk factors together and estimates an absolute disease risk for the individual. Accurate prediction of an individual's future risk for a certain disease enables the comparison of benefits and risks of treatment, the costs of alternative prevention strategies, and selection of the most efficient strategy for the individual. A large number of chronic disease prediction models, especially targeting cardiovascular diseases and cancers, have been suggested, and some of them have been adopted in the clinical practice guidelines and recommendations of many countries. Although few chronic disease prediction tools have been suggested in the Korean population, their clinical utility is not as high as expected. This article reviews methodologies that are commonly used for developing and evaluating a chronic disease prediction model and discusses the current status of chronic disease prediction in Korea. PMID:24954311

  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. Cellular thermal shift and clickable chemical probe assays for the determination of drug-target engagement in live cells.

    PubMed

    Xu, Hua; Gopalsamy, Ariamala; Hett, Erik C; Salter, Shores; Aulabaugh, Ann; Kyne, Robert E; Pierce, Betsy; Jones, Lyn H

    2016-07-14

    Proof of drug-target engagement in physiologically-relevant contexts is a key pillar of successful therapeutic target validation. We developed two orthogonal technologies, the cellular thermal shift assay (CETSA) and a covalent chemical probe reporter approach (harnessing sulfonyl fluoride tyrosine labeling and subsequent click chemistry) to measure the occupancy of the mRNA-decapping scavenger enzyme DcpS by a small molecule inhibitor in live cells. Enzyme affinity determined using isothermal dose response fingerprinting (ITDRFCETSA) and the concentration required to occupy 50% of the enzyme (OC50) using the chemical probe reporter assay were very similar. In this case, the chemical probe method worked well due to the long offset kinetics of the reversible inhibitor (determined using a fluorescent dye-tagged probe). This work suggests that CETSA could become the first choice assay to determine in-cell target engagement due to its simplicity. PMID:27216142

  13. The cytoskeleton as a drug target for neuroprotection: the case of the autism- mutated ADNP.

    PubMed

    Gozes, Illana

    2016-03-01

    Fifteen years ago we discovered activity-dependent neuroprotective protein (ADNP), and showed that it is essential for brain formation/function. Our protein interaction studies identified ADNP as a member of the chromatin remodeling complex, SWI/SNF also associated with alternative splicing of tau and prediction of tauopathy. Recently, we have identified cytoplasmic ADNP interactions with the autophagy regulating microtubule-associated protein 1 light chain 3 (LC3) and with microtubule end-binding (EB) proteins. The ADNP-EB-binding SIP domain is shared with the ADNP snippet drug candidate, NAPVSIPQ termed NAP (davunetide). Thus, we identified a precise target for ADNP/NAP (davunetide) neuroprotection toward improved drug development. PMID:25955282

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

    PubMed

    Wong, Lai H; Sinha, Sunita; Bergeron, Julien R; Mellor, Joseph C; Giaever, Guri; Flaherty, Patrick; Nislow, Corey

    2016-09-01

    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

  15. H3K36 methyltransferases as cancer drug targets: rationale and perspectives for inhibitor development.

    PubMed

    Rogawski, David S; Grembecka, Jolanta; Cierpicki, Tomasz

    2016-09-01

    Methylation at histone 3, lysine 36 (H3K36) is a conserved epigenetic mark regulating gene transcription, alternative splicing and DNA repair. Genes encoding H3K36 methyltransferases (KMTases) are commonly overexpressed, mutated or involved in chromosomal translocations in cancer. Molecular biology studies have demonstrated that H3K36 KMTases regulate oncogenic transcriptional programs. Structural studies of the catalytic SET domain of H3K36 KMTases have revealed intriguing opportunities for design of small molecule inhibitors. Nevertheless, potent inhibitors for most H3K36 KMTases have not yet been developed, underlining the challenges associated with this target class. As we now have strong evidence linking H3K36 KMTases to cancer, drug development efforts are predicted to yield novel compounds in the near future. PMID:27548565

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

    PubMed

    Guo, Jing; Liu, Hui; Zheng, Jie

    2016-01-01

    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

  17. Identification of Common Biological Pathways and Drug Targets Across Multiple Respiratory Viruses Based on Human Host Gene Expression Analysis

    PubMed Central

    Smith, Steven B.; Dampier, William; Tozeren, Aydin; Brown, James R.; Magid-Slav, Michal

    2012-01-01

    Background Pandemic and seasonal respiratory viruses are a major global health concern. Given the genetic diversity of respiratory viruses and the emergence of drug resistant strains, the targeted disruption of human host-virus interactions is a potential therapeutic strategy for treating multi-viral infections. The availability of large-scale genomic datasets focused on host-pathogen interactions can be used to discover novel drug targets as well as potential opportunities for drug repositioning. Methods/Results In this study, we performed a large-scale analysis of microarray datasets involving host response to infections by influenza A virus, respiratory syncytial virus, rhinovirus, SARS-coronavirus, metapneumonia virus, coxsackievirus and cytomegalovirus. Common genes and pathways were found through a rigorous, iterative analysis pipeline where relevant host mRNA expression datasets were identified, analyzed for quality and gene differential expression, then mapped to pathways for enrichment analysis. Possible repurposed drugs targets were found through database and literature searches. A total of 67 common biological pathways were identified among the seven different respiratory viruses analyzed, representing fifteen laboratories, nine different cell types, and seven different array platforms. A large overlap in the general immune response was observed among the top twenty of these 67 pathways, adding validation to our analysis strategy. Of the top five pathways, we found 53 differentially expressed genes affected by at least five of the seven viruses. We suggest five new therapeutic indications for existing small molecules or biological agents targeting proteins encoded by the genes F3, IL1B, TNF, CASP1 and MMP9. Pathway enrichment analysis also identified a potential novel host response, the Parkin-Ubiquitin Proteasomal System (Parkin-UPS) pathway, which is known to be involved in the progression of neurodegenerative Parkinson's disease. Conclusions Our study

  18. Integrated gene co-expression network analysis in the growth phase of Mycobacterium tuberculosis reveals new potential drug targets.

    PubMed

    Puniya, Bhanwar Lal; Kulshreshtha, Deepika; Verma, Srikant Prasad; Kumar, Sanjiv; Ramachandran, Srinivasan

    2013-11-01

    We have carried out weighted gene co-expression network analysis of Mycobacterium tuberculosis to gain insights into gene expression architecture during log phase growth. The differentially expressed genes between at least one pair of 11 different M. tuberculosis strains as source of biological variability were used for co-expression network analysis. This data included genes with highest coefficient of variation in expression. Five distinct modules were identified using topological overlap based clustering. All the modules together showed significant enrichment in biological processes: fatty acid biosynthesis, cell membrane, intracellular membrane bound organelle, DNA replication, Quinone biosynthesis, cell shape and peptidoglycan biosynthesis, ribosome and structural constituents of ribosome and transposition. We then extracted the co-expressed connections which were supported either by transcriptional regulatory network or STRING database or high edge weight of topological overlap. The genes trpC, nadC, pitA, Rv3404c, atpA, pknA, Rv0996, purB, Rv2106 and Rv0796 emerged as top hub genes. After overlaying this network on the iNJ661 metabolic network, the reactions catalyzed by 15 highly connected metabolic genes were knocked down in silico and evaluated by Flux Balance Analysis. The results showed that in 12 out of 15 cases, in 11 more than 50% of reactions catalyzed by genes connected through co-expressed connections also had altered fluxes. The modules 'Turquoise', 'Blue' and 'Red' also showed enrichment in essential genes. We could map 152 of the previously known or proposed drug targets in these modules and identified 15 new potential drug targets based on their high degree of co-expressed connections and strong correlation with module eigengenes. PMID:24056838

  19. 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.

  20. 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.

  1. 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.

  2. Many particle magnetic dipole-dipole and hydrodynamic interactions in magnetizable stent assisted magnetic drug targeting

    NASA Astrophysics Data System (ADS)

    Cregg, P. J.; Murphy, Kieran; Mardinoglu, Adil; Prina-Mello, Adriele

    2010-08-01

    The implant assisted magnetic targeted drug delivery system of Avilés, Ebner and Ritter is considered both experimentally ( in vitro) and theoretically. The results of a 2D mathematical model are compared with 3D experimental results for a magnetizable wire stent. In this experiment a ferromagnetic, coiled wire stent is implanted to aid collection of particles which consist of single domain magnetic nanoparticles (radius ≈10 nm). In order to model the agglomeration of particles known to occur in this system, the magnetic dipole-dipole and hydrodynamic interactions for multiple particles are included. Simulations based on this mathematical model were performed using open source C++ code. Different initial positions are considered and the system performance is assessed in terms of collection efficiency. The results of this model show closer agreement with the measured in vitro experimental results and with the literature. The implications in nanotechnology and nanomedicine are based on the prediction of the particle efficiency, in conjunction with the magnetizable stent, for targeted drug delivery.

  3. 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.

  4. Silica-deposited phospholipid nanotubules as a plausible drug targeting system.

    PubMed

    Kim, Il; Park, Yun Hwan; Rey, Diego A; Batt, Carl A

    2008-11-01

    An aqueous dispersion of self-organized phospholipid tubules has been utilized as the template for silica-deposited nanotubules (approximately 0.5 microm thick and >10 microm long) by a sol-gel method. The formation of the hybrid tubules was mechanistically investigated by controlled sol-gel reaction. The incorporation of silica increases the mechanical and thermal stability of tubule geometry. After bioconjugating Ni(2+)-nitrilotriacetic acid (Ni-NTA) to the surface of chemically modified tubules containing primary amine groups, green fluorescent protein (GFP)-6 His and A33scFv-6 His were further bioconjugated in order to investigate a potential application of these hollow silica tubules as vehicle for targeted controlled release. The resulting tubules bound and internalized to SW1222 endothelial human colon carcinoma cells that express the A33 cell-surface glycoprotein more specifically than HT29 cells that do not express this antigen. PMID:18982520

  5. 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. PMID:26139368

  6. 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

  7. 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.

  8. 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

  9. Transdermal drug targeting and functional imaging of tumor blood vessels in the mouse auricle.

    PubMed

    Schröder, Hannes; Komljenovic, Dorde; Hecker, Markus; Korff, Thomas

    2016-02-01

    Subcutaneously growing tumors are widely utilized to study tumor angiogenesis and the efficacy of antiangiogenic therapies in mice. To additionally assess functional and morphologic alterations of the vasculature in the periphery of a growing tumor, we exploited the easily accessible and hierarchically organized vasculature of the mouse auricle. By site-specific subcutaneous implantation of a defined preformed mouse B16/F0 melanoma aggregate, a solid tumor nodule developed within 14 d. Growth of the tumor nodule was accompanied by a 4-fold increase in its perfusion as well as a 2- to 4-fold elevated diameter and perfusion of peripheral blood vessels that had connected to the tumor capillary microvasculature. By transdermal application of the anticancer drug bortezomib, tumor growth was significantly diminished by about 50% without provoking side effects. Moreover, perfusion and tumor microvessel diameter as well as growth and perfusion of arterial or venous blood vessels supplying or draining the tumor microvasculature were decreased under these conditions by up to 80%. Collectively, we observed that the progressive tumor growth is accompanied by the enlargement of supplying and draining extratumoral blood vessels. This process was effectively suppressed by bortezomib, thereby restricting the perfusion capacity of both extra and intratumoral blood vessels. PMID:26546130

  10. Importance of polar solvation and configurational entropy for design of antiretroviral drugs targeting HIV-1 protease.

    PubMed

    Kar, Parimal; Lipowsky, Reinhard; Knecht, Volker

    2013-05-16

    Both KNI-10033 and KNI-10075 are high affinity preclinical HIV-1 protease (PR) inhibitors with affinities in the picomolar range. In this work, the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method has been used to investigate the potency of these two HIV-1 PR inhibitors against the wild-type and mutated proteases assuming that potency correlates with the affinity of the drugs for the target protein. The decomposition of the binding free energy reveals the origin of binding affinities or mutation-induced affinity changes. Our calculations indicate that the mutation I50V causes drug resistance against both inhibitors. On the other hand, we predict that the mutant I84V causes drug resistance against KNI-10075 while KNI-10033 is more potent against the I84V mutant compared to wild-type protease. Drug resistance arises mainly from unfavorable shifts in van der Waals interactions and configurational entropy. The latter indicates that neglecting changes in configurational entropy in the computation of relative binding affinities as often done is not appropriate in general. For the bound complex PR(I50V)-KNI-10075, an increased polar solvation free energy also contributes to the drug resistance. The importance of polar solvation free energies is revealed when interactions governing the binding of KNI-10033 or KNI-10075 to the wild-type protease are compared to the inhibitors darunavir or GRL-06579A. Although the contributions from intermolecular electrostatic and van der Waals interactions as well as the nonpolar component of the solvation free energy are more favorable for PR-KNI-10033 or PR-KNI-10075 compared to PR-DRV or PR-GRL-06579A, both KNI-10033 and KNI-10075 show a similar affinity as darunavir and a lower binding affinity relative to GRL-06579A. This is because of the polar solvation free energy which is less unfavorable for darunavir or GRL-06579A relative to KNI-10033 or KNI-10075. The importance of the polar solvation as revealed here

  11. Application of two direct runoff prediction methods in Puerto Rico

    USGS Publications Warehouse

    Sepulveda, N.

    1997-01-01

    Two methods for predicting direct runoff from rainfall data were applied to several basins and the resulting hydrographs compared to measured values. The first method uses a geomorphology-based unit hydrograph to predict direct runoff through its convolution with the excess rainfall hyetograph. The second method shows how the resulting hydraulic routing flow equation from a kinematic wave approximation is solved using a spectral method based on the matrix representation of the spatial derivative with Chebyshev collocation and a fourth-order Runge-Kutta time discretization scheme. The calibrated Green-Ampt (GA) infiltration parameters are obtained by minimizing the sum, over several rainfall events, of absolute differences between the total excess rainfall volume computed from the GA equations and the total direct runoff volume computed from a hydrograph separation technique. The improvement made in predicting direct runoff using a geomorphology-based unit hydrograph with the ephemeral and perennial stream network instead of the strictly perennial stream network is negligible. The hydraulic routing scheme presented here is highly accurate in predicting the magnitude and time of the hydrograph peak although the much faster unit hydrograph method also yields reasonable results.

  12. Application of Weibull Criterion to failure prediction in compsites

    SciTech Connect

    Cain, W. D.; Knight, Jr., C. E.

    1981-04-20

    Fiber-reinforced composite materials are being widely used in engineered structures. This report examines how the general form of the Weibull Criterion, including the evaluation of the parameters and the scaling of the parameter values, can be used for the prediction of component failure.

  13. Local predictability in biological sequences, algorithm and applications.

    PubMed

    Lebbe, J; Vignes, R

    1993-01-01

    The goal of this paper is to propose an algorithm based on the k nearest neighbours to compute a local predictability measure in biological sequences. Some ideas about the usefulness of this measure are discussed on the basis of preliminary experimentations. PMID:8347724

  14. Identification of potential vaccine and drug target candidates by expressed sequence tag analysis and immunoscreening of Onchocerca volvulus larval cDNA libraries.

    PubMed

    Lizotte-Waniewski, M; Tawe, W; Guiliano, D B; Lu, W; Liu, J; Williams, S A; Lustigman, S

    2000-06-01

    The search for appropriate vaccine candidates and drug targets against onchocerciasis has so far been confronted with several limitations due to the unavailability of biological material, appropriate molecular resources, and knowledge of the parasite biology. To identify targets for vaccine or chemotherapy development we have undertaken two approaches. First, cDNA expression libraries were constructed from life cycle stages that are critical for establishment of Onchocerca volvulus infection, the third-stage larvae (L3) and the molting L3. A gene discovery effort was then initiated by random expressed sequence tag analysis of 5,506 cDNA clones. Cluster analyses showed that many of the transcripts were up-regulated and/or stage specific in either one or both of the cDNA libraries when compared to the microfilariae, L2, and both adult stages of the parasite. Homology searches against the GenBank database facilitated the identification of several genes of interest, such as proteinases, proteinase inhibitors, antioxidant or detoxification enzymes, and neurotransmitter receptors, as well as structural and housekeeping genes. Other O. volvulus genes showed homology only to predicted genes from the free-living nematode Caenorhabditis elegans or were entirely novel. Some of the novel proteins contain potential secretory leaders. Secondly, by immunoscreening the molting L3 cDNA library with a pool of human sera from putatively immune individuals, we identified six novel immunogenic proteins that otherwise would not have been identified as potential vaccinogens using the gene discovery effort. This study lays a solid foundation for a better understanding of the biology of O. volvulus as well as for the identification of novel targets for filaricidal agents and/or vaccines against onchocerciasis based on immunological and rational hypothesis-driven research. PMID:10816503

  15. 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

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

    PubMed

    Russo, Thomas A; Manohar, Akshay; Beanan, Janet M; Olson, Ruth; MacDonald, Ulrike; Graham, Jessica; Umland, Timothy C

    2016-01-01

    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 health

  17. Comparative evaluation of novel biodegradable nanoparticles for the drug targeting to breast cancer cells.

    PubMed

    Mattu, C; Pabari, R M; Boffito, M; Sartori, S; Ciardelli, G; Ramtoola, Z

    2013-11-01

    . Interestingly, PUR nps were superior to commercial polyester-based nps in terms of higher cellular internalisation and cytotoxic activity on the tested cell lines. Results obtained warrants further investigation on the application of these PUR nps for controlled drug delivery and targeting. PMID:23916461

  18. 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

  19. Formability Prediction Of Aluminum Sheet In Automotive Applications

    SciTech Connect

    Leppin, Christian; Daniel, Dominique; Shahani, Ravi; Gese, Helmut; Dell, Harry

    2007-05-17

    In the following paper, a full mechanical characterization of the AA6016 T4 aluminum alloy car body sheet DR100 is presented. A comprehensive experimental program was performed to identify and model the orthotopic elasto-plastic deformation behavior of the material and its fracture characteristics including criteria for localized necking, ductile fracture and shear fracture. The commercial software package MF GenYld + CrachFEM in combination with the explicit finite element code Ls-Dyna is used to validate the quality of the material model with experiments, namely, prediction of the FLD, deep drawing with a cross-shaped punch and finally, analysis of a simplified hemming process using a solid discretization of the problem. The focus is on the correct prediction of the limits of the material in such processes.

  20. Applications of remote sensing to stream discharge predictions

    NASA Technical Reports Server (NTRS)

    Krause, F. R.; Winn, C. B.

    1972-01-01

    A feasibility study has been initiated on the use of remote earth observations for augmenting stream discharge prediction for the design and/or operation of major reservoir systems, pumping systems and irrigation systems. The near-term objectives are the interpolation of sparsely instrumented precipitation surveillance networks and the direct measurement of water loss by evaporation. The first steps of the study covered a survey of existing reservoir systems, stream discharge prediction methods, gage networks and the development of a self-adaptive variation of the Kentucky Watershed model, SNOPSET, that includes snowmelt. As a result of these studies, a special three channel scanner is being built for a small aircraft, which should provide snow, temperature and water vapor maps for the spatial and temporal interpolation of stream gages.

  1. Bloat free genetic programming: application to human oral bioavailability prediction.

    PubMed

    Silva, Sara; Vanneschi, Leonardo

    2012-01-01

    Being able to predict the human oral bioavailability for a potential new drug is extremely important for the drug discovery process. This problem has been addressed by several prediction tools, with Genetic Programming providing some of the best results ever achieved. In this paper we use the newest developments of Genetic Programming, in particular the latest bloat control method, Operator Equalisation, to find out how much improvement we can achieve on this problem. We show examples of some actual solutions and discuss their quality, comparing them with previously published results. We identify some unexpected behaviours related to overfitting, and discuss the way for further improving the practical usage of the Genetic Programming approach. PMID:23356009

  2. Formability Prediction Of Aluminum Sheet In Automotive Applications

    NASA Astrophysics Data System (ADS)

    Leppin, Christian; Daniel, Dominique; Shahani, Ravi; Gese, Helmut; Dell, Harry

    2007-05-01

    In the following paper, a full mechanical characterization of the AA6016 T4 aluminum alloy car body sheet DR100 is presented. A comprehensive experimental program was performed to identify and model the orthotopic elasto-plastic deformation behavior of the material and its fracture characteristics including criteria for localized necking, ductile fracture and shear fracture. The commercial software package MF GenYld + CrachFEM in combination with the explicit finite element code Ls-Dyna is used to validate the quality of the material model with experiments, namely, prediction of the FLD, deep drawing with a cross-shaped punch and finally, analysis of a simplified hemming process using a solid discretization of the problem. The focus is on the correct prediction of the limits of the material in such processes.

  3. 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.

  4. 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.

  5. 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

  6. 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

  7. Theoretical outdoor noise propagation models: Application to practical predictions

    NASA Astrophysics Data System (ADS)

    Tuominen, H. T.; Lahti, T.

    1982-02-01

    The theoretical calculation approaches for outdoor noise propagation are reviewed. Possibilities for their application to practical engineering calculations are outlined. A calculation procedure, which is a combination and extension of several theoretical models, is described. Calculation examples are compared with the results of some propagation studies.

  8. 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.

  9. Identifying drug-target selectivity of small-molecule CRM1/XPO1 inhibitors by CRISPR/Cas9 genome editing.

    PubMed

    Neggers, Jasper E; Vercruysse, Thomas; Jacquemyn, Maarten; Vanstreels, Els; Baloglu, Erkan; Shacham, Sharon; Crochiere, Marsha; Landesman, Yosef; Daelemans, Dirk

    2015-01-22

    Validation of drug-target interaction is essential in drug discovery and development. The ultimate proof for drug-target validation requires the introduction of mutations that confer resistance in cells, an approach that is not straightforward in mammalian cells. Using CRISPR/Cas9 genome editing, we show that a homozygous genomic C528S mutation in the XPO1 gene confers cells with resistance to selinexor (KPT-330). Selinexor is an orally bioavailable inhibitor of exportin-1 (CRM1/XPO1) with potent anticancer activity and is currently under evaluation in human clinical trials. Mutant cells were resistant to the induction of cytotoxicity, apoptosis, cell cycle arrest, and inhibition of XPO1 function, including direct binding of the drug to XPO1. These results validate XPO1 as the prime target of selinexor in cells and identify the selectivity of this drug toward the cysteine 528 residue of XPO1. Our findings demonstrate that CRISPR/Cas9 genome editing enables drug-target validation and drug-target selectivity studies in cancer cells. PMID:25579209

  10. 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.

  11. Applications of tree-structured regression for regional precipitation prediction

    NASA Astrophysics Data System (ADS)

    Li, Xiangshang

    2000-11-01

    This thesis presents a Tree-Structured Regression (TSR) method to relate daily precipitation with a variety of free atmosphere variables. Historical data were used to identify distinct weather patterns associated with differing types of precipitation events. Models were developed using 67% of the data for training and the remaining data for model validation. Seasonal models were built for each of four U.S. sites; New Orleans Louisiana, San Antonio and Amarillo of Texas as well as San Francisco California. The average correlation by site between observed and simulated daily precipitation data series range from 0.69 to 0.79 for the training set, and 0.64 to 0.79 for the validation set. Relative humidity related variables were found to be the dominant variables in these TSR models. Output from an NCAR Climate System Model (CSM) transient simulation of climate change were then used to drive the TSR models for predicting precipitation characteristics under climate change. A preliminary screening of the GCM output variables for current climate, however, revealed significant problems for the New Orleans, San Antonio and Amarillo sites. Specifically, the CSM missed the annual trends in humidity for the grid cells containing these sites. CSM output for the San Francisco site was found to be much more reliable. Therefore, we present future precipitation estimates only for the San Francisco site. While both GCM and TSR predict very small change in overall annual precipitation, they differ significantly from season to season.

  12. Evaluation of a convective downburst prediction application for India

    NASA Astrophysics Data System (ADS)

    Pryor, Kenneth L.; Johny, C. J.; Prasad, V. S.

    2016-05-01

    During the month of June 2015, the South Asian (or Southwest) monsoon advanced steadily from the southern to the northwestern states of India. The progression of the monsoon had an apparent effect on the relative strength of convective storm downbursts that occurred during June and July 2015. A convective downburst prediction algorithm, involving the Microburst Windspeed Potential Index (MWPI) and a satellite-derived three-band microburst risk product, and applied with meteorological geostationary satellite (KALPANA-1 VHRR and METEOSAT-7) and MODIS Aqua data, was evaluated and found to effectively indicate relative downburst intensity in both pre-monsoon and monsoon environments over various regions of India. The MWPI product, derived from T574L64 Global Forecast System (NGFS) model data, is being generated in real-time by National Center for Medium Range Weather Forecasting (NCMRWF), Ministry of Earth Sciences, India. The validation process entailed direct comparison of measured downburst-related wind gusts at airports and India Meteorological Department (IMD) observatories to adjacent MWPI values calculated from GFS and India NGFS model datasets. Favorable results include a statistically significant positive correlation between MWPI values and proximate measured downburst wind gusts with a confidence level near 100%. Case studies demonstrate the influence of the South Asian monsoon on convective storm environments and the response of the downburst prediction algorithm.

  13. 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

    2015-01-01

    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. PMID:26958172

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

    PubMed Central

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

    2015-01-01

    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. PMID:26958172

  15. Anti-cancer drug loaded iron-gold core-shell nanoparticles (Fe@Au) for magnetic drug targeting.

    PubMed

    Kayal, Sibnath; Ramanujan, Raju Vijayaraghavan

    2010-09-01

    Magnetic drug targeting, using core-shell magnetic carrier particles loaded with anti-cancer drugs, is an emerging and significant method of cancer treatment. Gold shell-iron core nanoparticles (Fe@Au) were synthesized by the reverse micelle method with aqueous reactants, surfactant, co-surfactant and oil phase. XRD, XPS, TEM and magnetic property measurements were utilized to characterize these core-shell nanoparticles. Magnetic measurements showed that the particles were superparamagnetic at room temperature and that the saturation magnetization decreased with increasing gold concentration. The anti-cancer drug doxorubicin (DOX) was loaded onto these Fe@Au nanoparticle carriers and the drug release profiles showed that upto 25% of adsorbed drug was released in 80 h. It was found that the amine (-NH2) group of DOX binds to the gold shell. An in vitro apparatus simulating the human circulatory system was used to determine the retention of these nanoparticle carriers when exposed to an external magnetic field. A high percentage of magnetic carriers could be retained for physiologically relevant flow speeds of fluid. The present findings show that DOX loaded gold coated iron nanoparticles are promising for magnetically targeted drug delivery. PMID:21133071

  16. Contributions from Caenorhabditis elegans functional genetics to antiparasitic drug target identification and validation: nicotinic acetylcholine receptors, a case study.

    PubMed

    Brown, L A; Jones, A K; Buckingham, S D; Mee, C J; Sattelle, D B

    2006-05-31

    Following the complete sequencing of the genome of the free-living nematode, Caenorhabditis elegans, in 1998, rapid advances have been made in assigning functions to many genes. Forward and reverse genetics have been used to identify novel components of synaptic transmission as well as determine the key components of antiparasitic drug targets. The nicotinic acetylcholine receptors (nAChRs) are prototypical ligand-gated ion channels. The functions of these transmembrane proteins and the roles of the different members of their extensive subunit families are increasingly well characterised. The simple nervous system of C. elegans possesses one of the largest nicotinic acetylcholine receptor gene families known for any organism and a combination of genetic, microarray, physiological and reporter gene expression studies have added greatly to our understanding of the components of nematode muscle and neuronal nAChR subtypes. Chemistry-to-gene screens have identified five subunits that are components of nAChRs sensitive to the antiparasitic drug, levamisole. A novel, validated target acting downstream of the levamisole-sensitive nAChR has also been identified in such screens. Physiology and molecular biology studies on nAChRs of parasitic nematodes have also identified levamisole-sensitive and insensitive subtypes and further subdivisions are under investigation. PMID:16620825

  17. 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. PMID:22287994

  18. 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

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

    PubMed Central

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

    2015-01-01

    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. PMID:26261215

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

    PubMed

    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

  1. 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.

  2. Amidated pectin/sodium carboxymethylcellulose microspheres as a new carrier for colonic drug targeting: Development and optimization by factorial design.

    PubMed

    Gadalla, Hytham H; El-Gibaly, Ibrahim; Soliman, Ghareb M; Mohamed, Fergany A; El-Sayed, Ahmed M

    2016-11-20

    The colon is a promising site for drug targeting owing to its long transit time and mild proteolytic activity. The aim of this study was to prepare new low methoxy amidated pectin/NaCMC microspheres cross-linked by a mixture of Zn(2+) and Al(3+) ions and test their potential for colonic targeting of progesterone. A 2(4) factorial design was carried out to optimize the preparation conditions. High drug entrapment efficiency (82-99%) was obtained and it increased with increasing drug concentration but decreased with increasing polymer concentration. Drug release rate was directly proportional to the microsphere drug content and inversely related to Al(3+) ion concentration. Drug release was minimal during the first 3h but was significantly improved in the presence of 1% rat caecal contents, confirming the microsphere potential for colonic delivery. The microspheres achieved >2.3-fold enhancement of colonic progesterone permeability. These results confirm the viability of the produced microspheres as colon-targeted drug delivery vehicle. PMID:27561525

  3. Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets

    PubMed Central

    Chu, Liang-Hui; Chen, Bor-Sen

    2008-01-01

    Background Cancer is caused by genetic abnormalities, such as mutations of oncogenes or tumor suppressor genes, which alter downstream signal transduction pathways and protein-protein interactions. Comparisons of the interactions of proteins in cancerous and normal cells can shed light on the mechanisms of carcinogenesis. Results We constructed initial networks of protein-protein interactions involved in the apoptosis of cancerous and normal cells by use of two human yeast two-hybrid data sets and four online databases. Next, we applied a nonlinear stochastic model, maximum likelihood parameter estimation, and Akaike Information Criteria (AIC) to eliminate false-positive protein-protein interactions in our initial protein interaction networks by use of microarray data. Comparisons of the networks of apoptosis in HeLa (human cervical carcinoma) cells and in normal primary lung fibroblasts provided insight into the mechanism of apoptosis and allowed identification of potential drug targets. The potential targets include BCL2, caspase-3 and TP53. Our comparison of cancerous and normal cells also allowed derivation of several party hubs and date hubs in the human protein-protein interaction networks involved in caspase activation. Conclusion Our method allows identification of cancer-perturbed protein-protein interactions involved in apoptosis and identification of potential molecular targets for development of anti-cancer drugs. PMID:18590547

  4. 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. PMID:27604745

  5. CRISPR-Mediated Drug-Target Validation Reveals Selective Pharmacological Inhibition of the RNA Helicase, eIF4A.

    PubMed

    Chu, Jennifer; Galicia-Vázquez, Gabriela; Cencic, Regina; Mills, John R; Katigbak, Alexandra; Porco, John A; Pelletier, Jerry

    2016-06-14

    Targeting translation initiation is an emerging anti-neoplastic strategy that capitalizes on de-regulated upstream MAPK and PI3K-mTOR signaling pathways in cancers. A key regulator of translation that controls ribosome recruitment flux is eukaryotic initiation factor (eIF) 4F, a hetero-trimeric complex composed of the cap binding protein eIF4E, the scaffolding protein eIF4G, and the RNA helicase eIF4A. Small molecule inhibitors targeting eIF4F display promising anti-neoplastic activity in preclinical settings. Among these are some rocaglate family members that are well tolerated in vivo, deplete eIF4F of its eIF4A helicase subunit, have shown activity as single agents in several xenograft models, and can reverse acquired resistance to MAPK and PI3K-mTOR targeted therapies. Herein, we highlight the power of using genetic complementation approaches and CRISPR/Cas9-mediated editing for drug-target validation ex vivo and in vivo, linking the anti-tumor properties of rocaglates to eIF4A inhibition. PMID:27239032

  6. Genome-scale reconstruction of the Streptococcus pyogenes M49 metabolic network reveals growth requirements and indicates potential drug targets.

    PubMed

    Levering, Jennifer; Fiedler, Tomas; Sieg, Antje; van Grinsven, Koen W A; Hering, Silvio; Veith, Nadine; Olivier, Brett G; Klett, Lara; Hugenholtz, Jeroen; Teusink, Bas; Kreikemeyer, Bernd; Kummer, Ursula

    2016-08-20

    Genome-scale metabolic models comprise stoichiometric relations between metabolites, as well as associations between genes and metabolic reactions and facilitate the analysis of metabolism. We computationally reconstructed the metabolic network of the lactic acid bacterium Streptococcus pyogenes M49. Initially, we based the reconstruction on genome annotations and already existing and curated metabolic networks of Bacillus subtilis, Escherichia coli, Lactobacillus plantarum and Lactococcus lactis. This initial draft was manually curated with the final reconstruction accounting for 480 genes associated with 576 reactions and 558 metabolites. In order to constrain the model further, we performed growth experiments of wild type and arcA deletion strains of S. pyogenes M49 in a chemically defined medium and calculated nutrient uptake and production fluxes. We additionally performed amino acid auxotrophy experiments to test the consistency of the model. The established genome-scale model can be used to understand the growth requirements of the human pathogen S. pyogenes and define optimal and suboptimal conditions, but also to describe differences and similarities between S. pyogenes and related lactic acid bacteria such as L. lactis in order to find strategies to reduce the growth of the pathogen and propose drug targets. PMID:26970054

  7. Endothelial Small- and Intermediate-Conductance K Channels and Endothelium-Dependent Hyperpolarization as Drug Targets in Cardiovascular Disease.

    PubMed

    Köhler, R; Oliván-Viguera, A; Wulff, H

    2016-01-01

    Endothelial calcium/calmodulin-gated K channels of small (KCa2.3) and intermediate conductance (KCa3.1) produce membrane hyperpolarization and endothelium-dependent hyperpolarization (EDH)-mediated vasodilation. Dysfunctions of the two channels and ensuing EDH impairments are found in several cardiovascular pathologies such as diabetes, atherosclerosis, postangioplastic neointima formation, but also inflammatory disease, cancer, and organ fibrosis. Moreover, KCa3.1 plays an important role in endothelial barrier dysfunction, edema formation in cardiac and pulmonary disease, and in ischemic stroke. Concerning KCa2.3, genome-wide association studies revealed an association of KCa2.3 channels with atrial fibrillation in humans. Accordingly, both channels are considered potential drug targets for cardio- and cerebrovascular disease states. In this chapter, we briefly review the function of the two channels in EDH-type vasodilation and systemic circulatory regulation and then highlight their pathophysiological roles in ischemic stroke as well as in pulmonary and brain edema. Finally, the authors summarize recent advances in the pharmacology of the channels and explore potential therapeutic utilities of novel channel modulators. PMID:27451095

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

    PubMed Central

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

    2014-01-01

    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. PMID:25253464

  9. 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. PMID:26126937

  10. Ephrin receptor A10 is a promising drug target potentially useful for breast cancers including triple negative breast cancers.

    PubMed

    Nagano, Kazuya; Maeda, Yuka; Kanasaki, So-Ichiro; Watanabe, Takanobu; Yamashita, Takuya; Inoue, Masaki; Higashisaka, Kazuma; Yoshioka, Yasuo; Abe, Yasuhiro; Mukai, Yohei; Kamada, Haruhiko; Tsutsumi, Yasuo; Tsunoda, Shin-ichi

    2014-09-10

    Ephrin receptor A10 (EphA10) is a relatively uncharacterized protein which is expressed in many breast cancers but not expressed in normal breast tissues. Here, we examined the potential of EphA10 as a drug target in breast cancer. Immunohistochemical staining of clinical tissue sections revealed that EphA10 was expressed in various breast cancer subtypes, including triple negative breast cancers (TNBCs), with no expression observed in normal tissues apart from testis. Ligand-dependent proliferation was observed in EphA10-transfected MDA-MB-435 cells (MDA-MB-435(EphA10)) and native TNBC cells (MDA-MB-436). However, this phenomenon was not observed in parental MDA-MB-435 cells which express a low level of EphA10. Finally, tumor growth was significantly suppressed by administration of an anti-EphA10 monoclonal antibody in a xenograft mouse model. These results suggest that inhibition of EphA10 signaling may be a novel therapeutic option for management of breast cancer, including TNBCs which are currently not treated with molecularly targeted agents. PMID:24946238

  11. Preclinical Evaluation of miR-15/107 Family Members as Multifactorial Drug Targets for Alzheimer's Disease

    PubMed Central

    Parsi, Sepideh; Smith, Pascal Y; Goupil, Claudia; Dorval, Véronique; Hébert, Sébastien S

    2015-01-01

    Alzheimer's disease (AD) is a multifactorial, fatal neurodegenerative disorder characterized by the abnormal accumulation of Aβ and Tau deposits in the brain. There is no cure for AD, and failure at different clinical trials emphasizes the need for new treatments. In recent years, significant progress has been made toward the development of miRNA-based therapeutics for human disorders. This study was designed to evaluate the efficiency and potential safety of miRNA replacement therapy in AD, using miR-15/107 paralogues as candidate drug targets. We identified miR-16 as a potent inhibitor of amyloid precursor protein (APP) and BACE1 expression, Aβ peptide production, and Tau phosphorylation in cells. Brain delivery of miR-16 mimics in mice resulted in a reduction of AD-related genes APP, BACE1, and Tau in a region-dependent manner. We further identified Nicastrin, a γ-secretase component involved in Aβ generation, as a target of miR-16. Proteomics analysis identified a number of additional putative miR-16 targets in vivo, including α-Synuclein and Transferrin receptor 1. Top-ranking biological networks associated with miR-16 delivery included AD and oxidative stress. Collectively, our data suggest that miR-16 is a good candidate for future drug development by targeting simultaneously endogenous regulators of AD biomarkers (i.e., Aβ and Tau), inflammation, and oxidative stress. PMID:26440600

  12. 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

  13. 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…

  14. Decision tree methods: applications for classification and prediction.

    PubMed

    Song, Yan-Yan; Lu, Ying

    2015-04-25

    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

  15. Implant-Assisted Intrathecal Magnetic Drug Targeting to Aid in Therapeutic Nanoparticle Localization for Potential Treatment of Central Nervous System Disorders.

    PubMed

    Lueshen, Eric; Venugopal, Indu; Soni, Tejen; Alaraj, Ali; Linninger, Andreas

    2015-02-01

    There is an ongoing struggle to develop efficient drug delivery and targeting methods within the central nervous system. One technique known as intrathecal drug delivery, involves direct drug infusion into the spinal canal and has become standard practice for treating many central nervous system diseases due to reduced systemic toxicity from the drug bypassing the blood-brain barrier. Although intrathecal drug delivery boasts the advantage of reduced systemic toxicity compared to oral and intravenous drug delivery techniques, current intrathecal delivery protocols lack a means of sufficient drug targeting at specific locations of interest within the central nervous system. We previously proposed the method of intrathecal magnetic drug targeting in order to overcome the limited targeting capabilities of standard intrathecal drug delivery protocols, while simultaneously reducing the systemic toxicity as well as the amount of drug required to produce a therapeutic effect. Building off of our previous work, this paper presents the concept of implant-assisted intrathecal magnetic drug targeting. Ferritic stainless steel implants were incorporated within the subarachnoid space of our in vitro human spine model, and the targeting magnet was placed at a physiological distance away from the model and implant to mimic the distance between the epidermis and spinal canal. Computer simulations were performed to optimize implant design for generating high gradient magnetic fields and to study how these fields may aid in therapeutic nanoparticle localization. Experiments aiming to determine the effects of different magnetically-susceptible implants placed within an external magnetic field on the targeting efficiency of gold-coated magnetite nanoparticles were then performed on our in vitro human spine model. Our results indicate that implant-assisted intrathecal magnetic drug targeting is an excellent supplementary technique to further enhance the targeting capabilities of our

  16. Tropical Cyclone Intensity Forecast Error Predictions and Their Applications

    NASA Astrophysics Data System (ADS)

    Bhatia, Kieran T.

    This dissertation aims to improve tropical cyclone (TC) intensity forecasts by exploring the connection between intensity forecast error and parameters representing initial condition uncertainty, atmospheric flow stability, TC strength, and the large-scale environment surrounding a TC. After assessing which of these parameters have robust relationships with error, a set of predictors are selected to develop a priori estimates of intensity forecast accuracy for Atlantic basin TCs. The applications of these forecasts are then discussed, including a multimodel ensemble that unequally weights different intensity models according to the situation. The ultimate goal is to produce skillful forecasts of TC intensity error and use their output to enhance intensity forecasts.

  17. 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. PMID:25790514

  18. 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.

  19. 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.

  20. 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

  1. 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. PMID:27054065

  2. The clinicopathological significance and drug target potential of FHIT in breast cancer, a meta-analysis and literature review.

    PubMed

    Su, Yunshu; Wang, Xiaoli; Li, Jun; Xu, Junming; Xu, Lijun

    2015-01-01

    FHIT is a bona fide tumor-suppressor gene and its loss contributes to tumorigenesis of epithelial cancers including breast cancer (BC). However, the association and clinicopathological significance between FHIT promoter hypermethylation and BC remains unclear. The purpose of this study is to conduct a meta-analysis and literature review to investigate the clinicopathological significance of FHIT methylation in BC. A detailed literature search was performed in PubMed, EMBASE, Web of Science, and Google Scholar databases. The data were extracted and assessed by two reviewers independently. Odds ratios with 95% corresponding confidence intervals were calculated. A total of seven relevant articles were available for meta-analysis, which included 985 patients. The frequency of FHIT hypermethylation was significantly increased in invasive ductal carcinoma compared to benign breast disease, the pooled odds ratio was 8.43, P<0.00001. The rate of FHIT hypermethylation was not significantly different between stage I/II and stage III/IV, odds ratio was 2.98, P=0.06. In addition, FHIT hypermethylation was not significantly associated with ER and PR status. FHIT hypermethylation was not significantly correlated with premenopausal and postmenopausal patients with invasive ductal carcinoma. In summary, our meta-analysis indicated that the frequency of FHIT hypermethylation was significantly increased in BC compared to benign breast disease. The rate of FHIT hypermethylation in advanced stages of BC was higher than in earlier stages; however, the difference was not statistically significant. Our data suggested that FHIT methylation could be a diagnostic biomarker of BC carcinogenesis. FHIT is a potential drug target for development of demethylation treatment for patients with BC. PMID:26491255

  3. Potent antimicrobial action of triclosan-lysozyme complex against skin pathogens mediated through drug-targeted delivery mechanism.

    PubMed

    Hoq, Md Imranul; Ibrahim, Hisham R

    2011-01-18

    Triclosan (TCS), an antimicrobial agent that inhibits bacterial fatty acid synthesis by blocking the active site of enoyl-ACP reductase (FabI), is a water-insoluble agent that limits its therapeutic candidacy. We have recently shown that the water solubility and antimicrobial activity of TCS were greatly enhanced when complexed to lysozyme (LZ). This study is to examine the therapeutic potential of triclosan-lysozyme (T-LZ) complex against common skin pathogens expressing different levels of FabI, and to delineate the drug-targeting mechanism by lysozyme. The T-LZ exhibited superior antimicrobial activity against two bacterial skin pathogens, Propionibacterium acnes and Corynebacterium minutissimum, while yeast pathogens, Candida albicans and Malassezia furfur lacking FabI enzyme were insensitive to the complex. Unlike free TCS or LZ, the T-LZ complex exhibited a potent antibacterial activity under a wide range of pH condition and salt concentration. Interestingly, P. acnes expressing greater amount of FabI was more susceptible to the T-LZ complex than C. minutissimum that produces lesser amount of the enzyme. A sensitive assay of FabI activity revealed that P. acnes and C. minutissimum treated with the complex exhibited significant inhibition of the intracellular FabI activity than cells treated with free TCS, indicating the efficiency of lysozyme to specifically deliver TCS to its target (FabI) in the cytoplasm of bacterial cells. These results demonstrate, for the first time, that lysozyme is a potential drug carrier that allows specific targeting to the microbial cells of the water-insoluble triclosan and highlights the potency of the complex for the treatment of skin bacterial infections. PMID:21078387

  4. Application of simulation techniques for internal corrosion prediction

    SciTech Connect

    Palacios T, C.A.; Hernandez, Y.

    1997-08-01

    Characterization of corrosion in the oil and gas industry is becoming of increasing importance for safety reasons as well as for the preservation of production facilities; to prevent down time and damage to the environment. This article presents the methodology used by this company to characterize the corrosion behavior of the whole production facility, taking into consideration the hydrodynamic and thermodynamic conditions of the produced fluids (flow velocities, flow pattern, liquid holdup, pressure, temperature, etc.) as they flow from the reservoir through the surface installations (flowlines, gas/oil gathering and transmission lines, gas processing plants, artificial lift systems, etc.). The methodology uses Petroleum Engineering and Two-Phase modeling techniques to: (1) optimize the entire production system to obtain the most efficient objective flow rate taking into consideration the corrosive/erosive nature of the produced fluid and (2) characterize the corrosive nature of the produced fluid as it flows through the above mentioned installations. The modeling techniques were performed using commercially available simulators and CO{sub 2} corrosion rates were determined using well known published correlations. For H{sub 2}S corrosion, NACE MR0175 criteria is applied. The application of this methodology has allowed corrosion control strategies, protection and monitoring criteria, inhibitor optimization and increased the effectiveness of already existing corrosion control systems.

  5. Application of rule based methods to predicting storm surge

    NASA Astrophysics Data System (ADS)

    Royston, S. J.; Horsburgh, K. J.; Lawry, J.

    2012-04-01

    The accurate forecast of storm surge, the long wavelength sea level response to meteorological forcing, is imperative for flood warning purposes. There remain regions of the world where operational forecast systems have not been developed and in these locations it is worthwhile considering numerically simpler, data-driven techniques to provide operational services. In this paper, we investigate the applicability of a class of data driven methods referred to as rule based models to the problem of forecasting storm surge. The accuracy of the rule based model is found to be comparable to several alternative data-driven techniques, all of which result in marginally worse but acceptable forecasts compared with the UK's operational hydrodynamic forecast model, given the reduction in computational effort. Promisingly, the rule based model is considered to be skillful in forecasting total water levels above a given flood warning threshold, with a Brier Skill Score of 0.58 against a climatological forecast (the operational storm surge system has a Brier Skill Score of up to 0.75 for the same data set). The structure of the model can be interrogated as IF-THEN rules and we find that the model structure in this case is consistent with our understanding of the physical system. Furthermore, the rule based approach provides probabilistic forecasts of storm surge, which is much more informative to flood warning managers than alternative approaches. Therefore, the rule based model provides reasonably skillful forecasts in comparison with the operational forecast model, for a significant reduction in development and run time, and is therefore considered to be an appropriate data driven approach that could be employed to forecast storm surge in regions of the world where a fully fledged hydrodynamic forecast system does not exist, provided a good observational and meteorological forecast can be made.

  6. Computational prediction of protein interfaces: A review of data driven methods.

    PubMed

    Xue, Li C; Dobbs, Drena; Bonvin, Alexandre M J J; Honavar, Vasant

    2015-11-30

    Reliably pinpointing which specific amino acid residues form the interface(s) between a protein and its binding partner(s) is critical for understanding the structural and physicochemical determinants of protein recognition and binding affinity, and has wide applications in modeling and validating protein interactions predicted by high-throughput methods, in engineering proteins, and in prioritizing drug targets. Here, we review the basic concepts, principles and recent advances in computational approaches to the analysis and prediction of protein-protein interfaces. We point out caveats for objectively evaluating interface predictors, and discuss various applications of data-driven interface predictors for improving energy model-driven protein-protein docking. Finally, we stress the importance of exploiting binding partner information in reliably predicting interfaces and highlight recent advances in this emerging direction. PMID:26460190

  7. Molecular Markers Predictive of Chemotherapy Response in Colorectal Cancer

    PubMed Central

    Shiovitz, Stacey; Grady, William M.

    2015-01-01

    Recognition of the molecular heterogeneity of colorectal cancer (CRC) has led to the classification of CRC based on a variety of clinical and molecular characteristics. Although the clinical significance of the majority of these molecular alterations is still being ascertained, it is widely anticipated that these characteristics will improve the accuracy of our ability to determine the prognosis and therapeutic response of CRC patients. A few of these markers, such as microsatellite instability and the CpG island methylator phenotype (CIMP), show promise as predictive markers for cytotoxic chemotherapy. KRAS is a validated biomarker for EGFR-targeted therapy, while NRAS and PI3KCA are evolving markers for targeted therapies. Multiple new actionable drug targets are being identified on a regular basis, but most are not ready for clinical use at this time. This review focuses on key molecular features of CRCs and the application of these molecular alterations as predictive biomarkers for CRC. PMID:25663616

  8. 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.

  9. Direct binding of radioiodinated monoclonal antibody to tumor cells: significance of antibody purity and affinity for drug targeting or tumor imaging

    SciTech Connect

    Kennel, S.J.; Foote, L.J.; Lankford, P.K.; Johnson, M.; Mitchell, T.; Braslawsky, G.R.

    1983-01-01

    For MoAb to be used efficiently for drug targeting and tumor imaging, the fraction of antibody binding to tumor cells must be maximized. We have studied the binding of 125I MoAb in three different tumor systems. The fraction of antibody that could be bound to the cell surface was directly proportional to the antibody purity. The affinity constant also limits the fraction of antibody that can bind to cells at a given antigen concentration. Rearrangement of the standard expression for univalent equilibrium binding between two reactants shows that in antigen excess, the maximum fraction of antibody that can bind (formula; see text). Binding data using four different MoAb with three cell systems confirm this relationship. Estimates for reasonable concentrations of tumor antigens in vivo indicate that antibodies with binding constants less than 10(8) M-1 are not likely to be useful for drug targeting or tumor imaging.

  10. 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

  11. Embedded prediction in feature extraction: application to single-trial EEG discrimination.

    PubMed

    Hsu, Wei-Yen

    2013-01-01

    In this study, an analysis system embedding neuron-fuzzy prediction in feature extraction is proposed for brain-computer interface (BCI) applications. Wavelet-fractal features combined with neuro-fuzzy predictions are applied for feature extraction in motor imagery (MI) discrimination. The features are extracted from the electroencephalography (EEG) signals recorded from participants performing left and right MI. Time-series predictions are performed by training 2 adaptive neuro-fuzzy inference systems (ANFIS) for respective left and right MI data. Features are then calculated from the difference in multi-resolution fractal feature vector (MFFV) between the predicted and actual signals through a window of EEG signals. Finally, the support vector machine is used for classification. The proposed method estimates its performance in comparison with the linear adaptive autoregressive (AAR) model and the AAR time-series prediction of 6 participants from 2 data sets. The results indicate that the proposed method is promising in MI classification. PMID:23248335

  12. DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins.

    PubMed

    Jamali, Ali Akbar; Ferdousi, Reza; Razzaghi, Saeed; Li, Jiuyong; Safdari, Reza; Ebrahimie, Esmaeil

    2016-05-01

    Application of computational methods in drug discovery has received increased attention in recent years as a way to accelerate drug target prediction. Based on 443 sequence-derived protein features, we applied the most commonly used machine learning methods to predict whether a protein is druggable as well as to opt for superior algorithm in this task. In addition, feature selection procedures were used to provide the best performance of each classifier according to the optimum number of features. When run on all features, Neural Network was the best classifier, with 89.98% accuracy, based on a k-fold cross-validation test. Among all the algorithms applied, the optimum number of most-relevant features was 130, according to the Support Vector Machine-Feature Selection (SVM-FS) algorithm. This study resulted in the discovery of new drug target which potentially can be employed in cell signaling pathways, gene expression, and signal transduction. The DrugMiner web tool was developed based on the findings of this study to provide researchers with the ability to predict druggable proteins. DrugMiner is freely available at www.DrugMiner.org. PMID:26821132

  13. The Landscape of Host Transcriptional Response Programs Commonly Perturbed by Bacterial Pathogens: Towards Host-Oriented Broad-Spectrum Drug Targets

    PubMed Central

    Kidane, Yared H.; Lawrence, Christopher; Murali, T. M.

    2013-01-01

    Background The emergence of drug-resistant pathogen strains and new infectious agents pose major challenges to public health. A promising approach to combat these problems is to target the host’s genes or proteins, especially to discover targets that are effective against multiple pathogens, i.e., host-oriented broad-spectrum (HOBS) drug targets. An important first step in the discovery of such drug targets is the identification of host responses that are commonly perturbed by multiple pathogens. Results In this paper, we present a methodology to identify common host responses elicited by multiple pathogens. First, we identified host responses perturbed by each pathogen using a gene set enrichment analysis of publicly available genome-wide transcriptional datasets. Then, we used biclustering to identify groups of host pathways and biological processes that were perturbed only by a subset of the analyzed pathogens. Finally, we tested the enrichment of each bicluster in human genes that are known drug targets, on the basis of which we elicited putative HOBS targets for specific groups of bacterial pathogens. We identified 84 up-regulated and three down-regulated statistically significant biclusters. Each bicluster contained a group of pathogens that commonly dysregulated a group of biological processes. We validated our approach by checking whether these biclusters correspond to known hallmarks of bacterial infection. Indeed, these biclusters contained biological process such as inflammation, activation of dendritic cells, pro- and anti- apoptotic responses and other innate immune responses. Next, we identified biclusters containing pathogens that infected the same tissue. After a literature-based analysis of the drug targets contained in these biclusters, we suggested new uses of the drugs Anakinra, Etanercept, and Infliximab for gastrointestinal pathogens Yersinia enterocolitica, Helicobacter pylori kx2 strain, and enterohemorrhagic Escherichia coli and the drug

  14. Validity of the UKCAT in applicant selection and predicting exam performance in UK dental students.

    PubMed

    Lala, Rizwana; Wood, Duncan; Baker, Sarah

    2013-09-01

    The United Kingdom's Clinical Aptitude Test (UKCAT) aims to assess candidates' "natural talent" for dentistry. The aim of this study was to determine the validity of the UKCAT for dental school applicant selection. The relationship of the UKCAT with demographic and academic variables was examined, assessing if the likelihood of being offered a place at a UK dental school was predicted by demographic factors and academic selection tools (predicted grades and existing school results). Finally, the validity of these selection tools in predicting first-year dental exam performance was assessed. Correlational and regression analyses showed that females and poorer students were more likely to have lower UKCAT scores. Gender and social class did not, however, predict first-year dental exam performance. UKCAT scores predicted the likelihood of the candidate being offered a place in the dental course; however, they did not predict exam performance during the first year of the course. Indeed, the only predictor of dental exam performance was existing school results. These findings argue against the use of the UKCAT as the sole determinant in dental applicant selection, instead highlighting the value of using existing school results. PMID:24002854

  15. Application of the Water Erosion Prediction Project (WEPP) Model for Soil Erosion Estimation and Conservation Planning

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Water Erosion Prediction Project (WEPP) model is a process-based, continuous- simulation, distributed parameter erosion simulation model for application to field-scale hillslope profiles and small watersheds. Developed over the past 25 years by the United States Department of Agriculture, it con...

  16. Application of Monte Carlo simulations to the prediction of the effective elastic moduli of hydrated Nafion

    NASA Astrophysics Data System (ADS)

    Weiland, Lisa Mauck; Lada, Emily K.; Smith, Ralph C.; Leo, Donald J.

    2005-05-01

    Application of Rotational Isomeric State (RIS) theory to the prediction of Young's modulus of a solvated ionomer is considered. RIS theory directly addresses polymer chain conformation as it relates to mechanical response trends. Successful adaptation of this methodology to the prediction of elastic moduli would thus provide a powerful tool for guiding ionomer fabrication. The Mark-Curro Monte Carlo methodology is applied to generate a statistically valid number of end-to-end chain lengths via RIS theory for a solvated Nafion case. The distribution of chain lengths is then fitted to a Probability Density Function by the Johnson Bounded distribution method. The fitting parameters, as they relate to the model predictions and physical structure of the polymer, are studied so that a means to extend RIS theory to the reliable prediction of ionomer stiffness may be identified.

  17. 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.

  18. Predictable component-based software design of real-time MPEG-4 video applications

    NASA Astrophysics Data System (ADS)

    Bondarev, Egor; Pastrnak, Milan; de With, Peter H. N.; Chaudron, Michel R. V.

    2005-07-01

    Component-based software development is very attractive, because it allows a clear decomposition of logical processing blocks into software blocks and it offers wide reuse. The strong real-time requirements of media processing systems should be validated as soon as possible to avoid costly system redesign. This can be achieved by prediction of timing and performance properties. In this paper, we propose a scenario simulation design approach featuring early performance prediction of a component-based software system. We validated this approach through a case study, for which we developed an advanced MPEG-4 coding application. The benefits of the approach are threefold: (a) high accuracy of the predicted performance data; (b) it delivers an efficient real-time software-hardware implementation, because the generic computational costs become known in advance, and (c) improved ease of use because of a high abstraction level of modelling. Experiments showed that the prediction accuracy of the system performance is about 90% or higher, while the prediction accuracy of the time-detailed processor usage (performance) does not get lower than 70%. However, the real-time performance requirements are sometimes not met, e.g. when other applications require intensive memory usage, thereby imposing delays on the retrieval from memory of the decoder data.

  19. Operational Rainfall Prediction on Meso-γ Scales for Hydrologic Applications

    NASA Astrophysics Data System (ADS)

    Lee, Tim H.; Georgakakos, Konstantine P.

    1996-04-01

    Presented is a rainfall prediction methodology for application in operational hydrologic forecasting with forecast lead times of 1-6 hours and spatial-resolution scales of 10-30 km. The essential elements of the prediction methodology are a mathematical model for precipitation prediction from surface and upper air meteorological variables; operational forecasts of temperature, pressure, humidity, and wind fields by large-scale numerical weather prediction models; surface and upper air meteorological observations; remote and on-site rainfall observations; and a state estimator for real-time updating from local frequent rainfall observations and for probabilistic predictions. This paper formulates a class of rainfall models suitable for this prediction methodology. The models are based on the differential equation of conservation of cloud and rainwater equivalent mass and on a newly introduced advection equation for a parameter that determines updraft strength. The latter advection equation is a prognostic equation for the strength of convection in space and time. The innovative features of the model formulated and tested are the inclusion of the prognostic equation for the advection of regions of active convection, the formulation of the state estimator component for state updating and probabilistic forecasts, and the utilization of a numerical solution scheme which reduces artificial numerical diffusion and can be used with the state estimator because of its explicit form. Utilization of the prediction model formulated was exemplified in several case studies of summer convection in Oklahoma using data available during routine forecast operations. The case studies show that when verified with radar rainfall data, the model's hourly precipitation predictions over a 20,000 km2 area with a 100-900 km2 resolution are better than simple persistence and explain more than 60% of the observed hourly rainfall variance. Sensitivity studies quantify dependence of rainfall

  20. Scenario-based, closed-loop model predictive control with application to emergency vehicle scheduling

    NASA Astrophysics Data System (ADS)

    Goodwin, Graham. C.; Medioli, Adrian. M.

    2013-08-01

    Model predictive control has been a major success story in process control. More recently, the methodology has been used in other contexts, including automotive engine control, power electronics and telecommunications. Most applications focus on set-point tracking and use single-sequence optimisation. Here we consider an alternative class of problems motivated by the scheduling of emergency vehicles. Here disturbances are the dominant feature. We develop a novel closed-loop model predictive control strategy aimed at this class of problems. We motivate, and illustrate, the ideas via the problem of fluid deployment of ambulance resources.

  1. The application study on the multi-scales integrated prediction method to fractured reservoir description

    NASA Astrophysics Data System (ADS)

    Chen, Shuang-Quan; Zeng, Lian-Bo; Huang, Ping; Sun, Shao-Han; Zhang, Wan-Lu; Li, Xiang-Yang

    2016-03-01

    In this paper, we implement three scales of fracture integrated prediction study by classifying it to macro- (> 1/4 λ), meso- (> 1/100 λ and < 1/4 λ) and micro- (< 1/100 λ) scales. Based on the multi-scales rock physics modelling technique, the seismic azimuthal anisotropy characteristic is analyzed for distinguishing the fractures of meso-scale. Furthermore, by integrating geological core fracture description, image well-logging fracture interpretation, seismic attributes macro-scale fracture prediction and core slice micro-scale fracture characterization, an comprehensive multi-scale fracture prediction methodology and technique workflow are proposed by using geology, well-logging and seismic multi-attributes. Firstly, utilizing the geology core slice observation (Fractures description) and image well-logging data interpretation results, the main governing factors of fracture development are obtained, and then the control factors of the development of regional macro-scale fractures are carried out via modelling of the tectonic stress field. For the meso-scale fracture description, the poststack geometric attributes are used to describe the macro-scale fracture as well, the prestack attenuation seismic attribute is used to predict the meso-scale fracture. Finally, by combining lithological statistic inversion with superposed results of faults, the relationship of the meso-scale fractures, lithology and faults can be reasonably interpreted and the cause of meso-scale fractures can be verified. The micro-scale fracture description is mainly implemented by using the electron microscope scanning of cores. Therefore, the development of fractures in reservoirs is assessed by valuating three classes of fracture prediction results. An integrated fracture prediction application to a real field in Sichuan basin, where limestone reservoir fractures developed, is implemented. The application results in the study area indicates that the proposed multi-scales integrated

  2. 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.

  3. 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.

  4. 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

  5. CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications

    PubMed Central

    2012-01-01

    Background Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. Results In this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture. Conclusions Speedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications. PMID:22369626

  6. Application of GA-SVM method with parameter optimization for landslide development prediction

    NASA Astrophysics Data System (ADS)

    Li, X. Z.; Kong, J. M.

    2014-03-01

    Prediction of the landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. The support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ) of the SVM model. In this study, we present an application of genetic algorithm and support vector machine (GA-SVM) method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in a hydro-electrical engineering area of southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multi-factor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multi-factor SVM models of the landslide. The results show that the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models, and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest root mean square error (RMSE) of 0.0009 and the highest relation index (RI) of 0.9992.

  7. Application of GA-SVM method with parameter optimization for landslide development prediction

    NASA Astrophysics Data System (ADS)

    Li, X. Z.; Kong, J. M.

    2013-10-01

    Prediction of landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. Support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ) of SVM model. In this study, we presented an application of GA-SVM method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in some hydro - electrical engineering area of Southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multi-factor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multi-factor SVM models of the landslide. The results show that, the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest RSME of 0.0009 and the biggest RI of 0.9992.

  8. Life prediction of coated and uncoated metallic interconnect for solid oxide fuel cell applications

    NASA Astrophysics Data System (ADS)

    Liu, W. N.; Sun, X.; Stephens, E.; Khaleel, M. A.

    In this paper, we present an integrated experimental and modeling methodology in predicting the life of coated and uncoated metallic interconnect (IC) for solid oxide fuel cell (SOFC) applications. The ultimate goal is to provide cell designer and manufacture with a predictive methodology such that the life of the IC system can be managed and optimized through different coating thickness to meet the overall cell designed life. Crofer 22 APU is used as the example IC material system. The life of coated and uncoated Crofer 22 APU under isothermal cooling was predicted by comparing the predicted interfacial strength and the interfacial stresses induced by the cooling process from the operating temperature to room temperature, together with the measured oxide scale growth kinetics. It was found that the interfacial strength between the oxide scale and the Crofer 22 APU substrate decreases with the growth of the oxide scale, and that the interfacial strength for the oxide scale/spinel coating interface is much higher than that of the oxide scale/Crofer 22 APU substrate interface. As expected, the predicted life of the coated Crofer 22 APU is significantly longer than that of the uncoated Crofer 22 APU.

  9. Linear genetic programming application for successive-station monthly streamflow prediction

    NASA Astrophysics Data System (ADS)

    Danandeh Mehr, Ali; Kahya, Ercan; Yerdelen, Cahit

    2014-09-01

    In recent decades, artificial intelligence (AI) techniques have been pronounced as a branch of computer science to model wide range of hydrological phenomena. A number of researches have been still comparing these techniques in order to find more effective approaches in terms of accuracy and applicability. In this study, we examined the ability of linear genetic programming (LGP) technique to model successive-station monthly streamflow process, as an applied alternative for streamflow prediction. A comparative efficiency study between LGP and three different artificial neural network algorithms, namely feed forward back propagation (FFBP), generalized regression neural networks (GRNN), and radial basis function (RBF), has also been presented in this study. For this aim, firstly, we put forward six different successive-station monthly streamflow prediction scenarios subjected to training by LGP and FFBP using the field data recorded at two gauging stations on Çoruh River, Turkey. Based on Nash-Sutcliffe and root mean squared error measures, we then compared the efficiency of these techniques and selected the best prediction scenario. Eventually, GRNN and RBF algorithms were utilized to restructure the selected scenario and to compare with corresponding FFBP and LGP. Our results indicated the promising role of LGP for successive-station monthly streamflow prediction providing more accurate results than those of all the ANN algorithms. We found an explicit LGP-based expression evolved by only the basic arithmetic functions as the best prediction model for the river, which uses the records of the both target and upstream stations.

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

    PubMed

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

    2012-12-01

    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

  11. Direct binding of radioiodinated monoclonal antibody to tumor cells: significance of antibody purity and affinity for drug targeting or tumor imaging

    SciTech Connect

    Kennel, S.J.; Foote, L.J.; Lankford, P.K.; Johnson, M.; Mitchell, T.; Braslawsky, G.R.

    1983-01-01

    For MoAb to be used efficiently for drug targeting and tumor imaging, the fraction of antibody binding to tumor cells must be maximized. The authors have studied the binding of /sup 125/I MoAb in three different tumor systems. The fraction of antibody that could be bound to the cell surface was directly proportional to the antibody purity. The affinity constant also limits the fraction of antibody that can bind to cells at a given antigen concentration. Rearrangement of the standard expression for univalent equilibrium binding between two reactants shows that in antigen excess, the maximum fraction of antibody that can bind =Ka(Ag total)/1 + Ka(Ag total). Binding data using four different MoAb with three cell systems confirm this relationship. Estimates for reasonable concentrations of tumor antigens in vivo indicate that antibodies with binding constants less than 10/sup 8/ M/sup -1/ are not likely to be useful for drug targeting or tumor imaging.

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

    PubMed Central

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

    2015-01-01

    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. PMID:27076918

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

    PubMed

    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. 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

  15. 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

  16. 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

  17. Life Prediction/Reliability Data of Glass-Ceramic Material Determined for Radome Applications

    NASA Technical Reports Server (NTRS)

    Choi, Sung R.; Gyekenyesi, John P.

    2002-01-01

    Brittle materials, ceramics, are candidate materials for a variety of structural applications for a wide range of temperatures. However, the process of slow crack growth, occurring in any loading configuration, limits the service life of structural components. Therefore, it is important to accurately determine the slow crack growth parameters required for component life prediction using an appropriate test methodology. This test methodology also should be useful in determining the influence of component processing and composition variables on the slow crack growth behavior of newly developed or existing materials, thereby allowing the component processing and composition to be tailored and optimized to specific needs. Through the American Society for Testing and Materials (ASTM), the authors recently developed two test methods to determine the life prediction parameters of ceramics. The two test standards, ASTM 1368 for room temperature and ASTM C 1465 for elevated temperatures, were published in the 2001 Annual Book of ASTM Standards, Vol. 15.01. Briefly, the test method employs constant stress-rate (or dynamic fatigue) testing to determine flexural strengths as a function of the applied stress rate. The merit of this test method lies in its simplicity: strengths are measured in a routine manner in flexure at four or more applied stress rates with an appropriate number of test specimens at each applied stress rate. The slow crack growth parameters necessary for life prediction are then determined from a simple relationship between the strength and the applied stress rate. Extensive life prediction testing was conducted at the NASA Glenn Research Center using the developed ASTM C 1368 test method to determine the life prediction parameters of a glass-ceramic material that the Navy will use for radome applications.

  18. 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.

  19. Predicting when precipitation-driven synthesis is feasible: application to biocatalysis.

    PubMed

    Ulijn, R V; Janssen, A E; Moore, B D; Halling, P J

    2001-05-18

    Precipitation-driven synthesis offers the possibility of obtaining high reaction yields using very low volume reactors and is finding increasing applications in biocatalysis. Here, a model that allows straightforward prediction of when such a precipitation-driven reaction will be thermodynamically feasible is presented. This requires comparison of the equilibrium constant, Keq, with the saturated mass action ratio, Zsat, defined as the ratio of product solubilities to reactant solubilities. A hypothetical thermodynamic cycle that can be used to accurately predict Zsat, in water is described. The cycle involves three main processes: fusion of a solid to a supercooled liquid, ideal mixing of the liquid with octanol, and partitioning from octanol to water. To obtain the saturated mass action ratio using this cycle, only the melting points of the reactants and products, and in certain cases the pKa of ionisable groups, are required as input parameters. The model was tested on a range of enzyme-catalysed peptide syntheses from the literature and found to predict accurately when precipitation-driven reaction was possible. The methodology employed is quite general and the model is therefore expected to be applicable to a wide range of other (bio)-catalysed reactions. PMID:11411981

  20. COBRA: a computational brewing application for predicting the molecular composition of organic aerosols.

    PubMed

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

    2012-06-01

    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 their complex and dynamic chemical composition. We introduce a novel Computational Brewing Application (COBRA) and apply it to modeling oligomerization chemistry stemming from condensation and addition reactions in OA 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. The simulation generated thousands of structures in the mass range of 120-500 Da and correctly predicted ∼70% of the individual OA constituents observed by high-resolution mass spectrometry. Select predicted structures were confirmed with tandem mass spectrometry. Esterification was shown to play the most significant role in oligomer formation, with hemiacetal formation less important, and aldol condensation insignificant. COBRA is not limited to atmospheric aerosol chemistry; it should be 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. PMID:22568707

  1. 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.

  2. Application of discrete grey model in settlement prediction of high-speed railway

    NASA Astrophysics Data System (ADS)

    Nie, Guangyu; Wen, Hongyan; Gao, Hong; Yang, Zhi; Yang, Ming

    2015-12-01

    The GM (1,1) model uses a discrete form equation to estimate the parameters and employ a continuous form equation to fit the model and predict the data sequence. The jump between the two form of equation is the fundamental reason to causing the error of GM (1,1) model. This paper first introduces the theory of the Discrete Grey Model (DGM (1,1) model), the solving method of model parameter and the solving algorithm of simulation value and the predicted value. Then, a modified DGM (1,1) model is proposed after analyzing the problems of Discrete Grey Model exited in the practical application. Finally, some contrast experiments for high speed railway subgrade settlement prediction are carried on by applying the improved DGM (1,1) model, the GM(1,1) model and the DGM(1,1) model respectively. The experimental results show that the improved DGM (1,1) model could acquire better model accuracy and forecasting result in engineering application.

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

    PubMed Central

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

    2012-01-01

    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 their complex and dynamic chemical composition. We introduce a novel Computational Brewing Application (COBRA) and apply it to modeling oligomerization chemistry stemming from condensation and addition reactions in OA 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. The simulation generated thousands of structures in the mass range of 120–500 Da, and correctly predicted ~70% of the individual OA constituents observed by high-resolution mass spectrometry. Select predicted structures were confirmed with tandem mass spectrometry. Esterification was shown to play the most significant role in oligomer formation, with hemiacetal formation less important, and aldol condensation insignificant. COBRA is not limited to atmospheric aerosol chemistry; it should be 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. PMID:22568707

  4. Applicability Domain ANalysis (ADAN): a robust method for assessing the reliability of drug property predictions.

    PubMed

    Carrió, Pau; Pinto, Marta; Ecker, Gerhard; Sanz, Ferran; Pastor, Manuel

    2014-05-27

    We report a novel method called ADAN (Applicability Domain ANalysis) for assessing the reliability of drug property predictions obtained by in silico methods. The assessment provided by ADAN is based on the comparison of the query compound with the training set, using six diverse similarity criteria. For every criterion, the query compound is considered out of range when the similarity value obtained is larger than the 95th percentile of the values obtained for the training set. The final outcome is a number in the range of 0-6 that expresses the number of unmet similarity criteria and allows classifying the query compound within seven reliability categories. Such categories can be further exploited to assign simpler reliability classes using a traffic light schema, to assign approximate confidence intervals or to mark the predictions as unreliable. The entire methodology has been validated simulating realistic conditions, where query compounds are structurally diverse from those in the training set. The validation exercise involved the construction of more than 1000 models. These models were built using a combination of training set, molecular descriptors, and modeling methods representative of the real predictive tasks performed in the eTOX project (a project whose objective is to predict in vivo toxicological end points in drug development). Validation results confirm the robustness of the proposed assessment methodology, which compares favorably with other classical methods based solely on the structural similarity of the compounds. ADAN characteristics make the method well-suited for estimate the quality of drug predictions obtained in extremely unfavorable conditions, like the prediction of drug toxicity end points. PMID:24821140

  5. A Mixed-Mode I/II Fracture Criterion and Its Application in Crack Growth Predictions

    NASA Technical Reports Server (NTRS)

    Sutton, Michael A.; Deng, Xiaomin; Ma, Fashang; Newman, James S., Jr.

    1999-01-01

    A crack tip opening displacement (CTOD)-based, mixed mode fracture criterion is developed for predicting the onset and direction of crack growth. The criterion postulates that crack growth occurs in either the Mode I or Mode II direction, depending on whether the maximum in either the opening or the shear component of CTOD, measured at a specified distance behind the crack tip, attains a critical value. For crack growth direction prediction, the proposed CTOD criterion is shown to be equivalent to seven commonly used crack growth criteria under linearly elastic and asymptotic conditions. Under elastic-plastic conditions the CTOD criterion's prediction of the dependence of the crack growth direction on the crack-up mode mixity is in excellent agreement with the Arcan test results. Furthermore, the CTOD criterion correctly predicts the existence of a crack growth transition from mode I to mode II as the mode mixity approaches the mode II loading condition. The proposed CTOD criterion has been implemented in finite element crack growth simulation codes Z1P2DL and FRANC2DL to predict the crack growth paths in (a) a modified Arcan test specimen and fixture made of AL 2024-T34 and (b) a double cantilever beam (DCB) specimen made of AL 7050. A series of crack growth simulations have been carried out for the crack growth tests in the Arcan and DCB specimens and the results further demonstrate the applicability of the mixed mode CTOD fracture criterion crack growth predictions and residual strength analyses for airframe materials.

  6. 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

  7. 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. PMID:24952468

  8. 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.

  9. 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. PMID:27100841

  10. 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

  11. 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. PMID:24358125

  12. 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.

  13. 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

  14. 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. PMID:27017041

  15. 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.

  16. 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

  17. The Cellular Thermal Shift Assay: A Novel Biophysical Assay for In Situ Drug Target Engagement and Mechanistic Biomarker Studies.

    PubMed

    Martinez Molina, Daniel; Nordlund, Pär

    2016-01-01

    A drug must engage its intended target to achieve its therapeutic effect. However, conclusively measuring target engagement (TE) in situ is challenging. This complicates preclinical development and is considered a key factor in the high rate of attrition in clinical trials. Here, we discuss a recently developed, label-free, biophysical assay, the cellular thermal shift assay (CETSA), which facilitates the direct assessment of TE in cells and tissues at various stages of drug development. CETSA also reveals biochemical events downstream of drug binding and therefore provides a promising means of establishing mechanistic biomarkers. The implementation of proteome-wide CETSA using quantitative mass spectrometry represents a novel strategy for defining off-target toxicity and polypharmacology and for identifying downstream mechanistic biomarkers. The first year of CETSA applications in the literature has focused on TE studies in cell culture systems and has confirmed the broad applicability of CETSA to many different target families. The next phase of CETSA applications will likely encompass comprehensive animal and patient studies, and CETSA will likely serve as a very valuable tool in many stages of preclinical and clinical drug development. PMID:26566155

  18. Selecting molecular therapeutic drug targets based on the expression profiles of intrahepatic cholangiocarcinomas and miRNA-mRNA regulatory networks.

    PubMed

    Sun, Boshi; Xie, Changming; Zheng, Tongsen; Yin, Dalong; Wang, Jiabei; Liang, Yingjian; Li, Yuejin; Yang, Guangchao; Shi, Huawen; Pei, Tiemin; Han, Jihua; Liu, Lianxin

    2016-01-01

    The incidence of intrahepatic cholangiocarcinoma (ICC) is increasing yearly, making it the second most common carcinoma after hepatocellular carcinoma among primary malignant liver tumors. Integrated miRNA and mRNA analysis is becoming more frequently used in antitumor ICC treatment. However, this approach generates vast amounts of data, which leads to difficulties performing comprehensive analyses to identify specific therapeutic drug targets. In this study, we provide an in-depth analysis of ICC function, identifying potential highly potent antitumor drugs for antitumor therapy. Two sets of whole genome expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Using modular bioinformatic analysis, six core functional modules were identified for ICC. Based on a Fisher's test of the Cmap small molecule drug database, 65 drug components were identified that regulated the genes of these six core modules. Literature mining was then used to identify 15 new potential antitumor drugs. PMID:26498995

  19. 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

  20. Neural network application for radionuclide modelling and prediction of radioactivity levels

    NASA Astrophysics Data System (ADS)

    Lynch, Myron Corbett, Jr.

    Existing applications of artificial neural networks in physics research and development have been analyzed as a basis for proposing new opportunities using that AI technology for data analysis in physics. A taxonomy was developed, based on an extensive literature search, for physics problems where neural network applications have been useful. Then, a particular use of neural networks was carried out to study ways to predict normal concentrations of radioactivity measured at monitoring stations in different geographic locations. The purpose of the data collection and analysis was to establish background levels that would serve as bases for detecting unusual levels of radioactivity, for example due to nuclear weapons testing, in these physical environments. Useful data sets were developed in this area and a process was discovered for modeling the background levels.

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

    PubMed Central

    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. PMID:26696900

  2. 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

  3. 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 Central

    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. PMID:25789858

  4. Ampicillin/penicillin-binding protein interactions as a model drug-target system to optimize affinity pull-down and mass spectrometric strategies for target and pathway identification.

    PubMed

    von Rechenberg, Moritz; Blake, Brian Kelly; Ho, Yew-Seng J; Zhen, Yuejun; Chepanoske, Cindy Lou; Richardson, Bonnie E; Xu, Nafei; Kery, Vladimir

    2005-05-01

    The identification and validation of the targets of active compounds identified in cell-based assays is an important step in preclinical drug development. New analytical approaches that combine drug affinity pull-down assays with mass spectrometry (MS) could lead to the identification of new targets and druggable pathways. In this work, we investigate a drug-target system consisting of ampicillin- and penicillin-binding proteins (PBPs) to evaluate and compare different amino-reactive resins for the immobilization of the affinity compound and mass spectrometric methods to identify proteins from drug affinity pull-down assays. First, ampicillin was immobilized onto various amino-reactive resins, which were compared in the ampicillin-PBP model with respect to their nonspecific binding of proteins from an Escherichia coli membrane extract. Dynal M-270 magnetic beads were chosen to further study the system as a model for capturing and identifying the targets of ampicillin, PBPs that were specifically and covalently bound to the immobilized ampicillin. The PBPs were identified, after in situ digestion of proteins bound to ampicillin directly on the beads, by using either one-dimensional (1-D) or two-dimensional (2-D) liquid chromatography (LC) separation techniques followed by tandem mass spectrometry (MS/MS) analysis. Alternatively, an elution with N-lauroylsarcosine (sarcosyl) from the ampicillin beads followed by in situ digestion and 2-D LC-MS/MS analysis identified proteins potentially interacting noncovalently with the PBPs or the ampicillin. The in situ approach required only little time, resources, and sample for the analysis. The combination of drug affinity pull-down assays with in situ digestion and 2-D LC-MS/MS analysis is a useful tool in obtaining complex information about a primary drug target as well as its protein interactors. PMID:15761956

  5. High-throughput transcriptomic and RNAi analysis identifies AIM1, ERGIC1, TMED3 and TPX2 as potential drug targets in prostate cancer.

    PubMed

    Vainio, Paula; Mpindi, John-Patrick; Kohonen, Pekka; Fey, Vidal; Mirtti, Tuomas; Alanen, Kalle A; Perälä, Merja; Kallioniemi, Olli; Iljin, Kristiina

    2012-01-01

    Prostate cancer is a heterogeneous group of diseases and there is a need for more efficient and targeted methods of treatment. In this study, the potential of gene expression data and RNA interference technique were combined to advance future personalized prostate cancer therapeutics. To distinguish the most promising in vivo prevalidated prostate cancer drug targets, a bioinformatic analysis was carried out using genome-wide gene expression data from 9873 human tissue samples. In total, 295 genes were selected for further functional studies in cultured prostate cancer cells due to their high mRNA expression in prostate, prostate cancer or in metastatic prostate cancer samples. Second, RNAi based cell viability assay was performed in VCaP and LNCaP prostate cancer cells. Based on the siRNA results, gene expression patterns in human tissues and novelty, endoplasmic reticulum function associated targets AIM1, ERGIC1 and TMED3, as well as mitosis regulating TPX2 were selected for further validation. AIM1, ERGIC1, and TPX2 were shown to be highly expressed especially in prostate cancer tissues, and high mRNA expression of ERGIC1 and TMED3 associated with AR and ERG oncogene expression. ERGIC1 silencing specifically regulated the proliferation of ERG oncogene positive prostate cancer cells and inhibited ERG mRNA expression in these cells, indicating that it is a potent drug target in ERG positive subgroup of prostate cancers. TPX2 expression associated with PSA failure and TPX2 silencing reduced PSA expression, indicating that TPX2 regulates androgen receptor mediated signaling. In conclusion, the combinatorial usage of microarray and RNAi techniques yielded in a large number of potential novel biomarkers and therapeutic targets, for future development of targeted and personalized approaches for prostate cancer management. PMID:22761906

  6. 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. PMID:26564235

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

    PubMed

    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

  8. 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.

  9. Density and elemental ratios of secondary organic aerosol: Application of a density prediction method

    NASA Astrophysics Data System (ADS)

    Nakao, Shunsuke; Tang, Ping; Tang, Xiaochen; Clark, Christopher H.; Qi, Li; Seo, Eric; Asa-Awuku, Akua; Cocker, David

    2013-04-01

    Organic material density is a fundamental parameter in aerosol science, yet direct measurement is not readily available. This study investigates density and elemental ratios of secondary organic aerosol (SOA) formed by the oxidation of 22 different volatile organic compounds with a wide range of molecular size (C5˜C15) in an environmental chamber. Reactants with a larger number of carbons yielded SOA with lower density (e.g., β-caryophyllene SOA: 1.22 g cm-3) compared with smaller ones (e.g., phenol SOA: 1.43 g cm-3) consistent with different extents of oxidation of the parent molecule. A recent study proposed a semi-empirical relationship between elemental ratios (O/C and H/C) and organic material density (Kuwata et al., 2012). The prediction method therein is evaluated against the large experimental data set of this study acquired in the UC Riverside/CE-CERT environmental chamber. The predicted particle densities agree with experimental measurements within 12% as stated by Kuwata et al. (2012) except for C6 compounds (benzene, phenol, and catechol). Therefore, the range of application has been further extended to include anthropogenic (aromatic) systems. The effects of nitrogen and sulfur on the density prediction remain unclear.

  10. Application of the Baseline Rotonet system to the prediction of helicopter tone noise

    NASA Technical Reports Server (NTRS)

    Golub, R. A.; Weir, D. S.; Tracy, M. B.

    1986-01-01

    The capabilities of the baseline Rotonet system designed to predict helicopter noise are analyzed. The modules of the system utilized for main and tail rotor geometry and blade section aerodynamic characteristics, for analyses, and for source-to-observer geometry, and atmospheric and ground effects calculations are described; a diagram of the system is provided. The Rotonet system produces axial force, tone noise, and sound pressure level information and a one third octave spectrum related to rotor tone noise and broadband noise sources. Main rotor noise predictions are compared with flight data. It is observed that both sets of data reveal increase loading on the advancing side and decrease loading on the retreating side. The tone noise and sound pressure levels for the first and second harmonics correlate well with the flight data; however, there is only fair agreement for the third harmonics of the sound pressure level. Analysis of the spectra display lower noise levels for higher altitudes and lower speeds. It is noted that the baseline Rotonet system is applicable for predicting performance and noise signatures for the lower harmonics. A phase II Rotonet system for evaluating higher harmonics is being developed.

  11. New applications of computer-based section construction: strain analysis, local balancing, and subsurface fault prediction

    SciTech Connect

    Geiser, J.; Geiser, P.A.; Kligfield, R.; Ratliff, R.; Rowan, M.

    1988-04-01

    An increase in the use of computers in structural geology now encourages practical investigation of several topics which are of considerable importance to the explorationist. Computer-based cross section construction and analysis is one such application. Algorithms based on the geometry of flexural slip or flow deformation styles permit rapid construction restoration, and balancing of geological cross sections, which in turn allow evaluation of multiple working hypotheses in a time frame previously unattainable. These same techniques also simplify the application of several analytical methods which have tended to be restricted to structural geologists: predicted finite and incremental strain patterns within folds can be utilized in studies of porosity and permeability variation; the detailed geometry of fold can be evaluated and modified using local balancing methods when constraints provided by well, seismic, and surface data leave room for differing interpretations; and subsurface fault trajectories can be quickly and accurately predicted from knowledge or near-surface fold geometry. These and other methods discussed in the text permit the non-specialist to apply complex structural concepts of exploration in a practical and timely manner.

  12. A MITgcm/DART Ocean Analysis and Prediction System with Application to the Gulf of Mexico

    NASA Astrophysics Data System (ADS)

    Hoteit, I.; Hoar, T.; Collins, N.; Anderson, J.; Cornuelle, B.; Heimbach, P.

    2008-12-01

    The ECCO system is a new generation of ocean assimilation systems based on the Massachusetts Institute of Technology general circulation model (MITgcm) and its adjoint. The system has been used to produce the first global 1° ocean state estimates. It is now also used for regional and coastal MITgcm applications. To improve the predictive capabilities of the ECCO system, the Data Assimilation Research Testbed (DART), which is an ensemble Kalman filter (EnKF)-based data assimilation package, has been recently integrated to the ECCO system. DART is a software facility employing different EnKFs and advanced inflation/localization schemes. It has been developed at the National Center of Atmospheric Research (NCAR) and is now used for different operational weather forecasting problems. This contribution describes the integration of DART and the MITgcm, and discusses how this ensemble-based system can complement the existing adjoint-based assimilation system. An example of a 1/10° MITgcm/DART application for predicting the evolution of the loop current in the Gulf of Mexico is presented.

  13. 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

  14. Predicting and measuring environmental concentration of pesticides in air after soil application.

    PubMed

    Ferrari, Federico; Trevisan, Marco; Capri, Ettore

    2003-01-01

    Pesticides can volatilize into the atmosphere, which affects the air quality. The ability to predict pesticide volatilization is an essential tool for human risk and environmental assessment. Even though there are several mathematical models to assess and predict the fate of pesticides in different compartments of the environment, there is no reliable model to predict volatilization. The objectives of this study were to evaluate pesticide volatilization under agricultural conditions using malathion [ O,O-dimethyl-S-(1,2-dicarbethoxyethyl)-dithiophosphate], ethoprophos (O-ethyl S,S-dipropylphosphorodithioate), and procymidone [N-(3,5-dichlorophenyl)-1,2-dimethylcyclopropane-1,2-dicarboximide] as test compounds and to evaluate the ability of the Pesticide Leaching Model (PELMO) to calculate the predicted environmental concentrations of pesticides in air under field conditions. The volatilization rate of procymidone, malathion, and ethoprophos was determined in a field study during two different periods (December 1998 and September 1999) using the Theoretical Profile Shape (TPS) method. The experiments were performed on bare silty soil in the Bologna province, Italy. Residues in the air were continuously monitored for 2 to 3 wk after the pesticide applications. The amount of pesticide volatilized was 16, 5, and 11% in December 1998 and 41, 23, and 19% in September 1999 for procymidone, malathion, and ethoprophos, respectively. In both these experiments, the PELMO simulations of the concentration of ethoprophos and procymidone were in good agreement with the measured data (factor +/- 1.1 on average). The volatilization of malathion was underestimated by a factor of 30 on average. These results suggest that volatilization described by PELMO may be reliable for volatile substances, but PELMO may underpredict volatilization for less-volatile substances. PMID:14535302

  15. 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. PMID:27144276

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  1. 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. PMID:27583971

  2. Estimation of significant solvent concentration ranges and its application to the enhancement of the accuracy of gradient predictions.

    PubMed

    Vivó-Truyols, G; Torres-Lapasió, J R; García-Alvarez-Coque, M C

    2004-11-19

    The solvent concentration range actually useful for gradient predictions is significantly narrower than the total range scanned in a gradient run. This range, called "solvent informative range" (SIR), if known with the highest accuracy, allows to predict gradient retention times (t(g) with minimal error. The small size of the SIR supports the application of the linear solvent strength theory (LSST). Furthermore, LSST allows a closed-form solution to the integral required to predict gradient retention times, which eliminates numerical integration, needed with other retention models. A methodology that calculates the SIR by applying error analysis, and uses it to improve the accuracy in the prediction of t(g) from isocratic experiments, is proposed. The importance of those mobile-phase compositions that do not contribute significantly to the prediction of t(g) is selectively attenuated within the prediction algorithm, relying the predictions more heavily on the SIR. As a result, t(g) was found to be predicted with similar accuracy using isocratic training data with regard to predictions based on gradient training data. The approach is useful for all situations where the chromatographer is able to provide predictions of retention at constant solvent concentration, and wish to predict the retention in gradient mode. PMID:15584220

  3. 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. PMID:23920700

  4. 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.

  5. 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. PMID:24505723

  6. Model predictive control application to spacecraft rendezvous in mars sample return scenario

    NASA Astrophysics Data System (ADS)

    Saponara, M.; Barrena, V.; Bemporad, A.; Hartley, E. N.; Maciejowski, J.; Richards, A.; Tramutola, A.; Trodden, P.

    2013-12-01

    Model Predictive Control (MPC) is an optimization-based control strategy that is considered extremely attractive in the autonomous space rendezvous scenarios. The Online Recon¦guration Control System and Avionics Architecture (ORCSAT) study addresses its applicability in Mars Sample Return (MSR) mission, including the implementation of the developed solution in a space representative avionic architecture system. With respect to a classical control solution High-integrity Autonomous RendezVous and Docking control system (HARVD), MPC allows a signi¦cant performance improvement both in trajectory and in propellant save. Furthermore, thanks to the online optimization, it allows to identify improvements in other areas (i. e., at mission de¦nition level) that could not be known a priori.

  7. Application of Phase-field Method in Predicting Gas Bubble Microstructure Evolution in Nuclear Fuels

    SciTech Connect

    Hu, Shenyang Y.; Li, Yulan; Sun, Xin; Gao, Fei; Devanathan, Ramaswami; Henager, Charles H.; Khaleel, Mohammad A.

    2010-04-30

    Fission product accumulation and gas bubble microstructure evolution in nuclear fuels strongly affect thermo-mechanical properties such as thermal conductivity, gas release, volumetric swelling and cracking, and hence the fuel performance. In this paper, a general phase-field model is developed to predict gas bubble formation and evolution. Important materials processes and thermodynamic properties including the generation of gas atoms and vacancies, sinks for vacancies and gas atoms, the elastic interaction among defects, gas re-solution, and inhomogeneity of elasticity and diffusivity are accounted for in the model. The simulations demonstrate the potential application of the phase-field method in investigating 1) heterogeneous nucleation of gas bubbles at defects; 2) effect of elastic interaction, inhomogeneity of material properties, and gas re-solution on gas bubble microstructures; and 3) effective properties from the output of phase-field simulations such as distribution of defects, gas bubbles, and stress fields.

  8. Avionic Architecture for Model Predictive Control Application in Mars Sample & Return Rendezvous Scenario

    NASA Astrophysics Data System (ADS)

    Saponara, M.; Tramutola, A.; Creten, P.; Hardy, J.; Philippe, C.

    2013-08-01

    Optimization-based control techniques such as Model Predictive Control (MPC) are considered extremely attractive for space rendezvous, proximity operations and capture applications that require high level of autonomy, optimal path planning and dynamic safety margins. Such control techniques require high-performance computational needs for solving large optimization problems. The development and implementation in a flight representative avionic architecture of a MPC based Guidance, Navigation and Control system has been investigated in the ESA R&T study “On-line Reconfiguration Control System and Avionics Architecture” (ORCSAT) of the Aurora programme. The paper presents the baseline HW and SW avionic architectures, and verification test results obtained with a customised RASTA spacecraft avionics development platform from Aeroflex Gaisler.

  9. A nonlinear modeling approach using weighted piecewise series and its applications to predict unsteady flows

    NASA Astrophysics Data System (ADS)

    Yao, Weigang; Liou, Meng-Sing

    2016-08-01

    To preserve nonlinearity of a full-order system over a range of parameters of interest, we propose an accurate and robust nonlinear modeling approach by assembling a set of piecewise linear local solutions expanded about some sampling states. The work by Rewienski and White [1] on micromachined devices inspired our use of piecewise linear local solutions to study nonlinear unsteady aerodynamics. These local approximations are assembled via nonlinear weights of radial basis functions. The efficacy of the proposed procedure is validated for a two-dimensional airfoil moving with different pitching motions, specifically AGARD's CT2 and CT5 problems [27], in which the flows exhibit different nonlinear behaviors. Furthermore, application of the developed aerodynamic model to a two-dimensional aero-elastic system proves the approach is capable of predicting limit cycle oscillations (LCOs) by using AGARD's CT6 [28] as a benchmark test. All results, based on inviscid solutions, confirm that our nonlinear model is stable and accurate, against the full model solutions and measurements, and for predicting not only aerodynamic forces but also detailed flowfields. Moreover, the model is robust for inputs that considerably depart from the base trajectory in form and magnitude. This modeling provides a very efficient way for predicting unsteady flowfields with varying parameters because it needs only a tiny fraction of the cost of a full-order modeling for each new condition-the more cases studied, the more savings rendered. Hence, the present approach is especially useful for parametric studies, such as in the case of design optimization and exploration of flow phenomena.

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

    PubMed

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

    2013-01-01

    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. PMID:24351574

  11. Application of remote sensing for prediction and detection of thermal pollution, phase 2

    NASA Technical Reports Server (NTRS)

    Veziroglu, T. N.; Lee, S. S.

    1975-01-01

    The development of a predictive mathematical model for thermal pollution in connection with remote sensing measurements was continued. A rigid-lid model has been developed and its application to far-field study has been completed. The velocity and temperature fields have been computed for different atmospheric conditions and for different boundary currents produced by tidal effects. In connection with the theoretical work, six experimental studies of the two sites in question (Biscayne Bay site and Hutchinson Island site) have been carried out. The temperature fields obtained during the tests at the Biscayne Bay site have been compared with the predictions of the rigid-lid model and these results are encouraging. The rigid-lid model is also being applied to near-field study. Preliminary results for a simple case have been obtained and execution of more realistic cases has been initiated. The development of a free-surface model also been initiated. The governing equations have been formulated and the computer programs have been written.

  12. Neurobiological markers predicting treatment response in anxiety disorders: A systematic review and implications for clinical application.

    PubMed

    Lueken, Ulrike; Zierhut, Kathrin C; Hahn, Tim; Straube, Benjamin; Kircher, Tilo; Reif, Andreas; Richter, Jan; Hamm, Alfons; Wittchen, Hans-Ulrich; Domschke, Katharina

    2016-07-01

    Anxiety disorders constitute the largest group of mental disorders with a high individual and societal burden. Neurobiological markers of treatment response bear potential to improve response rates by informing stratified medicine approaches. A systematic review was performed on the current evidence of the predictive value of genetic, neuroimaging and other physiological markers for treatment response (pharmacological and/or psychotherapeutic treatment) in anxiety disorders. Studies published until March 2015 were selected through search in PubMed, Web of Science, PsycINFO, Embase, and CENTRAL. Sixty studies were included, among them 27 on genetic, 17 on neuroimaging and 16 on other markers. Preliminary evidence was found for the functional 5-HTTLPR/rs25531 genotypes, anterior cingulate cortex function and cardiovascular flexibility to modulate treatment outcome. Studies varied considerably in methodological quality. Application of more stringent study methodology, predictions on the individual patient level and cross-validation in independent samples are recommended to set the next stage of biomarker research and to avoid flawed conclusions in the emerging field of "Mental Health Predictomics". PMID:27168345

  13. 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, propeller performance, and the nacelle forebody pressure and viscous drag distributions. The computer analysis is applicable to single and coaxial counterrotating propellers. The blade geometries can include spanwise variations in sweep, droop, taper, thickness, and airfoil section type. In the coaxial mode of operation the analysis can treat both equal and unequal blade number and rotational speeds on the propeller disks. The nacelle portion of the analysis can treat both free air and tunnel wall configurations including wall bleed. The analysis was applied to many different sets of flight conditions using selected aerodynamic modeling options. The influence of different propeller nacelle-tunnel wall configurations was studied. Comparisons with available test data for both single and coaxial propeller configurations are presented along with a discussion of the results.

  14. Predicting Nitrogen in Streams: A Comparison of Two Estimates of Fertilizer Application

    NASA Astrophysics Data System (ADS)

    Mehaffey, M.; Neale, A.

    2011-12-01

    and uptake helping offset the impacts to water. To test the accuracy of our finer resolution nitrogen application data, we compare its ability to predict nitrogen concentrations in streams with the ability of the county sales data to do the same.

  15. Structure-Bioactivity Relationship for Benzimidazole Thiophene Inhibitors of Polo-Like Kinase 1 (PLK1), a Potential Drug Target in Schistosoma mansoni

    PubMed Central

    Long, Thavy; Neitz, R. Jeffrey; Beasley, Rachel; Kalyanaraman, Chakrapani; Suzuki, Brian M.; Jacobson, Matthew P.; Dissous, Colette; McKerrow, James H.; Drewry, David H.; Zuercher, William J.; Singh, Rahul; Caffrey, Conor R.

    2016-01-01

    Background Schistosoma flatworm parasites cause schistosomiasis, a chronic and debilitating disease of poverty in developing countries. Praziquantel is employed for treatment and disease control. However, its efficacy spectrum is incomplete (less active or inactive against immature stages of the parasite) and there is a concern of drug resistance. Thus, there is a need to identify new drugs and drug targets. Methodology/Principal Findings We show that RNA interference (RNAi) of the Schistosoma mansoni ortholog of human polo-like kinase (huPLK)1 elicits a deleterious phenotypic alteration in post-infective larvae (schistosomula or somules). Phenotypic screening and analysis of schistosomula and adult S. mansoni with small molecule inhibitors of huPLK1 identified a number of potent anti-schistosomals. Among these was a GlaxoSmithKline (GSK) benzimidazole thiophene inhibitor that has completed Phase I clinical trials for treatment of solid tumor malignancies. We then obtained GSKs Published Kinase Inhibitor Sets (PKIS) 1 and 2, and phenotypically screened an expanded series of 38 benzimidazole thiophene PLK1 inhibitors. Computational analysis of controls and PLK1 inhibitor-treated populations of somules demonstrated a distinctive phenotype distribution. Using principal component analysis (PCA), the phenotypes exhibited by these populations were mapped, visualized and analyzed through projection to a low-dimensional space. The phenotype distribution was found to have a distinct shape and topology, which could be elicited using cluster analysis. A structure-activity relationship (SAR) was identified for the benzimidazole thiophenes that held for both somules and adult parasites. The most potent inhibitors produced marked phenotypic alterations at 1–2 μM within 1 h. Among these were compounds previously characterized as potent inhibitors of huPLK1 in cell assays. Conclusions/Significance The reverse genetic and chemical SAR data support a continued investigation of Sm

  16. Network pharmacology-based prediction of the multi-target capabilities of the compounds in Taohong Siwu decoction, and their application in osteoarthritis

    PubMed Central

    ZHENG, CHUN-SONG; XU, XIAO-JIE; YE, HONG-ZHI; WU, GUANG-WEN; LI, XI-HAI; XU, HUI-FENG; LIU, XIAN-XIANG

    2013-01-01

    Taohong Siwu decoction (THSWD), a formulation prescribed in traditional Chinese medicine (TCM), has been widely used in the treatment of osteoarthritis (OA). TCM has the potential to prevent diseases, such as OA, in an integrative and holistic manner. However, the system-level characterization of the drug-target interactions of THSWD has not been elucidated. In the present study, we constructed a novel modeling system, by integrating chemical space, virtual screening and network pharmacology, to investigate the molecular mechanism of action of THSWD. The chemical distribution of the ligand database and the potential compound prediction demonstrated that THSWD, as a natural combinatorial chemical library, comprises abundant drug-like and lead-like compounds that may act as potential inhibitors for a number of important target proteins associated with OA. Moreover, the results of the ‘compound-target network’ analysis demonstrated that 19 compounds within THSWD were correlated with more than one target, whilst the maximum degree of correlation for the compounds was seven. Furthermore, the ‘target-disease network’ indicated that THSWD may potentially be effective against 69 diseases. These results may aid in the understanding of the use of THSWD as a multi-target therapy in OA. Moreover, they may be useful in establishing other pharmacological effects that may be brought about by THSWD. The in silico method used in this study has the potential to advance the understanding of the molecular mechanisms of TCM. PMID:23935733

  17. Applications Of Monte Carlo Radiation Transport Simulation Techniques For Predicting Single Event Effects In Microelectronics

    SciTech Connect

    Warren, Kevin; Reed, Robert; Weller, Robert; Mendenhall, Marcus; Sierawski, Brian; Schrimpf, Ronald

    2011-06-01

    MRED (Monte Carlo Radiative Energy Deposition) is Vanderbilt University's Geant4 application for simulating radiation events in semiconductors. Geant4 is comprised of the best available computational physics models for the transport of radiation through matter. In addition to basic radiation transport physics contained in the Geant4 core, MRED has the capability to track energy loss in tetrahedral geometric objects, includes a cross section biasing and track weighting technique for variance reduction, and additional features relevant to semiconductor device applications. The crucial element of predicting Single Event Upset (SEU) parameters using radiation transport software is the creation of a dosimetry model that accurately approximates the net collected charge at transistor contacts as a function of deposited energy. The dosimetry technique described here is the multiple sensitive volume (MSV) model. It is shown to be a reasonable approximation of the charge collection process and its parameters can be calibrated to experimental measurements of SEU cross sections. The MSV model, within the framework of MRED, is examined for heavy ion and high-energy proton SEU measurements of a static random access memory.

  18. Cloud Condensation Nuclei Prediction Error from Application of Kohler Theory: Importance for the Aerosol Indirect Effect

    NASA Technical Reports Server (NTRS)

    Sotiropoulou, Rafaella-Eleni P.; Nenes, Athanasios; Adams, Peter J.; Seinfeld, John H.

    2007-01-01

    In situ observations of aerosol and cloud condensation nuclei (CCN) and the GISS GCM Model II' with an online aerosol simulation and explicit aerosol-cloud interactions are used to quantify the uncertainty in radiative forcing and autoconversion rate from application of Kohler theory. Simulations suggest that application of Koehler theory introduces a 10-20% uncertainty in global average indirect forcing and 2-11% uncertainty in autoconversion. Regionally, the uncertainty in indirect forcing ranges between 10-20%, and 5-50% for autoconversion. These results are insensitive to the range of updraft velocity and water vapor uptake coefficient considered. This study suggests that Koehler theory (as implemented in climate models) is not a significant source of uncertainty for aerosol indirect forcing but can be substantial for assessments of aerosol effects on the hydrological cycle in climatically sensitive regions of the globe. This implies that improvements in the representation of GCM subgrid processes and aerosol size distribution will mostly benefit indirect forcing assessments. Predictions of autoconversion, by nature, will be subject to considerable uncertainty; its reduction may require explicit representation of size-resolved aerosol composition and mixing state.

  19. High applicability of two-dimensional phosphorous in Kagome lattice predicted from first-principles calculations.

    PubMed

    Chen, Peng-Jen; Jeng, Horng-Tay

    2016-01-01

    A new semiconducting phase of two-dimensional phosphorous in the Kagome lattice is proposed from first-principles calculations. The band gaps of the monolayer (ML) and bulk Kagome phosphorous (Kagome-P) are 2.00 and 1.11 eV, respectively. The magnitude of the band gap is tunable by applying the in-plane strain and/or changing the number of stacking layers. High optical absorption coefficients at the visible light region are predicted for multilayer Kagome-P, indicating potential applications for solar cell devices. The nearly dispersionless top valence band of the ML Kagome-P with high density of states at the Fermi level leads to superconductivity with Tc of ~9 K under the optimal hole doping concentration. We also propose that the Kagome-P can be fabricated through the manipulation of the substrate-induced strain during the process of the sample growth. Our work demonstrates the high applicability of the Kagome-P in the fields of electronics, photovoltaics, and superconductivity. PMID:26980060

  20. High applicability of two-dimensional phosphorous in Kagome lattice predicted from first-principles calculations

    PubMed Central

    Chen, Peng-Jen; Jeng, Horng-Tay

    2016-01-01

    A new semiconducting phase of two-dimensional phosphorous in the Kagome lattice is proposed from first-principles calculations. The band gaps of the monolayer (ML) and bulk Kagome phosphorous (Kagome-P) are 2.00 and 1.11 eV, respectively. The magnitude of the band gap is tunable by applying the in-plane strain and/or changing the number of stacking layers. High optical absorption coefficients at the visible light region are predicted for multilayer Kagome-P, indicating potential applications for solar cell devices. The nearly dispersionless top valence band of the ML Kagome-P with high density of states at the Fermi level leads to superconductivity with Tc of ~9 K under the optimal hole doping concentration. We also propose that the Kagome-P can be fabricated through the manipulation of the substrate-induced strain during the process of the sample growth. Our work demonstrates the high applicability of the Kagome-P in the fields of electronics, photovoltaics, and superconductivity. PMID:26980060

  1. Verification of Numerical Weather Prediction Model Results for Energy Applications in Latvia

    NASA Astrophysics Data System (ADS)

    Sīle, Tija; Cepite-Frisfelde, Daiga; Sennikovs, Juris; Bethers, Uldis

    2014-05-01

    A resolution to increase the production and consumption of renewable energy has been made by EU governments. Most of the renewable energy in Latvia is produced by Hydroelectric Power Plants (HPP), followed by bio-gas, wind power and bio-mass energy production. Wind and HPP power production is sensitive to meteorological conditions. Currently the basis of weather forecasting is Numerical Weather Prediction (NWP) models. There are numerous methodologies concerning the evaluation of quality of NWP results (Wilks 2011) and their application can be conditional on the forecast end user. The goal of this study is to evaluate the performance of Weather Research and Forecast model (Skamarock 2008) implementation over the territory of Latvia, focusing on forecasting of wind speed and quantitative precipitation forecasts. The target spatial resolution is 3 km. Observational data from Latvian Environment, Geology and Meteorology Centre are used. A number of standard verification metrics are calculated. The sensitivity to the model output interpretation (output spatial interpolation versus nearest gridpoint) is investigated. For the precipitation verification the dichotomous verification metrics are used. Sensitivity to different precipitation accumulation intervals is examined. Skamarock, William C. and Klemp, Joseph B. A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. Journal of Computational Physics. 227, 2008, pp. 3465-3485. Wilks, Daniel S. Statistical Methods in the Atmospheric Sciences. Third Edition. Academic Press, 2011.

  2. Application of the cell potential method to predict phase equilibria of multicomponent gas hydrate systems.

    PubMed

    Anderson, Brian J; Bazant, Martin Z; Tester, Jefferson W; Trout, Bernhardt L

    2005-04-28

    We present the application of a mathematical method reported earlier by which the van der Waals-Platteeuw statistical mechanical model with the Lennard-Jones and Devonshire approximation can be posed as an integral equation with the unknown function being the intermolecular potential between the guest molecules and the host molecules. This method allows us to solve for the potential directly for hydrates for which the Langmuir constants are computed, either from experimental data or from ab initio data. Given the assumptions made in the van der Waals-Platteeuw model with the spherical-cell approximation, there are an infinite number of solutions; however, the only solution without cusps is a unique central-well solution in which the potential is at a finite minimum at the center to the cage. From this central-well solution, we have found the potential well depths and volumes of negative energy for 16 single-component hydrate systems: ethane (C2H6), cyclopropane (C3H6), methane (CH4), argon (Ar), and chlorodifluoromethane (R-22) in structure I; and ethane (C2H6), cyclopropane (C3H6), propane (C3H8), isobutane (C4H10), methane (CH4), argon (Ar), trichlorofluoromethane (R-11), dichlorodifluoromethane (R-12), bromotrifluoromethane (R-13B1), chloroform (CHCl3), and 1,1,1,2-tetrafluoroethane (R-134a) in structure II. This method and the calculated cell potentials were validated by predicting existing mixed hydrate phase equilibrium data without any fitting parameters and calculating mixture phase diagrams for methane, ethane, isobutane, and cyclopropane mixtures. Several structural transitions that have been determined experimentally as well as some structural transitions that have not been examined experimentally were also predicted. In the methane-cyclopropane hydrate system, a structural transition from structure I to structure II and back to structure I is predicted to occur outside of the known structure II range for the cyclopropane hydrate. Quintuple (L

  3. Proteomic analysis of two metabolic proteins with potential to translocate to plasma membrane associated with tumor metastasis development and drug targets.

    PubMed

    Xue, Ting; Zhang, Yan; Zhang, Luofu; Yao, Ling; Hu, Xiaofang; Xu, Lisa X

    2013-04-01

    Metastasis is the main cause for death of breast cancer patients. However, the underlying mechanism is still poorly understood. Plasma membrane (PM) proteins play a key role in various biological processes, especially for cell migration. In this study, we used a set of well-characterized mammary mouse cell lines, 67NR, 168FARN, 4T1, representing the metastatic progression, to study the differentially expressed membrane proteins. These proteins were analyzed by a linear ion trap tandem mass spectrometry (LTQ-MS/MS) following cell surface biotinylation and streptavidin purification. A total of 1667 membrane proteins were identified, out of which 472 were characterized as differentially expressed with at least 2-fold change and p-value < 0.01. Functional clustering of the 472 proteins revealed that 178 of them were metabolic proteins. Finally, we focused on two metabolic proteins, fatty acid synthase (FASN) and NAD(P)H steroid dehydrogenase-like protein (NSDHL), which were validated by Western blot and immunofluorescence. We found that FASN and NSDHL translocated to the plasma membrane from the intracellular compartment, and their expressions increased from 67NR to 4T1. This alteration of localization along with differential expressions might be necessary for metastasis development. Potentially, FASN and NSDHL could serve as drug targets in new antimetastasis therapy. PMID:23445495

  4. Novel high throughput pooled shRNA screening identifies NQO1 as a potential drug target for host directed therapy for tuberculosis

    PubMed Central

    Li, Qing; Karim, Ahmad F.; Ding, Xuedong; Das, Biswajit; Dobrowolski, Curtis; Gibson, Richard M.; Quiñones-Mateu, Miguel E.; Karn, Jonathan; Rojas, Roxana E.

    2016-01-01

    Chemical regulation of macrophage function is one key strategy for developing host-directed adjuvant therapies for tuberculosis (TB). A critical step to develop these therapies is the identification and characterization of specific macrophage molecules and pathways with a high potential to serve as drug targets. Using a barcoded lentivirus-based pooled short-hairpin RNA (shRNA) library combined with next generation sequencing, we identified 205 silenced host genes highly enriched in mycobacteria-resistant macrophages. Twenty-one of these “hits” belonged to the oxidoreductase functional category. NAD(P)H:quinone oxidoreductase 1 (NQO1) was the top oxidoreductase “hit”. NQO1 expression was increased after mycobacterial infection, and NQO1 knockdown increased macrophage differentiation, NF-κB activation, and the secretion of pro-inflammatory cytokines TNF-α and IL-1β in response to infection. This suggests that mycobacteria hijacks NQO1 to down-regulate pro-inflammatory and anti-bacterial functions. The competitive inhibitor of NQO1 dicoumarol synergized with rifampin to promote intracellular killing of mycobacteria. Thus, NQO1 is a new host target in mycobacterial infection that could potentially be exploited to increase antibiotic efficacy in vivo. Our findings also suggest that pooled shRNA libraries could be valuable tools for genome-wide screening in the search for novel druggable host targets for adjunctive TB therapies. PMID:27297123

  5. Gene Network Analysis of Metallo Beta Lactamase Family Proteins Indicates the Role of Gene Partners in Antibiotic Resistance and Reveals Important Drug Targets.

    PubMed

    Parimelzaghan, Anitha; Anbarasu, Anand; Ramaiah, Sudha

    2016-06-01

    Metallo Beta (β) Lactamases (MBL) are metal dependent bacterial enzymes that hydrolyze the β-lactam antibiotics. In recent years, MBL have received considerable attention because it inactivates most of the β-lactam antibiotics. Increase in dissemination of MBL encoding antibiotic resistance genes in pathogenic bacteria often results in unsuccessful treatments. Gene interaction network of MBL provides a complete understanding on the molecular basis of MBL mediated antibiotic resistance. In our present study, we have constructed the MBL network of 37 proteins with 751 functional partners from pathogenic bacterial spp. We found 12 highly interconnecting clusters. Among the 37 MBL proteins considered in the present study, 22 MBL proteins are from B3 subclass, 14 are from B1 subclass and only one is from B2 subclass. Global topological parameters are used to calculate and compare the probability of interactions in MBL proteins. Our results indicate that the proteins associated within the network have a strong influence in antibiotic resistance mechanism. Interestingly, several drug targets are identified from the constructed network. We believe that our results would be helpful for researchers exploring MBL-mediated antibiotic resistant mechanisms. J. Cell. Biochem. 117: 1330-1339, 2016. © 2015 Wiley Periodicals, Inc. PMID:26517410

  6. Simulation of magnetic drug targeting through tracheobronchial airways in the presence of an external non-uniform magnetic field using Lagrangian magnetic particle tracking

    NASA Astrophysics Data System (ADS)

    Pourmehran, O.; Rahimi-Gorji, M.; Gorji-Bandpy, M.; Gorji, T. B.

    2015-11-01

    Drug delivery technologies are an important area within biomedicine. Targeted drug delivery aims to reduce the undesired side effects of drug usage by directing or capturing the active agents near a desired site within the body. Herein, a numerical investigation of magnetic drug targeting (MDT) using aerosol drugs named polystyrene particle (PMS40) in human lung is presented considering one-way coupling on the transport and capture of the magnetic particle. A realistic 3D geometry based on CT scan images is provided for CFD simulation. An external non-uniform magnetic field is applied. Parametric investigation is conducted and the influence of particle diameter, magnetic source position, and magnetic number (Mn) on the deposition efficiency and particle behavior is reported. According to the results, the magnetic field increased deposition efficiency of particles in a target region, the efficiency of deposition and MDT technique has a direct relation with increasing the particle diameter for magnetic number of 1 Tesla (T) and lower (Mn≤1(T)). Also it can be seen that there is an inverse relation between the particle diameter and deposition efficiency when Mn is more than 1 (T).

  7. in Silico analysis of Escherichia coli polyphosphate kinase (PPK) as a novel antimicrobial drug target and its high throughput virtual screening against PubChem library

    PubMed Central

    Saha, Saurav Bhaskar; Verma, Vivek

    2013-01-01

    Multiple drug resistance (MDR) in bacteria is a global health challenge that needs urgent attention. The 2011 outbreak caused by Escherichia coli O104:H4 in Europe has exposed the inability of present antibiotic arsenal to tackle the problem of antimicrobial infections. It has further posed a tremendous burden on entire pharmaceutical industry to find novel drugs and/or drug targets. Polyphosphate kinase (PPK) in bacteria plays a crucial role in helping latter to adapt to stringent conditions of low nutritional availability thus making it a good target for antibacterials. In spite of this critical role, to best of our knowledge no in-silico work has been carried out to develop PPK as an antibiotic target. In the present study, virtual screening of PPK was carried out against all the 3D compounds with pharmacological action present in PubChem database. Our screening results were further refined by interaction maps to eliminate the false positive data respectively. From our results, compound number 5281927 (PubChem ID) has been found to have significant affinity towards affinity towards PPK active ATP-binding site indicating its therapeutic relevance. PMID:23861568

  8. Prediction of circulation control performance characteristics for Super STOL and STOL applications

    NASA Astrophysics Data System (ADS)

    Naqvi, Messam Abbas

    due to the lack of a simple prediction capability. This research effort was focused on the creation of a rapid prediction capability of Circulation Control Aerodynamic Characteristics which could help designers with rapid performance estimates for design space exploration. A morphological matrix was created with the available set of options which could be chosen to create this prediction capability starting with purely analytical physics based modeling to high fidelity CFD codes. Based on the available constraints, and desired accuracy meta-models have been created around the two dimensional circulation control performance results computed using Navier Stokes Equations (Computational Fluid Dynamics). DSS2, a two dimensional RANS code written by Professor Lakshmi Sankar was utilized for circulation control airfoil characteristics. The CFD code was first applied to the NCCR 1510-7607N airfoil to validate the model with available experimental results. It was then applied to compute the results of a fractional factorial design of experiments array. Metamodels were formulated using the neural networks to the results obtained from the Design of Experiments. Additional validation runs were performed to validate the model predictions. Metamodels are not only capable of rapid performance prediction, but also help generate the relation trends of response matrices with control variables and capture the complex interactions between control variables. Quantitative as well as qualitative assessments of results were performed by computation of aerodynamic forces & moments and flow field visualizations. Wing characteristics in three dimensions were obtained by integration over the whole wing using Prandtl's Wing Theory. The baseline Super STOL configuration [3] was then analyzed with the application of circulation control technology. The desired values of lift and drag to achieve the target values of Takeoff & Landing performance were compared with the optimal configurations obtained

  9. Drug targeting through pilosebaceous route.

    PubMed

    Chourasia, Rashmi; Jain, Sanjay K

    2009-10-01

    Local skin targeting is of interest for the pharmaceutical and the cosmetic industry. A topically applied substance has basically three possibilities to penetrate into the skin: transcellular, intercellular, and follicular. The transfollicular path has been largely ignored because hair follicles constitute only 0.1% of the total skin. The hair follicle is a skin appendage with a complex structure containing many cell types that produce highly specialised proteins. The hair follicle is in a continuous cycle: anagen is the hair growth phase, catagen the involution phase and telogen is the resting phase. Nonetheless, the hair follicle has great potential for skin treatment, owing to its deep extension into the dermis and thus provides much deeper penetration and absorption of compounds beneath the skin than seen with the transdermal route. In the case of skin diseases and of cosmetic products, delivery to sweat glands or to the pilosebaceous unit is essential for the effectiveness of the drug. Increased accumulation in the pilosebaceous unit could treat alopecia, acne and skin cancer more efficiently and improve the effect of cosmetic substances and nutrients. Therefore, we review herein various drug delivery systems, including liposomes, niosomes, microspheres, nanoparticles, nanoemulsions, lipid nanocarriers, gene therapy and discuss the results of recent researches. We also review the drugs which have been investigated for pilosebaceous delivery. PMID:19663765

  10. Novel drugs targeting transthyretin amyloidosis.

    PubMed

    Hanna, Mazen

    2014-03-01

    Transthyretin amyloidosis (ATTR) is either a hereditary disease related to a mutation in the transthyretin gene that leads to neuropathy and/or cardiomyopathy or an acquired disease of the elderly that leads to restrictive cardiomyopathy. The prevalence of this disease is higher than once thought and awareness is likely to increase amongst physicians and in particular cardiologists. Until recently there have been no treatment options for this disease except to treat the heart failure with diuretics and the neuropathy symptomatically. However, there are several emerging pharmacologic therapies designed to slow or stop the progression of ATTR. This article reviews novel therapeutic drugs that work at different points in the pathogenesis of this disease attempting to change its natural history and improve outcomes. PMID:24464360

  11. Drug targeting of leptin resistance.

    PubMed

    Santoro, Anna; Mattace Raso, Giuseppina; Meli, Rosaria

    2015-11-01

    Leptin regulates glucose, lipid and energy homeostasis as well as feeding behavior, serving as a bridge between peripheral metabolically active tissues and the central nervous system (CNS). Indeed, this adipocyte-derived hormone, whose circulating levels mirror fat mass, not only exerts its anti-obesity effects mainly modulating the activity of specific hypothalamic neurons expressing the long form of the leptin receptor (Ob-Rb), but it also shows pleiotropic functions due to the activation of Ob-Rb in peripheral tissues. Nevertheless, several mechanisms have been suggested to mediate leptin resistance, including obesity-associated hyperleptinemia, impairment of leptin access to CNS and the reduction in Ob-Rb signal transduction effectiveness, among others. During the onset and progression of obesity, the dampening of leptin sensitivity often occurs, preventing the efficacy of leptin replacement therapy from overcoming obesity and/or its comorbidities. This review focuses on obesity-associated leptin resistance and the mechanisms underpinning this condition, to highlight the relevance of leptin sensitivity restoration as a useful therapeutic strategy to treat common obesity and its complications. Interestingly, although promising strategies to counteract leptin resistance have been proposed, these pharmacological approaches have shown limited efficacy or even relevant adverse effects in preclinical and clinical studies. Therefore, the numerous findings from this review clearly indicate a lack of a single and efficacious treatment for leptin resistance, highlighting the necessity to find new therapeutic tools to improve leptin sensitivity, especially in patients with most severe disease profiles. PMID:26071010

  12. Prediction of acoustic scattering in the time domain and its applications to rotorcraft noise

    NASA Astrophysics Data System (ADS)

    Lee, Seongkyu

    This work aims at the development of a numerical method for the analysis of acoustic scattering in the time domain and its applications to rotorcraft noise. This purpose is achieved by developing two independent methods: (1) an analytical formulation of the pressure gradient for an arbitrary moving source and (2) a time-domain moving equivalent source method. First, the analytical formulation for the pressure gradient is developed to fulfill the boundary condition on a scattering surface to account for arbitrary moving incident sources. A semi-analytical formulation was derived from the gradient of the Ffowcs Williams-Hawkings (FW-H) equation. This formulation needs to calculate the observer time differentiation outside the integrals numerically. A numerical algorithm is developed to implement this formulation in an aeroacoustic prediction code. A new analytical formulation is presented in the thesis. In this formulation, the time differentiation is taken inside the integrals analytically. This formulation avoids the numerical time differentiation with respect to the observer time, which is computationally more efficient. The acoustic pressure gradient predicted by these two formulations is validated through comparison with available exact solutions for a stationary and moving monopole sources. The agreement between the predictions and exact solutions is excellent. One of the advantages of this analytic formulation is that it efficiently provides the boundary condition for the acoustic scattering of sound generated from an arbitrary moving source, such as rotating blades, which undergoes rotation, flapping and lead-lag motions. The formulation is applied to the rotor noise problems for two model rotors (UH-1H and HART-I). For HART-I rotor, CFD/CSD coupling was used to provide unsteady aerodynamics and trim solutions of the blade motion. A purely numerical approach is compared with the analytical formulations. The agreement between the analytical formulations and

  13. Accurate First-Principles Spectra Predictions for Planetological and Astrophysical Applications at Various T-Conditions

    NASA Astrophysics Data System (ADS)

    Rey, M.; Nikitin, A. V.; Tyuterev, V.

    2014-06-01

    Knowledge of near infrared intensities of rovibrational transitions of polyatomic molecules is essential for the modeling of various planetary atmospheres, brown dwarfs and for other astrophysical applications 1,2,3. For example, to analyze exoplanets, atmospheric models have been developed, thus making the need to provide accurate spectroscopic data. Consequently, the spectral characterization of such planetary objects relies on the necessity of having adequate and reliable molecular data in extreme conditions (temperature, optical path length, pressure). On the other hand, in the modeling of astrophysical opacities, millions of lines are generally involved and the line-by-line extraction is clearly not feasible in laboratory measurements. It is thus suggested that this large amount of data could be interpreted only by reliable theoretical predictions. There exists essentially two theoretical approaches for the computation and prediction of spectra. The first one is based on empirically-fitted effective spectroscopic models. Another way for computing energies, line positions and intensities is based on global variational calculations using ab initio surfaces. They do not yet reach the spectroscopic accuracy stricto sensu but implicitly account for all intramolecular interactions including resonance couplings in a wide spectral range. The final aim of this work is to provide reliable predictions which could be quantitatively accurate with respect to the precision of available observations and as complete as possible. All this thus requires extensive first-principles quantum mechanical calculations essentially based on three necessary ingredients which are (i) accurate intramolecular potential energy surface and dipole moment surface components well-defined in a large range of vibrational displacements and (ii) efficient computational methods combined with suitable choices of coordinates to account for molecular symmetry properties and to achieve a good numerical

  14. Performances and reliability predictions of optical data transmission links using a system simulator for aerospace applications

    NASA Astrophysics Data System (ADS)

    Bechou, L.; Deshayes, Y.; Aupetit-Berthelemot, C.; Guerin, A.; Tronche, C.

    Space missions for Earth Observation are called upon to carry a growing number of instruments in their payload, whose performances are increasing. Future space systems are therefore intended to generate huge amounts of data and a key challenge in coming years will therefore lie in the ability to transmit that significant quantity of data to ground. Thus very high data rate Payload Telemetry (PLTM) systems will be required to face the demand of the future Earth Exploration Satellite Systems and reliability is one of the major concern of such systems. An attractive approach associated with the concept of predictive modeling consists in analyzing the impact of components malfunctioning on the optical link performances taking into account the network requirements and experimental degradation laws. Reliability estimation is traditionally based on life-testing and a basic approach is to use Telcordia requirements (468GR) for optical telecommunication applications. However, due to the various interactions between components, operating lifetime of a system cannot be taken as the lifetime of the less reliable component. In this paper, an original methodology is proposed to estimate reliability of an optical communication system by using a dedicated system simulator for predictive modeling and design for reliability. At first, we present frameworks of point-to-point optical communication systems for space applications where high data rate (or frequency bandwidth), lower cost or mass saving are needed. Optoelectronics devices used in these systems can be similar to those found in terrestrial optical network. Particularly we report simulation results of transmission performances after introduction of DFB Laser diode parameters variations versus time extrapolated from accelerated tests based on terrestrial or submarine telecommunications qualification standards. Simulations are performed to investigate and predict the consequence of degradations of the Laser diode (acting as a

  15. Applications of the predictability of the Coherent Noise Model to aftershock sequences

    NASA Astrophysics Data System (ADS)

    Christopoulos, Stavros-Richard; Sarlis, Nicholas

    2014-05-01

    A study [1] of the coherent noise model [2-4] in natural time [5-7] has shown that it exhibits predictability. Interestingly, one of the predictors suggested [1] for the coherent noise model can be generalized and applied to the case of (real) aftershock sequences. The results obtained [8] so far are beyond chance. Here, we apply this approach to several aftershock sequences of strong earthquakes with magnitudes Mw ≥6.9 in Indonesia, California and Greece, including the Mw9.2 earthquake that occurred on 26 December 2004 in Sumatra. References. [1] N. V. Sarlis and S.-R. G. Christopoulos, Predictability of the coherent-noise model and its applications, Physical Review E, 85, 051136, 2012. [2] M.E.J. Newman, Self-organized criticality, evolution and the fossil extinction record, Proc. R. Soc. London B, 263, 1605-1610, 1996. [3] M. E. J. Newman and K. Sneppen, Avalanches, scaling, and coherent noise, Phys. Rev. E, 54, 6226-6231, 1996. [4] K. Sneppen and M. Newman, Coherent noise, scale invariance and intermittency in large systems, Physica D, 110, 209 - 222. [5] P. Varotsos, N. Sarlis, and E. Skordas, Spatiotemporal complexity aspects on the interrelation between Seismic Electric Signals and seismicity, Practica of Athens Academy, 76, 294-321, 2001. [6] P.A. Varotsos, N.V. Sarlis, and E.S. Skordas, Long-range correlations in the electric signals that precede rupture, Phys. Rev. E, 66, 011902, 2002. [7] Varotsos P. A., Sarlis N. V. and Skordas E. S., Natural Time Analysis: The new view of time. Precursory Seismic Electric Signals, Earthquakes and other Complex Time-Series (Springer-Verlag, Berlin Heidelberg) 2011. [8] N. V. Sarlis and S.-R. G. Christopoulos, "Visualization of the significance of Receiver Operating Characteristics based on confidence ellipses", Computer Physics Communications, http://dx.doi.org/10.1016/j.cpc.2013.12.009

  16. Application of a global hydrologic prediction system to the Zambezi River Basin (Invited)

    NASA Astrophysics Data System (ADS)

    Voisin, N.; Pappenberger, F.; Buizza, R.; Lettenmaier, D. P.

    2010-12-01

    We evaluate a 10-day globally applicable flood prediction scheme over the Zambezi River basin for the period 2003-2007. The hydrological core of the scheme is the Variable Infiltration Capacity (VIC) hydrology model, which we forced with the European Centre for Medium Range Weather Forecasts (ECMWF) temperature and wind analyses, and the near real-time Tropical Rainfall Monitoring Mission (TRMM) precipitation product (3B42RT) up to the day of forecast. During the forecast period, the VIC model was forced with calibrated and downscaled 10-day forecasts from the ECMWF ensemble prediction system (EPS). We also tested a parallel setup where the EPS ensemble forecasts were interpolated to the 0.25 degree spatial resolution of the hydrology model. Each set of forecasts was extended by 5 days using monthly mean climatological variables and zero precipitation in order to account for the effect of initial conditions (the EPS was extended to 15 days only in 2006). The 15-day spatially distributed ensemble runoff forecasts were then routed to several locations in the basin. Surrogates for observed daily runoff and streamflow were provided by the reference run, i.e. the VIC simulations forced with ECMWF analysis fields and TRMM precipitation. Mean forecast errors and skills for the two sets of ensemble forecasts are evaluated with respect to the reference on a seasonal basis, and are compared to previous results from a similarly designed study over the Ohio River Basin. The influence on forecast accuracy of basin drainage area, hydroclimatic diversity within the basin, and storm type on forecast skill scores is evaluated.

  17. Application of physicochemical properties and process parameters in the development of a neural network model for prediction of tablet characteristics.

    PubMed

    Sovány, Tamás; Papós, Kitti; Kása, Péter; Ilič, Ilija; Srčič, Stane; Pintye-Hódi, Klára

    2013-06-01

    The importance of in silico modeling in the pharmaceutical industry is continuously increasing. The aim of the present study was the development of a neural network model for prediction of the postcompressional properties of scored tablets based on the application of existing data sets from our previous studies. Some important process parameters and physicochemical characteristics of the powder mixtures were used as training factors to achieve the best applicability in a wide range of possible compositions. The results demonstrated that, after some pre-processing of the factors, an appropriate prediction performance could be achieved. However, because of the poor extrapolation capacity, broadening of the training data range appears necessary. PMID:23413109

  18. Application of GIS based data driven evidential belief function model to predict groundwater potential zonation

    NASA Astrophysics Data System (ADS)

    Nampak, Haleh; Pradhan, Biswajeet; Manap, Mohammad Abd

    2014-05-01

    The objective of this paper is to exploit potential application of an evidential belief function (EBF) model for spatial prediction of groundwater productivity at Langat basin area, Malaysia using geographic information system (GIS) technique. About 125 groundwater yield data were collected from well locations. Subsequently, the groundwater yield was divided into high (⩾11 m3/h) and low yields (<11 m3/h) respectively, based on the groundwater classification standard recommended by Department of Mineral and Geosciences (JMG), Malaysia. Out of all of the borehole data, only 60 wells possessed higher yield at ⩾ 11 m3/h. Further, these wells were randomly divided into a testing dataset 70% (42 wells) for training the model and the remaining 30% (18 wells) was used for validation purpose. To perform cross validation, the frequency ratio (FR) approach was applied into remaining groundwater wells with low yield to show the spatial correlation between the low potential zones of groundwater productivity. A total of twelve groundwater conditioning factors that affect the storage of groundwater occurrences were derived from various data sources such as satellite based imagery, topographic maps and associated database. Those twelve groundwater conditioning factors are elevation, slope, curvature, stream power index (SPI), topographic wetness index (TWI), drainage density, lithology, lineament density, land use, normalized difference vegetation index (NDVI), soil and rainfall. Subsequently, the Dempster-Shafer theory of evidence model was applied to prepare the groundwater potential map. Finally, the result of groundwater potential map derived from belief map was validated using testing data. Furthermore, to compare the performance of the EBF result, logistic regression model was applied. The success-rate and prediction-rate curves were computed to estimate the efficiency of the employed EBF model compared to LR method. The validation results demonstrated that the success

  19. On spatiotemporal series analysis and its application to predict the regional short term climate process

    NASA Astrophysics Data System (ADS)

    Wang, Geli; Yang, Peicai; Lü, Daren

    2004-04-01

    Based on the theory of reconstructing state space, a technique for spatiotemporal series prediction is presented. By means of this technique and NCEP/NCAR data of the monthly mean geopotential height anomaly of the 500-hPa isobaric surface in the Northern Hemisphere, a regional prediction experiment is also carried out. If using the correlation coefficient R between the observed field and the prediction field to measure the prediction accuracy, the averaged R given by 48 prediction samples reaches 21%, which corresponds to the current prediction level for the short range climate process.

  20. Crystal Structure Prediction and its Application in Earth and Materials Sciences

    NASA Astrophysics Data System (ADS)

    Zhu, Qiang

    First of all, we describe how to predict crystal structure by evolutionary approach, and extend this method to study the packing of organic molecules, by our specially designed constrained evolutionary algorithm. The main feature of this new approach is that each unit or molecule is treated as a whole body, which drastically reduces the search space and improves the efficiency. The improved method is possibly to be applied in the fields of (1) high pressure phase of simple molecules (H2O, NH3, CH4, etc); (2) pharmaceutical molecules (glycine, aspirin, etc); (3) complex inorganic crystals containing cluster or molecular unit, (Mg(BH4)2, Ca(BH4)2, etc). One application of the constrained evolutionary algorithm is given by the study of (Mg(BH4)2, which is a promising materials for hydrogen storage. Our prediction does not only reproduce the previous work on Mg(BH4)2 at ambient condition, but also yields two new tetragonal structures at high pressure, with space groups P4 and I41/acd are predicted to be lower in enthalpy, by 15.4 kJ/mol and 21.2 kJ/mol, respectively, than the earlier proposed P42nm phase. We have simulated X-ray diffraction spectra, lattice dynamics, and equations of state of these phases. The density, volume contraction, bulk modulus, and the simulated XRD patterns of P4 and I41/acd structures are in excellent agreement with the experimental results. Two kinds of oxides (Xe-O and Mg-O) have been studied under megabar pressures. For XeO, we predict the existence of thermodynamically stable Xe-O compounds at high pressures (XeO, XeO2 and XeO3 become stable at pressures of 83, 102 and 114 GPa, respectively). For Mg-O, our calculations find that two extraordinary compounds MgO2 and Mg3O 2 become thermodynamically stable at 116 GPa and 500 GPa, respectively. Our calculations indicate large charge transfer in these oxides for both systems, suggesting that large electronegativity difference and pressure are the key factors favouring their formations. We also

  1. A Computational Model for Predicting RNase H Domain of Retrovirus.

    PubMed

    Wu, Sijia; Zhang, Xinman; Han, Jiuqiang

    2016-01-01

    RNase H (RNH) is a pivotal domain in retrovirus to cleave the DNA-RNA hybrid for continuing retroviral replication. The crucial role indicates that RNH is a promising drug target for therapeutic intervention. However, annotated RNHs in UniProtKB database have still been insufficient for a good understanding of their statistical characteristics so far. In this work, a computational RNH model was proposed to annotate new putative RNHs (np-RNHs) in the retroviruses. It basically predicts RNH domains through recognizing their start and end sites separately with SVM method. The classification accuracy rates are 100%, 99.01% and 97.52% respectively corresponding to jack-knife, 10-fold cross-validation and 5-fold cross-validation test. Subsequently, this model discovered 14,033 np-RNHs after scanning sequences without RNH annotations. All these predicted np-RNHs and annotated RNHs were employed to analyze the length, hydrophobicity and evolutionary relationship of RNH domains. They are all related to retroviral genera, which validates the classification of retroviruses to a certain degree. In the end, a software tool was designed for the application of our prediction model. The software together with datasets involved in this paper can be available for free download at https://sourceforge.net/projects/rhtool/files/?source=navbar. PMID:27574780

  2. Applicability of the theory of thermodynamic similarity to predict the enthalpies of vaporization of aliphatic aldehydes

    NASA Astrophysics Data System (ADS)

    Esina, Z. N.; Korchuganova, M. R.

    2015-06-01

    The theory of thermodynamic similarity is used to predict the enthalpies of vaporization of aliphatic aldehydes. The predicted data allow us to calculate the phase diagrams of liquid-vapor equilibrium in a binary water-aliphatic aldehyde system.

  3. Applications of Population Genetics to Animal Breeding, from Wright, Fisher and Lush to Genomic Prediction

    PubMed Central

    Hill, William G.

    2014-01-01

    Although animal breeding was practiced long before the science of genetics and the relevant disciplines of population and quantitative genetics were known, breeding programs have mainly relied on simply selecting and mating the best individuals on their own or relatives’ performance. This is based on sound quantitative genetic principles, developed and expounded by Lush, who attributed much of his understanding to Wright, and formalized in Fisher’s infinitesimal model. Analysis at the level of individual loci and gene frequency distributions has had relatively little impact. Now with access to genomic data, a revolution in which molecular information is being used to enhance response with “genomic selection” is occurring. The predictions of breeding value still utilize multiple loci throughout the genome and, indeed, are largely compatible with additive and specifically infinitesimal model assumptions. I discuss some of the history and genetic issues as applied to the science of livestock improvement, which has had and continues to have major spin-offs into ideas and applications in other areas. PMID:24395822

  4. Predicting a quaternary tungsten oxide for sustainable photovoltaic application by density functional theory

    NASA Astrophysics Data System (ADS)

    Sarker, Pranab; Al-Jassim, Mowafak M.; Huda, Muhammad N.

    2015-12-01

    A quaternary oxide, CuSnW2O8 (CTTO), has been predicted by density functional theory (DFT) to be a suitable material for sustainable photovoltaic applications. CTTO possesses band gaps of 1.25 eV (indirect) and 1.37 eV (direct), which were evaluated using the hybrid functional (HSE06) as a post-DFT method. The hole mobility of CTTO was higher than that of silicon. Further, optical absorption calculations demonstrate that CTTO is a better absorber of sunlight than Cu2ZnSnS4 and CuInxGa1-xSe2 (x = 0.5). In addition, CTTO exhibits rigorous thermodynamic stability comparable to WO3, as investigated by different thermodynamic approaches such as bonding cohesion, fragmentation tendency, and chemical potential analysis. Chemical potential analysis further revealed that CTTO can be synthesized at flexible experimental growth conditions, although the co-existence of at least one secondary phase is likely. Finally, like other Cu-based compounds, the formation of Cu vacancies is highly probable, even at Cu-rich growth condition, which could introduce p-type activity in CTTO.

  5. Homogenizing surface pressure time-series from operational numerical weather prediction models for geodetic applications

    NASA Astrophysics Data System (ADS)

    Dobslaw, H.

    2016-07-01

    Global surface pressure grids from 14.5 years of 6-hourly analyses out of both the operational ECMWF weather prediction model and ERA-Interim are mapped to a common reference orography by means of ECMWF's mean sea-level pressure diagnostic. The approach reduces both relative biases and residual variability by about one order of magnitude and thereby achieves a consistency among both data sets at the level of about 1 hPa. Remaining differences rather reflect temperature biases and also resolution limitations of the reanalysis data set, but are not anymore related to the local roughness in orography or to changes in the spatial resolution of the operational model. The presented reduction method therefore allows to obtain surface pressure time series with the long-time consistency of a reanalysis from an operational numerical weather model with much higher resolution and much shorter latency, making the results suitable for geodetic near realtime applications requiring continuously updated time series that are homogeneous over many years.

  6. An application of characteristic function in order to predict reliability and lifetime of aeronautical hardware

    NASA Astrophysics Data System (ADS)

    Żurek, Józef; Kaleta, Ryszard; Zieja, Mariusz

    2016-06-01

    The forecasting of reliability and life of aeronautical hardware requires recognition of many and various destructive processes that deteriorate the health/maintenance status thereof. The aging of technical components of aircraft as an armament system proves of outstanding significance to reliability and safety of the whole system. The aging process is usually induced by many and various factors, just to mention mechanical, biological, climatic, or chemical ones. The aging is an irreversible process and considerably affects (i.e. reduces) reliability and lifetime of aeronautical equipment. Application of the characteristic function of the aging process is suggested to predict reliability and lifetime of aeronautical hardware. An increment in values of diagnostic parameters is introduced to formulate then, using the characteristic function and after some rearrangements, the partial differential equation. An analytical dependence for the characteristic function of the aging process is a solution to this equation. With the inverse transformation applied, the density function of the aging of aeronautical hardware is found. Having found the density function, one can determine the aeronautical equipment's reliability and lifetime. The in-service collected or the life tests delivered data are used to attain this goal. Coefficients in this relationship are found using the likelihood function.

  7. Predicting a quaternary tungsten oxide for sustainable photovoltaic application by density functional theory

    SciTech Connect

    Sarker, Pranab; Huda, Muhammad N.; Al-Jassim, Mowafak M.

    2015-12-07

    A quaternary oxide, CuSnW{sub 2}O{sub 8} (CTTO), has been predicted by density functional theory (DFT) to be a suitable material for sustainable photovoltaic applications. CTTO possesses band gaps of 1.25 eV (indirect) and 1.37 eV (direct), which were evaluated using the hybrid functional (HSE06) as a post-DFT method. The hole mobility of CTTO was higher than that of silicon. Further, optical absorption calculations demonstrate that CTTO is a better absorber of sunlight than Cu{sub 2}ZnSnS{sub 4} and CuIn{sub x}Ga{sub 1−x}Se{sub 2} (x = 0.5). In addition, CTTO exhibits rigorous thermodynamic stability comparable to WO{sub 3}, as investigated by different thermodynamic approaches such as bonding cohesion, fragmentation tendency, and chemical potential analysis. Chemical potential analysis further revealed that CTTO can be synthesized at flexible experimental growth conditions, although the co-existence of at least one secondary phase is likely. Finally, like other Cu-based compounds, the formation of Cu vacancies is highly probable, even at Cu-rich growth condition, which could introduce p-type activity in CTTO.

  8. Application of Physiologically Based Pharmacokinetic Modeling to Predict Acetaminophen Metabolism and Pharmacokinetics in Children

    PubMed Central

    Jiang, X-L; Zhao, P; Barrett, J S; Lesko, L J; Schmidt, S

    2013-01-01

    Acetaminophen (APAP) is a widely used analgesic and antipyretic drug that undergoes extensive phase I and II metabolism. To better understand the kinetics of this process and to characterize the dynamic changes in metabolism and pharmacokinetics (PK) between children and adults, we developed a physiologically based PK (PBPK) model for APAP integrating in silico, in vitro, and in vivo PK data into a single model. The model was developed and qualified for adults and subsequently expanded for application in children by accounting for maturational changes from birth. Once developed and qualified, it was able to predict clinical PK data in neonates (0–28 days), infants (29 days to <2 years), children (2 to <12 years), and adolescents (12–17 years) following intravenous and orally administered APAP. This approach represents a general strategy for projecting drug exposure in children, in the absence of pediatric PK information, using previous drug- and system-specific information of adults and children through PBPK modeling. PMID:24132164

  9. A Comparison of Isoconversional and Model-Fitting Approaches to Kinetic Parameter Estimation and Application Predictions

    SciTech Connect

    Burnham, A K

    2006-05-17

    Chemical kinetic modeling has been used for many years in process optimization, estimating real-time material performance, and lifetime prediction. Chemists have tended towards developing detailed mechanistic models, while engineers have tended towards global or lumped models. Many, if not most, applications use global models by necessity, since it is impractical or impossible to develop a rigorous mechanistic model. Model fitting acquired a bad name in the thermal analysis community after that community realized a decade after other disciplines that deriving kinetic parameters for an assumed model from a single heating rate produced unreliable and sometimes nonsensical results. In its place, advanced isoconversional methods (1), which have their roots in the Friedman (2) and Ozawa-Flynn-Wall (3) methods of the 1960s, have become increasingly popular. In fact, as pointed out by the ICTAC kinetics project in 2000 (4), valid kinetic parameters can be derived by both isoconversional and model fitting methods as long as a diverse set of thermal histories are used to derive the kinetic parameters. The current paper extends the understanding from that project to give a better appreciation of the strengths and weaknesses of isoconversional and model-fitting approaches. Examples are given from a variety of sources, including the former and current ICTAC round-robin exercises, data sets for materials of interest, and simulated data sets.

  10. Application of random forest approach to QSAR prediction of aquatic toxicity.

    PubMed

    Polishchuk, Pavel G; Muratov, Eugene N; Artemenko, Anatoly G; Kolumbin, Oleg G; Muratov, Nail N; Kuz'min, Victor E

    2009-11-01

    This work is devoted to the application of the random forest approach to QSAR analysis of aquatic toxicity of chemical compounds tested on Tetrahymena pyriformis. The simplex representation of the molecular structure approach implemented in HiT QSAR Software was used for descriptors generation on a two-dimensional level. Adequate models based on simplex descriptors and the RF statistical approach were obtained on a modeling set of 644 compounds. Model predictivity was validated on two external test sets of 339 and 110 compounds. The high impact of lipophilicity and polarizability of investigated compounds on toxicity was determined. It was shown that RF models were tolerant for insertion of irrelevant descriptors as well as for randomization of some part of toxicity values that were representing a "noise". The fast procedure of optimization of the number of trees in the random forest has been proposed. The discussed RF model had comparable or better statistical characteristics than the corresponding PLS or KNN models. PMID:19860412

  11. Extension and Application of Feature Prediction Model for Synthesis of Hydrologic Records

    NASA Astrophysics Data System (ADS)

    Panu, Umed Singh; Unny, T. E.

    1980-02-01

    The method described in this paper for the synthesis of streamflows differs from the traditional approaches in synthetic hydrology in the sense that it utilizes the information contained in or among the groups of data in a streamflow record. The existense of such groups in geophysical records, including hydrologic records, is well emphasized by Hurst (1951). Further, in the proposed method, based on concepts of pattern recognition, neither a basic structure nor any preconceived model is imposed on the data; rather the data are allowed to speak for themselves in a most `democratic' way. The preliminary details of the method were provided in an earlier paper by Panu et al. (1978). The intent of this paper is to describe a procedure whereby it is possible to specify explicitly multivariate probability distribution for the intrapattern structure and first-order Markovian dependence for the interpattern structure in the feature prediction model (Panu et al., 1978). The various steps involved in the construction and operation of the model for streamflow synthesis are presented. The application of the model for synthesizing monthly streamflow records of three Canadian rivers exhibiting biannual cycles is explained. Statistical and hydrological tests show that these synthetic realizations possess relevant properties that are comparable with the corresponding properties contained in the historical record. This article should be read in conjunction with the previous publication by Panu et al. (1978).

  12. A Pilot Study to Assess Adenosine 5’-triphosphate Metabolism in Red Blood Cells as a Drug Target for Potential Cardiovascular Protection

    PubMed Central

    Yeung, Pollen K.F.; Tinkel, Jodi; Seeto, Dena

    2015-01-01

    Objective: To study the effect of exercise preconditioning on adenosine 5’triphosphate (ATP) metabolism in red blood cells and cardiovascular protection against injury induced by isoproterenol in vivo. Methods: Male Sprague Dawley rats (SDR) were each exercised on a treadmill for 15 minutes at 10 m/min and 10% grade (n = 7) (LowEx), or 14 m/min and 22% grade (n = 8) (VigEx). Two hours after the exercise, each rat received a single dose of isoproterenol (30 mg/kg) by subcutaneous (sc) injection. Two separate groups of SDR were used as control: One received no exercise (n = 10) (NoEx) and the other received no exercise and no isoproterenol (n = 11) (NoIso). Serial blood samples were collected over 5 hours for measurement of ATP and its catabolites by a validated HPLC. Hemodynamic recording was collected continuously for 
the duration of the experiment. Data were analysed using ANOVA and t-tests and difference considered significant at 
p < 0.05. Results: Exercise pre-conditioning (both LowEx and VigEx) reduced mortality after isoproterenol from 50% to < 30% 
(p > 0.05). It attenuated the rebound in blood pressure significantly (p < 0.05 between NoEx vs VigEx), attenuated the increase of RBC adenosine 5’-monophosphate (AMP) concentrations induced by isoproterenol, and also decreased the breakdown of ATP to AMP in the RBC (p < 0.05 vs NoEx). Conclusion: Exercise pre-conditioning decreased the blood pressure rebound and also breakdown of ATP in RBC after isoproterenol which may be exploited further as a drug target for cardiovascular protection and prevention.

  13. Cathepsin B is a New Drug Target for Traumatic Brain Injury Therapeutics: Evidence for E64d as a Promising Lead Drug Candidate

    PubMed Central

    Hook, Gregory; Jacobsen, J. Steven; Grabstein, Kenneth; Kindy, Mark; Hook, Vivian

    2015-01-01

    There is currently no therapeutic drug treatment for traumatic brain injury (TBI) despite decades of experimental clinical trials. This may be because the mechanistic pathways for improving TBI outcomes have yet to be identified and exploited. As such, there remains a need to seek out new molecular targets and their drug candidates to find new treatments for TBI. This review presents supporting evidence for cathepsin B, a cysteine protease, as a potentially important drug target for TBI. Cathepsin B expression is greatly up-regulated in TBI animal models, as well as in trauma patients. Importantly, knockout of the cathepsin B gene in TBI mice results in substantial improvements of TBI-caused deficits in behavior, pathology, and biomarkers, as well as improvements in related injury models. During the process of TBI-induced injury, cathepsin B likely escapes the lysosome, its normal subcellular location, into the cytoplasm or extracellular matrix (ECM) where the unleashed proteolytic power causes destruction via necrotic, apoptotic, autophagic, and activated glia-induced cell death, together with ECM breakdown and inflammation. Significantly, chemical inhibitors of cathepsin B are effective for improving deficits in TBI and related injuries including ischemia, cerebral bleeding, cerebral aneurysm, edema, pain, infection, rheumatoid arthritis, epilepsy, Huntington’s disease, multiple sclerosis, and Alzheimer’s disease. The inhibitor E64d is unique among cathepsin B inhibitors in being the only compound to have demonstrated oral efficacy in a TBI model and prior safe use in man and as such it is an excellent tool compound for preclinical testing and clinical compound development. These data support the conclusion that drug development of cathepsin B inhibitors for TBI treatment should be accelerated. PMID:26388830

  14. THE APPLICATION OF SIDEROPHORES FOR METAL RECOVERY AND WASTE REMEDIATION: EXAMINATION OF CORRELATIONS FOR PREDICTION OF METAL AFFINITIES

    EPA Science Inventory

    The naturally occurring metal-chelating compounds known as siderophores may be useful in environmental applications but limited metal specificity data is available for this class of compounds. Correlations that predict ligand-metal affinity versus mtal ion charge density and hyd...

  15. Curcumin binds in silico to anti-cancer drug target enzyme MMP-3 (human stromelysin-1) with affinity comparable to two known inhibitors of the enzyme

    PubMed Central

    Jerah, Ahmed; Hobani, Yahya; Kumar, B Vinod; Bidwai, Anil

    2015-01-01

    In silico interaction of curcumin with the enzyme MMP-3 (human stromelysin-1) was studied by molecular docking using AutoDock 4.2 as the docking software application. AutoDock 4.2 software serves as a valid and acceptable docking application to study the interactions of small compounds with proteins. Interactions of curcumin with MMP-3 were compared to those of two known inhibitors of the enzyme, PBSA and MPPT. The calculated free energy of binding (ΔG binding) shows that curcumin binds with affinity comparable to or better than the two known inhibitors. Binding interactions of curcumin with active site residues of the enzyme are also predicted. Curcumin appears to bind in an extendended conformation making extensive VDW contacts in the active site of the enzyme. Hydrogen bonding and pi-pi interactions with key active site residues is also observed. Thus, curcumin can be considered as a good lead compound in the development of new inhibitors of MMP-3 which is a potential target of anticancer drugs. The results of these studies can serve as a starting point for further computational and experimental studies. PMID:26420919

  16. Curcumin binds in silico to anti-cancer drug target enzyme MMP-3 (human stromelysin-1) with affinity comparable to two known inhibitors of the enzyme.

    PubMed

    Jerah, Ahmed; Hobani, Yahya; Kumar, B Vinod; Bidwai, Anil

    2015-01-01

    In silico interaction of curcumin with the enzyme MMP-3 (human stromelysin-1) was studied by molecular docking using AutoDock 4.2 as the docking software application. AutoDock 4.2 software serves as a valid and acceptable docking application to study the interactions of small compounds with proteins. Interactions of curcumin with MMP-3 were compared to those of two known inhibitors of the enzyme, PBSA and MPPT. The calculated free energy of binding (ΔG binding) shows that curcumin binds with affinity comparable to or better than the two known inhibitors. Binding interactions of curcumin with active site residues of the enzyme are also predicted. Curcumin appears to bind in an extendended conformation making extensive VDW contacts in the active site of the enzyme. Hydrogen bonding and pi-pi interactions with key active site residues is also observed. Thus, curcumin can be considered as a good lead compound in the development of new inhibitors of MMP-3 which is a potential target of anticancer drugs. The results of these studies can serve as a starting point for further computational and experimental studies. PMID:26420919

  17. Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection

    NASA Technical Reports Server (NTRS)

    Kumar, Sricharan; Srivistava, Ashok N.

    2012-01-01

    Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap methods is proposed. Bootstrap methods allow for a non-parametric approach to computing prediction intervals with no specific assumptions about the sampling distribution of the noise or the data. The asymptotic fidelity of the proposed prediction intervals is theoretically proved. Subsequently, the validity of the bootstrap based prediction intervals is illustrated via simulations. Finally, the bootstrap prediction intervals are applied to the problem of anomaly detection on aviation data.

  18. Investigating micronucleus assay applicability for prediction of normal tissue intrinsic radiosensitivity in gynecological cancer patients

    PubMed Central

    Encheva, Elitsa; Deleva, Sofia; Hristova, Rositsa; Hadjidekova, Valeria; Hadjieva, Tatiana

    2011-01-01

    great variations in MNT parameters. Only three patients with grade 2 “summarized clinical radiosensitivity” had values of cells with MN/1000 above the chosen radiosensitivity threshold. Conclusion The present study was not able to confirm in vitro MNT applicability for radiosensitivity prediction in pelvic irradiation. PMID:24376993

  19. A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles.

    PubMed

    Jones, David E; Ghandehari, Hamidreza; Facelli, Julio C

    2016-08-01

    This article presents a comprehensive review of applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles of medical interest. The papers reviewed here present the results of research using these techniques to predict the biological fate and properties of a variety of nanoparticles relevant to their biomedical applications. These include the influence of particle physicochemical properties on cellular uptake, cytotoxicity, molecular loading, and molecular release in addition to manufacturing properties like nanoparticle size, and polydispersity. Overall, the results are encouraging and suggest that as more systematic data from nanoparticles becomes available, machine learning and data mining would become a powerful aid in the design of nanoparticles for biomedical applications. There is however the challenge of great heterogeneity in nanoparticles, which will make these discoveries more challenging than for traditional small molecule drug design. PMID:27282231

  20. Application of Suomi-NPP Green Vegetation Fraction and NUCAPS for Improving Regional Numerical Weather Prediction

    NASA Technical Reports Server (NTRS)

    Case, Jonathan L.; Berndt, Emily B.; Srikishen, Jayanthi; Zavodsky, Bradley T.

    2014-01-01

    The NASA SPoRT Center is working to incorporate Suomi-NPP products into its research and transition activities to improve regional numerical weather prediction (NWP). Specifically, SPoRT seeks to utilize two data products from NOAA/NESDIS: (1) daily global VIIRS green vegetation fraction (GVF), and (2) NOAA Unique CrIS and ATMS Processing System (NUCAPS) temperature and moisture retrieved profiles. The goal of (1) is to improve the representation of vegetation in the Noah land surface model (LSM) over existing climatological GVF datasets in order to improve the land-atmosphere energy exchanges in NWP models and produce better temperature, moisture, and precipitation forecasts. The goal of (2) is to assimilate NUCAPS retrieved profiles into the Gridpoint Statistical Interpolation (GSI) data assimilation system to assess the impact on a summer pre-frontal convection case. Most regional NWP applications make use of