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Sample records for drug-target interaction prediction

  1. Drug-target interaction prediction by random walk on the heterogeneous network.

    PubMed

    Chen, Xing; Liu, Ming-Xi; Yan, Gui-Ying

    2012-07-01

    Predicting potential drug-target interactions from heterogeneous biological data is critical not only for better understanding of the various interactions and biological processes, but also for the development of novel drugs and the improvement of human medicines. In this paper, the method of Network-based Random Walk with Restart on the Heterogeneous network (NRWRH) is developed to predict potential drug-target interactions on a large scale under the hypothesis that similar drugs often target similar target proteins and the framework of Random Walk. Compared with traditional supervised or semi-supervised methods, NRWRH makes full use of the tool of the network for data integration to predict drug-target associations. It integrates three different networks (protein-protein similarity network, drug-drug similarity network, and known drug-target interaction networks) into a heterogeneous network by known drug-target interactions and implements the random walk on this heterogeneous network. When applied to four classes of important drug-target interactions including enzymes, ion channels, GPCRs and nuclear receptors, NRWRH significantly improves previous methods in terms of cross-validation and potential drug-target interaction prediction. Excellent performance enables us to suggest a number of new potential drug-target interactions for drug development.

  2. 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.

  3. 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

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

    PubMed

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

    2016-02-01

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

  5. 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

  6. 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

  7. Benchmarking a Wide Range of Chemical Descriptors for Drug-Target Interaction Prediction Using a Chemogenomic Approach.

    PubMed

    Sawada, Ryusuke; Kotera, Masaaki; Yamanishi, Yoshihiro

    2014-12-01

    The identification of drug-target interactions, or interactions between drug candidate compounds and target candidate proteins, is a crucial process in genomic drug discovery. In silico chemogenomic methods are recently recognized as a promising approach for genome-wide scale prediction of drug-target interactions, but the prediction performance depends heavily on the descriptors and similarity measures of drugs and proteins. In this paper, we investigated the performance of various descriptors and similarity measures of drugs and proteins for the drug-target interaction prediction using a chemogenomic approach. We compared the prediction accuracy of 18 chemical descriptors of drugs (e.g., ECFP, FCFP,E-state, CDK, KlekotaRoth, MACCS, PubChem, Dragon, KCF-S, and graph kernels) and 4 descriptors of proteins (e.g., amino acid composition, domain profile, local sequence similarity, and string kernel) on about one hundred thousand drug-target interactions. We examined the combinatorial effects of drug descriptors and protein descriptors using the same benchmark data under several experimental conditions. Large-scale experiments showed that our proposed KCF-S descriptor worked the best in terms of prediction accuracy. The comparative results are expected to be useful for selecting chemical descriptors in various pharmaceutical applications.

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

    PubMed Central

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

    2010-01-01

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

  9. 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

  10. Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach

    PubMed Central

    Emig, Dorothea; Ivliev, Alexander; Pustovalova, Olga; Lancashire, Lee; Bureeva, Svetlana; Nikolsky, Yuri; Bessarabova, Marina

    2013-01-01

    The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example. PMID:23593264

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

    PubMed

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

    2015-01-01

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

  12. Large-Scale Prediction of Drug Targets Based on Local and Global Consistency of Chemical-Chemical Networks.

    PubMed

    Huang, Guohua; Feng, Kaiyan; Li, Xiaomei; Peng, Yan

    2016-01-01

    It is crucial to identify the molecular targets of a compound during the course of the new drug discovery and drug development. Due to the complexity of biological systems, finding drug targets by biological experiments is very tedious and expensive. In the paper, we used chemicalchemical interactions in the STITCH database to construct a network of drug-drug association. Based on the network, a learning method keeping local and global consistency was presented to infer drug targets. We achieved an accuracy of 57.75% in the first order prediction using leave-one-out cross validation, which was higher than the accuracy of 53.77% achieved by the local neighbor model. We manually validated 27 absent drug targets in the crossvalidation using drug-target interactions from other databases. Applying the presented method to large-scale prediction of unknown targets, we manually confirmed 14 pairs of drug-target interactions among the newly predicted drug targets. These results suggested that the presented method was a promising tool for large-scale identification of drug targets.

  13. 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

  14. Protein interaction network analysis--approach for potential drug target identification in Mycobacterium tuberculosis.

    PubMed

    Kushwaha, Sandeep K; Shakya, Madhvi

    2010-01-21

    In host-parasite diseases like tuberculosis, non-homologous proteins (enzymes) as drug target are first preference. Most potent drug target can be identified among large number of non-homologous protein through protein interaction network analysis. In this study, the entire promising dimension has been explored for identification of potential drug target. A comparative metabolic pathway analysis of the host Homo sapiens and the pathogen M. tuberculosis H37Rv has been performed with three level of analysis. In first level, the unique metabolic pathways of M. tuberculosis have been identified through its comparative study with H. sapiens and identification of non-homologous proteins has been done through BLAST similarity search. In second level, choke-point analysis has been performed with identified non-homologous proteins of metabolic pathways. In third level, two type of analysis have been performed through protein interaction network. First analysis has been done to find out the most potential metabolic functional associations among all identified choke point proteins whereas second analysis has been performed to find out the functional association of high metabolic interacting proteins to pathogenesis causing proteins. Most interactive metabolic proteins which have highest number of functional association with pathogenesis causing proteins have been considered as potential drug target. A list of 18 potential drug targets has been proposed which are various stages of progress at the TBSGC and proposed drug targets are also studied for other pathogenic strains. As a case study, we have built a homology model of identified drug targets histidinol-phosphate aminotransferase (HisC1) using MODELLER software and various information have been generated through molecular dynamics which will be useful in wetlab structure determination. The generated model could be further explored for insilico docking studies with suitable inhibitors.

  15. Mining predicted essential genes of Brugia malayi for nematode drug targets.

    PubMed

    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.

  16. 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

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

    PubMed

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

    2011-07-01

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

  18. Chaperones as thermodynamic sensors of drug-target interactions reveal kinase inhibitor specificities in living cells.

    PubMed

    Taipale, Mikko; Krykbaeva, Irina; Whitesell, Luke; Santagata, Sandro; Zhang, Jianming; Liu, Qingsong; Gray, Nathanael S; Lindquist, Susan

    2013-07-01

    The interaction between the HSP90 chaperone and its client kinases is sensitive to the conformational status of the kinase, and stabilization of the kinase fold by small molecules strongly decreases chaperone interaction. Here we exploit this observation and assay small-molecule binding to kinases in living cells, using chaperones as 'thermodynamic sensors'. The method allows determination of target specificities of both ATP-competitive and allosteric inhibitors in the kinases' native cellular context in high throughput. We profile target specificities of 30 diverse kinase inhibitors against >300 kinases. Demonstrating the value of the assay, we identify ETV6-NTRK3 as a target of the FDA-approved drug crizotinib (Xalkori). Crizotinib inhibits proliferation of ETV6-NTRK3-dependent tumor cells with nanomolar potency and induces the regression of established tumor xenografts in mice. Finally, we show that our approach is applicable to other chaperone and target classes by assaying HSP70/steroid hormone receptor and CDC37/kinase interactions, suggesting that chaperone interactions will have broad application in detecting drug-target interactions in vivo.

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

    PubMed Central

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

    2016-01-01

    The protein–protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as “indispensable,” “neutral,” or “dispensable,” which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network’s control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets. PMID:27091990

  20. SynSysNet: integration of experimental data on synaptic protein–protein interactions with drug-target relations

    PubMed Central

    von Eichborn, Joachim; Dunkel, Mathias; Gohlke, Björn O.; Preissner, Sarah C.; Hoffmann, Michael F.; Bauer, Jakob M. J.; Armstrong, J. D.; Schaefer, Martin H.; Andrade-Navarro, Miguel A.; Le Novere, Nicolas; Croning, Michael D. R.; Grant, Seth G. N.; van Nierop, Pim; Smit, August B.; Preissner, Robert

    2013-01-01

    We created SynSysNet, available online at http://bioinformatics.charite.de/synsysnet, to provide a platform that creates a comprehensive 4D network of synaptic interactions. Neuronal synapses are fundamental structures linking nerve cells in the brain and they are responsible for neuronal communication and information processing. These processes are dynamically regulated by a network of proteins. New developments in interaction proteomics and yeast two-hybrid methods allow unbiased detection of interactors. The consolidation of data from different resources and methods is important to understand the relation to human behaviour and disease and to identify new therapeutic approaches. To this end, we established SynSysNet from a set of ∼1000 synapse specific proteins, their structures and small-molecule interactions. For two-thirds of these, 3D structures are provided (from Protein Data Bank and homology modelling). Drug-target interactions for 750 approved drugs and 50 000 compounds, as well as 5000 experimentally validated protein–protein interactions, are included. The resulting interaction network and user-selected parts can be viewed interactively and exported in XGMML. Approximately 200 involved pathways can be explored regarding drug-target interactions. Homology-modelled structures are downloadable in Protein Data Bank format, and drugs are available as MOL-files. Protein–protein interactions and drug-target interactions can be viewed as networks; corresponding PubMed IDs or sources are given. PMID:23143269

  1. 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

  2. A Comparative Chemogenomics Strategy to Predict Potential Drug Targets in the Metazoan Pathogen, Schistosoma mansoni

    PubMed Central

    Caffrey, Conor R.; Rohwer, Andreas; Oellien, Frank; Marhöfer, Richard J.; Braschi, Simon; Oliveira, Guilherme; McKerrow, James H.; Selzer, Paul M.

    2009-01-01

    Schistosomiasis is a prevalent and chronic helmintic disease in tropical regions. Treatment and control relies on chemotherapy with just one drug, praziquantel and this reliance is of concern should clinically relevant drug resistance emerge and spread. Therefore, to identify potential target proteins for new avenues of drug discovery we have taken a comparative chemogenomics approach utilizing the putative proteome of Schistosoma mansoni compared to the proteomes of two model organisms, the nematode, Caenorhabditis elegans and the fruitfly, Drosophila melanogaster. Using the genome comparison software Genlight, two separate in silico workflows were implemented to derive a set of parasite proteins for which gene disruption of the orthologs in both the model organisms yielded deleterious phenotypes (e.g., lethal, impairment of motility), i.e., are essential genes/proteins. Of the 67 and 68 sequences generated for each workflow, 63 were identical in both sets, leading to a final set of 72 parasite proteins. All but one of these were expressed in the relevant developmental stages of the parasite infecting humans. Subsequent in depth manual curation of the combined workflow output revealed 57 candidate proteins. Scrutiny of these for ‘druggable’ protein homologs in the literature identified 35 S. mansoni sequences, 18 of which were homologous to proteins with 3D structures including co-crystallized ligands that will allow further structure-based drug design studies. The comparative chemogenomics strategy presented generates a tractable set of S. mansoni proteins for experimental validation as drug targets against this insidious human pathogen. PMID:19198654

  3. 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

  4. 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.

  5. 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

  6. 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

  7. Identification of Interaction Hot Spots in Structures of Drug Targets on the Basis of Three-Dimensional Activity Cliff Information.

    PubMed

    Furtmann, Norbert; Hu, Ye; Gütschow, Michael; Bajorath, Jürgen

    2015-12-01

    Activity cliffs are defined as pairs or groups of structurally similar or analogous compounds that share the same specific activity but have large differences in potency. Although activity cliffs are mostly studied in medicinal chemistry at the level of molecular graphs, they can also be assessed by comparing compound binding modes. If such three-dimensional activity cliffs (3D-cliffs) are studied on the basis of X-ray complex structures, experimental ligand-target interaction details can be taken into account. Rapid growth in the number of 3D-cliffs that can be derived from X-ray complex structures has made it possible to identify targets for which a substantial body of 3D-cliff information is available. Activity cliffs are typically studied to identify structure-activity relationship determinants and aid in compound optimization. However, 3D-cliff information can also be used to search for interaction hot spots and key residues, as reported herein. For six of seven drug targets for which more than 20 3D-cliffs were available, series of 3D-cliffs were identified that were consistently involved in interactions with different hot spots. These 3D-cliffs often encoded chemical modifications resulting in interactions that were characteristic of highly potent compounds but absent in weakly potent ones, thus providing information for structure-based design.

  8. Interaction of carbon monoxide with transition metals: evolutionary insights into drug target discovery.

    PubMed

    Foresti, Roberta; Motterlini, Roberto

    2010-12-01

    The perception that carbon monoxide (CO) is poisonous and life-threatening for mammalian organisms stems from its intrinsic propensity to bind iron in hemoglobin, a reaction that ultimately leads to impaired oxygen delivery to tissues. From evolutionary and chemical perspectives, however, CO is one of the most essential molecules in the formation of biological components and its interaction with transition metals is at the origin of primordial cell signaling. Not surprisingly, mammals have gradually evolved systems to finely control the synthesis and the sensing of this gaseous molecule. Cells are indeed continuously exposed to small quantities of CO produced endogenously during the degradation of heme by constitutive and inducible heme oxygenase enzymes. We have gradually learnt that heme oxygenase-derived carbon monoxide (CO) serves as a ubiquitous signaling mediator which could be exploited for therapeutic purposes. The development of transition metal carbonyls as prototypic carbon monoxide-releasing molecules (CO-RMs) represents a novel stratagem for a safer delivery of CO-based pharmaceuticals in the treatment of various pathological disorders. This review will look back at evolution to analyze and argue that a dynamic interaction of CO with specific intracellular metal centers is the common denominator for the diversified beneficial effects mediated by this gaseous molecule. PMID:20704543

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

    PubMed Central

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

    2016-01-01

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

  10. 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

  11. 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

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

    PubMed

    Xu, Jingyu; Wang, Panpan; Yang, Hong; Zhou, Jin; Li, Yinghong; Li, Xiaoxu; Xue, Weiwei; Yu, Chunyan; Tian, Yubin; Zhu, Feng

    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.

  13. 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

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

    PubMed

    Sugaya, Nobuyoshi

    2014-10-27

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

  15. INFERENCE OF PERSONALIZED DRUG TARGETS VIA NETWORK PROPAGATION.

    PubMed

    Shnaps, Ortal; Perry, Eyal; Silverbush, Dana; Sharan, Roded

    2016-01-01

    We present a computational strategy to simulate drug treatment in a personalized setting. The method is based on integrating patient mutation and differential expression data with a protein-protein interaction network. We test the impact of in-silico deletions of different proteins on the flow of information in the network and use the results to infer potential drug targets. We apply our method to AML data from TCGA and validate the predicted drug targets using known targets. To benchmark our patient-specific approach, we compare the personalized setting predictions to those of the conventional setting. Our predicted drug targets are highly enriched with known targets from DrugBank and COSMIC (p < 10(-5) outperforming the non-personalized predictions. Finally, we focus on the largest AML patient subgroup (~30%) which is characterized by an FLT3 mutation, and utilize our prediction score to rank patient sensitivity to inhibition of each predicted target, reproducing previous findings of in-vitro experiments.

  16. 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

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

    PubMed

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

    2015-01-01

    The S100A8 and epidermal growth factor receptor (EGFR) proteins are proto-oncogenes that are strongly expressed in a number of cancer types. EGFR promotes cellular proliferation, differentiation, migration and survival by activating molecular pathways. Involvement of proinflammatory S100A8 in tumor cell differentiation and progression is largely unclear and not studied in kidney cancer (KC). S100A8 and EGFR are potential therapeutic biomarkers and anticancer drug targets for KC. In this study, we explored molecular mechanisms of interaction profiles of both molecules with potential anticancer drugs. We undertook transcriptional profiling in Saudi KCs using Affymetrix HuGene 1.0 ST arrays. We identified 1478 significantly expressed genes, including S100A8 and EGFR overexpression, using cut-off p value <0.05 and fold change ≥2. Additionally, we compared and confirmed our findings with expression data available at NCBI's GEO database. A significant number of genes associated with cancer showed involvement in cell cycle progression, DNA repair, tumor morphology, tissue development, and cell survival. Atherosclerosis signaling, leukocyte extravasation signaling, notch signaling, and IL-12 signaling were the most significantly disrupted signaling pathways. The present study provides an initial transcriptional profiling of Saudi KC patients. Our analysis suggests distinct transcriptomic signatures and pathways underlying molecular mechanisms of KC progression. Molecular docking analysis revealed that the kinase inhibitor "midostaurin" has amongst the selected drug targets, the best ligand properties to S100A8 and EGFR, with the implication that its binding inhibits downstream signaling in KC. This is the first structure-based docking study for the selected protein targets and anticancer drug, and the results indicate S100A8 and EGFR as attractive anticancer targets and midostaurin with effective drug properties for therapeutic intervention in KC. PMID:25789858

  18. 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

  19. T-iDT : tool for identification of drug target in bacteria and validation by Mycobacterium tuberculosis.

    PubMed

    Singh, Nitesh Kumar; Selvam, S Mahalaxmi; Chakravarthy, Paulsharma

    2006-01-01

    With the completion of the Human Genome Project in 2003, many new projects to sequence bacterial genomes were started and soon many complete bacterial genome sequences were available. The sequenced genomes of pathogenic bacteria provide useful information for understanding host-pathogen interactions. These data prove to be a new weapon in fighting against pathogenic bacteria by providing information about potential drug targets. But the limitation of computational tools for finding potential drug targets has hindered the process and further experimental analysis. There are many in silico approaches proposed for finding drug targets but only few have been automated. One such approach finds essential genes in bacterial genomes with no human homologue and predicts these as potential drug targets. The same approach is used in our tool. T-iDT, a tool for the identification of drug targets, finds essential genes by comparing a bacterial gene set against DEG (Database of Essential Genes) and excludes homologue genes by comparing against a human protein database. The tool predicts both the set of essential genes as well as potential target genes for the given genome. The tool was tested with Mycobacterium tuberculosis and results were validated. With default parameters, the tool predicted 236 essential genes and 52 genes to encode potential drug targets. A pathway-based approach was used to validate these potential drug target genes. The pathway in which the products of these genes are involved was determined. Our analysis shows that almost all these pathways are very essential for the bacterial survival and hence these genes encode possible drug targets. Our tool provides a fast method for finding possible drug targets in bacterial genomes with varying stringency level. The tool will be helpful in finding possible drug targets in various pathogenic organisms and can be used for further analysis in novel therapeutic drug development. The tool can be downloaded from http

  20. 2D MI-DRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins.

    PubMed

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

    2011-12-01

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

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

    PubMed

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

    2015-03-30

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

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

    PubMed

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

    2016-01-26

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

  3. 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

  4. 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

  5. Network-based characterization of drug-regulated genes, drug targets, and toxicity.

    PubMed

    Kotlyar, Max; Fortney, Kristen; Jurisica, Igor

    2012-08-01

    Proteins do not exert their effects in isolation of one another, but interact together in complex networks. In recent years, sophisticated methods have been developed to leverage protein-protein interaction (PPI) network structure to improve several stages of the drug discovery process. Network-based methods have been applied to predict drug targets, drug side effects, and new therapeutic indications. In this paper we have two aims. First, we review the past contributions of network approaches and methods to drug discovery, and discuss their limitations and possible future directions. Second, we show how past work can be generalized to gain a more complete understanding of how drugs perturb networks. Previous network-based characterizations of drug effects focused on the small number of known drug targets, i.e., direct binding partners of drugs. However, drugs affect many more genes than their targets - they can profoundly affect the cell's transcriptome. For the first time, we use networks to characterize genes that are differentially regulated by drugs. We found that drug-regulated genes differed from drug targets in terms of functional annotations, cellular localizations, and topological properties. Drug targets mainly included receptors on the plasma membrane, down-regulated genes were largely in the nucleus and were enriched for DNA binding, and genes lacking drug relationships were enriched in the extracellular region. Network topology analysis indicated several significant graph properties, including high degree and betweenness for the drug targets and drug-regulated genes, though possibly due to network biases. Topological analysis also showed that proteins of down-regulated genes appear to be frequently involved in complexes. Analyzing network distances between regulated genes, we found that genes regulated by structurally similar drugs were significantly closer than genes regulated by dissimilar drugs. Finally, network centrality of a drug

  6. Drug target identification and quantitative proteomics.

    PubMed

    He, Tao; Jin Kim, Yeoun; Heidbrink, Jenny L; Moore, Paul A; Ruben, Steven M

    2006-10-01

    The emerging technologies in proteomic analysis provide great opportunity for the discovery of novel therapeutic drug targets for unmet medical needs through delivering of key information on protein expression, post-translational modifications and protein-protein interactions. This review presents a summary of current quantitative proteomic concepts and mass spectrometric technologies, which enable the acceleration of target discovery. Examples of the strategies and current technologies in the target identification/validation process are provided to illustrate the successful application of proteomics in target identification, in particular for monoclonal antibody therapies. Current bottlenecks and future directions of proteomic studies for target and biomarker identification are also discussed to better facilitate the application of this technology.

  7. 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

  8. 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

  9. Assessing drug target association using semantic linked data.

    PubMed

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

    2012-01-01

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

  10. 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.

  11. The drug-target residence time model: a 10-year retrospective.

    PubMed

    Copeland, Robert A

    2016-02-01

    The drug-target residence time model was first introduced in 2006 and has been broadly adopted across the chemical biology, biotechnology and pharmaceutical communities. While traditional in vitro methods view drug-target interactions exclusively in terms of equilibrium affinity, the residence time model takes into account the conformational dynamics of target macromolecules that affect drug binding and dissociation. The key tenet of this model is that the lifetime (or residence time) of the binary drug-target complex, and not the binding affinity per se, dictates much of the in vivo pharmacological activity. Here, this model is revisited and key applications of it over the past 10 years are highlighted.

  12. 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.

  13. Mining nematode genome data for novel drug targets.

    PubMed

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

    2005-03-01

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

  14. Fluid mechanics aspects of magnetic drug targeting.

    PubMed

    Odenbach, Stefan

    2015-10-01

    Experiments and numerical simulations using a flow phantom for magnetic drug targeting have been undertaken. The flow phantom is a half y-branched tube configuration where the main tube represents an artery from which a tumour-supplying artery, which is simulated by the side branch of the flow phantom, branches off. In the experiments a quantification of the amount of magnetic particles targeted towards the branch by a magnetic field applied via a permanent magnet is achieved by impedance measurement using sensor coils. Measuring the targeting efficiency, i.e. the relative amount of particles targeted to the side branch, for different field configurations one obtains targeting maps which combine the targeting efficiency with the magnetic force densities in characteristic points in the flow phantom. It could be shown that targeting efficiency depends strongly on the magnetic field configuration. A corresponding numerical model has been set up, which allows the simulation of targeting efficiency for variable field configuration. With this simulation good agreement of targeting efficiency with experimental data has been found. Thus, the basis has been laid for future calculations of optimal field configurations in clinical applications of magnetic drug targeting. Moreover, the numerical model allows the variation of additional parameters of the drug targeting process and thus an estimation of the influence, e.g. of the fluid properties on the targeting efficiency. Corresponding calculations have shown that the non-Newtonian behaviour of the fluid will significantly influence the targeting process, an aspect which has to be taken into account, especially recalling the fact that the viscosity of magnetic suspensions depends strongly on the magnetic field strength and the mechanical load. PMID:26415215

  15. 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.

  16. 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

  17. 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

  18. 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.

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

    PubMed

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

    2015-10-01

    We carry out a time-averaged contact matrix study to reveal the existence of protein backbone hydrogen bonds (BHBs) whose net persistence in time differs markedly form their corresponding PDB-reported state. We term such interactions as "chameleonic" BHBs, CBHBs, precisely to account for their tendency to change the structural prescription of the PDB for the opposite bonding propensity in solution. We also find a significant enrichment of protein binding sites in CBHBs, relate them to local water exposure and analyze their behavior as ligand/drug targets. Thus, the dynamic analysis of hydrogen bond propensity might lay the foundations for new tools of interest in protein binding-site prediction and in lead optimization for drug design. PMID:26486885

  20. Therapeutic approaches to drug targets in atherosclerosis

    PubMed Central

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

    2013-01-01

    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

  1. 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

  2. Is hippocampal atrophy a future drug target?

    PubMed

    Dhikav, Vikas; Anand, Kuljeet Singh

    2007-01-01

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

  3. Predicting the fission yeast protein interaction network.

    PubMed

    Pancaldi, Vera; Saraç, Omer S; Rallis, Charalampos; McLean, Janel R; Převorovský, Martin; Gould, Kathleen; Beyer, Andreas; Bähler, Jürg

    2012-04-01

    A systems-level understanding of biological processes and information flow requires the mapping of cellular component interactions, among which protein-protein interactions are particularly important. Fission yeast (Schizosaccharomyces pombe) is a valuable model organism for which no systematic protein-interaction data are available. We exploited gene and protein properties, global genome regulation datasets, and conservation of interactions between budding and fission yeast to predict fission yeast protein interactions in silico. We have extensively tested our method in three ways: first, by predicting with 70-80% accuracy a selected high-confidence test set; second, by recapitulating interactions between members of the well-characterized SAGA co-activator complex; and third, by verifying predicted interactions of the Cbf11 transcription factor using mass spectrometry of TAP-purified protein complexes. Given the importance of the pathway in cell physiology and human disease, we explore the predicted sub-networks centered on the Tor1/2 kinases. Moreover, we predict the histidine kinases Mak1/2/3 to be vital hubs in the fission yeast stress response network, and we suggest interactors of argonaute 1, the principal component of the siRNA-mediated gene silencing pathway, lost in budding yeast but preserved in S. pombe. Of the new high-quality interactions that were discovered after we started this work, 73% were found in our predictions. Even though any predicted interactome is imperfect, the protein network presented here can provide a valuable basis to explore biological processes and to guide wet-lab experiments in fission yeast and beyond. Our predicted protein interactions are freely available through PInt, an online resource on our website (www.bahlerlab.info/PInt).

  4. Predictive Systems for Customer Interactions

    NASA Astrophysics Data System (ADS)

    Vijayaraghavan, Ravi; Albert, Sam; Singh, Vinod Kumar; Kannan, Pallipuram V.

    With the coming of age of web as a mainstream customer service channel, B2C companies have invested substantial resources in enhancing their web presence. Today customers can interact with a company, not only through the traditional phone channel but also through chat, email, SMS or web self-service. Each of these channels is best suited for some services and ill-matched for others. Customer service organizations today struggle with the challenge of delivering seamlessly integrated services through these different channels. This paper will evaluate some of the key challenges in multi-channel customer service. It will address the challenge of creating the right channel mix i.e. providing the right choice of channels for a given customer/behavior/issue profile. It will also provide strategies for optimizing the performance of a given channel in creating the right customer experience.

  5. A sequence-based computational approach to predicting PDZ domain-peptide interactions.

    PubMed

    Nakariyakul, Songyot; Liu, Zhi-Ping; Chen, Luonan

    2014-01-01

    The PDZ domain is one of the most ubiquitous protein domains that is involved in coordinating signaling complex formation and protein networking by reversibly interacting with multiple binding partners. It has been linked to many devastating diseases such as avian influenza, Fraser syndrome, Usher syndrome and Dejerine-Sottas neuropathy. Understanding the selectivity of PDZ domains can help elucidate how defects in PDZ proteins and their binding partners lead to human diseases. Since experimental methods to determine the interaction specificity of the PDZ domains are expensive and labor intensive, an accurate computational method is thus needed. Our developed support vector machine-based predictor using dipeptide composition is shown to qualitatively predict PDZ domain-peptide interaction with a high accuracy rate. Furthermore, since most of the dipeptide compositions are redundant and irrelevant, we propose a new hybrid feature selection technique to select only a subset of these compositions for interaction prediction. The experimental results show that only approximately 25% of dipeptide features are needed and that our method improves the prediction results significantly. The selected dipeptide features are also analyzed and shown to play important roles in specificity patterns of PDZ domains. Our method is based only on primary sequence information, and it can be used for the research of drug target and drug design in identifying PDZ domain-ligand interactions. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai. PMID:23608946

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

    PubMed

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

    2016-02-08

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

  7. A Computational Drug-Target Network for Yuanhu Zhitong Prescription

    PubMed Central

    Lu, Peng; Zhang, Fangbo; Yuan, Yuan; Wang, Songsong

    2013-01-01

    Yuanhu Zhitong prescription (YZP) is a typical and relatively simple traditional Chinese medicine (TCM), widely used in the clinical treatment of headache, gastralgia, and dysmenorrhea. However, the underlying molecular mechanism of action of YZP is not clear. In this study, based on the previous chemical and metabolite analysis, a complex approach including the prediction of the structure of metabolite, high-throughput in silico screening, and network reconstruction and analysis was developed to obtain a computational drug-target network for YZP. This was followed by a functional and pathway analysis by ClueGO to determine some of the pharmacologic activities. Further, two new pharmacologic actions, antidepressant and antianxiety, of YZP were validated by animal experiments using zebrafish and mice models. The forced swimming test and the tail suspension test demonstrated that YZP at the doses of 4 mg/kg and 8 mg/kg had better antidepressive activity when compared with the control group. The anxiolytic activity experiment showed that YZP at the doses of 100 mg/L, 150 mg/L, and 200 mg/L had significant decrease in diving compared to controls. These results not only shed light on the better understanding of the molecular mechanisms of YZP for curing diseases, but also provide some evidence for exploring the classic TCM formulas for new clinical application. PMID:23762151

  8. 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

  9. Use of systems biology to decipher host-pathogen interaction networks and predict biomarkers.

    PubMed

    Dix, A; Vlaic, S; Guthke, R; Linde, J

    2016-07-01

    In systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host-pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions, (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets and (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilizing high-throughput data (e.g. single nucleotide polymorphism (SNP) detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as a novel strategy, and discuss possible applications to host-pathogen interactions. Pioneering work was done to predict molecular host-pathogen interactions networks based on dual RNA-seq data. However, currently this network modelling is restricted to a small number of genes. With increasing number and quality of databases and data repositories, the prediction of large-scale networks will also be feasible that can used for multidimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalized medicine with high-throughput data and generation of multiscale host-pathogen interaction models.

  10. How to Predict Molecular Interactions between Species?

    PubMed Central

    Schulze, Sylvie; Schleicher, Jana; Guthke, Reinhard; Linde, Jörg

    2016-01-01

    Organisms constantly interact with other species through physical contact which leads to changes on the molecular level, for example the transcriptome. These changes can be monitored for all genes, with the help of high-throughput experiments such as RNA-seq or microarrays. The adaptation of the gene expression to environmental changes within cells is mediated through complex gene regulatory networks. Often, our knowledge of these networks is incomplete. Network inference predicts gene regulatory interactions based on transcriptome data. An emerging application of high-throughput transcriptome studies are dual transcriptomics experiments. Here, the transcriptome of two or more interacting species is measured simultaneously. Based on a dual RNA-seq data set of murine dendritic cells infected with the fungal pathogen Candida albicans, the software tool NetGenerator was applied to predict an inter-species gene regulatory network. To promote further investigations of molecular inter-species interactions, we recently discussed dual RNA-seq experiments for host-pathogen interactions and extended the applied tool NetGenerator (Schulze et al., 2015). The updated version of NetGenerator makes use of measurement variances in the algorithmic procedure and accepts gene expression time series data with missing values. Additionally, we tested multiple modeling scenarios regarding the stimuli functions of the gene regulatory network. Here, we summarize the work by Schulze et al. (2015) and put it into a broader context. We review various studies making use of the dual transcriptomics approach to investigate the molecular basis of interacting species. Besides the application to host-pathogen interactions, dual transcriptomics data are also utilized to study mutualistic and commensalistic interactions. Furthermore, we give a short introduction into additional approaches for the prediction of gene regulatory networks and discuss their application to dual transcriptomics data. We

  11. Interactions of timing and prediction error learning.

    PubMed

    Kirkpatrick, Kimberly

    2014-01-01

    Timing and prediction error learning have historically been treated as independent processes, but growing evidence has indicated that they are not orthogonal. Timing emerges at the earliest time point when conditioned responses are observed, and temporal variables modulate prediction error learning in both simple conditioning and cue competition paradigms. In addition, prediction errors, through changes in reward magnitude or value alter timing of behavior. Thus, there appears to be a bi-directional interaction between timing and prediction error learning. Modern theories have attempted to integrate the two processes with mixed success. A neurocomputational approach to theory development is espoused, which draws on neurobiological evidence to guide and constrain computational model development. Heuristics for future model development are presented with the goal of sparking new approaches to theory development in the timing and prediction error fields.

  12. Interactions of timing and prediction error learning.

    PubMed

    Kirkpatrick, Kimberly

    2014-01-01

    Timing and prediction error learning have historically been treated as independent processes, but growing evidence has indicated that they are not orthogonal. Timing emerges at the earliest time point when conditioned responses are observed, and temporal variables modulate prediction error learning in both simple conditioning and cue competition paradigms. In addition, prediction errors, through changes in reward magnitude or value alter timing of behavior. Thus, there appears to be a bi-directional interaction between timing and prediction error learning. Modern theories have attempted to integrate the two processes with mixed success. A neurocomputational approach to theory development is espoused, which draws on neurobiological evidence to guide and constrain computational model development. Heuristics for future model development are presented with the goal of sparking new approaches to theory development in the timing and prediction error fields. PMID:23962670

  13. Generation and Analysis of Large-Scale Data-Driven Mycobacterium tuberculosis Functional Networks for Drug Target Identification.

    PubMed

    Mazandu, Gaston K; Mulder, Nicola J

    2011-01-01

    Technological developments in large-scale biological experiments, coupled with bioinformatics tools, have opened the doors to computational approaches for the global analysis of whole genomes. This has provided the opportunity to look at genes within their context in the cell. The integration of vast amounts of data generated by these technologies provides a strategy for identifying potential drug targets within microbial pathogens, the causative agents of infectious diseases. As proteins are druggable targets, functional interaction networks between proteins are used to identify proteins essential to the survival, growth, and virulence of these microbial pathogens. Here we have integrated functional genomics data to generate functional interaction networks between Mycobacterium tuberculosis proteins and carried out computational analyses to dissect the functional interaction network produced for identifying drug targets using network topological properties. This study has provided the opportunity to expand the range of potential drug targets and to move towards optimal target-based strategies.

  14. Proteome-wide prediction of self-interacting proteins based on multiple properties.

    PubMed

    Liu, Zhongyang; Guo, Feifei; Zhang, Jiyang; Wang, Jian; Lu, Liang; Li, Dong; He, Fuchu

    2013-06-01

    Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER (SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins and various related annotation information. This

  15. Proteome-wide prediction of self-interacting proteins based on multiple properties.

    PubMed

    Liu, Zhongyang; Guo, Feifei; Zhang, Jiyang; Wang, Jian; Lu, Liang; Li, Dong; He, Fuchu

    2013-06-01

    Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER (SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins and various related annotation information. This

  16. 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

  17. PREFACE: Protein protein interactions: principles and predictions

    NASA Astrophysics Data System (ADS)

    Nussinov, Ruth; Tsai, Chung-Jung

    2005-06-01

    Proteins are the `workhorses' of the cell. Their roles span functions as diverse as being molecular machines and signalling. They carry out catalytic reactions, transport, form viral capsids, traverse membranes and form regulated channels, transmit information from DNA to RNA, making possible the synthesis of new proteins, and they are responsible for the degradation of unnecessary proteins and nucleic acids. They are the vehicles of the immune response and are responsible for viral entry into the cell. Given their importance, considerable effort has been centered on the prediction of protein function. A prime way to do this is through identification of binding partners. If the function of at least one of the components with which the protein interacts is known, that should let us assign its function(s) and the pathway(s) in which it plays a role. This holds since the vast majority of their chores in the living cell involve protein-protein interactions. Hence, through the intricate network of these interactions we can map cellular pathways, their interconnectivities and their dynamic regulation. Their identification is at the heart of functional genomics; their prediction is crucial for drug discovery. Knowledge of the pathway, its topology, length, and dynamics may provide useful information for forecasting side effects. The goal of predicting protein-protein interactions is daunting. Some associations are obligatory, others are continuously forming and dissociating. In principle, from the physical standpoint, any two proteins can interact, but under what conditions and at which strength? The principles of protein-protein interactions are general: the non-covalent interactions of two proteins are largely the outcome of the hydrophobic effect, which drives the interactions. In addition, hydrogen bonds and electrostatic interactions play important roles. Thus, many of the interactions observed in vitro are the outcome of experimental overexpression. Protein disorder

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

    PubMed Central

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

    2015-01-01

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

  19. TSEMA: interactive prediction of protein pairings between interacting families.

    PubMed

    Izarzugaza, José M G; Juan, David; Pons, Carles; Ranea, Juan A G; Valencia, Alfonso; Pazos, Florencio

    2006-07-01

    An entire family of methodologies for predicting protein interactions is based on the observed fact that families of interacting proteins tend to have similar phylogenetic trees due to co-evolution. One application of this concept is the prediction of the mapping between the members of two interacting protein families (which protein within one family interacts with which protein within the other). The idea is that the real mapping would be the one maximizing the similarity between the trees. Since the exhaustive exploration of all possible mappings is not feasible for large families, current approaches use heuristic techniques which do not ensure the best solution to be found. This is why it is important to check the results proposed by heuristic techniques and to manually explore other solutions. Here we present TSEMA, the server for efficient mapping assessment. This system calculates an initial mapping between two families of proteins based on a Monte Carlo approach and allows the user to interactively modify it based on performance figures and/or specific biological knowledge. All the explored mappings are graphically shown over a representation of the phylogenetic trees. The system is freely available at http://pdg.cnb.uam.es/TSEMA. Standalone versions of the software behind the interface are available upon request from the authors.

  20. 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

  1. Prioritizing Genomic Drug Targets in Pathogens: Application to Mycobacterium tuberculosis

    PubMed Central

    Hasan, Samiul; Daugelat, Sabine; Rao, P. S. Srinivasa; Schreiber, Mark

    2006-01-01

    We have developed a software program that weights and integrates specific properties on the genes in a pathogen so that they may be ranked as drug targets. We applied this software to produce three prioritized drug target lists for Mycobacterium tuberculosis, the causative agent of tuberculosis, a disease for which a new drug is desperately needed. Each list is based on an individual criterion. The first list prioritizes metabolic drug targets by the uniqueness of their roles in the M. tuberculosis metabolome (“metabolic chokepoints”) and their similarity to known “druggable” protein classes (i.e., classes whose activity has previously been shown to be modulated by binding a small molecule). The second list prioritizes targets that would specifically impair M. tuberculosis, by weighting heavily those that are closely conserved within the Actinobacteria class but lack close homology to the host and gut flora. M. tuberculosis can survive asymptomatically in its host for many years by adapting to a dormant state referred to as “persistence.” The final list aims to prioritize potential targets involved in maintaining persistence in M. tuberculosis. The rankings of current, candidate, and proposed drug targets are highlighted with respect to these lists. Some features were found to be more accurate than others in prioritizing studied targets. It can also be shown that targets can be prioritized by using evolutionary programming to optimize the weights of each desired property. We demonstrate this approach in prioritizing persistence targets. PMID:16789813

  2. Identification of drug targets related to the induction of ventricular tachyarrhythmia through a systems chemical biology approach.

    PubMed

    Ivanov, Sergey M; Lagunin, Alexey A; Pogodin, Pavel V; Filimonov, Dmitry A; Poroikov, Vladimir V

    2015-06-01

    Ventricular tachyarrhythmia (VT) is one of the most serious adverse drug reactions leading to death. The in vitro assessment of the interaction of lead compounds with HERG potassium channels, which is one of the primary known causes of VT induction, is an obligatory test during drug development. However, experimental and clinical data support the hypothesis that the inhibition of ion channels is not the only mechanism of VT induction. Therefore, the identification of other drug targets contributing to the induction of VT is crucial. We developed a systems chemical biology approach for searching for such targets. This approach involves the following steps: (1) creation of special sets of VT-causing and non-VT-causing drugs, (2) statistical analysis of in silico predicted drug-target interaction profiles of studied drugs with 1738 human protein targets for the identification of potential VT-related targets, (3) gene ontology and pathway enrichment analysis of the revealed targets for the identification of biological processes underlying drug-induced VT etiology, (4) creation of a cardiomyocyte regulatory network (CRN) based on general and heart-specific signaling and regulatory pathways, and (5) simulation of changes in the behavior of the CRN caused by the inhibition of each node for the identification of potential VT-related targets. As a result, we revealed 312 potential VT-related targets and classified them into 3 confidence categories: high (36 proteins), medium (111 proteins), and low (165 proteins) classes. The most probable targets may serve as a basis for experimental confirmation and may be used for in vitro or in silico assessments of the relationships between drug candidates and drug-induced VT, the understanding of contraindications of drug application and dangerous drug combinations.

  3. 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

  4. Predicting protein-peptide interactions from scratch

    NASA Astrophysics Data System (ADS)

    Yan, Chengfei; Xu, Xianjin; Zou, Xiaoqin; Zou lab Team

    Protein-peptide interactions play an important role in many cellular processes. The ability to predict protein-peptide complex structures is valuable for mechanistic investigation and therapeutic development. Due to the high flexibility of peptides and lack of templates for homologous modeling, predicting protein-peptide complex structures is extremely challenging. Recently, we have developed a novel docking framework for protein-peptide structure prediction. Specifically, given the sequence of a peptide and a 3D structure of the protein, initial conformations of the peptide are built through protein threading. Then, the peptide is globally and flexibly docked onto the protein using a novel iterative approach. Finally, the sampled modes are scored and ranked by a statistical potential-based energy scoring function that was derived for protein-peptide interactions from statistical mechanics principles. Our docking methodology has been tested on the Peptidb database and compared with other protein-peptide docking methods. Systematic analysis shows significantly improved results compared to the performances of the existing methods. Our method is computationally efficient and suitable for large-scale applications. Nsf CAREER Award 0953839 (XZ) NIH R01GM109980 (XZ).

  5. Differential genome analyses of metabolic enzymes in Pseudomonas aeruginosa for drug target identification.

    PubMed

    Perumal, Deepak; Lim, Chu Sing; Sakharkar, Kishore R; Sakharkar, Meena K

    2007-01-01

    Complete genome sequences of several pathogenic bacteria have been determined, and many more such projects are currently under way. While these data potentially contain all the determinants of host-pathogen interactions and possible drug targets, computational tools for selecting suitable candidates for further experimental analyses are currently limited. Detection of bacterial genes that are non-homologous to human genes, and are essential for the survival of the pathogen represents a promising means of identifying novel drug targets. We used a differential pathway analyses approach (based on KEGG data) to identify essential genes from Pseudomonas aeruginosa. Our approach identified 214 unique enzymes in P. aeruginosa that may be potential drug targets and can be considered for rational drug design. About 40% of these putative targets have been reported as essential by transposon mutagenesis data elsewhere. Homology model for one of the proteins (LpxC) is presented as a case study and can be explored for in silico docking with suitable inhibitors. This approach is a step towards facilitating the search for new antibiotics.

  6. 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

  7. New drugs targeting Th2 lymphocytes in asthma

    PubMed Central

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

    2008-01-01

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

  8. 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

  9. 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

  10. 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

  11. Investigating drug-target association and dissociation mechanisms using metadynamics-based algorithms.

    PubMed

    Cavalli, Andrea; Spitaleri, Andrea; Saladino, Giorgio; Gervasio, Francesco L

    2015-02-17

    CONSPECTUS: This Account highlights recent advances and discusses major challenges in the field of drug-target recognition, binding, and unbinding studied using metadynamics-based approaches, with particular emphasis on their role in structure-based design. Computational chemistry has significantly contributed to drug design and optimization in an extremely broad range of areas, including prediction of target druggability and drug likeness, de novo design, fragment screening, ligand docking, estimation of binding affinity, and modulation of ADMET (absorption, distribution, metabolism, excretion, toxicity) properties. Computationally driven drug discovery must continuously adapt to keep pace with the evolving knowledge of the factors that modulate the pharmacological action of drugs. There is thus an urgent need for novel computational approaches that integrate the vast amount of complex information currently available for small (bio)organic compounds, biologically relevant targets and their complexes, while also accounting accurately for the thermodynamics and kinetics of drug-target association, the intrinsic dynamical behavior of biomolecular systems, and the complexity of protein-protein networks. Understanding the mechanism of drug binding to and unbinding from biological targets is fundamental for optimizing lead compounds and designing novel biologically active ones. One major challenge is the accurate description of the conformational complexity prior to and upon formation of drug-target complexes. Recently, enhanced sampling methods, including metadynamics and related approaches, have been successfully applied to investigate complex mechanisms of drugs binding to flexible targets. Metadynamics is a family of enhanced sampling techniques aimed at enhancing the rare events and reconstructing the underlying free energy landscape as a function of a set of order parameters, usually referred to as collective variables. Studies of drug binding mechanisms have

  12. 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

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

    PubMed Central

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

    2014-01-01

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

  14. Unlocking the secrets to protein–protein interface drug targets using structural mass spectrometry techniques

    PubMed Central

    Dailing, Angela; Luchini, Alessandra; Liotta, Lance

    2016-01-01

    Protein–protein interactions (PPIs) drive all biologic systems at the subcellular and extracellular level. Changes in the specificity and affinity of these interactions can lead to cellular malfunctions and disease. Consequently, the binding interfaces between interacting protein partners are important drug targets for the next generation of therapies that block such interactions. Unfortunately, protein–protein contact points have proven to be very difficult pharmacological targets because they are hidden within complex 3D interfaces. For the vast majority of characterized binary PPIs, the specific amino acid sequence of their close contact regions remains unknown. There has been an important need for an experimental technology that can rapidly reveal the functionally important contact points of native protein complexes in solution. In this review, experimental techniques employing mass spectrometry to explore protein interaction binding sites are discussed. Hydrogen–deuterium exchange, hydroxyl radical footprinting, crosslinking and the newest technology protein painting, are compared and contrasted. PMID:26400464

  15. Emerging high-throughput drug target validation technologies.

    PubMed

    Ilag, Leodevico L; Ng, Jocelyn H; Beste, Gerald; Henning, Stefan W

    2002-09-15

    Identifying the right target for drug development is a critical bottleneck in the pharmaceutical and biotech industries. The genomics revolution has shifted the problem from a scarcity of targets to a surplus of putative drug targets. As the validity of a target cannot be simply inferred from correlative data, the key is confirmation of the causative role of a gene product in a particular disease. It should therefore be recognized that an effective therapeutic strategy requires an appropriate target validation technology to verify the right target.

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

    PubMed

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

    2013-01-01

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

  17. 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

  18. Spherons as a drug target in Alzheimer's disease.

    PubMed

    Averback, P

    1998-10-01

    Spherons are unique brain entities that are causally linked to the amyloid plaques (SPs [senile plaques]) of Alzheimer's disease (AD). SPs are the quantitatively major tissue abnormality of AD. Spherons increase in size (but not in number) gradually throughout life until they reach a size range where they burst and form SPs. Drugs targeted at attenuating the process of spheron transformation into SPs are a logical approach to AD therapy. There are 20 criteria of validity for an SP causal entity that are satisfied by spherons-and no more than a few of these 20 criteria are satisfied by any other known hypothesis. These criteria of validity are reviewed, in addition to common difficulties in understanding spheron theory and a number of common-sense considerations in AD therapeutic research. Spheron-based drug therapy in AD potentially can retard the process of spheron bursting and subsequent plaque formation by: 1) blocking the formation of SPs; 2) reducing the size of SPs; 3) delaying spheron breakdown; and 4) retarding spheron growth. Isolated spherons from human brain are intact human drug targets and can be used as human in vitro or in vivo screening targets. The paramount importance of spherons as a target for drug therapy in AD is emphasized by considering that regardless of any other type of real or potential therapy, there still already exists in every middle-aged adult a full population of spherons in the brain, filled with more than enough amyloid to bring about full-blown AD.

  19. Structural genomics of infectious disease drug targets: the SSGCID

    PubMed Central

    Stacy, Robin; Begley, Darren W.; Phan, Isabelle; Staker, Bart L.; Van Voorhis, Wesley C.; Varani, Gabriele; Buchko, Garry W.; Stewart, Lance J.; Myler, Peter J.

    2011-01-01

    The Seattle Structural Genomics Center for Infectious Disease (SSGCID) is a consortium of researchers at Seattle BioMed, Emerald BioStructures, the University of Washington and Pacific Northwest National Laboratory that was established to apply structural genomics approaches to drug targets from infectious disease organisms. The SSGCID is currently funded over a five-year period by the National Institute of Allergy and Infectious Diseases (NIAID) to determine the three-dimensional structures of 400 proteins from a variety of Category A, B and C pathogens. Target selection engages the infectious disease research and drug-therapy communities to identify drug targets, essential enzymes, virulence factors and vaccine candidates of biomedical relevance to combat infectious diseases. The protein-expression systems, purified proteins, ligand screens and three-dimensional structures produced by SSGCID con­stitute a valuable resource for drug-discovery research, all of which is made freely available to the greater scientific community. This issue of Acta Crystallographica Section F, entirely devoted to the work of the SSGCID, covers the details of the high-throughput pipeline and presents a series of structures from a broad array of pathogenic organisms. Here, a background is provided on the structural genomics of infectious disease, the essential components of the SSGCID pipeline are discussed and a survey of progress to date is presented. PMID:21904037

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

    PubMed Central

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

    2015-01-01

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

  1. Drug Targets for Cell Cycle Dysregulators in Leukemogenesis: In Silico Docking Studies

    PubMed Central

    Jayaraman, Archana; Jamil, Kaiser

    2014-01-01

    Alterations in cell cycle regulating proteins are a key characteristic in neoplastic proliferation of lymphoblast cells in patients with Acute Lymphoblastic Leukemia (ALL). The aim of our study was to investigate whether the routinely administered ALL chemotherapeutic agents would be able to bind and inhibit the key deregulated cell cycle proteins such as - Cyclins E1, D1, D3, A1 and Cyclin Dependent Kinases (CDK) 2 and 6. We used Schrödinger Glide docking protocol to dock the chemotherapeutic drugs such as Doxorubicin and Daunorubicin and others which are not very common including Clofarabine, Nelarabine and Flavopiridol, to the crystal structures of these proteins. We observed that the drugs were able to bind and interact with cyclins E1 and A1 and CDKs 2 and 6 while their docking to cyclins D1 and D3 were not successful. This binding proved favorable to interact with the G1/S cell cycle phase proteins that were examined in this study and may lead to the interruption of the growth of leukemic cells. Our observations therefore suggest that these drugs could be explored for use as inhibitors for these cell cycle proteins. Further, we have also highlighted residues which could be important in the designing of pharmacophores against these cell cycle proteins. This is the first report in understanding the mechanism of action of the drugs targeting these cell cycle proteins in leukemia through the visualization of drug-target binding and molecular docking using computational methods. PMID:24454966

  2. 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.

  3. 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

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

    PubMed

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

    2014-07-01

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

  5. Voltage-gated Potassium Channels as Therapeutic Drug Targets

    PubMed Central

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

    2009-01-01

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

  6. Wzy-dependent bacterial capsules as potential drug targets.

    PubMed

    Ericsson, Daniel J; Standish, Alistair; Kobe, Bostjan; Morona, Renato

    2012-10-01

    The bacterial capsule is a recognized virulence factor in pathogenic bacteria. It likely works as an antiphagocytic barrier by minimizing complement deposition on the bacterial surface. With the continual rise of bacterial pathogens resistant to multiple antibiotics, there is an increasing need for novel drugs. In the Wzy-dependent pathway, the biosynthesis of capsular polysaccharide (CPS) is regulated by a phosphoregulatory system, whose main components consist of bacterial-tyrosine kinases (BY-kinases) and their cognate phosphatases. The ability to regulate capsule biosynthesis has been shown to be vital for pathogenicity, because different stages of infection require a shift in capsule thickness, making the phosphoregulatory proteins suitable as drug targets. Here, we review the role of regulatory proteins focusing on Streptococcus pneumoniae, Staphylococcus aureus, and Escherichia coli and discuss their suitability as targets in structure-based drug design.

  7. 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

  8. Preparation of bovine serum albumin nanospheres as drug targeting carriers.

    PubMed

    Nakagawa, Y; Takayama, K; Ueda, H; Machida, Y; Nagai, T

    1987-12-01

    Bovine serum albumin nanospheres (BSA-NS) of mean diameter about 170 nm were prepared by means of the tanning method with glutaraldehyde, and their efficacy as drug targeting carriers was evaluated. To gain insight of biodegradability, BSA microspheres (BSA-MS) were first administered to rats and their distributions in the lungs and liver were observed by a scanning electron microscope. A large amount of BSA-MS was found in the lungs and their surface was slightly degraded at 1 week after the administration. For investigating biocompatibility, the weight increase of the spleen and liver was measured after the administration of the BSA-NS to mice. The spleen weight of the group receiving BSA-NS was equivalent to that of the control group, though the liver weight was significantly increased. It was observed that conjugates of BSA-NS with antibody selectively concentrated on the surface of Sepharose beads which were coated with antigen.

  9. Single-cell transcriptomics for drug target discovery.

    PubMed

    Spaethling, Jennifer M; Eberwine, James H

    2013-10-01

    Single cell sequencing is currently in its relative infancy although an unprecedented amount of information is already being generated. These techniques are providing new insight into intercellular variability as well as identification of previously unrecognized drug targets. As more groups are gaining an interest in this fruitful technique, new sample preparation techniques, sequencing platforms, and bioinformatics tools are being developed which only improve the quantity and quality of data generated in these studies. Great advancements in harvest (in vivo pipette), sample preparation, and sequencing (Illumina HiSeq 2500/MiSeq, Ion Torrent PGM, Pacific Biosciences RS) are allowing for previously untestable questions to be answered and for expanded accessibility of these technologies.

  10. 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

  11. 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.

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

    PubMed

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

    2015-02-01

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

  13. Increasing the Structural Coverage of Tuberculosis Drug Targets

    PubMed Central

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

    2015-01-01

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

  14. 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.

  15. Increasing the structural coverage of tuberculosis drug targets

    DOE PAGES

    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

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

    PubMed

    Nakayama, Masamichi; Akimoto, Jun; Okano, Teruo

    2014-08-01

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

  17. Histone deacetylase 6 represents a novel drug target in the oncogenic Hedgehog signaling pathway.

    PubMed

    Dhanyamraju, Pavan Kumar; Holz, Philipp Simon; Finkernagel, Florian; Fendrich, Volker; Lauth, Matthias

    2015-03-01

    Uncontrolled Hedgehog (Hh) signaling is the cause of several malignancies, including the pediatric cancer medulloblastoma, a neuroectodermal tumor affecting the cerebellum. Despite the development of potent Hh pathway antagonists, medulloblastoma drug resistance is still an unresolved issue that requires the identification of novel drug targets. Following up on our observation that histone deacetylase 6 (HDAC6) expression was increased in Hh-driven medulloblastoma, we found that this enzyme is essential for full Hh pathway activation. Intriguingly, these stimulatory effects of HDAC6 are partly integrated downstream of primary cilia, a known HDAC6-regulated structure. In addition, HDAC6 is also required for the complete repression of basal Hh target gene expression. These contrasting effects are mediated by HDAC6's impact on Gli2 mRNA and GLI3 protein expression. As a result of this complex interaction with Hh signaling, global transcriptome analysis revealed that HDAC6 regulates only a subset of Smoothened- and Gli-driven genes, including all well-established Hh targets such as Ptch1 or Gli1. Importantly, medulloblastoma cell survival was severely compromised by HDAC6 inhibition in vitro and pharmacologic HDAC6 blockade strongly reduced tumor growth in an in vivo allograft model. In summary, our data describe an important role for HDAC6 in regulating the mammalian Hh pathway and encourage further studies focusing on HDAC6 as a novel drug target in medulloblastoma. PMID:25552369

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

    PubMed Central

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

    2015-01-01

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

  19. 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.

  20. Predicting community composition from pairwise interactions

    NASA Astrophysics Data System (ADS)

    Friedman, Jonathan; Higgins, Logan; Gore, Jeff

    The ability to predict the structure of complex, multispecies communities is crucial for understanding the impact of species extinction and invasion on natural communities, as well as for engineering novel, synthetic communities. Communities are often modeled using phenomenological models, such as the classical generalized Lotka-Volterra (gLV) model. While a lot of our intuition comes from such models, their predictive power has rarely been tested experimentally. To directly assess the predictive power of this approach, we constructed synthetic communities comprised of up to 8 soil bacteria. We measured the outcome of competition between all species pairs, and used these measurements to predict the composition of communities composed of more than 2 species. The pairwise competitions resulted in a diverse set of outcomes, including coexistence, exclusion, and bistability, and displayed evidence for both interference and facilitation. Most pair outcomes could be captured by the gLV framework, and the composition of multispecies communities could be predicted for communities composed solely of such pairs. Our results demonstrate the predictive ability and utility of simple phenomenology, which enables accurate predictions in the absence of mechanistic details.

  1. Adipokines as drug targets in diabetes and underlying disturbances.

    PubMed

    Andrade-Oliveira, Vinícius; Câmara, Niels O S; Moraes-Vieira, Pedro M

    2015-01-01

    Diabetes and obesity are worldwide health problems. White fat dynamically participates in hormonal and inflammatory regulation. White adipose tissue is recognized as a multifactorial organ that secretes several adipose-derived factors that have been collectively termed "adipokines." Adipokines are pleiotropic molecules that gather factors such as leptin, adiponectin, visfatin, apelin, vaspin, hepcidin, RBP4, and inflammatory cytokines, including TNF and IL-1β, among others. Multiple roles in metabolic and inflammatory responses have been assigned to these molecules. Several adipokines contribute to the self-styled "low-grade inflammatory state" of obese and insulin-resistant subjects, inducing the accumulation of metabolic anomalies within these individuals, including autoimmune and inflammatory diseases. Thus, adipokines are an interesting drug target to treat autoimmune diseases, obesity, insulin resistance, and adipose tissue inflammation. The aim of this review is to present an overview of the roles of adipokines in different immune and nonimmune cells, which will contribute to diabetes as well as to adipose tissue inflammation and insulin resistance development. We describe how adipokines regulate inflammation in these diseases and their therapeutic implications. We also survey current attempts to exploit adipokines for clinical applications, which hold potential as novel approaches to drug development in several immune-mediated diseases.

  2. 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.

  3. 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.

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

    PubMed

    Müller, Joachim; Hemphill, Andrew

    2011-01-01

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

  5. 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. 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.

  7. 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

  8. Novel Drugs Targeting the c-Ring of the F1FO-ATP Synthase.

    PubMed

    Pagliarani, Alessandra; Nesci, S; Ventrella, V

    2016-01-01

    Increasing evidence highlights the role of the ATP synthase/hydrolase, also known as F1FO-complex, as key molecular and enzymatic switch between cell life and death, thus increasing the enzyme attractiveness as drug target in pharmacology. Being inhibition of ATP production usually linked to antiproliferative properties, drugs targeting the enzyme complex have been mainly considered to fight pathogen parasites and cancer. In recent years, a number of natural macrolides, produced by bacterial fermentation and structurally related to the classical enzyme inhibitor oligomycin, have been shown to bind to the membrane-embedded FO sector and to inhibit the enzyme complex by an oligomycin-like mechanism, namely by interacting with the c-ring. Other than natural macrolide antibiotics, which display variegated inhibition power on different F1FO-complexes, synthetic compounds from the diarylquinoline and organotin families also target the c-ring and strongly inhibit the enzyme. Bioinformatic insights address drug design to target FO subunits. Additionally, the possible modulation of the drug inhibition power, by amino acid substitutions or post-translational modifications of c-subunits, adds further interest to the target. The present survey on compounds targeting the c-ring and bi-directionally blocking the transmembrane proton flux which drives ATP synthesis/hydrolysis, discloses new therapeutic options to fight cancer and infections sustained by therapeutically recalcitrant microorganisms. Additionally, c-ring targeting compounds may constitute new tools to eradicate undesired biofilms and to address at the molecular level the therapy of mammalian diseases linked to mitochondrial dysfunctions. In summary, studies on the only partially known molecular interactions within the c-ring of the F1FO-complex may renew hope to counteract mammalian diseases. PMID:26864551

  9. Predicting RNA-RNA Interactions Using RNAstructure.

    PubMed

    DiChiacchio, Laura; Mathews, David H

    2016-01-01

    RNA-RNA binding is a required step for many regulatory and catalytic processes in the cell. Identifying RNA-RNA hybridization sites is challenging because of the competition between intramolecular and intermolecular structure formation. A complete picture of RNA-RNA binding includes an understanding of single-stranded folding and binding site accessibility, and is strongly concentration-dependent. This chapter provides guidance for using RNAstructure to predict RNA-RNA binding sites and RNA-RNA structures, utilizing free energy minimization and partition function calculations. RNAstructure is freely available at http://rna.urmc.rochester.edu/RNAstructure.html . PMID:27665592

  10. Predicting biotic interactions and their variability in a changing environment.

    PubMed

    Kadowaki, Kohmei; Barbera, Claire G; Godsoe, William; Delsuc, Frédéric; Mouquet, Nicolas

    2016-05-01

    Global environmental change is altering the patterns of biodiversity worldwide. Observation and theory suggest that species' distributions and abundances depend on a suite of processes, notably abiotic filtering and biotic interactions, both of which are constrained by species' phylogenetic history. Models predicting species distribution have historically mostly considered abiotic filtering and are only starting to integrate biotic interaction. However, using information on present interactions to forecast the future of biodiversity supposes that biotic interactions will not change when species are confronted with new environments. Using bacterial microcosms, we illustrate how biotic interactions can vary along an environmental gradient and how this variability can depend on the phylogenetic distance between interacting species. PMID:27220858

  11. 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

  12. Adenylating Enzymes in Mycobacterium tuberculosis as Drug Targets

    PubMed Central

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

    2013-01-01

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

  13. 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

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

    PubMed

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

    2006-09-01

    The Aurora kinases, a family of mitotic regulators, have received much attention as potential targets for novel anti-cancer therapeutics. Several Aurora kinase inhibitors have been described including ZM447439, which prevents chromosome alignment, spindle checkpoint function and cytokinesis. Subsequently, ZM447439-treated cells exit mitosis without dividing and lose viability. Because ZM447439 inhibits both Aurora A and B, we set out to determine which phenotypes are due to inhibition of which kinase. Using molecular genetic approaches, we show that inhibition of Aurora B kinase activity phenocopies ZM447439. Furthermore, a novel ZM compound, which is 100 times more selective for Aurora B over Aurora A in vitro, induces identical phenotypes. Importantly, inhibition of Aurora B kinase activity induces a penetrant anti-proliferative phenotype, indicating that Aurora B is an attractive anti-cancer drug target. Using molecular genetic and chemical-genetic approaches, we also probe the role of Aurora A kinase activity. We show that simultaneous repression of Aurora A plus induction of a catalytic mutant induces a monopolar phenotype. Consistently, another novel ZM-related inhibitor, which is 20 times as potent against Aurora A compared with ZM447439, induces a monopolar phenotype. Expression of a drug-resistant Aurora A mutant reverts this phenotype, demonstrating that Aurora A kinase activity is required for spindle bipolarity in human cells. Because small molecule-mediated inhibition of Aurora A and Aurora B yields distinct phenotypes, our observations indicate that the Auroras may present two avenues for anti-cancer drug discovery. PMID:16912073

  15. Activity based chemical proteomics: profiling proteases as drug targets.

    PubMed

    Heal, William Percy; Wickramasinghe, Sasala Roshinie; Tate, Edward William

    2008-09-01

    The pivotal role of proteases in many diseases has generated considerable interest in their basic biology, and in the potential to target them for chemotherapy. Although fundamental to the initiation and progression of diseases such as cancer, diabetes, arthritis and malaria, in many cases their precise role remains unknown. Activity-based chemical proteomics-an emerging field involving a combination of organic synthesis, biochemistry, cell biology, biophysics and bioinformatics-allows the detection, visualisation and activity quantification of whole families or selected sub-sets of proteases based upon their substrate specificity. This approach can be applied for drug target/lead identification and validation, the fundamentals of drug discovery. The activity-based probes discussed in this review contain three key features; a 'warhead' (binds irreversibly but selectively to the active site), a 'tag' (allowing enzyme 'handling', with a combination of fluorescent, affinity and/or radio labels), and a linker region between warhead and tag. From the design and synthesis of the linker arise some of the latest developments discussed here; not only can the physical properties (e.g., solubility, localisation) of the probe be tuned, but the inclusion of a cleavable moiety allows selective removal of tagged enzyme from affinity beads etc. The design and synthesis of recently reported probes is discussed, including modular assembly of highly versatile probes via solid phase synthesis. Recent applications of activity-based protein profiling to specific proteases (serine, threonine, cysteine and metalloproteases) are reviewed as are demonstrations of their use in the study of disease function in cancer and malaria.

  16. Characterizing EPR-Mediated Passive Drug Targeting using Contrast-Enhanced Functional Ultrasound Imaging

    PubMed Central

    Theek, Benjamin; Gremse, Felix; Kunjachan, Sijumon; Fokong, Stanley; Pola, Robert; Pechar, Michal; Deckers, Roel; Storm, Gert; Ehling, Josef; Kiessling, Fabian; Lammers, Twan

    2014-01-01

    The Enhanced Permeability and Retention (EPR) effect is extensively used in drug delivery research. Taking into account that EPR is a highly variable phenomenon, we have here set out to evaluate if contrast-enhanced functional ultrasound (ceUS) imaging can be employed to characterize EPR-mediated passive drug targeting to tumors. Using standard fluorescence molecular tomography (FMT) and two different protocols for hybrid computed tomography-fluorescence molecular tomography (CT-FMT), the tumor accumulation of a ~10 nm-sized near-infrared-fluorophore-labeled polymeric drug carrier (pHPMA-Dy750) was evaluated in CT26 tumor-bearing mice. In the same set of animals, two different ceUS techniques (2D MIOT and 3D B-mode imaging) were employed to assess tumor vascularization. Subsequently, the degree of tumor vascularization was correlated with the degree of EPR-mediated drug targeting. Depending on the optical imaging protocol used, the tumor accumulation of the polymeric drug carrier ranged from 5-12% of the injected dose. The degree of tumor vascularization, determined using ceUS, varied from 4-11%. For both hybrid CT-FMT protocols, a good correlation between the degree of tumor vascularization and the degree of tumor accumulation was observed, with in the case of reconstructed CT-FMT, correlation coefficients of ~0.8 and p-values of <0.02. These findings indicate that ceUS can be used to characterize and predict EPR, and potentially also to pre-selecting patients likely to respond to passively tumor-targeted nanomedicine treatments. PMID:24631862

  17. Simple prediction of interaction strengths in complex food webs

    PubMed Central

    Berlow, Eric L.; Dunne, Jennifer A.; Martinez, Neo D.; Stark, Philip B.; Williams, Richard J.; Brose, Ulrich

    2009-01-01

    Darwin's classic image of an “entangled bank” of interdependencies among species has long suggested that it is difficult to predict how the loss of one species affects the abundance of others. We show that for dynamical models of realistically structured ecological networks in which pair-wise consumer-resource interactions allometrically scale to the ¾ power—as suggested by metabolic theory—the effect of losing one species on another can be predicted well by simple functions of variables easily observed in nature. By systematically removing individual species from 600 networks ranging from 10–30 species, we analyzed how the strength of 254,032 possible pair-wise species interactions depended on 90 stochastically varied species, link, and network attributes. We found that the interaction strength between a pair of species is predicted well by simple functions of the two species' biomasses and the body mass of the species removed. On average, prediction accuracy increases with network size, suggesting that greater web complexity simplifies predicting interaction strengths. Applied to field data, our model successfully predicts interactions dominated by trophic effects and illuminates the sign and magnitude of important nontrophic interactions. PMID:19114659

  18. Improving miRNA-mRNA interaction predictions

    PubMed Central

    2014-01-01

    Background MicroRNAs are short RNA molecules that post-transcriptionally regulate gene expression. Today, microRNA target prediction remains challenging since very few have been experimentally validated and sequence-based predictions have large numbers of false positives. Furthermore, due to the different measuring rules used in each database of predicted interactions, the selection of the most reliable ones requires extensive knowledge about each algorithm. Results Here we propose two methods to measure the confidence of predicted interactions based on experimentally validated information. The output of the methods is a combined database where new scores and statistical confidences are re-assigned to each predicted interaction. The new scores allow the robust combination of several databases without the effect of low-performing algorithms dragging down good-performing ones. The combined databases obtained using both algorithms described in this paper outperform each of the existing predictive algorithms that were considered for the combination. Conclusions Our approaches are a useful way to integrate predicted interactions from different databases. They reduce the selection of interactions to a unique database based on an intuitive score and allow comparing databases between them. PMID:25559987

  19. Predicting Marital Happiness and Stability from Newlywed Interactions.

    ERIC Educational Resources Information Center

    Gottman, John M.; Coan, James; Carrere, Sybil; Swanson, Catherine

    1998-01-01

    Marital interaction processes that are predictive of divorce or marital stability and processes that discriminate between happily and unhappily married stable couples are explored (N=130). Seven types of process models are examined, and results are discussed. Divorce and stability were predicted with 83% accuracy, and satisfaction with 80%…

  20. Complete genome-wide screening and subtractive genomic approach revealed new virulence factors, potential drug targets against bio-war pathogen Brucella melitensis 16M.

    PubMed

    Pradeepkiran, Jangampalli Adi; Sainath, Sri Bhashyam; Kumar, Konidala Kranthi; Bhaskar, Matcha

    2015-01-01

    Brucella melitensis 16M is a Gram-negative coccobacillus that infects both animals and humans. It causes a disease known as brucellosis, which is characterized by acute febrile illness in humans and causes abortions in livestock. To prevent and control brucellosis, identification of putative drug targets is crucial. The present study aimed to identify drug targets in B. melitensis 16M by using a subtractive genomic approach. We used available database repositories (Database of Essential Genes, Kyoto Encyclopedia of Genes and Genomes Automatic Annotation Server, and Kyoto Encyclopedia of Genes and Genomes) to identify putative genes that are nonhomologous to humans and essential for pathogen B. melitensis 16M. The results revealed that among 3 Mb genome size of pathogen, 53 putative characterized and 13 uncharacterized hypothetical genes were identified; further, from Basic Local Alignment Search Tool protein analysis, one hypothetical protein showed a close resemblance (50%) to Silicibacter pomeroyi DUF1285 family protein (2RE3). A further homology model of the target was constructed using MODELLER 9.12 and optimized through variable target function method by molecular dynamics optimization with simulating annealing. The stereochemical quality of the restrained model was evaluated by PROCHECK, VERIFY-3D, ERRAT, and WHATIF servers. Furthermore, structure-based virtual screening was carried out against the predicted active site of the respective protein using the glycerol structural analogs from the PubChem database. We identified five best inhibitors with strong affinities, stable interactions, and also with reliable drug-like properties. Hence, these leads might be used as the most effective inhibitors of modeled protein. The outcome of the present work of virtual screening of putative gene targets might facilitate design of potential drugs for better treatment against brucellosis.

  1. Attitude-Normative Belief Interactions in Predicting Adolescent Substance Use.

    ERIC Educational Resources Information Center

    Grube, Joel W.; Morgan, Mark

    Additive and interactive models of attitudes and normative beliefs were compared in a survey of smoking, drinking, and drug use among post-primary students from Dublin, Ireland. It was hypothesized that contingent consistency interactions would be found: (1) when predicting drug use, but not smoking or alcohol use; (2) for younger, but not older…

  2. What Predicts Use of Learning-Centered, Interactive Engagement Methods?

    ERIC Educational Resources Information Center

    Madson, Laura; Trafimow, David; Gray, Tara; Gutowitz, Michael

    2014-01-01

    What makes some faculty members more likely to use interactive engagement methods than others? We use the theory of reasoned action to predict faculty members' use of interactive engagement methods. Results indicate that faculty members' beliefs about the personal positive consequences of using these methods (e.g., "Using…

  3. RNA-RNA interaction prediction using genetic algorithm

    PubMed Central

    2014-01-01

    Background RNA-RNA interaction plays an important role in the regulation of gene expression and cell development. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. In the RNA-RNA interaction prediction problem, two RNA sequences are given as inputs and the goal is to find the optimal secondary structure of two RNAs and between them. Some different algorithms have been proposed to predict RNA-RNA interaction structure. However, most of them suffer from high computational time. Results In this paper, we introduce a novel genetic algorithm called GRNAs to predict the RNA-RNA interaction. The proposed algorithm is performed on some standard datasets with appropriate accuracy and lower time complexity in comparison to the other state-of-the-art algorithms. In the proposed algorithm, each individual is a secondary structure of two interacting RNAs. The minimum free energy is considered as a fitness function for each individual. In each generation, the algorithm is converged to find the optimal secondary structure (minimum free energy structure) of two interacting RNAs by using crossover and mutation operations. Conclusions This algorithm is properly employed for joint secondary structure prediction. The results achieved on a set of known interacting RNA pairs are compared with the other related algorithms and the effectiveness and validity of the proposed algorithm have been demonstrated. It has been shown that time complexity of the algorithm in each iteration is as efficient as the other approaches. PMID:25114714

  4. DIMA 2.0--predicted and known domain interactions.

    PubMed

    Pagel, Philipp; Oesterheld, Matthias; Tovstukhina, Oksana; Strack, Norman; Stümpflen, Volker; Frishman, Dmitrij

    2008-01-01

    DIMA-the domain interaction map has evolved from a simple web server for domain phylogenetic profiling into an integrative prediction resource combining both experimental data on domain-domain interactions and predictions from two different algorithms. With this update, DIMA obtains greatly improved coverage at the level of genomes and domains as well as with respect to available prediction approaches. The domain phylogenetic profiling method now uses SIMAP as its backend for exhaustive domain hit coverage: 7038 Pfam domains were profiled over 460 completely sequenced genomes. Domain pair exclusion predictions were produced from 83 969 distinct protein-protein interactions obtained from IntAct resulting in 21 513 domain pairs with significant domain pair exclusion algorithm scores. Additional predictions applying the same algorithm to predicted protein interactions from STRING yielded 2378 high-confidence pairs. Experimental data comes from iPfam (3074) and 3did (3034 pairs), two databases identifying domain contacts in solved protein structures. Taken together, these two resources yielded 3653 distinct interacting domain pairs. DIMA is available at http://mips.gsf.de/genre/proj/dima.

  5. Prediction and Annotation of Plant Protein Interaction Networks

    SciTech Connect

    McDermott, Jason E.; Wang, Jun; Yu, Jun; Wong, Gane Ka-Shu; Samudrala, Ram

    2009-02-01

    Large-scale experimental studies of interactions between components of biological systems have been performed for a variety of eukaryotic organisms. However, there is a dearth of such data for plants. Computational methods for prediction of relationships between proteins, primarily based on comparative genomics, provide a useful systems-level view of cellular functioning and can be used to extend information about other eukaryotes to plants. We have predicted networks for Arabidopsis thaliana, Oryza sativa indica and japonica and several plant pathogens using the Bioverse (http://bioverse.compbio.washington.edu) and show that they are similar to experimentally-derived interaction networks. Predicted interaction networks for plants can be used to provide novel functional annotations and predictions about plant phenotypes and aid in rational engineering of biosynthesis pathways.

  6. A Grammatical Approach to RNA-RNA Interaction Prediction

    NASA Astrophysics Data System (ADS)

    Kato, Yuki; Akutsu, Tatsuya; Seki, Hiroyuki

    2007-11-01

    Much attention has been paid to two interacting RNA molecules involved in post-transcriptional control of gene expression. Although there have been a few studies on RNA-RNA interaction prediction based on dynamic programming algorithm, no grammar-based approach has been proposed. The purpose of this paper is to provide a new modeling for RNA-RNA interaction based on multiple context-free grammar (MCFG). We present a polynomial time parsing algorithm for finding the most likely derivation tree for the stochastic version of MCFG, which is applicable to RNA joint secondary structure prediction including kissing hairpin loops. Also, elementary tests on RNA-RNA interaction prediction have shown that the proposed method is comparable to Alkan et al.'s method.

  7. Theoretical study and prediction of BVI noise including close interactions

    NASA Astrophysics Data System (ADS)

    Spiegel, Pierre; Rahier, Gilles

    1991-05-01

    The following study deals with the highly impulsive blade-vortex interaction (BVI) noise which is generated by helicopter main rotors in descent flight. Two computer codes have been especially designed to predict it at reduced computing costs, starting from given vortices. The aerodynamic code, called MAIR, predicts the unsteady airloads, even for head-on collisions, by using a singularity method and modeling the vortices as cloud vortices with viscous cores. The acoustic code, called PARIS, computes the resulting radiated loading noise and is based on the Ffowcs Williams and Hawkings equation. These two codes are ready to perform noise prediction in the flight cases but the vortex prediction required for input data still needs to be improved. While waiting for better wake data, the two codes have been used for a theoretical parametric study of arbitrary single blade-vortex interactions. This study gives a physical insight of the phenomenon and its predicted tendencies could help designing quieter blades.

  8. Predict octane numbers using a generalized interaction method

    SciTech Connect

    Twu, C.H.; Coon, J.E.

    1996-02-01

    An interaction-based correlation using a new approach can be used to predict research and motor octane numbers of gasoline blends. An ultimately detailed analysis of the gasoline cut is not necessary. This correlation can describe blending behavior over the entire composition range of gasoline cuts. The component-oriented interaction approach is general and will accurately predict, without performing additional blending studies, blending behavior for new gasoline cuts. The proposed correlation fits the data quite closely for blends of many gasoline cuts. The regression gives realistic values for binary interaction parameters between components. A unique set of binary interaction parameters was found for the equation for predicting octane number of any gasoline blend. The binary interaction parameters between components contained in gasoline cuts have been converted to binary interaction parameters between gasoline cuts through a general equation to simplify the calculations. Because of the proposed method`s accuracy, optimum allocation of components among gasoline grades can be obtained and predicted values can be used for quality control of the octane number of marketed gasolines.

  9. The interaction between attention and motor prediction. An ERP study.

    PubMed

    Jones, Alexander; Hughes, Gethin; Waszak, Florian

    2013-12-01

    Performing a voluntary action involves the anticipation of the intended effect of that action. Interaction with the environment also requires the allocation of attention. However, the effects of attention upon motor predictive processes remain unclear. Here we use a novel paradigm to investigate attention and motor prediction orthogonally. In an acquisition phase, high and low tones were associated with left and right key presses. In the following test phase, tones were presented at random and participants attended to only one ear whilst ignoring tones presented in the unattended ear. In the test phase a tone could therefore be presented at the attended or unattended ear, as well as being congruent or incongruent with prior action-effect learning. We demonstrated early and late effects of attention as well as a later independent motor prediction effect with a larger P3a for incongruent tones. Interestingly, we demonstrated an intermediate interaction, showing an action-effect negativity (NAE) for tones which were unattended, whilst no motor prediction effect was found for attended tones. This interaction pattern suggests that attention and motor prediction are not opposing processes but can both operate to modulate prediction, providing valuable new insight into the relationship between attention and the effects of motor prediction.

  10. 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.

  11. Signature Product Code for Predicting Protein-Protein Interactions

    SciTech Connect

    Martin, Shawn B.; Brown, William M.

    2004-09-25

    The SigProdV1.0 software consists of four programs which together allow the prediction of protein-protein interactions using only amino acid sequences and experimental data. The software is based on the use of tensor products of amino acid trimers coupled with classifiers known as support vector machines. Essentially the program looks for amino acid trimer pairs which occur more frequently in protein pairs which are known to interact. These trimer pairs are then used to make predictions about unknown protein pairs. A detailed description of the method can be found in the paper: S. Martin, D. Roe, J.L. Faulon. "Predicting protein-protein interactions using signature products," Bioinformatics, available online from Advance Access, Aug. 19, 2004.

  12. Predicting the protein-protein interactions using primary structures with predicted protein surface

    PubMed Central

    2010-01-01

    Background Many biological functions involve various protein-protein interactions (PPIs). Elucidating such interactions is crucial for understanding general principles of cellular systems. Previous studies have shown the potential of predicting PPIs based on only sequence information. Compared to approaches that require other auxiliary information, these sequence-based approaches can be applied to a broader range of applications. Results This study presents a novel sequence-based method based on the assumption that protein-protein interactions are more related to amino acids at the surface than those at the core. The present method considers surface information and maintains the advantage of relying on only sequence data by including an accessible surface area (ASA) predictor recently proposed by the authors. This study also reports the experiments conducted to evaluate a) the performance of PPI prediction achieved by including the predicted surface and b) the quality of the predicted surface in comparison with the surface obtained from structures. The experimental results show that surface information helps to predict interacting protein pairs. Furthermore, the prediction performance achieved by using the surface estimated with the ASA predictor is close to that using the surface obtained from protein structures. Conclusion This work presents a sequence-based method that takes into account surface information for predicting PPIs. The proposed procedure of surface identification improves the prediction performance with an F-measure of 5.1%. The extracted surfaces are also valuable in other biomedical applications that require similar information. PMID:20122202

  13. Predicting high levels of multitasking reduces between-tasks interactions.

    PubMed

    Fischer, Rico; Dreisbach, Gesine

    2015-12-01

    The simultaneous handling of 2 tasks requires shielding of the prioritized primary task (T1) from interference caused by the secondary task (T2) processing. Such interactions between tasks (e.g., between-task interference, or crosstalk) depend on the similarity of both tasks and are especially pronounced when both tasks overlap strongly in time. In the present study we investigated whether between-tasks interference can be reduced when specific items do not predict the level of interference but instead the degree of temporal proximity between both tasks. We implemented an item-specific proportion manipulation of temporal task overlap (stimulus onset asynchrony [SOA]). Selected stimuli of T1 predicted high temporal task overlap (short SOAs) in 80% of trials, whereas other stimuli of T1 predicted low temporal task overlap (long SOAs) in 80% of trials. Results showed that the predictive value of T1 stimuli determined the adjustment of T1 shielding. That is, interference from the secondary task was significantly reduced for items predicting high temporal task overlap compared to items predicting low temporal task overlap. It is important to note that task shielding was not initiated by predicting the actual conflict level (i.e., whether T1 and T2 required compatible/incompatible responses) between tasks but by specific items predicting conditions in which 2 tasks are likely to interact (i.e., short vs. long SOA). These findings offer new insights into the specificity of contextual bottom-up regulations of cognitive control. PMID:26480246

  14. Predicting high levels of multitasking reduces between-tasks interactions.

    PubMed

    Fischer, Rico; Dreisbach, Gesine

    2015-12-01

    The simultaneous handling of 2 tasks requires shielding of the prioritized primary task (T1) from interference caused by the secondary task (T2) processing. Such interactions between tasks (e.g., between-task interference, or crosstalk) depend on the similarity of both tasks and are especially pronounced when both tasks overlap strongly in time. In the present study we investigated whether between-tasks interference can be reduced when specific items do not predict the level of interference but instead the degree of temporal proximity between both tasks. We implemented an item-specific proportion manipulation of temporal task overlap (stimulus onset asynchrony [SOA]). Selected stimuli of T1 predicted high temporal task overlap (short SOAs) in 80% of trials, whereas other stimuli of T1 predicted low temporal task overlap (long SOAs) in 80% of trials. Results showed that the predictive value of T1 stimuli determined the adjustment of T1 shielding. That is, interference from the secondary task was significantly reduced for items predicting high temporal task overlap compared to items predicting low temporal task overlap. It is important to note that task shielding was not initiated by predicting the actual conflict level (i.e., whether T1 and T2 required compatible/incompatible responses) between tasks but by specific items predicting conditions in which 2 tasks are likely to interact (i.e., short vs. long SOA). These findings offer new insights into the specificity of contextual bottom-up regulations of cognitive control.

  15. Predicting genetic interactions with random walks on biological networks

    PubMed Central

    Chipman, Kyle C; Singh, Ambuj K

    2009-01-01

    Background Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree classifier, integrate diverse

  16. Proline Rich Motifs as Drug Targets in Immune Mediated Disorders

    PubMed Central

    Srinivasan, Mythily; Dunker, A. Keith

    2012-01-01

    The current version of the human immunome network consists of nearly 1400 interactions involving approximately 600 proteins. Intermolecular interactions mediated by proline-rich motifs (PRMs) are observed in many facets of the immune response. The proline-rich regions are known to preferentially adopt a polyproline type II helical conformation, an extended structure that facilitates transient intermolecular interactions such as signal transduction, antigen recognition, cell-cell communication and cytoskeletal organization. The propensity of both the side chain and the backbone carbonyls of the polyproline type II helix to participate in the interface interaction makes it an excellent recognition motif. An advantage of such distinct chemical features is that the interactions can be discriminatory even in the absence of high affinities. Indeed, the immune response is mediated by well-orchestrated low-affinity short-duration intermolecular interactions. The proline-rich regions are predominantly localized in the solvent-exposed regions such as the loops, intrinsically disordered regions, or between domains that constitute the intermolecular interface. Peptide mimics of the PRM have been suggested as potential antagonists of intermolecular interactions. In this paper, we discuss novel PRM-mediated interactions in the human immunome that potentially serve as attractive targets for immunomodulation and drug development for inflammatory and autoimmune pathologies. PMID:22666276

  17. Scalable prediction of compound-protein interactions using minwise hashing.

    PubMed

    Tabei, Yasuo; Yamanishi, Yoshihiro

    2013-01-01

    The identification of compound-protein interactions plays key roles in the drug development toward discovery of new drug leads and new therapeutic protein targets. There is therefore a strong incentive to develop new efficient methods for predicting compound-protein interactions on a genome-wide scale. In this paper we develop a novel chemogenomic method to make a scalable prediction of compound-protein interactions from heterogeneous biological data using minwise hashing. The proposed method mainly consists of two steps: 1) construction of new compact fingerprints for compound-protein pairs by an improved minwise hashing algorithm, and 2) application of a sparsity-induced classifier to the compact fingerprints. We test the proposed method on its ability to make a large-scale prediction of compound-protein interactions from compound substructure fingerprints and protein domain fingerprints, and show superior performance of the proposed method compared with the previous chemogenomic methods in terms of prediction accuracy, computational efficiency, and interpretability of the predictive model. All the previously developed methods are not computationally feasible for the full dataset consisting of about 200 millions of compound-protein pairs. The proposed method is expected to be useful for virtual screening of a huge number of compounds against many protein targets.

  18. Predicting direct protein interactions from affinity purification mass spectrometry data

    PubMed Central

    2010-01-01

    Background Affinity purification followed by mass spectrometry identification (AP-MS) is an increasingly popular approach to observe protein-protein interactions (PPI) in vivo. One drawback of AP-MS, however, is that it is prone to detecting indirect interactions mixed with direct physical interactions. Therefore, the ability to distinguish direct interactions from indirect ones is of much interest. Results We first propose a simple probabilistic model for the interactions captured by AP-MS experiments, under which the problem of separating direct interactions from indirect ones is formulated. Then, given idealized quantitative AP-MS data, we study the problem of identifying the most likely set of direct interactions that produced the observed data. We address this challenging graph theoretical problem by first characterizing signatures that can identify weakly connected nodes as well as dense regions of the network. The rest of the direct PPI network is then inferred using a genetic algorithm. Our algorithm shows good performance on both simulated and biological networks with very high sensitivity and specificity. Then the algorithm is used to predict direct interactions from a set of AP-MS PPI data from yeast, and its performance is measured against a high-quality interaction dataset. Conclusions As the sensitivity of AP-MS pipeline improves, the fraction of indirect interactions detected will also increase, thereby making the ability to distinguish them even more desirable. Despite the simplicity of our model for indirect interactions, our method provides a good performance on the test networks. PMID:21034440

  19. Predicting Drug–Target Interactions Using Probabilistic Matrix Factorization

    PubMed Central

    2013-01-01

    Quantitative analysis of known drug–target interactions emerged in recent years as a useful approach for drug repurposing and assessing side effects. In the present study, we present a method that uses probabilistic matrix factorization (PMF) for this purpose, which is particularly useful for analyzing large interaction networks. DrugBank drugs clustered based on PMF latent variables show phenotypic similarity even in the absence of 3D shape similarity. Benchmarking computations show that the method outperforms those recently introduced provided that the input data set of known interactions is sufficiently large—which is the case for enzymes and ion channels, but not for G-protein coupled receptors (GPCRs) and nuclear receptors. Runs performed on DrugBank after hiding 70% of known interactions show that, on average, 88 of the top 100 predictions hit the hidden interactions. De novo predictions permit us to identify new potential interactions. Drug–target pairs implicated in neurobiological disorders are overrepresented among de novo predictions. PMID:24289468

  20. Structural and energetic analyses of SNPs in drug targets and implications for drug therapy.

    PubMed

    Sun, Hui-Yong; Ji, Feng-Qin; Fu, Liang-Yu; Wang, Zhong-Yi; Zhang, Hong-Yu

    2013-12-23

    Mutations in drug targets can alter the therapeutic effects of drugs. Therefore, evaluating the effects of single-nucleotide polymorphisms (SNPs) on drug-target binding is of significant interest. This study focuses on the analysis of the structural and energy properties of SNPs in successful drug targets by using the data derived from HapMap and the Therapeutic Target Database. The results show the following: (i) Drug targets undergo strong purifying selection, and the majority (92.4%) of the SNPs are located far from the drug-binding sites (>12 Å). (ii) For SNPs near the drug-binding pocket (≤12 Å), nearly half of the drugs are weakly affected by the SNPs, and only a few drugs are significantly affected by the target mutations. These results have direct implications for population-based drug therapy and for chemical treatment of genetic diseases as well.

  1. Using support vector machine for improving protein-protein interaction prediction utilizing domain interactions

    SciTech Connect

    Singhal, Mudita; Shah, Anuj R.; Brown, Roslyn N.; Adkins, Joshua N.

    2010-10-02

    Understanding protein interactions is essential to gain insights into the biological processes at the whole cell level. The high-throughput experimental techniques for determining protein-protein interactions (PPI) are error prone and expensive with low overlap amongst them. Although several computational methods have been proposed for predicting protein interactions there is definite room for improvement. Here we present DomainSVM, a predictive method for PPI that uses computationally inferred domain-domain interaction values in a Support Vector Machine framework to predict protein interactions. DomainSVM method utilizes evidence of multiple interacting domains to predict a protein interaction. It outperforms existing methods of PPI prediction by achieving very high explanation ratios, precision, specificity, sensitivity and F-measure values in a 10 fold cross-validation study conducted on the positive and negative PPIs in yeast. A Functional comparison study using GO annotations on the positive and the negative test sets is presented in addition to discussing novel PPI predictions in Salmonella Typhimurium.

  2. Toward more realistic drug–target interaction predictions

    PubMed Central

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

    2015-01-01

    A number of supervised machine learning models have recently been introduced for the prediction of drug–target interactions based on chemical structure and genomic sequence information. Although these models could offer improved means for many network pharmacology applications, such as repositioning of drugs for new therapeutic uses, the prediction models are often being constructed and evaluated under overly simplified settings that do not reflect the real-life problem in practical applications. Using quantitative drug–target bioactivity assays for kinase inhibitors, as well as a popular benchmarking data set of binary drug–target interactions for enzyme, ion channel, nuclear receptor and G protein-coupled receptor targets, we illustrate here the effects of four factors that may lead to dramatic differences in the prediction results: (i) problem formulation (standard binary classification or more realistic regression formulation), (ii) evaluation data set (drug and target families in the application use case), (iii) evaluation procedure (simple or nested cross-validation) and (iv) experimental setting (whether training and test sets share common drugs and targets, only drugs or targets or neither). Each of these factors should be taken into consideration to avoid reporting overoptimistic drug–target interaction prediction results. We also suggest guidelines on how to make the supervised drug–target interaction prediction studies more realistic in terms of such model formulations and evaluation setups that better address the inherent complexity of the prediction task in the practical applications, as well as novel benchmarking data sets that capture the continuous nature of the drug–target interactions for kinase inhibitors. PMID:24723570

  3. Predictions interact with missing sensory evidence in semantic processing areas.

    PubMed

    Scharinger, Mathias; Bendixen, Alexandra; Herrmann, Björn; Henry, Molly J; Mildner, Toralf; Obleser, Jonas

    2016-02-01

    Human brain function draws on predictive mechanisms that exploit higher-level context during lower-level perception. These mechanisms are particularly relevant for situations in which sensory information is compromised or incomplete, as for example in natural speech where speech segments may be omitted due to sluggish articulation. Here, we investigate which brain areas support the processing of incomplete words that were predictable from semantic context, compared with incomplete words that were unpredictable. During functional magnetic resonance imaging (fMRI), participants heard sentences that orthogonally varied in predictability (semantically predictable vs. unpredictable) and completeness (complete vs. incomplete, i.e. missing their final consonant cluster). The effects of predictability and completeness interacted in heteromodal semantic processing areas, including left angular gyrus and left precuneus, where activity did not differ between complete and incomplete words when they were predictable. The same regions showed stronger activity for incomplete than for complete words when they were unpredictable. The interaction pattern suggests that for highly predictable words, the speech signal does not need to be complete for neural processing in semantic processing areas. Hum Brain Mapp 37:704-716, 2016. © 2015 Wiley Periodicals, Inc. PMID:26583355

  4. Recent advances in endocrine metabolic immune disorders drug targeting: an editorial overview.

    PubMed

    Magrone, Thea; Jirillo, Emilio

    2015-01-01

    This editorial overview is aimed at reviewing all the work published by the Journal Endocrine Metabolic Immune Disorders-Drug Targets over the period 2012-2014. The main body of publications has been divided either into a section based on special issues and meeting proceedings or various specific sections according to different types of pathologies related to the field of endocrine metabolic immune disorder-drug targeting.

  5. iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.

    PubMed

    Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen

    2013-01-01

    Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called "iGPCR-drug", was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional) fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition) generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug - target interaction networks.

  6. Signature Product Code for Predicting Protein-Protein Interactions

    2004-09-25

    The SigProdV1.0 software consists of four programs which together allow the prediction of protein-protein interactions using only amino acid sequences and experimental data. The software is based on the use of tensor products of amino acid trimers coupled with classifiers known as support vector machines. Essentially the program looks for amino acid trimer pairs which occur more frequently in protein pairs which are known to interact. These trimer pairs are then used to make predictionsmore » about unknown protein pairs. A detailed description of the method can be found in the paper: S. Martin, D. Roe, J.L. Faulon. "Predicting protein-protein interactions using signature products," Bioinformatics, available online from Advance Access, Aug. 19, 2004.« less

  7. 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

  8. Predicting protein-protein interactions in the post synaptic density.

    PubMed

    Bar-shira, Ossnat; Chechik, Gal

    2013-09-01

    The post synaptic density (PSD) is a specialization of the cytoskeleton at the synaptic junction, composed of hundreds of different proteins. Characterizing the protein components of the PSD and their interactions can help elucidate the mechanism of long-term changes in synaptic plasticity, which underlie learning and memory. Unfortunately, our knowledge of the proteome and interactome of the PSD is still partial and noisy. In this study we describe a computational framework to improve the reconstruction of the PSD network. The approach is based on learning the characteristics of PSD protein interactions from a set of trusted interactions, expanding this set with data collected from large scale repositories, and then predicting novel interaction with proteins that are suspected to reside in the PSD. Using this method we obtained thirty predicted interactions, with more than half of which having supporting evidence in the literature. We discuss in details two of these new interactions, Lrrtm1 with PSD-95 and Src with Capg. The first may take part in a mechanism underlying glutamatergic dysfunction in schizophrenia. The second suggests an alternative mechanism to regulate dendritic spines maturation.

  9. 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

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

    PubMed

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

    2013-10-01

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

  11. Program Predicts Time Courses of Human/Computer Interactions

    NASA Technical Reports Server (NTRS)

    Vera, Alonso; Howes, Andrew

    2005-01-01

    CPM X is a computer program that predicts sequences of, and amounts of time taken by, routine actions performed by a skilled person performing a task. Unlike programs that simulate the interaction of the person with the task environment, CPM X predicts the time course of events as consequences of encoded constraints on human behavior. The constraints determine which cognitive and environmental processes can occur simultaneously and which have sequential dependencies. The input to CPM X comprises (1) a description of a task and strategy in a hierarchical description language and (2) a description of architectural constraints in the form of rules governing interactions of fundamental cognitive, perceptual, and motor operations. The output of CPM X is a Program Evaluation Review Technique (PERT) chart that presents a schedule of predicted cognitive, motor, and perceptual operators interacting with a task environment. The CPM X program allows direct, a priori prediction of skilled user performance on complex human-machine systems, providing a way to assess critical interfaces before they are deployed in mission contexts.

  12. Plant Interactions Alter the Predictions of Metabolic Scaling Theory

    PubMed Central

    Lin, Yue; Berger, Uta; Grimm, Volker; Huth, Franka; Weiner, Jacob

    2013-01-01

    Metabolic scaling theory (MST) is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of −4/3 between mean individual biomass and density during density-dependent mortality (self-thinning). Empirical tests have produced variable results, and the validity of MST is intensely debated. MST focuses on organisms’ internal physiological mechanisms but we hypothesize that ecological interactions can be more important in determining plant mass-density relationships induced by density. We employ an individual-based model of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric), and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories. Slopes were significantly shallower than −4/3 if competition was size-symmetric. We conclude that when the size of survivors is influenced by strong ecological interactions, these can override predictions of MST, whereas when surviving plants are less affected by interactions, individual-level metabolic processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive. PMID:23460884

  13. Protein function prediction using guilty by association from interaction networks.

    PubMed

    Piovesan, Damiano; Giollo, Manuel; Ferrari, Carlo; Tosatto, Silvio C E

    2015-12-01

    Protein function prediction from sequence using the Gene Ontology (GO) classification is useful in many biological problems. It has recently attracted increasing interest, thanks in part to the Critical Assessment of Function Annotation (CAFA) challenge. In this paper, we introduce Guilty by Association on STRING (GAS), a tool to predict protein function exploiting protein-protein interaction networks without sequence similarity. The assumption is that whenever a protein interacts with other proteins, it is part of the same biological process and located in the same cellular compartment. GAS retrieves interaction partners of a query protein from the STRING database and measures enrichment of the associated functional annotations to generate a sorted list of putative functions. A performance evaluation based on CAFA metrics and a fair comparison with optimized BLAST similarity searches is provided. The consensus of GAS and BLAST is shown to improve overall performance. The PPI approach is shown to outperform similarity searches for biological process and cellular compartment GO predictions. Moreover, an analysis of the best practices to exploit protein-protein interaction networks is also provided.

  14. Interactive-predictive detection of handwritten text blocks

    NASA Astrophysics Data System (ADS)

    Ramos Terrades, O.; Serrano, N.; Gordó, A.; Valveny, E.; Juan, A.

    2010-01-01

    A method for text block detection is introduced for old handwritten documents. The proposed method takes advantage of sequential book structure, taking into account layout information from pages previously transcribed. This glance at the past is used to predict the position of text blocks in the current page with the help of conventional layout analysis methods. The method is integrated into the GIDOC prototype: a first attempt to provide integrated support for interactive-predictive page layout analysis, text line detection and handwritten text transcription. Results are given in a transcription task on a 764-page Spanish manuscript from 1891.

  15. Prediction of Genetic Interactions Using Machine Learning and Network Properties

    PubMed Central

    Madhukar, Neel S.; Elemento, Olivier; Pandey, Gaurav

    2015-01-01

    A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI – synthetic sickness or synthetic lethality – involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases. PMID:26579514

  16. Herb–Drug Interactions: Challenges and Opportunities for Improved Predictions

    PubMed Central

    Brantley, Scott J.; Argikar, Aneesh A.; Lin, Yvonne S.; Nagar, Swati

    2014-01-01

    Supported by a usage history that predates written records and the perception that “natural” ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb–drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb–drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb–drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb–drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens. PMID:24335390

  17. 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

    promoting the elucidation of the molecular structures of drug targets in MTB, and are consequently markedly useful for the design of new, promising antituberculous drugs using QSAR techniques. In this issue, we review the following areas. Firstly, Dr. Li M. Fu reviews the perspective that combines machine learning and genomics for drug discovery in tuberculosis, in relation to the problem that the exhaustive search for useful drug targets over the entire MTB genome would not be as productive as expected in practice [1]. Secondly, the review article by Drs. R. S. Chauhan. S. K. Chanumolu, C. Rout, and R. Shrivastava focuses on analysis of the current state of MTB genomic resources, host-pathogen interaction studies in the context of mycobacterial persistence, and drug target discovery based on the utilization of computational tools and metabolic network analyses [2]. Thirdly, Drs. Daria Bottai, Agnese Serafini, Alessandro Cascioferro, Roland Brosch, and Riccardo Manganelli review the current knowledge on MTB T7SS/ESX secretion systems and their impact on MTB physiology and virulence, and the possible approaches to develop T7SS/ESX inhibitors [3]. Fourthly, Drs. E. Jeffrey North, Mary Jackson, and Richard E. Lee review and analyze new and emerging inhibitors of the mycolic acid biosynthetic pathway, including mycobacterial enzymes for fatty acid synthesis, mycolic acid-modifying enzymes, fatty acid-activating and -condensing enzymes, transporters, and transferases, that have been discovered in the post-genomic era of tuberculosis drug discovery [4]. Fifthly, Drs. Katarina Mikusova, Vadim Makarov, and Joao Neres review the mycobacterial enzyme DprE1, which catalyzes a unique epimerization reaction in the biosynthesis of decaprenylphosphoryl arabinose, a single donor of the arabinosyl residue for the build-up of arabinans, one of the mycobacterial cell wall components, as an important drug target especially for the development of benzothiazinones [5]. Sixthly, I review the

  18. Transferring network topological knowledge for predicting protein-protein interactions.

    PubMed

    Xu, Qian; Xiang, Evan Wei; Yang, Qiang

    2011-10-01

    Protein-protein interactions (PPIs) play an important role in cellular processes within a cell. An important task is to determine the existence of interactions among proteins. Unfortunately, the existing biological experimental techniques are expensive, time-consuming and labor-intensive. The network structures of many such networks are sparse, incomplete and noisy. Thus, state-of-the-art methods for link prediction in these networks often cannot give satisfactory prediction results, especially when some networks are extremely sparse. Noticing that we typically have more than one PPI network available, we naturally wonder whether it is possible to 'transfer' the linkage knowledge from some existing, relatively dense networks to a sparse network, to improve the prediction performance. Noticing that a network structure can be modeled using a matrix model, we introduce the well-known collective matrix factorization technique to 'transfer' usable linkage knowledge from relatively dense interaction network to a sparse target network. Our approach is to establish a correspondence between a source network and a target network via network-wide similarities. We test this method on two real PPI networks, Helicobacter pylori (as a target network) and human (as a source network). Our experimental results show that our method can achieve higher performance as compared with some baseline methods. PMID:21770035

  19. On the earthquake predictability of fault interaction models

    PubMed Central

    Marzocchi, W; Melini, D

    2014-01-01

    Space-time clustering is the most striking departure of large earthquakes occurrence process from randomness. These clusters are usually described ex-post by a physics-based model in which earthquakes are triggered by Coulomb stress changes induced by other surrounding earthquakes. Notwithstanding the popularity of this kind of modeling, its ex-ante skill in terms of earthquake predictability gain is still unknown. Here we show that even in synthetic systems that are rooted on the physics of fault interaction using the Coulomb stress changes, such a kind of modeling often does not increase significantly earthquake predictability. Earthquake predictability of a fault may increase only when the Coulomb stress change induced by a nearby earthquake is much larger than the stress changes caused by earthquakes on other faults and by the intrinsic variability of the earthquake occurrence process. PMID:26074643

  20. 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

  1. 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.

  2. Prediction and integration of regulatory and protein-protein interactions

    SciTech Connect

    Wichadakul, Duangdao; McDermott, Jason E.; Samudrala, Ram

    2009-04-20

    Knowledge of transcriptional regulatory interactions (TRIs) is essential for exploring functional genomics and systems biology in any organism. While several results from genome-wide analysis of transcriptional regulatory networks are available, they are limited to model organisms such as yeast [1] and worm [2]. Beyond these networks, experiments on TRIs study only individual genes and proteins of specific interest. In this chapter, we present a method for the integration of various data sets to predict TRIs for 54 organisms in the Bioverse [3]. We describe how to compile and handle various formats and identifiers of data sets from different sources, and how to predict the TRIs using a homology-based approach, utilizing the compiled data sets. Integrated data sets include experimentally verified TRIs, binding sites of transcription factors, promoter sequences, protein sub-cellular localization, and protein families. Predicted TRIs expand the networks of gene regulation for a large number of organisms. The integration of experimentally verified and predicted TRIs with other known protein-protein interactions (PPIs) gives insight into specific pathways, network motifs, and the topological dynamics of an integrated network with gene expression under different conditions, essential for exploring functional genomics and systems biology.

  3. 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

  4. 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

  5. Neurodegenerative diseases: quantitative predictions of protein-RNA interactions.

    PubMed

    Cirillo, Davide; Agostini, Federico; Klus, Petr; Marchese, Domenica; Rodriguez, Silvia; Bolognesi, Benedetta; Tartaglia, Gian Gaetano

    2013-02-01

    Increasing evidence indicates that RNA plays an active role in a number of neurodegenerative diseases. We recently introduced a theoretical framework, catRAPID, to predict the binding ability of protein and RNA molecules. Here, we use catRAPID to investigate ribonucleoprotein interactions linked to inherited intellectual disability, amyotrophic lateral sclerosis, Creutzfeuld-Jakob, Alzheimer's, and Parkinson's diseases. We specifically focus on (1) RNA interactions with fragile X mental retardation protein FMRP; (2) protein sequestration caused by CGG repeats; (3) noncoding transcripts regulated by TAR DNA-binding protein 43 TDP-43; (4) autogenous regulation of TDP-43 and FMRP; (5) iron-mediated expression of amyloid precursor protein APP and α-synuclein; (6) interactions between prions and RNA aptamers. Our results are in striking agreement with experimental evidence and provide new insights in processes associated with neuronal function and misfunction.

  6. Signaling through Rho GTPase pathway as viable drug target.

    PubMed

    Lu, Qun; Longo, Frank M; Zhou, Huchen; Massa, Stephen M; Chen, Yan-Hua

    2009-01-01

    Signaling through the Rho family of small GTPases has been increasingly investigated for their involvement in a wide variety of diseases such as cardiovascular, pulmonary, and neurological disorders as well as cancer. Rho GTPases are a subfamily of the Ras superfamily proteins which play essential roles in a number of biological processes, especially in the regulation of cell shape change, cytokinesis, cell adhesion, and cell migration. Many of these processes demonstrate a common theme: the rapid and dynamic reorganization of actin cytoskeleton of which Rho signaling has now emerged as a major switch control. The involvement of dynamic changes of Rho GTPases in disease states underscores the need to produce effective inhibitors for their therapeutic applications. Fasudil and Y-27632, with many newer additions, are two classes of widely used chemical compounds that inhibit Rho kinase (ROCK), an important downstream effector of RhoA subfamily GTPases. These inhibitors have been successful in many preclinical studies, indicating the potential benefit of clinical Rho pathway inhibition. On the other hand, except for Rac1 inhibitor NSC23766, there are few effective inhibitors directly targeting Rho GTPases, likely due to the lack of optimal structural information on individual Rho-RhoGEF, Rho-RhoGAP, or Rho-RhoGDI interaction to achieve specificity. Recently, LM11A-31 and other derivatives of peptide mimetic ligands for p75 neurotrophin receptor (p75(NTR)) show promising effects upstream of Rho GTPase signaling in neuronal regeneration. CCG-1423, a chemical compound showing profiles of inhibiting downstream of RhoA, is a further attempt for the development of novel pharmacological tools to disrupt Rho signaling pathway in cancer. Because of a rapidly growing number of studies deciphering the role of the Rho proteins in many diseases, specific and potent pharmaceutical modulators of various steps of Rho GTPase signaling pathway are critically needed to target for

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

    PubMed

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

    2013-04-01

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

  8. 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

  9. HART-II: Prediction of Blade-Vortex Interaction Loading

    NASA Technical Reports Server (NTRS)

    Lim, Joon W.; Tung, Chee; Yu, Yung H.; Burley, Casey L.; Brooks, Thomas; Boyd, Doug; vanderWall, Berend; Schneider, Oliver; Richard, Hugues; Raffel, Markus

    2003-01-01

    During the HART-I data analysis, the need for comprehensive wake data was found including vortex creation and aging, and its re-development after blade-vortex interaction. In October 2001, US Army AFDD, NASA Langley, German DLR, French ONERA and Dutch DNW performed the HART-II test as an international joint effort. The main objective was to focus on rotor wake measurement using a PIV technique along with the comprehensive data of blade deflections, airloads, and acoustics. Three prediction teams made preliminary correlation efforts with HART-II data: a joint US team of US Army AFDD and NASA Langley, German DLR, and French ONERA. The predicted results showed significant improvements over the HART-I predicted results, computed about several years ago, which indicated that there has been better understanding of complicated wake modeling in the comprehensive rotorcraft analysis. All three teams demonstrated satisfactory prediction capabilities, in general, though there were slight deviations of prediction accuracies for various disciplines.

  10. CINPER: an interactive web system for pathway prediction for prokaryotes.

    PubMed

    Mao, Xizeng; Chen, Xin; Zhang, Yu; Pangle, Spencer; Xu, Ying

    2012-01-01

    We present a web-based network-construction system, CINPER (CSBL INteractive Pathway BuildER), to assist a user to build a user-specified gene network for a prokaryotic organism in an intuitive manner. CINPER builds a network model based on different types of information provided by the user and stored in the system. CINPER's prediction process has four steps: (i) collection of template networks based on (partially) known pathways of related organism(s) from the SEED or BioCyc database and the published literature; (ii) construction of an initial network model based on the template networks using the P-Map program; (iii) expansion of the initial model, based on the association information derived from operons, protein-protein interactions, co-expression modules and phylogenetic profiles; and (iv) computational validation of the predicted models based on gene expression data. To facilitate easy applications, CINPER provides an interactive visualization environment for a user to enter, search and edit relevant data and for the system to display (partial) results and prompt for additional data. Evaluation of CINPER on 17 well-studied pathways in the MetaCyc database shows that the program achieves an average recall rate of 76% and an average precision rate of 90% on the initial models; and a higher average recall rate at 87% and an average precision rate at 28% on the final models. The reduced precision rate in the final models versus the initial models reflects the reality that the final models have large numbers of novel genes that have no experimental evidences and hence are not yet collected in the MetaCyc database. To demonstrate the usefulness of this server, we have predicted an iron homeostasis gene network of Synechocystis sp. PCC6803 using the server. The predicted models along with the server can be accessed at http://csbl.bmb.uga.edu/cinper/.

  11. QSAR Modeling and Prediction of Drug-Drug Interactions.

    PubMed

    Zakharov, Alexey V; Varlamova, Ekaterina V; Lagunin, Alexey A; Dmitriev, Alexander V; Muratov, Eugene N; Fourches, Denis; Kuz'min, Victor E; Poroikov, Vladimir V; Tropsha, Alexander; Nicklaus, Marc C

    2016-02-01

    Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database. PMID:26669717

  12. 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

  13. Thiamin (vitamin B1) biosynthesis and regulation: a rich source of antimicrobial drug targets?

    PubMed

    Du, Qinglin; Wang, Honghai; Xie, Jianping

    2011-01-09

    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.

  14. Magnetic Drug Targeting: Preclinical in Vivo Studies, Mathematical Modeling, and Extrapolation to Humans.

    PubMed

    Al-Jamal, Khuloud T; Bai, Jie; Wang, Julie Tzu-Wen; Protti, Andrea; Southern, Paul; Bogart, Lara; Heidari, Hamed; Li, Xinjia; Cakebread, Andrew; Asker, Dan; Al-Jamal, Wafa T; Shah, Ajay; Bals, Sara; Sosabowski, Jane; Pankhurst, Quentin A

    2016-09-14

    A sound theoretical rationale for the design of a magnetic nanocarrier capable of magnetic capture in vivo after intravenous administration could help elucidate the parameters necessary for in vivo magnetic tumor targeting. In this work, we utilized our long-circulating polymeric magnetic nanocarriers, encapsulating increasing amounts of superparamagnetic iron oxide nanoparticles (SPIONs) in a biocompatible oil carrier, to study the effects of SPION loading and of applied magnetic field strength on magnetic tumor targeting in CT26 tumor-bearing mice. Under controlled conditions, the in vivo magnetic targeting was quantified and found to be directly proportional to SPION loading and magnetic field strength. Highest SPION loading, however, resulted in a reduced blood circulation time and a plateauing of the magnetic targeting. Mathematical modeling was undertaken to compute the in vivo magnetic, viscoelastic, convective, and diffusive forces acting on the nanocapsules (NCs) in accordance with the Nacev-Shapiro construct, and this was then used to extrapolate to the expected behavior in humans. The model predicted that in the latter case, the NCs and magnetic forces applied here would have been sufficient to achieve successful targeting in humans. Lastly, an in vivo murine tumor growth delay study was performed using docetaxel (DTX)-encapsulated NCs. Magnetic targeting was found to offer enhanced therapeutic efficacy and improve mice survival compared to passive targeting at drug doses of ca. 5-8 mg of DTX/kg. This is, to our knowledge, the first study that truly bridges the gap between preclinical experiments and clinical translation in the field of magnetic drug targeting. PMID:27541372

  15. Aqueous solubility prediction: do crystal lattice interactions help?

    PubMed

    Salahinejad, Maryam; Le, Tu C; Winkler, David A

    2013-07-01

    Aqueous solubility is a very important physical property of small molecule drugs and drug candidates but also one of the most difficult to predict accurately. Aqueous solubility plays a major role in drug delivery and pharmacokinetics. It is believed that crystal lattice interactions are important in solubility and that including them in solubility models should improve the accuracy of the models. We used calculated values for lattice energy and sublimation enthalpy of organic molecules as descriptors to determine whether these would improve the accuracy of the aqueous solubility models. Multiple linear regression employing an expectation maximization algorithm and a sparse prior (MLREM) method and a nonlinear Bayesian regularized artificial neural network with a Laplacian prior (BRANNLP) were used to derive optimal predictive models of aqueous solubility of a large and highly diverse data set of 4558 organic compounds over a normal ambient temperature range of 20-30 °C (293-303 K). A randomly selected test set and compounds from a solubility challenge were used to estimate the predictive ability of the models. The BRANNLP method showed the best statistical results with squared correlation coefficients of 0.90 and standard errors of 0.645-0.665 log(S) for training and test sets. Surprisingly, including descriptors that captured crystal lattice interactions did not significantly improve the quality of these aqueous solubility models.

  16. Interaction prediction optimization in multidisciplinary design optimization problems.

    PubMed

    Meng, Debiao; Zhang, Xiaoling; Huang, Hong-Zhong; Wang, Zhonglai; Xu, Huanwei

    2014-01-01

    The distributed strategy of Collaborative Optimization (CO) is suitable for large-scale engineering systems. However, it is hard for CO to converge when there is a high level coupled dimension. Furthermore, the discipline objectives cannot be considered in each discipline optimization problem. In this paper, one large-scale systems control strategy, the interaction prediction method (IPM), is introduced to enhance CO. IPM is utilized for controlling subsystems and coordinating the produce process in large-scale systems originally. We combine the strategy of IPM with CO and propose the Interaction Prediction Optimization (IPO) method to solve MDO problems. As a hierarchical strategy, there are a system level and a subsystem level in IPO. The interaction design variables (including shared design variables and linking design variables) are operated at the system level and assigned to the subsystem level as design parameters. Each discipline objective is considered and optimized at the subsystem level simultaneously. The values of design variables are transported between system level and subsystem level. The compatibility constraints are replaced with the enhanced compatibility constraints to reduce the dimension of design variables in compatibility constraints. Two examples are presented to show the potential application of IPO for MDO.

  17. Predictable patterns of trait mismatches between interacting plants and insects

    PubMed Central

    2010-01-01

    Background There are few predictions about the directionality or extent of morphological trait (mis)matches between interacting organisms. We review and analyse studies on morphological trait complementarity (e.g. floral tube length versus insect mouthpart length) at the population and species level. Results Plants have consistently more exaggerated morphological traits than insects at high trait magnitudes and in some cases less exaggerated traits than insects at smaller trait magnitudes. This result held at the population level, as well as for phylogenetically adjusted analyses at the species-level and for both pollination and host-parasite interactions, perhaps suggesting a general pattern. Across communities, the degree of trait mismatch between one specialist plant and its more generalized pollinator was related to the level of pollinator specialization at each site; the observed pattern supports the "life-dinner principle" of selection acting more strongly on species with more at stake in the interaction. Similarly, plant mating system also affected the degree of trait correspondence because selfing reduces the reliance on pollinators and is analogous to pollination generalization. Conclusions Our analyses suggest that there are predictable "winners" and "losers" of evolutionary arms races and the results of this study highlight the fact that breeding system and the degree of specialization can influence the outcome. PMID:20604973

  18. 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

  19. Explicit and implicit approach motivation interact to predict interpersonal arrogance.

    PubMed

    Robinson, Michael D; Ode, Scott; Palder, Spencer L; Fetterman, Adam K

    2012-07-01

    Self-reports of approach motivation are unlikely to be sufficient in understanding the extent to which the individual reacts to appetitive cues in an approach-related manner. A novel implicit probe of approach tendencies was thus developed, one that assessed the extent to which positive affective (versus neutral) stimuli primed larger size estimates, as larger perceptual sizes co-occur with locomotion toward objects in the environment. In two studies (total N = 150), self-reports of approach motivation interacted with this implicit probe of approach motivation to predict individual differences in arrogance, a broad interpersonal dimension previously linked to narcissism, antisocial personality tendencies, and aggression. The results of the two studies were highly parallel in that self-reported levels of approach motivation predicted interpersonal arrogance in the particular context of high, but not low, levels of implicit approach motivation. Implications for understanding approach motivation, implicit probes of it, and problematic approach-related outcomes are discussed. PMID:22399360

  20. Genetic interaction networks: better understand to better predict

    PubMed Central

    Boucher, Benjamin; Jenna, Sarah

    2013-01-01

    A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances. PMID:24381582

  1. System for Predicting Pitzer Ion-Interaction Model Parameters

    NASA Astrophysics Data System (ADS)

    Schreiber, D. R.; Obias, T.

    2002-12-01

    Pitzer's Ion-Interaction Model has been widely utilized for the prediction of non-ideal solution behavior. The Pitzer model does an excellent job of predicting the solubility of minerals over a wide range of conditions for natural water systems. While Pitzer's equations have been successful in modeling systems when there are parameters available, there are still some systems that can't be modeled because parameters aren't available for all of the salts of interest. For example, there is little to no data for aluminum salts yet in acidified natural waters it may be present at significant concentrations. In addition, aluminum chemistry will also be important in the remediation of acidified High-level waste. Given the quantity of work involved in generating the needed parameters it would be advantageous to be able to predict Pitzer parameters for salt systems when there is no data available. Recently we began work on modeling High-level waste systems where Pitzer parameters are not available for some of the constituents of interest. We will discuss a set of relations we have developed for the prediction of Pitzer's binary ion-interaction parameters. In the binary parameter case, we reformulated the Pitzer's equations by replacing the parameters, β(0), β(1), β(2), and C, with expressions in ionic radii. Equations have been developed for salts of a particular anion with cations of similar charge. For example, there is a single equation for the 1:1 chloride salts. Relations for acids were developed separately. Also we have developed a separate set of equations for all salts of a particular charge type independent of the anion. While the latter set of equations are of lesser predictive value, they can be used in cases where we don't have a relation for a particular anion. Since any system used to predict parameters would result in a loss of accuracy, experimentally determined parameters should be used when available. The ability of parameters derived from our model

  2. 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

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

    PubMed

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

    2014-07-01

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

  4. Interactive decision support system to predict print quality.

    PubMed

    Leman, Sugani; Lehto, Mark R

    2003-01-15

    Customers using printers occasionally experience problems such as fuzzy images, bands, or streaks. The customer may call or otherwise contact the manufacturer, who attempts to diagnose the problem based on the customer's description of the problem. This study evaluated Bayesian inference as a tool for identifying or diagnosing 16 different types of print defects from such descriptions. The Bayesian model was trained using 1701 narrative descriptions of print defects obtained from 60 subjects with varying technical backgrounds. The Bayesian model was then implemented as an interactive decision support system, which was used by eight 'agents' to diagnose print defects reported by 16 'customers' in a simulated call centre. The 'agents' and 'customers' in the simulated call centre were all students at Purdue University. Each customer made eight telephone calls, resulting in a total of 128 telephone calls in which the customer reported defects to the agents. The results showed that the Bayesian model closely fitted the data in the training set of narratives. Overall, the model correctly predicted the actual defect category with its top prediction 70% of the time. The actual defect was in the top five predictions 94% of the time. The model in the simulated call centre performed nearly as well for the test subjects. The top prediction was correct 50% of the time, and the defect was one of the top five predictions 80% of the time. Agent accuracy in diagnosing the problem improved when using the tool. These results demonstrated that the Bayesian system learned enough from the existing narratives to accurately classify print defect categories.

  5. [Study on prediction of compound-target-disease network of chuanxiong rhizoma based on random forest algorithm].

    PubMed

    Yuan, Jie; Li, Xiao-Jie; Chen, Chao; Song, Xiang-Gang; Wang, Shu-Mei

    2014-06-01

    To collect small molecule drugs and their drug target data such as enzymes, ion channels, G-protein-coupled receptors and nuclear receptors from KEGG database as the training sets, in order to establish drug-target interaction models based on the random forest algorithm. The accuracies of the models were evaluated by the 10-fold cross-validation test, showing that the predicted success rates of the four drug target models were 71.34%, 67.08%, 73.17% and 67.83%, respectively. The models were adopted to predict the targets of 26 chemical components and establish the compound-target-disease network. The results were well verified by literatures. The models established in this paper are highly accurate, and can be used to discover potential targets in other traditional Chinese medicine ingredients. PMID:25244771

  6. Making Transporter Models for Drug-Drug Interaction Prediction Mobile.

    PubMed

    Ekins, Sean; Clark, Alex M; Wright, Stephen H

    2015-10-01

    The past decade has seen increased numbers of studies publishing ligand-based computational models for drug transporters. Although they generally use small experimental data sets, these models can provide insights into structure-activity relationships for the transporter. In addition, such models have helped to identify new compounds as substrates or inhibitors of transporters of interest. We recently proposed that many transporters are promiscuous and may require profiling of new chemical entities against multiple substrates for a specific transporter. Furthermore, it should be noted that virtually all of the published ligand-based transporter models are only accessible to those involved in creating them and, consequently, are rarely shared effectively. One way to surmount this is to make models shareable or more accessible. The development of mobile apps that can access such models is highlighted here. These apps can be used to predict ligand interactions with transporters using Bayesian algorithms. We used recently published transporter data sets (MATE1, MATE2K, OCT2, OCTN2, ASBT, and NTCP) to build preliminary models in a commercial tool and in open software that can deliver the model in a mobile app. In addition, several transporter data sets extracted from the ChEMBL database were used to illustrate how such public data and models can be shared. Predicting drug-drug interactions for various transporters using computational models is potentially within reach of anyone with an iPhone or iPad. Such tools could help prioritize which substrates should be used for in vivo drug-drug interaction testing and enable open sharing of models. PMID:26199424

  7. 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

  8. On setting the first dose in man: quantitating biotherapeutic drug-target binding through pharmacokinetic and pharmacodynamic models.

    PubMed

    Lowe, Philip J; Tannenbaum, Stacey; Wu, Kai; Lloyd, Peter; Sims, Jennifer

    2010-03-01

    Although the three (perhaps four) phases of clinical drug development are well known, it is relatively unappreciated that there are similar phases in pre-clinical development. These consist of 'Phase I' the initial, normally Research Discovery driven pharmacology; 'Phase II' non-good laboratory practice (GLP) dose range finding, followed by pivotal 'Phase III' GLP toxicology. Together with an array of in vitro experiments comparing species, these stages should enable an integrated safety assessment prior to entry into man, documenting to investigators and authorities evidence that the new pharmaceutic is unlikely to cause harm. Following the lessons learned from TeGenero TGN1412 and subsequent updates to regulatory guidelines, there are aspects peculiar to biotherapeutics, especially those that target key body systems, where calculations could be made for doses for human studies using pharmacokinetic and pharmacodynamic models. Two of these are exemplified in this paper. In the first, target-mediated drug disposition, where the binding of the drug to a cellular target quantitatively affects the pharmacokinetics, enables occupancy to be estimated without recourse to independent assays. In the second, assaying captured soluble target, as drug-target complexes, allows estimation of the concentration of the free ligand ensuring that in initial clinical studies, soluble targets are not overly suppressed. To support this methodology, it has been demonstrated using omalizumab, free and total IgE data that such analyses do predict the suppression of the free unbound ligand with reasonable accuracy. Overall, the objective of the process is to deliver a justification, through consideration of drug-target binding, of a safe starting and therapeutically relevant escalation doses for human studies. PMID:20050847

  9. Identification of Drug Targets in Helicobacter pylori by in silico Analysis: Possible Therapeutic Implications for Gastric cancer.

    PubMed

    Nammi, Deepthi; Srimath-Tirumala-Peddinti, Ravi C P K; Neelapu, Nageswara Rao R

    2016-01-01

    Helicobacter pylori colonize stomach, inducing gastritis, ulcers and gastric cancer. Drugs are used to relieve pain, but not H. pylori infections. Hence, there is a need for discovery of drug targets and drugs for H. pylori. An objective of this current study is to identify drug targets for H. pylori. RAST was used to compare genomes of 23 H. pylori strains with Homo sapiens sapiens, other Helicobacter species (H. acinonychis, H. hepaticus, H. mustalae) and among them, to identify 13471 unique genes. Bacterial genes which are non-homologous to humans and essential for pathogen are identified using BLASTp. Later, 29 potential drug targets were identified by subjecting these genes to property analysis. Eleven of the 29 drug targets are already experimentally validated, lending credence to our approach. These methods have enabled rapid identification of drug targets with possible therapeutic implications for gastric cancer.

  10. Association of hypertension drug target genes with blood pressure and hypertension in 86,588 individuals.

    PubMed

    Johnson, Andrew D; Newton-Cheh, Christopher; Chasman, Daniel I; Ehret, Georg B; Johnson, Toby; Rose, Lynda; Rice, Kenneth; Verwoert, Germaine C; Launer, Lenore J; Gudnason, Vilmundur; Larson, Martin G; Chakravarti, Aravinda; Psaty, Bruce M; Caulfield, Mark; van Duijn, Cornelia M; Ridker, Paul M; Munroe, Patricia B; Levy, Daniel

    2011-05-01

    We previously conducted genome-wide association meta-analysis of systolic blood pressure, diastolic blood pressure, and hypertension in 29,136 people from 6 cohort studies in the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium. Here we examine associations of these traits with 30 gene regions encoding known antihypertensive drug targets. We find nominal evidence of association of ADRB1, ADRB2, AGT, CACNA1A, CACNA1C, and SLC12A3 polymorphisms with 1 or more BP traits in the Cohorts for Heart and Aging Research in Genomic Epidemiology genome-wide association meta-analysis. We attempted replication of the top meta-analysis single nucleotide polymorphisms for these genes in the Global BPgen Consortium (n=34,433) and the Women's Genome Health Study (n=23,019) and found significant results for rs1801253 in ADRB1 (Arg389Gly), with the Gly allele associated with a lower mean systolic blood pressure (β: 0.57 mm Hg; SE: 0.09 mm Hg; meta-analysis: P=4.7×10(-10)), diastolic blood pressure (β: 0.36 mm Hg; SE: 0.06 mm Hg; meta-analysis: P=9.5×10(-10)), and prevalence of hypertension (β: 0.06 mm Hg; SE: 0.02 mm Hg; meta-analysis: P=3.3×10(-4)). Variation in AGT (rs2004776) was associated with systolic blood pressure (β: 0.42 mm Hg; SE: 0.09 mm Hg; meta-analysis: P=3.8×10(-6)), as well as diastolic blood pressure (P=5.0×10(-8)) and hypertension (P=3.7×10(-7)). A polymorphism in ACE (rs4305) showed modest replication of association with increased hypertension (β: 0.06 mm Hg; SE: 0.01 mm Hg; meta-analysis: P=3.0×10(-5)). Two loci, ADRB1 and AGT, contain single nucleotide polymorphisms that reached a genome-wide significance threshold in meta-analysis for the first time. Our findings suggest that these genes warrant further studies of their genetic effects on blood pressure, including pharmacogenetic interactions.

  11. Sulfonylurea pharmacogenomics in Type 2 diabetes: the influence of drug target and diabetes risk polymorphisms

    PubMed Central

    Aquilante, Christina L

    2010-01-01

    The sulfonylureas stimulate insulin release from pancreatic β cells, and have been a cornerstone of Type 2 diabetes pharmacotherapy for over 50 years. Although sulfonylureas are effective antihyperglycemic agents, interindividual variability exists in drug response (i.e., pharmacodynamics), disposition (i.e., pharmacokinetics) and adverse effects. The field of pharmacogenomics has been applied to sulfonylurea clinical studies in order to elucidate the genetic underpinnings of this response variability. Historically, most studies have sought to determine the influence of polymorphisms in drug-metabolizing enzyme genes on sulfonylurea pharmacokinetics in humans. More recently, polymorphisms in sulfonylurea drug target genes and diabetes risk genes have been implicated as important determinants of sulfonylurea pharmacodynamics in patients with Type 2 diabetes. As such, the purpose of this review is to discuss sulfonylurea pharmacogenomics in the setting of Type 2 diabetes, specifically focusing on polymorphisms in drug target and diabetes risk genes, and their relationship with interindividual variability in sulfonylurea response and adverse effects. PMID:20222815

  12. Dynamic Structure and Inhibition of a Malaria Drug Target: Geranylgeranyl Diphosphate Synthase.

    PubMed

    G Ricci, Clarisse; Liu, Yi-Liang; Zhang, Yonghui; Wang, Yang; Zhu, Wei; Oldfield, Eric; McCammon, J Andrew

    2016-09-13

    We report a molecular dynamics investigation of the structure, function, and inhibition of geranylgeranyl diphosphate synthase (GGPPS), a potential drug target, from the malaria parasite Plasmodium vivax. We discovered several GGPPS inhibitors, benzoic acids, and determined their structures crystallographically. We then used molecular dynamics simulations to investigate the dynamics of three such inhibitors and two bisphosphonate inhibitors, zoledronate and a lipophilic analogue of zoledronate, as well as the enzyme's product, GGPP. We were able to identify the main motions that govern substrate binding and product release as well as the molecular features required for GGPPS inhibition by both classes of inhibitor. The results are of broad general interest because they represent the first detailed investigation of the mechanism of action, and inhibition, of an important antimalarial drug target, geranylgeranyl diphosphate synthase, and may help guide the development of other, novel inhibitors as new drug leads. PMID:27564465

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

    PubMed Central

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

    2015-01-01

    CRISPR-Cas9 genome editing technology holds great promise for discovering therapeutic targets in cancer and other diseases. Current screening strategies target CRISPR-induced mutations to the 5’ exons of candidate genes1–5, but this approach often produces in-frame variants that retain functionality, which can obscure even strong genetic dependencies. Here we overcome this limitation by targeting CRISPR mutagenesis to exons encoding functional protein domains. This generates a higher proportion of null mutations and substantially increases the potency of negative selection. We show that the magnitude of negative selection reports the functional importance of individual protein domains of interest. A screen of 192 chromatin regulatory domains in murine acute myeloid leukemia cells identifies six known drug targets and 19 additional dependencies. A broader application of this approach may allow comprehensive identification of protein domains that sustain cancer cells and are suitable for drug targeting. PMID:25961408

  14. 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

  15. 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

  16. Identification and characterization of potential drug targets by subtractive genome analyses of methicillin resistant Staphylococcus aureus.

    PubMed

    Uddin, Reaz; Saeed, Kiran

    2014-02-01

    Methicillin resistant Staphylococcus aureus (MRSA) causes serious infections in humans and becomes resistant to a number of antibiotics. Due to the emergence of antibiotic resistance strains, there is an essential need to develop novel drug targets to address the challenge of multidrug-resistant bacteria. In current study, the idea was to utilize the available genome or proteome in a subtractive genome analyses protocol to identify drug targets within two of the MRSA types, i.e., MRSA ST398 and MRSA 252. Recently, the use of subtractive genomic approaches helped in the identification and characterization of novel drug targets of a number of pathogens. Our protocol involved a similarity search between pathogen and host, essentiality study using the database of essential genes, metabolic functional association study using Kyoto Encyclopedia of Genes and Genomes database (KEGG), cellular membrane localization analysis and Drug Bank database. Functional family characterizations of the identified non homologous hypothetical essential proteins were done by SVMProt server. Druggability potential of each of the identified drug targets was also evaluated by Drug Bank database. Moreover, metabolic pathway analysis of the identified druggable essential proteins with KEGG revealed that the identified proteins are participating in unique and essential metabolic pathways amongst MRSA strains. In short, the complete proteome analyses by the use of advanced computational tools, databases and servers resulted in identification and characterization of few nonhomologous/hypothetical and essential proteins which are not homologous to the host genome. Therefore, these non-homologous essential targets ensure the survival of the pathogen and hence can be targeted for drug discovery.

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

    PubMed

    Müller, Joachim; Hemphill, Andrew

    2013-01-01

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

  18. 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.

  19. 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

  20. Computational models for predicting interactions with membrane transporters.

    PubMed

    Xu, Y; Shen, Q; Liu, X; Lu, J; Li, S; Luo, C; Gong, L; Luo, X; Zheng, M; Jiang, H

    2013-01-01

    Membrane transporters, including two members: ATP-binding cassette (ABC) transporters and solute carrier (SLC) transporters are proteins that play important roles to facilitate molecules into and out of cells. Consequently, these transporters can be major determinants of the therapeutic efficacy, toxicity and pharmacokinetics of a variety of drugs. Considering the time and expense of bio-experiments taking, research should be driven by evaluation of efficacy and safety. Computational methods arise to be a complementary choice. In this article, we provide an overview of the contribution that computational methods made in transporters field in the past decades. At the beginning, we present a brief introduction about the structure and function of major members of two families in transporters. In the second part, we focus on widely used computational methods in different aspects of transporters research. In the absence of a high-resolution structure of most of transporters, homology modeling is a useful tool to interpret experimental data and potentially guide experimental studies. We summarize reported homology modeling in this review. Researches in computational methods cover major members of transporters and a variety of topics including the classification of substrates and/or inhibitors, prediction of protein-ligand interactions, constitution of binding pocket, phenotype of non-synonymous single-nucleotide polymorphisms, and the conformation analysis that try to explain the mechanism of action. As an example, one of the most important transporters P-gp is elaborated to explain the differences and advantages of various computational models. In the third part, the challenges of developing computational methods to get reliable prediction, as well as the potential future directions in transporter related modeling are discussed.

  1. 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

  2. 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

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

    PubMed

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

    2016-02-01

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

  4. 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

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

    PubMed

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

    2011-11-01

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

  6. 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

  7. Computational Prediction of Protein–Protein Interaction Networks: Algo-rithms and Resources

    PubMed Central

    Zahiri, Javad; Bozorgmehr, Joseph Hannon; Masoudi-Nejad, Ali

    2013-01-01

    Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computational methods have attracted tremendous attention among biologists because of the ability to predict protein-protein interactions and validate the obtained experimental results. In this study, we have reviewed several computational methods for protein-protein interaction prediction as well as describing major databases, which store both predicted and detected protein-protein interactions, and the tools used for analyzing protein interaction networks and improving protein-protein interaction reliability. PMID:24396273

  8. A genome-wide RNAi screen identifies potential drug targets in a C. elegans model of α1-antitrypsin deficiency

    PubMed Central

    O'Reilly, Linda P.; Long, Olivia S.; Cobanoglu, Murat C.; Benson, Joshua A.; Luke, Cliff J.; Miedel, Mark T.; Hale, Pamela; Perlmutter, David H.; Bahar, Ivet; Silverman, Gary A.; Pak, Stephen C.

    2014-01-01

    α1-Antitrypsin deficiency (ATD) is a common genetic disorder that can lead to end-stage liver and lung disease. Although liver transplantation remains the only therapy currently available, manipulation of the proteostasis network (PN) by small molecule therapeutics offers great promise. To accelerate the drug-discovery process for this disease, we first developed a semi-automated high-throughput/content-genome-wide RNAi screen to identify PN modifiers affecting the accumulation of the α1-antitrypsin Z mutant (ATZ) in a Caenorhabditis elegans model of ATD. We identified 104 PN modifiers, and these genes were used in a computational strategy to identify human ortholog–ligand pairs. Based on rigorous selection criteria, we identified four FDA-approved drugs directed against four different PN targets that decreased the accumulation of ATZ in C. elegans. We also tested one of the compounds in a mammalian cell line with similar results. This methodology also proved useful in confirming drug targets in vivo, and predicting the success of combination therapy. We propose that small animal models of genetic disorders combined with genome-wide RNAi screening and computational methods can be used to rapidly, economically and strategically prime the preclinical discovery pipeline for rare and neglected diseases with limited therapeutic options. PMID:24838285

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed Central

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

    2016-01-01

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

  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. 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

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

    PubMed

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

    2013-01-01

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

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed Central

    Sakane, Fumio; Mizuno, Satoru; Komenoi, Suguru

    2016-01-01

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

  16. 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

  17. 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

  18. NOXclass: prediction of protein-protein interaction types

    PubMed Central

    Zhu, Hongbo; Domingues, Francisco S; Sommer, lngolf; Lengauer, Thomas

    2006-01-01

    Background Structural models determined by X-ray crystallography play a central role in understanding protein-protein interactions at the molecular level. Interpretation of these models requires the distinction between non-specific crystal packing contacts and biologically relevant interactions. This has been investigated previously and classification approaches have been proposed. However, less attention has been devoted to distinguishing different types of biological interactions. These interactions are classified as obligate and non-obligate according to the effect of the complex formation on the stability of the protomers. So far no automatic classification methods for distinguishing obligate, non-obligate and crystal packing interactions have been made available. Results Six interface properties have been investigated on a dataset of 243 protein interactions. The six properties have been combined using a support vector machine algorithm, resulting in NOXclass, a classifier for distinguishing obligate, non-obligate and crystal packing interactions. We achieve an accuracy of 91.8% for the classification of these three types of interactions using a leave-one-out cross-validation procedure. Conclusion NOXclass allows the interpretation and analysis of protein quaternary structures. In particular, it generates testable hypotheses regarding the nature of protein-protein interactions, when experimental results are not available. We expect this server will benefit the users of protein structural models, as well as protein crystallographers and NMR spectroscopists. A web server based on the method and the datasets used in this study are available at . PMID:16423290

  19. Identification of potential drug targets for tuberous sclerosis complex by synthetic screens combining CRISPR-based knockouts with RNAi.

    PubMed

    Housden, Benjamin E; Valvezan, Alexander J; Kelley, Colleen; Sopko, Richelle; Hu, Yanhui; Roesel, Charles; Lin, Shuailiang; Buckner, Michael; Tao, Rong; Yilmazel, Bahar; Mohr, Stephanie E; Manning, Brendan D; Perrimon, Norbert

    2015-09-01

    The tuberous sclerosis complex (TSC) family of tumor suppressors, TSC1 and TSC2, function together in an evolutionarily conserved protein complex that is a point of convergence for major cell signaling pathways that regulate mTOR complex 1 (mTORC1). Mutation or aberrant inhibition of the TSC complex is common in various human tumor syndromes and cancers. The discovery of novel therapeutic strategies to selectively target cells with functional loss of this complex is therefore of clinical relevance to patients with nonmalignant TSC and those with sporadic cancers. We developed a CRISPR-based method to generate homogeneous mutant Drosophila cell lines. By combining TSC1 or TSC2 mutant cell lines with RNAi screens against all kinases and phosphatases, we identified synthetic interactions with TSC1 and TSC2. Individual knockdown of three candidate genes (mRNA-cap, Pitslre, and CycT; orthologs of RNGTT, CDK11, and CCNT1 in humans) reduced the population growth rate of Drosophila cells lacking either TSC1 or TSC2 but not that of wild-type cells. Moreover, individual knockdown of these three genes had similar growth-inhibiting effects in mammalian TSC2-deficient cell lines, including human tumor-derived cells, illustrating the power of this cross-species screening strategy to identify potential drug targets. PMID:26350902

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

    PubMed

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

    2015-09-18

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

  1. On the possibility of the unification of drug targeting systems. Studies with liposome transport to the mixtures of target antigens.

    PubMed

    Trubetskoy, V S; Berdichevsky, V R; Efremov, E E; Torchilin, V P

    1987-03-15

    In order to make the drug targeting system more effective, simple and technological, we suggest creation of drug-bearing conjugates capable of simultaneous binding with different antigenic components of the target via specific antibodies. It is supposed that the targeted therapy should include sequential administration of the mixture of modified antibodies (or other specific vectors) against different components of affected tissue and, upon antibody accumulation in the desired region, administration of modified drugs or drug carrying systems which can recognize and bind with the target via accumulated antibodies due to the interaction between vector modifier and carrier modifier. Using as a model system monolayers consisting of the mixture of extracellular antigens and appropriated antibodies, it was shown that the treatment of the target with the mixture of biotinylated antibodies against all target components and subsequent binding with the target of biotinylated liposomes via avidin permits high liposome accumulation on the monolayer. The binding achieved is always higher than in the case of the utilization of single antibody-bearing liposomes. Besides, the system suggested is very simple and its components can be easily obtained on technological scale in standardized conditions.

  2. A predictive modeling approach for cell line-specific long-range regulatory interactions

    PubMed Central

    Roy, Sushmita; Siahpirani, Alireza Fotuhi; Chasman, Deborah; Knaack, Sara; Ay, Ferhat; Stewart, Ron; Wilson, Michael; Sridharan, Rupa

    2015-01-01

    Long range regulatory interactions among distal enhancers and target genes are important for tissue-specific gene expression. Genome-scale identification of these interactions in a cell line-specific manner, especially using the fewest possible datasets, is a significant challenge. We develop a novel computational approach, Regulatory Interaction Prediction for Promoters and Long-range Enhancers (RIPPLE), that integrates published Chromosome Conformation Capture (3C) data sets with a minimal set of regulatory genomic data sets to predict enhancer-promoter interactions in a cell line-specific manner. Our results suggest that CTCF, RAD21, a general transcription factor (TBP) and activating chromatin marks are important determinants of enhancer-promoter interactions. To predict interactions in a new cell line and to generate genome-wide interaction maps, we develop an ensemble version of RIPPLE and apply it to generate interactions in five human cell lines. Computational validation of these predictions using existing ChIA-PET and Hi-C data sets showed that RIPPLE accurately predicts interactions among enhancers and promoters. Enhancer-promoter interactions tend to be organized into subnetworks representing coordinately regulated sets of genes that are enriched for specific biological processes and cis-regulatory elements. Overall, our work provides a systematic approach to predict and interpret enhancer-promoter interactions in a genome-wide cell-type specific manner using a few experimentally tractable measurements. PMID:26338778

  3. Predicting and detecting reciprocity between indirect ecological interactions and evolution.

    PubMed

    Estes, James A; Brashares, Justin S; Power, Mary E

    2013-05-01

    Living nature can be thought of as a tapestry, defined not only by its constituent parts but also by how these parts are woven together. The weaving of this tapestry is a metaphor for species interactions, which can be divided into three broad classes: competitive, mutualistic, and consumptive. Direct interactions link together as more complex networks, for example, the joining of consumptive interactions into food webs. Food web dynamics are driven, in turn, by changes in the abundances of web members, whose numbers or biomass respond to bottom-up (resource limitation) and top-down (consumer limitation) forcing. The relative strengths of top-down and bottom-up forcing on the abundance of a given web member depend on its ecological context, including its topological position within the food web. Top-down effects by diverse consumers are nearly ubiquitous, in many cases influencing the structure and operation of ecosystems. While the ecological effects of such interactions are well known, far less is known of their evolutionary consequences. In this essay, we describe sundry consequences of these interaction chains on species and ecosystem processes, explain several known or suspected evolutionary effects of consumer-induced interaction chains, and identify areas where reciprocity between ecology and evolution involving the indirect effects of consumer-prey interaction chains might be further explored.

  4. Toward the Computational Prediction of Muon Sites and Interaction Parameters

    NASA Astrophysics Data System (ADS)

    Bonfà, Pietro; De Renzi, Roberto

    2016-09-01

    The rapid developments of computational quantum chemistry methods and supercomputing facilities motivate the renewed interest in the analysis of the muon/electron interactions in μSR experiments with ab initio approaches. Modern simulation methods seem to be able to provide the answers to the frequently asked questions of many μSR experiments: where is the muon? Is it a passive probe? What are the interaction parameters governing the muon-sample interaction? In this review we describe some of the approaches used to provide quantitative estimations of the aforementioned quantities and we provide the reader with a short discussion on the current developments in this field.

  5. Predicting Protein-Protein Interactions from the Molecular to the Proteome Level.

    PubMed

    Keskin, Ozlem; Tuncbag, Nurcan; Gursoy, Attila

    2016-04-27

    Identification of protein-protein interactions (PPIs) is at the center of molecular biology considering the unquestionable role of proteins in cells. Combinatorial interactions result in a repertoire of multiple functions; hence, knowledge of PPI and binding regions naturally serve to functional proteomics and drug discovery. Given experimental limitations to find all interactions in a proteome, computational prediction/modeling of protein interactions is a prerequisite to proceed on the way to complete interactions at the proteome level. This review aims to provide a background on PPIs and their types. Computational methods for PPI predictions can use a variety of biological data including sequence-, evolution-, expression-, and structure-based data. Physical and statistical modeling are commonly used to integrate these data and infer PPI predictions. We review and list the state-of-the-art methods, servers, databases, and tools for protein-protein interaction prediction. PMID:27074302

  6. Interspecific interactions through 2 million years: are competitive outcomes predictable?

    PubMed

    Liow, Lee Hsiang; Di Martino, Emanuela; Voje, Kjetil Lysne; Rust, Seabourne; Taylor, Paul D

    2016-08-31

    Ecological interactions affect the survival and reproduction of individuals. However, ecological interactions are notoriously difficult to measure in extinct populations, hindering our understanding of how the outcomes of interactions such as competition vary in time and influence long-term evolutionary changes. Here, the outcomes of spatial competition in a temporally continuous community over evolutionary timescales are presented for the first time. Our research domain is encrusting cheilostome bryozoans from the Wanganui Basin of New Zealand over a ca 2 Myr time period (Pleistocene to Recent). We find that a subset of species can be identified as consistent winners, and others as consistent losers, in the sense that they win or lose interspecific competitive encounters statistically more often than the null hypothesis of 50%. Most species do not improve or worsen in their competitive abilities through the 2 Myr period, but a minority of species are winners in some intervals and losers in others. We found that conspecifics tend to cluster spatially and interact more often than expected under a null hypothesis: most of these are stand-off interactions where the two colonies involved stopped growing at edges of encounter. Counterintuitively, competitive ability has no bearing on ecological dominance. PMID:27581885

  7. Interspecific interactions through 2 million years: are competitive outcomes predictable?

    PubMed Central

    Di Martino, Emanuela; Rust, Seabourne

    2016-01-01

    Ecological interactions affect the survival and reproduction of individuals. However, ecological interactions are notoriously difficult to measure in extinct populations, hindering our understanding of how the outcomes of interactions such as competition vary in time and influence long-term evolutionary changes. Here, the outcomes of spatial competition in a temporally continuous community over evolutionary timescales are presented for the first time. Our research domain is encrusting cheilostome bryozoans from the Wanganui Basin of New Zealand over a ca 2 Myr time period (Pleistocene to Recent). We find that a subset of species can be identified as consistent winners, and others as consistent losers, in the sense that they win or lose interspecific competitive encounters statistically more often than the null hypothesis of 50%. Most species do not improve or worsen in their competitive abilities through the 2 Myr period, but a minority of species are winners in some intervals and losers in others. We found that conspecifics tend to cluster spatially and interact more often than expected under a null hypothesis: most of these are stand-off interactions where the two colonies involved stopped growing at edges of encounter. Counterintuitively, competitive ability has no bearing on ecological dominance. PMID:27581885

  8. Prediction of subsonic aircraft flows with jet exhaust interactions

    NASA Technical Reports Server (NTRS)

    Roberts, D. W.

    1981-01-01

    A numerical procedure to calculate the flow fields resulting from the viscous inviscid interactions that occur when a strong jet exhaust and aircraft flow field coupling exists was developed. The approach divides the interaction region into zones which are either predominantly viscous or inviscid. The flow in the inviscid zone, which surrounds most of the aircraft, is calculated using an existing potential flow code. The viscous flow zone, which encompasses the jet plume, is modeled using a parabolized Navier-Stokes code. The procedure features the coupling of the zonal solutions such that sufficient information is transferred between the zones to preserve the effects of the interactions. The zonal boundaries overlap and the boundary conditions are the information link between zones. An iteration scheme iterates the coupled analysis until convergence has been obtained.

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

    PubMed

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

    2011-09-01

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

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2014-01-01

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

  13. 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

  14. Blood-brain barrier drug targeting: the future of brain drug development.

    PubMed

    Pardridge, William M

    2003-03-01

    As human longevity increases, the likelihood of the onset of diseases of the brain (and other organs) also increases. Clinical therapeutics offer useful long-term treatments, if not cures, if drugs can be delivered appropriately and effectively. Unfortunately, research in drug transport to the brain has not advanced very far. Through better characterization of the transport systems utilized within the blood-brain barrier, a greater understanding of how to exploit these systems will lead to effective treatments for brain disorders. Pardridge reviews the functions of the various known transport systems in the brain and discusses how the development of BBB drug-targeting programs in pharmaceutical and academic settings may lead to more efficacious treatments.

  15. 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

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

    PubMed

    Perbandt, Markus; Ndjonka, Dieudonne; Liebau, Eva

    2014-01-01

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

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

    PubMed

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

    2013-01-01

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

  18. 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.

  19. 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

  20. Shear-activated nanotherapeutics for drug targeting to obstructed blood vessels.

    PubMed

    Korin, Netanel; Kanapathipillai, Mathumai; Matthews, Benjamin D; Crescente, Marilena; Brill, Alexander; Mammoto, Tadanori; Ghosh, Kaustabh; Jurek, Samuel; Bencherif, Sidi A; Bhatta, Deen; Coskun, Ahmet U; Feldman, Charles L; Wagner, Denisa D; Ingber, Donald E

    2012-08-10

    Obstruction of critical blood vessels due to thrombosis or embolism is a leading cause of death worldwide. Here, we describe a biomimetic strategy that uses high shear stress caused by vascular narrowing as a targeting mechanism--in the same way platelets do--to deliver drugs to obstructed blood vessels. Microscale aggregates of nanoparticles were fabricated to break up into nanoscale components when exposed to abnormally high fluid shear stress. When coated with tissue plasminogen activator and administered intravenously in mice, these shear-activated nanotherapeutics induce rapid clot dissolution in a mesenteric injury model, restore normal flow dynamics, and increase survival in an otherwise fatal mouse pulmonary embolism model. This biophysical strategy for drug targeting, which lowers required doses and minimizes side effects while maximizing drug efficacy, offers a potential new approach for treatment of life-threatening diseases that result from acute vascular occlusion.

  1. 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

  2. Structural and functional parameters of the flaviviral protease: a promising antiviral drug target

    PubMed Central

    Shiryaev, Sergey A; Strongin, Alex Y

    2010-01-01

    Flaviviruses have a single-strand, positive-polarity RNA genome that encodes a single polyprotein. The polyprotein is comprised of seven nonstructural (NS) and three structural proteins. The N- and C-terminal parts of NS3 represent the serine protease and the RNA helicase, respectively. The cleavage of the polyprotein by the protease is required to produce the individual viral proteins, which assemble a new viral progeny. Conversely, inactivation of the protease blocks viral infection. Both the protease and the helicase are conserved among flaviviruses. As a result, NS3 is a promising drug target in flaviviral infections. This article examines the West Nile virus NS3 with an emphasis on the structural and functional parameters of the protease, the helicase and their cofactors. PMID:21076642

  3. 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

  4. [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

  5. Proteomic identification of multitasking proteins in unexpected locations complicates drug targeting.

    PubMed

    Butler, Georgina S; Overall, Christopher M

    2009-12-01

    Proteomics has revealed that many proteins are present in unexpected cellular locations. Moreover, it is increasingly recognized that proteins can translocate between intracellular and extracellular compartments in non-conventional ways. This increases gene pleiotrophy as the diverse functions of the protein that the gene encodes are dependent on the cellular location. Given that trafficking drug targets may exist in various forms--often with completely different functions--in multiple cellular compartments, careful interpretation of proteomics data is needed for an accurate understanding of gene function. This Perspective is intended to inspire the investigation of unusual protein localizations, rather than assuming that they are due to mislocalization or artefacts. Given a fair chance, proteomics could reveal novel and unforeseen biology with important ramifications for target validation in drug discovery.

  6. Perspective of microsomal prostaglandin E2 synthase-1 as drug target in inflammation-related disorders.

    PubMed

    Koeberle, Andreas; Werz, Oliver

    2015-11-01

    Prostaglandin (PG)E2 encompasses crucial roles in pain, fever, inflammation and diseases with inflammatory component, such as cancer, but is also essential for gastric, renal, cardiovascular and immune homeostasis. Cyclooxygenases (COX) convert arachidonic acid to the intermediate PGH2 which is isomerized to PGE2 by at least three different PGE2 synthases. Inhibitors of COX - non-steroidal anti-inflammatory drugs (NSAIDs) - are currently the only available therapeutics that target PGE2 biosynthesis. Due to adverse effects of COX inhibitors on the cardiovascular system (COX-2-selective), stomach and kidney (COX-1/2-unselective), novel pharmacological strategies are in demand. The inducible microsomal PGE2 synthase (mPGES)-1 is considered mainly responsible for the excessive PGE2 synthesis during inflammation and was suggested as promising drug target for suppressing PGE2 biosynthesis. However, 15 years after intensive research on the biology and pharmacology of mPGES-1, the therapeutic value of mPGES-1 as drug target is still vague and mPGES-1 inhibitors did not enter the market so far. This commentary will first shed light on the structure, mechanism and regulation of mPGES-1 and will then discuss its biological function and the consequence of its inhibition for the dynamic network of eicosanoids. Moreover, we (i) present current strategies for interfering with mPGES-1-mediated PGE2 synthesis, (ii) summarize bioanalytical approaches for mPGES-1 drug discovery and (iii) describe preclinical test systems for the characterization of mPGES-1 inhibitors. The pharmacological potential of selective mPGES-1 inhibitor classes as well as dual mPGES-1/5-lipoxygenase inhibitors is reviewed and pitfalls in their development, including species discrepancies and loss of in vivo activity, are discussed.

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

    PubMed

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

    2014-01-27

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

  8. Genetic Validation of Aminoacyl-tRNA Synthetases as Drug Targets in Trypanosoma brucei

    PubMed Central

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

    2014-01-01

    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

  9. Decaprenylphosphoryl-β-D-ribose 2'-epimerase from Mycobacterium tuberculosis is a magic drug target.

    PubMed

    Manina, G; Pasca, M R; Buroni, S; De Rossi, E; Riccardi, G

    2010-01-01

    Tuberculosis is still a leading cause of death in developing countries and a resurgent disease in developed countries. The selection and soaring spread of Mycobacterium tuberculosis multidrug-resistant (MDR-TB) and extensively drug-resistant strains (XDR-TB) is a severe public health problem. Currently, there is an urgent need of new drugs for tuberculosis treatment, with novel mechanisms of action and, moreover, the necessity to identify new drug targets. Several enzymes involved in various metabolic processes have been described as potential targets for the development of new drugs. Recently, two different classes of most promising drugs, the benzothiazinones (BTZ) and the dinitrobenzamide derivatives (DNB), have been found to be highly active against M. tuberculosis, including XDR-TB strains. Interestingly, both drugs have the same target: the heteromeric decaprenylphosphoryl-β-D-ribose 2'-epimerase encoded by dprE1 (Rv3790) and dprE2 (Rv3791) genes, respectively. DprE1 and DprE2 are involved in the biosynthesis of D-arabinose and, in particular, they are essential to perform the transformation of decaprenylphosphoryl-D-ribose to decaprenylphosphoryl-D-arabinose, which is a substrate for arabinosyltransferases in the synthesis of the cell-envelope arabinogalactan and liporabinomannan polysaccharides of mycobacteria. Arabinogalactan is a fundamental component of the mycobacterial cell wall, which covalently binds the outer layer of mycolic acids to peptidoglycan. The heteromeric decaprenylphosphoryl-β-D-ribose 2'-epimerase thus represents a valid vulnerable antimycobacterial drug target which could result in "magic" for tuberculosis treatment.

  10. Earthquake prediction: The interaction of public policy and science

    USGS Publications Warehouse

    Jones, L.M.

    1996-01-01

    Earthquake prediction research has searched for both informational phenomena, those that provide information about earthquake hazards useful to the public, and causal phenomena, causally related to the physical processes governing failure on a fault, to improve our understanding of those processes. Neither informational nor causal phenomena are a subset of the other. I propose a classification of potential earthquake predictors of informational, causal, and predictive phenomena, where predictors are causal phenomena that provide more accurate assessments of the earthquake hazard than can be gotten from assuming a random distribution. Achieving higher, more accurate probabilities than a random distribution requires much more information about the precursor than just that it is causally related to the earthquake.

  11. Towards terrain interaction prediction for bioinspired planetary exploration rovers.

    PubMed

    Yeomans, Brian; Saaj, Chakravathini M

    2014-03-01

    Deployment of a small legged vehicle to extend the reach of future planetary exploration missions is an attractive possibility but little is known about the behaviour of a walking rover on deformable planetary terrain. This paper applies ideas from the developing study of granular materials together with a detailed characterization of the sinkage process to propose and validate a combined model of terrain interaction based on an understanding of the physics and micro mechanics at the granular level. Whilst the model reflects the complexity of interactions expected from a walking rover, common themes emerge which enable the model to be streamlined to the extent that a simple mathematical representation is possible without resorting to numerical methods. Bespoke testing and analysis tools are described which reveal some unexpected conclusions and point the way towards intelligent control and foot geometry techniques to improve thrust generation.

  12. 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

  13. CopraRNA and IntaRNA: predicting small RNA targets, networks and interaction domains.

    PubMed

    Wright, Patrick R; Georg, Jens; Mann, Martin; Sorescu, Dragos A; Richter, Andreas S; Lott, Steffen; Kleinkauf, Robert; Hess, Wolfgang R; Backofen, Rolf

    2014-07-01

    CopraRNA (Comparative prediction algorithm for small RNA targets) is the most recent asset to the Freiburg RNA Tools webserver. It incorporates and extends the functionality of the existing tool IntaRNA (Interacting RNAs) in order to predict targets, interaction domains and consequently the regulatory networks of bacterial small RNA molecules. The CopraRNA prediction results are accompanied by extensive postprocessing methods such as functional enrichment analysis and visualization of interacting regions. Here, we introduce the functionality of the CopraRNA and IntaRNA webservers and give detailed explanations on their postprocessing functionalities. Both tools are freely accessible at http://rna.informatik.uni-freiburg.de. PMID:24838564

  14. CopraRNA and IntaRNA: predicting small RNA targets, networks and interaction domains

    PubMed Central

    Wright, Patrick R.; Georg, Jens; Mann, Martin; Sorescu, Dragos A.; Richter, Andreas S.; Lott, Steffen; Kleinkauf, Robert; Hess, Wolfgang R.; Backofen, Rolf

    2014-01-01

    CopraRNA (Comparative prediction algorithm for small RNA targets) is the most recent asset to the Freiburg RNA Tools webserver. It incorporates and extends the functionality of the existing tool IntaRNA (Interacting RNAs) in order to predict targets, interaction domains and consequently the regulatory networks of bacterial small RNA molecules. The CopraRNA prediction results are accompanied by extensive postprocessing methods such as functional enrichment analysis and visualization of interacting regions. Here, we introduce the functionality of the CopraRNA and IntaRNA webservers and give detailed explanations on their postprocessing functionalities. Both tools are freely accessible at http://rna.informatik.uni-freiburg.de. PMID:24838564

  15. Are pharmaceuticals with evolutionary conserved molecular drug targets more potent to cause toxic effects in non-target organisms?

    PubMed

    Furuhagen, Sara; Fuchs, Anne; Lundström Belleza, Elin; Breitholtz, Magnus; Gorokhova, Elena

    2014-01-01

    The ubiquitous use of pharmaceuticals has resulted in a continuous discharge into wastewater and pharmaceuticals and their metabolites are found in the environment. Due to their design towards specific drug targets, pharmaceuticals may be therapeutically active already at low environmental concentrations. Several human drug targets are evolutionary conserved in aquatic organisms, raising concerns about effects of these pharmaceuticals in non-target organisms. In this study, we hypothesized that the toxicity of a pharmaceutical towards a non-target invertebrate depends on the presence of the human drug target orthologs in this species. This was tested by assessing toxicity of pharmaceuticals with (miconazole and promethazine) and without (levonorgestrel) identified drug target orthologs in the cladoceran Daphnia magna. The toxicity was evaluated using general toxicity endpoints at individual (immobility, reproduction and development), biochemical (RNA and DNA content) and molecular (gene expression) levels. The results provide evidence for higher toxicity of miconazole and promethazine, i.e. the drugs with identified drug target orthologs. At the individual level, miconazole had the lowest effect concentrations for immobility and reproduction (0.3 and 0.022 mg L-1, respectively) followed by promethazine (1.6 and 0.18 mg L-1, respectively). At the biochemical level, individual RNA content was affected by miconazole and promethazine already at 0.0023 and 0.059 mg L-1, respectively. At the molecular level, gene expression for cuticle protein was significantly suppressed by exposure to both miconazole and promethazine; moreover, daphnids exposed to miconazole had significantly lower vitellogenin expression. Levonorgestrel did not have any effects on any endpoints in the concentrations tested. These results highlight the importance of considering drug target conservation in environmental risk assessments of pharmaceuticals.

  16. RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information

    PubMed Central

    Suresh, V.; Liu, Liang; Adjeroh, Donald; Zhou, Xiaobo

    2015-01-01

    RNA-protein complexes are essential in mediating important fundamental cellular processes, such as transport and localization. In particular, ncRNA-protein interactions play an important role in post-transcriptional gene regulation like mRNA localization, mRNA stabilization, poly-adenylation, splicing and translation. The experimental methods to solve RNA-protein interaction prediction problem remain expensive and time-consuming. Here, we present the RPI-Pred (RNA-protein interaction predictor), a new support-vector machine-based method, to predict protein-RNA interaction pairs, based on both the sequences and structures. The results show that RPI-Pred can correctly predict RNA-protein interaction pairs with ∼94% prediction accuracy when using sequence and experimentally determined protein and RNA structures, and with ∼83% when using sequences and predicted protein and RNA structures. Further, our proposed method RPI-Pred was superior to other existing ones by predicting more experimentally validated ncRNA-protein interaction pairs from different organisms. Motivated by the improved performance of RPI-Pred, we further applied our method for reliable construction of ncRNA-protein interaction networks. The RPI-Pred is publicly available at: http://ctsb.is.wfubmc.edu/projects/rpi-pred. PMID:25609700

  17. A Target Repurposing Approach Identifies N-myristoyltransferase as a New Candidate Drug Target in Filarial Nematodes

    PubMed Central

    Villemaine, Estelle; Poole, Catherine B.; Chapman, Melissa S.; Pollastri, Michael P.; Wyatt, Paul G.; Carlow, Clotilde K. S.

    2014-01-01

    Myristoylation is a lipid modification involving the addition of a 14-carbon unsaturated fatty acid, myristic acid, to the N-terminal glycine of a subset of proteins, a modification that promotes their binding to cell membranes for varied biological functions. The process is catalyzed by myristoyl-CoA:protein N-myristoyltransferase (NMT), an enzyme which has been validated as a drug target in human cancers, and for infectious diseases caused by fungi, viruses and protozoan parasites. We purified Caenorhabditis elegans and Brugia malayi NMTs as active recombinant proteins and carried out kinetic analyses with their essential fatty acid donor, myristoyl-CoA and peptide substrates. Biochemical and structural analyses both revealed that the nematode enzymes are canonical NMTs, sharing a high degree of conservation with protozoan NMT enzymes. Inhibitory compounds that target NMT in protozoan species inhibited the nematode NMTs with IC50 values of 2.5–10 nM, and were active against B. malayi microfilariae and adult worms at 12.5 µM and 50 µM respectively, and C. elegans (25 µM) in culture. RNA interference and gene deletion in C. elegans further showed that NMT is essential for nematode viability. The effects observed are likely due to disruption of the function of several downstream target proteins. Potential substrates of NMT in B. malayi are predicted using bioinformatic analysis. Our genetic and chemical studies highlight the importance of myristoylation in the synthesis of functional proteins in nematodes and have shown for the first time that NMT is required for viability in parasitic nematodes. These results suggest that targeting NMT could be a valid approach for the development of chemotherapeutic agents against nematode diseases including filariasis. PMID:25188325

  18. A target repurposing approach identifies N-myristoyltransferase as a new candidate drug target in filarial nematodes.

    PubMed

    Galvin, Brendan D; Li, Zhiru; Villemaine, Estelle; Poole, Catherine B; Chapman, Melissa S; Pollastri, Michael P; Wyatt, Paul G; Carlow, Clotilde K S

    2014-09-01

    Myristoylation is a lipid modification involving the addition of a 14-carbon unsaturated fatty acid, myristic acid, to the N-terminal glycine of a subset of proteins, a modification that promotes their binding to cell membranes for varied biological functions. The process is catalyzed by myristoyl-CoA:protein N-myristoyltransferase (NMT), an enzyme which has been validated as a drug target in human cancers, and for infectious diseases caused by fungi, viruses and protozoan parasites. We purified Caenorhabditis elegans and Brugia malayi NMTs as active recombinant proteins and carried out kinetic analyses with their essential fatty acid donor, myristoyl-CoA and peptide substrates. Biochemical and structural analyses both revealed that the nematode enzymes are canonical NMTs, sharing a high degree of conservation with protozoan NMT enzymes. Inhibitory compounds that target NMT in protozoan species inhibited the nematode NMTs with IC50 values of 2.5-10 nM, and were active against B. malayi microfilariae and adult worms at 12.5 µM and 50 µM respectively, and C. elegans (25 µM) in culture. RNA interference and gene deletion in C. elegans further showed that NMT is essential for nematode viability. The effects observed are likely due to disruption of the function of several downstream target proteins. Potential substrates of NMT in B. malayi are predicted using bioinformatic analysis. Our genetic and chemical studies highlight the importance of myristoylation in the synthesis of functional proteins in nematodes and have shown for the first time that NMT is required for viability in parasitic nematodes. These results suggest that targeting NMT could be a valid approach for the development of chemotherapeutic agents against nematode diseases including filariasis.

  19. Does global gene expression analysis in type 2 diabetes provide an opportunity to identify highly promising drug targets?

    PubMed

    Buechler, C; Schäffler, A

    2007-12-01

    The recent technological advances in high-throughput gene expression analysis allow the simultaneous investigation of thousands of genes. These technologies represent promising tools for the identification of new drug targets and considerable progress has been achieved in cancer research where microarray data provide a basis to design new drugs and to predict adverse reactions and the efficacy of chemotherapy. The metabolic syndrome represents a cluster of disorders including high blood pressure, insulin resistance/type 2 diabetes mellitus, visceral obesity and dyslipidaemia with fatty liver disease being a common associated complication. High-throughput gene expression analyses using GeneChips, microarrays and serial analysis of gene expression (SAGE) have been applied to study global gene expression in insulin resistance/type 2 diabetes mellitus. Type 2 diabetes mellitus is a multifactorial and polygenic disease by which several organs are affected. Therefore, the identification of both, disease causing and therapeutically relevant target genes is an ambitious challenge. In the present review we focus on genomic approaches that used biopsies from human skeletal muscle, liver and adipose tissue, the main organs affected by insulin resistance. Members of the PPARgamma coactivator-1 (PGC-1) family of transcriptional coactivators are decreased in skeletal muscle in insulin resistance accounting for the reduced expression of genes involved in mitochondrial oxidative phosphorylation. Hepatic steatosis is also linked to alterations in mitochondrial phosphorylation and oxidative metabolism. An up regulation of pro-inflammatory genes can be detected in early stages of fatty liver disease without histological signs of inflammation. Impaired adipogenesis, intra-adipose accumulation of macrophages and a sustained release of inflammatory and acute phase proteins are characteristic features of adipose tissue in obesity and may aggravate systemic insulin resistance.

  20. LIBP-Pred: web server for lipid binding proteins using structural network parameters; PDB mining of human cancer biomarkers and drug targets in parasites and bacteria.

    PubMed

    González-Díaz, Humberto; Munteanu, Cristian R; Postelnicu, Lucian; Prado-Prado, Francisco; Gestal, Marcos; Pazos, Alejandro

    2012-03-01

    Lipid-Binding Proteins (LIBPs) or Fatty Acid-Binding Proteins (FABPs) play an important role in many diseases such as different types of cancer, kidney injury, atherosclerosis, diabetes, intestinal ischemia and parasitic infections. Thus, the computational methods that can predict LIBPs based on 3D structure parameters became a goal of major importance for drug-target discovery, vaccine design and biomarker selection. In addition, the Protein Data Bank (PDB) contains 3000+ protein 3D structures with unknown function. This list, as well as new experimental outcomes in proteomics research, is a very interesting source to discover relevant proteins, including LIBPs. However, to the best of our knowledge, there are no general models to predict new LIBPs based on 3D structures. We developed new Quantitative Structure-Activity Relationship (QSAR) models based on 3D electrostatic parameters of 1801 different proteins, including 801 LIBPs. We calculated these electrostatic parameters with the MARCH-INSIDE software and they correspond to the entire protein or to specific protein regions named core, inner, middle, and surface. We used these parameters as inputs to develop a simple Linear Discriminant Analysis (LDA) classifier to discriminate 3D structure of LIBPs from other proteins. We implemented this predictor in the web server named LIBP-Pred, freely available at , along with other important web servers of the Bio-AIMS portal. The users can carry out an automatic retrieval of protein structures from PDB or upload their custom protein structural models from their disk created with LOMETS server. We demonstrated the PDB mining option performing a predictive study of 2000+ proteins with unknown function. Interesting results regarding the discovery of new Cancer Biomarkers in humans or drug targets in parasites have been discussed here in this sense.

  1. Chronic pain patient-spouse behavioral interactions predict patient disability.

    PubMed

    Romano, J M; Turner, J A; Jensen, M P; Friedman, L S; Bulcroft, R A; Hops, H; Wright, S F

    1995-12-01

    Based on behavioral theory, it has been hypothesized that spouse solicitous responses to the pain behaviors of chronic pain patients may contribute to the maintenance of pain behaviors and disability. Self-report data support this hypothesis, but direct observational measures have not been used to study this association. In this study, 50 chronic pain patients and their spouses were videotaped while engaging in common household activities. and patient pain behaviors and spouse solicitous behaviors were coded from the tapes. Spouse solicitous responses to non-verbal pain behaviors were significant predictors of physical disability in the more depressed patients, and were significant predictors of rate of non-verbal pain behavior in patients who reported greater pain. Spouse solicitous responses did not predict psychosocial dysfunction or total self-reported pain behaviors. The result support behavioral theory and indicate the need for further study of the association between spouse solicitousness and patient pain behaviors/disability.

  2. An Interactive Point Kernel Program For Photon Dose Rate Prediction of Cylindrical Source/Shield Arrangements.

    1990-10-26

    Version 00 The program ZYLIND is an interactive point kernel program for photon dose rate prediction of a homogeneous cylindrical source shielded by cylindrical (radial) or plane (axial) layered shields.

  3. Parent conflict predicts infants' vagal regulation in social interaction.

    PubMed

    Moore, Ginger A

    2010-01-01

    Parent conflict during infancy may affect rapidly developing physiological regulation. To examine the association between parent conflict and infants' vagal tone functioning, mothers (N = 48) reported levels of parent conflict and their 6-month-old male and female infants' respiratory sinus arrhythmia (RSA) was measured in the still-face paradigm. Higher parent conflict was related to lower RSA at baseline and each episode of the still-face paradigm. Infants in relatively higher conflict families showed attenuated RSA withdrawal in response to mothers' disengagement and attenuated RSA activation when interacting with mothers. Findings suggest atypical RSA regulation and reliance on self-regulation for infants in families with moderate levels of parent conflict. Implications for later development and future research are discussed.

  4. Prediction and redesign of protein–protein interactions

    PubMed Central

    Lua, Rhonald C.; Marciano, David C.; Katsonis, Panagiotis; Adikesavan, Anbu K.; Wilkins, Angela D.; Lichtarge, Olivier

    2014-01-01

    Understanding the molecular basis of protein function remains a central goal of biology, with the hope to elucidate the role of human genes in health and in disease, and to rationally design therapies through targeted molecular perturbations. We review here some of the computational techniques and resources available for characterizing a critical aspect of protein function – those mediated by protein–protein interactions (PPI). We describe several applications and recent successes of the Evolutionary Trace (ET) in identifying molecular events and shapes that underlie protein function and specificity in both eukaryotes and prokaryotes. ET is a part of analytical approaches based on the successes and failures of evolution that enable the rational control of PPI. PMID:24878423

  5. Homology-Based Prediction of Potential Protein–Protein Interactions between Human Erythrocytes and Plasmodium falciparum

    PubMed Central

    Ramakrishnan, Gayatri; Srinivasan, Narayanaswamy; Padmapriya, Ponnan; Natarajan, Vasant

    2015-01-01

    Plasmodium falciparum, a causative agent of malaria, is a well-characterized obligate intracellular parasite known for its ability to remodel host cells, particularly erythrocytes, to successfully persist in the host environment. However, the current levels of understanding from the laboratory experiments on the host–parasite interactions and the strategies pursued by the parasite to remodel host erythrocytes are modest. Several computational means developed in the recent past to predict host–parasite/pathogen interactions have generated testable hypotheses on feasible protein–protein interactions. We demonstrate the utility of protein structure-based protocol in the recognition of potential interacting proteins across P. falciparum and host erythrocytes. In concert with the information on the expression and subcellular localization of host and parasite proteins, we have identified 208 biologically feasible interactions potentially brought about by 59 P. falciparum and 30 host erythrocyte proteins. For selected cases, we have evaluated the physicochemical viability of the predicted interactions in terms of surface complementarity, electrostatic complementarity, and interaction energies at protein interface regions. Such careful inspection of molecular and mechanistic details generates high confidence on the predicted host–parasite protein–protein interactions. The predicted host–parasite interactions generate many experimentally testable hypotheses that can contribute to the understanding of possible mechanisms undertaken by the parasite in host erythrocyte remodeling. Thus, the key protein players recognized in P. falciparum can be explored for their usefulness as targets for chemotherapeutic intervention. PMID:26740742

  6. Predict drug-protein interaction in cellular networking.

    PubMed

    Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen

    2013-01-01

    Involved with many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, GPCRs (G-protein-coupled receptors) are the most frequent targets for drug development: over 50% of all prescription drugs currently on the market are actually acting by targeting GPCRs directly or indirectly. Found in every living thing and nearly all cells, ion channels play crucial roles for many vital functions in life, such as heartbeat, sensory transduction, and central nervous system response. Their dysfunction may have significant impact to human health, and hence ion channels are deemed as "the next GPCRs". To develop GPCR-targeting or ion-channel-targeting drugs, the first important step is to identify the interactions between potential drug compounds with the two kinds of protein receptors in the cellular networking. In this minireview, we are to introduce two predictors. One is called iGPCR-Drug accessible at http://www.jci-bioinfo.cn/iGPCR-Drug/; the other called iCDI-PseFpt at http://www.jci-bioinfo.cn/iCDI-PseFpt. The former is for identifying the interactions of drug compounds with GPCRs; while the latter for that with ion channels. In both predictors, the drug compound was formulated by the two-dimensional molecular fingerprint, and the protein receptor by the pseudo amino acid composition generated with the grey model theory, while the operation engine was the fuzzy K-nearest neighbor algorithm. For the convenience of most experimental pharmaceutical and medical scientists, a step-bystep guide is provided on how to use each of the two web-servers to get the desired results without the need to follow the complicated mathematics involved originally for their establishment. PMID:23889048

  7. Predict drug-protein interaction in cellular networking.

    PubMed

    Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen

    2013-01-01

    Involved with many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, GPCRs (G-protein-coupled receptors) are the most frequent targets for drug development: over 50% of all prescription drugs currently on the market are actually acting by targeting GPCRs directly or indirectly. Found in every living thing and nearly all cells, ion channels play crucial roles for many vital functions in life, such as heartbeat, sensory transduction, and central nervous system response. Their dysfunction may have significant impact to human health, and hence ion channels are deemed as "the next GPCRs". To develop GPCR-targeting or ion-channel-targeting drugs, the first important step is to identify the interactions between potential drug compounds with the two kinds of protein receptors in the cellular networking. In this minireview, we are to introduce two predictors. One is called iGPCR-Drug accessible at http://www.jci-bioinfo.cn/iGPCR-Drug/; the other called iCDI-PseFpt at http://www.jci-bioinfo.cn/iCDI-PseFpt. The former is for identifying the interactions of drug compounds with GPCRs; while the latter for that with ion channels. In both predictors, the drug compound was formulated by the two-dimensional molecular fingerprint, and the protein receptor by the pseudo amino acid composition generated with the grey model theory, while the operation engine was the fuzzy K-nearest neighbor algorithm. For the convenience of most experimental pharmaceutical and medical scientists, a step-bystep guide is provided on how to use each of the two web-servers to get the desired results without the need to follow the complicated mathematics involved originally for their establishment.

  8. Comparison of Predicted Low Speed Fan Rotor/Stator Interaction Modes to Measured

    NASA Technical Reports Server (NTRS)

    Sutliff, Daniel L.; Bridges, James; Envia, Edmane

    1996-01-01

    The V072 Rotor Wake/Stator Interaction Code is widely used as a state-of-the-art prediction code. This paper validates the code by comparing experimentally measured mode levels to those predicted by V072. The experimental mode levels were measured by the Rotating Rake system installed on the 48 inch Active Noise Control Fan at NASA Lewis Research Center. V072 predicted mode levels by inputting the actual wake profiles of the ANCF rotor measured by a 2-component hotwire. The mode levels were also predicted from the V072 wake models. V072 reasonably predicts the mode levels within the design limits of the code.

  9. A comprehensive comparison of general RNA-RNA interaction prediction methods.

    PubMed

    Lai, Daniel; Meyer, Irmtraud M

    2016-04-20

    RNA-RNA interactions are fast emerging as a major functional component in many newly discovered non-coding RNAs. Basepairing is believed to be a major contributor to the stability of these intermolecular interactions, much like intramolecular basepairs formed in RNA secondary structure. As such, using algorithms similar to those for predicting RNA secondary structure, computational methods have been recently developed for the prediction of RNA-RNA interactions. We provide the first comprehensive comparison comprising 14 methods that predict general intermolecular basepairs. To evaluate these, we compile an extensive data set of 54 experimentally confirmed fungal snoRNA-rRNA interactions and 102 bacterial sRNA-mRNA interactions. We test the performance accuracy of all methods, evaluating the effects of tool settings, sequence length, and multiple sequence alignment usage and quality. Our results show that-unlike for RNA secondary structure prediction--the overall best performing tools are non-comparative energy-based tools utilizing accessibility information that predict short interactions on this data set. Furthermore, we find that maintaining high accuracy across biologically different data sets and increasing input lengths remains a huge challenge, causing implications for de novo transcriptome-wide searches. Finally, we make our interaction data set publicly available for future development and benchmarking efforts.

  10. Theoretical predictions of jet interaction effects for USB and OWB configurations

    NASA Technical Reports Server (NTRS)

    Lan, C. E.; Campbell, J. F.

    1976-01-01

    A wing jet interaction theory is presented for predicting the aerodynamic characteristics of upper surface blowing and over wing blowing configurations. For the latter configurations, a new jet entrainment theory is developed. Comparison of predicted results with some available data showed good agreement. Some applications of the theory are also presented.

  11. Predicting the Creativity of Design Majors Based on the Interaction of Diverse Personality Traits

    ERIC Educational Resources Information Center

    Chang, Chi-Cheng; Peng, Li-Pei; Lin, Ju-Sen; Liang, Chaoyun

    2015-01-01

    In this study, design majors were analysed to examine how diverse personality traits interact and influence student creativity. The study participants comprised 476 design majors. The results indicated that openness predicted the originality of creativity, whereas openness, conscientiousness and agreeableness predicted the usefulness of…

  12. Factors of Learner-Instructor Interaction Which Predict Perceived Learning Outcomes in Online Learning Environment

    ERIC Educational Resources Information Center

    Kang, M.; Im, T.

    2013-01-01

    Interaction in the online learning environment has been regarded as one of the most critical elements that affect learning outcomes. This study examined what factors in learner-instructor interaction can predict the learner's outcomes in the online learning environment. Learners in K Online University participated by answering the survey, and data…

  13. PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction

    DOE PAGES

    Yao, Jianzhuang; Guo, Hong; Yang, Xiaohan

    2015-01-01

    Determining protein-protein interaction (PPI) in biological systems is of considerable importance, and prediction of PPI has become a popular research area. Although different classifiers have been developed for PPI prediction, no single classifier seems to be able to predict PPI with high confidence. We postulated that by combining individual classifiers the accuracy of PPI prediction could be improved. We developed a method called protein-protein interaction prediction classifiers merger (PPCM), and this method combines output from two PPI prediction tools, GO2PPI and Phyloprof, using Random Forests algorithm. The performance of PPCM was tested by area under the curve (AUC) using anmore » assembled Gold Standard database that contains both positive and negative PPI pairs. Our AUC test showed that PPCM significantly improved the PPI prediction accuracy over the corresponding individual classifiers. We found that additional classifiers incorporated into PPCM could lead to further improvement in the PPI prediction accuracy. Furthermore, cross species PPCM could achieve competitive and even better prediction accuracy compared to the single species PPCM. This study established a robust pipeline for PPI prediction by integrating multiple classifiers using Random Forests algorithm. This pipeline will be useful for predicting PPI in nonmodel species.« less

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

    PubMed

    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.

  15. 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.

  16. 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

  17. 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.

  18. Streptococcus pneumoniae TIGR4 Flavodoxin: Structural and Biophysical Characterization of a Novel Drug Target

    PubMed Central

    Rodríguez-Cárdenas, Ángela; Rojas, Adriana L.; Conde-Giménez, María; Velázquez-Campoy, Adrián; Hurtado-Guerrero, Ramón; Sancho, Javier

    2016-01-01

    Streptococcus pneumoniae (Sp) strain TIGR4 is a virulent, encapsulated serotype that causes bacteremia, otitis media, meningitis and pneumonia. Increased bacterial resistance and limited efficacy of the available vaccine to some serotypes complicate the treatment of diseases associated to this microorganism. Flavodoxins are bacterial proteins involved in several important metabolic pathways. The Sp flavodoxin (Spfld) gene was recently reported to be essential for the establishment of meningitis in a rat model, which makes SpFld a potential drug target. To facilitate future pharmacological studies, we have cloned and expressed SpFld in E. coli and we have performed an extensive structural and biochemical characterization of both the apo form and its active complex with the FMN cofactor. SpFld is a short-chain flavodoxin containing 146 residues. Unlike the well-characterized long-chain apoflavodoxins, the Sp apoprotein displays a simple two-state thermal unfolding equilibrium and binds FMN with moderate affinity. The X-ray structures of the apo and holo forms of SpFld differ at the FMN binding site, where substantial rearrangement of residues at the 91–100 loop occurs to permit cofactor binding. This work will set up the basis for future studies aiming at discovering new potential drugs to treat S. pneumoniae diseases through the inhibition of SpFld. PMID:27649488

  19. Streptococcus pneumoniae TIGR4 Flavodoxin: Structural and Biophysical Characterization of a Novel Drug Target.

    PubMed

    Rodríguez-Cárdenas, Ángela; Rojas, Adriana L; Conde-Giménez, María; Velázquez-Campoy, Adrián; Hurtado-Guerrero, Ramón; Sancho, Javier

    2016-01-01

    Streptococcus pneumoniae (Sp) strain TIGR4 is a virulent, encapsulated serotype that causes bacteremia, otitis media, meningitis and pneumonia. Increased bacterial resistance and limited efficacy of the available vaccine to some serotypes complicate the treatment of diseases associated to this microorganism. Flavodoxins are bacterial proteins involved in several important metabolic pathways. The Sp flavodoxin (Spfld) gene was recently reported to be essential for the establishment of meningitis in a rat model, which makes SpFld a potential drug target. To facilitate future pharmacological studies, we have cloned and expressed SpFld in E. coli and we have performed an extensive structural and biochemical characterization of both the apo form and its active complex with the FMN cofactor. SpFld is a short-chain flavodoxin containing 146 residues. Unlike the well-characterized long-chain apoflavodoxins, the Sp apoprotein displays a simple two-state thermal unfolding equilibrium and binds FMN with moderate affinity. The X-ray structures of the apo and holo forms of SpFld differ at the FMN binding site, where substantial rearrangement of residues at the 91-100 loop occurs to permit cofactor binding. This work will set up the basis for future studies aiming at discovering new potential drugs to treat S. pneumoniae diseases through the inhibition of SpFld. PMID:27649488

  20. 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

  1. DYRK1A: a potential drug target for multiple Down syndrome neuropathologies.

    PubMed

    Becker, Walter; Soppa, Ulf; Tejedor, Francisco J

    2014-02-01

    Down syndrome (DS), the most common genetic cause of intellectual disability, is caused by the trisomy of chromosome 21. MNB/DYRK1A (Minibrain/dual specificity tyrosine phosphorylation-regulated kinase 1A) has possibly been the most extensively studied chromosome 21 gene during the last decade due to the remarkable correlation of its functions in the brain with important DS neuropathologies, such as neuronal deficits, dendrite atrophy, spine dysgenesis, precocious Alzheimer's-like neurodegeneration, and cognitive deficits. MNB/DYRK1A has become an attractive drug target because increasing evidence suggests that its overexpression may induce DS-like neurobiological alterations, and several small-molecule inhibitors of its protein kinase activity are available. Here, we summarize the functional complexity of MNB/DYRK1A from a DS-research perspective, paying particular attention to the capacity of different MNB/DYRK1A inhibitors to reverse the neurobiological alterations caused by the increased activity of MNB/DYRK1A in experimental models. Finally, we discuss the advantages and drawbacks of possible MNB/DYRK1A-based therapeutic strategies that result from the functional, molecular, and pharmacological complexity of MNB/DYRK1A. PMID:24152332

  2. 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

  3. Comparison of mutated JAK2 and ABL1 as oncogenes and drug targets in myeloproliferative disorders

    PubMed Central

    Walz, Christoph; Cross, Nicholas C. P.; Van Etten, Richard A.; Reiter, Andreas

    2012-01-01

    Constitutively activated mutants of the non-receptor tyrosine kinases (TK) ABL1 and JAK2 play a central role in the pathogenesis of clinically and morphologically distinct chronic myeloproliferative disorders but are also found in some cases of de novo acute leukemia and lymphoma. Ligand-independent activation occurs as a consequence of point mutations or insertions/deletions within functionally relevant regulatory domains (JAK2), or the creation of TK fusion proteins by balanced reciprocal translocations, insertions or episomal amplification (ABL1 and JAK2). Specific abnormalities are correlated with clinical phenotype, although some are broad and encompass several WHO-defined entities. TKs are excellent drug targets as exemplified by the activity of imatinib in BCR-ABL1-positive disease, particularly chronic myeloid leukemia. Resistance to imatinib is seen in a minority of cases and is often associated with the appearance of secondary point mutations within the TK domain of BCR-ABL1. These mutations are highly variable in their sensitivity to increased doses of imatinib or alternative TK-inhibitors such as nilotinib or dasatinib. Selective and non-selective inhibitors of JAK2 are currently being developed and encouraging data from pre-clinical experiments and initial phase-I-studies regarding efficacy and potential toxicity of these compounds have already been reported. PMID:18528425

  4. 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.

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

    PubMed

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

    2015-11-01

    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. PMID:26458056

  6. 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.

  7. 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.

  8. 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

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

    PubMed

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

    2015-11-01

    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.

  10. Prediction of B-strand packing interactions using the signature product.

    SciTech Connect

    Brown, W. Michael; Martin, Shawn Bryan; Faulon, Jean-Loup Michel; Strauss, Charlie

    2005-03-01

    The prediction of {beta}-sheet topology requires the consideration of long-range interactions between {beta}-strands that are not necessarily consecutive in sequence. Since these interactions are difficult to simulate using ab initio methods, we propose a supplementary method able to assign {beta}-sheet topology using only sequence information. We envision using the results of our method to reduce the three-dimensional search space of ab initio methods. Our method is based on the signature molecular descriptor, which has been used previously to predict protein-protein interactions successfully, and to develop quantitative structure-activity relationships for small organic drugs and peptide inhibitors. Here, we show how the signature descriptor can be used in a Support Vector Machine to predict whether or not two {beta}-strands will pack adjacently within a protein. We then show how these predictions can be used to order {beta}-strands within {beta}-sheets. Using the entire PDB database with ten-fold cross-validation, we have achieved 74.0% accuracy in packing prediction and 75.6% accuracy in the prediction of edge strands. For the case of {beta}-strand ordering, we are able to predict the correct ordering accurately for 51.3% of the {beta}-sheets. Furthermore, using a simple confidence metric, we can determine those sheets for which accurate predictions can be obtained. For the top 25% highest confidence predictions, we are able to achieve 95.7% accuracy in {beta}-strand ordering.

  11. Low energy magnetospheric plasma interactions with space systems: The role of predictions. [spacecraft charging

    NASA Technical Reports Server (NTRS)

    Garrett, H. B.

    1979-01-01

    The present status of low energy magnetospheric plasma interactions with space systems is reviewed. The role of predictions in meeting user needs in assessing the impact of such interactions is described. In light of the perceived needs of the user community and of the current status of modeling and prediction efforts, it is suggested that for most user needs more detailed statistical models of the low energy environment are required. In order to meet current prediction requirements, real-time in situ measurements are proposed as a near-term solution.

  12. Conflict and expectancies interact to predict sexual behavior under the influence among gay and bisexual men

    PubMed Central

    Wells, Brooke E; Starks, Tyrel J; Parsons, Jeffrey T; Golub, Sarit

    2013-01-01

    As the mechanisms of the associations between substance use and risky sex remain unclear, this study investigates the interactive roles of conflicts about casual sex and condom use and expectancies of the sexual effects of substances in those associations among gay men. Conflict interacted with expectancies to predict sexual behavior under the influence; low casual sex conflict coupled with high expectancies predicted the highest number of casual partners, and high condom use conflict and high expectancies predicted the highest number of unprotected sex acts. Results have implications for intervention efforts that aim to improve sexual decision-making and reduce sexual expectancies. PMID:23584507

  13. Conflict and expectancies interact to predict sexual behavior under the influence among gay and bisexual men.

    PubMed

    Wells, Brooke E; Starks, Tyrel J; Parsons, Jeffrey T; Golub, Sarit

    2014-07-01

    As the mechanisms of the associations between substance use and risky sex remain unclear, this study investigates the interactive roles of conflicts about casual sex and condom use and expectancies of the sexual effects of substances in those associations among gay men. Conflict interacted with expectancies to predict sexual behavior under the influence; low casual sex conflict coupled with high expectancies predicted the highest number of casual partners, and high condom use conflict and high expectancies predicted the highest number of unprotected sex acts. Results have implications for intervention efforts that aim to improve sexual decision-making and reduce sexual expectancies.

  14. Hierarchical Interactions Model for Predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) Conversion

    PubMed Central

    Li, Han; Liu, Yashu; Gong, Pinghua; Zhang, Changshui; Ye, Jieping

    2014-01-01

    Identifying patients with Mild Cognitive Impairment (MCI) who are likely to convert to dementia has recently attracted increasing attention in Alzheimer's disease (AD) research. An accurate prediction of conversion from MCI to AD can aid clinicians to initiate treatments at early stage and monitor their effectiveness. However, existing prediction systems based on the original biosignatures are not satisfactory. In this paper, we propose to fit the prediction models using pairwise biosignature interactions, thus capturing higher-order relationship among biosignatures. Specifically, we employ hierarchical constraints and sparsity regularization to prune the high-dimensional input features. Based on the significant biosignatures and underlying interactions identified, we build classifiers to predict the conversion probability based on the selected features. We further analyze the underlying interaction effects of different biosignatures based on the so-called stable expectation scores. We have used 293 MCI subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) database that have MRI measurements at the baseline to evaluate the effectiveness of the proposed method. Our proposed method achieves better classification performance than state-of-the-art methods. Moreover, we discover several significant interactions predictive of MCI-to-AD conversion. These results shed light on improving the prediction performance using interaction features. PMID:24416143

  15. 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

  16. Physiologically Based Pharmacokinetic Modeling Framework for Quantitative Prediction of an Herb–Drug Interaction

    PubMed Central

    Brantley, S J; Gufford, B T; Dua, R; Fediuk, D J; Graf, T N; Scarlett, Y V; Frederick, K S; Fisher, M B; Oberlies, N H; Paine, M F

    2014-01-01

    Herb–drug interaction predictions remain challenging. Physiologically based pharmacokinetic (PBPK) modeling was used to improve prediction accuracy of potential herb–drug interactions using the semipurified milk thistle preparation, silibinin, as an exemplar herbal product. Interactions between silibinin constituents and the probe substrates warfarin (CYP2C9) and midazolam (CYP3A) were simulated. A low silibinin dose (160 mg/day × 14 days) was predicted to increase midazolam area under the curve (AUC) by 1%, which was corroborated with external data; a higher dose (1,650 mg/day × 7 days) was predicted to increase midazolam and (S)-warfarin AUC by 5% and 4%, respectively. A proof-of-concept clinical study confirmed minimal interaction between high-dose silibinin and both midazolam and (S)-warfarin (9 and 13% increase in AUC, respectively). Unexpectedly, (R)-warfarin AUC decreased (by 15%), but this is unlikely to be clinically important. Application of this PBPK modeling framework to other herb–drug interactions could facilitate development of guidelines for quantitative prediction of clinically relevant interactions. PMID:24670388

  17. Predicting disease-related proteins based on clique backbone in protein-protein interaction network.

    PubMed

    Yang, Lei; Zhao, Xudong; Tang, Xianglong

    2014-01-01

    Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.

  18. Predicting Long Noncoding RNA and Protein Interactions Using Heterogeneous Network Model

    PubMed Central

    2015-01-01

    Recent study shows that long noncoding RNAs (lncRNAs) are participating in diverse biological processes and complex diseases. However, at present the functions of lncRNAs are still rarely known. In this study, we propose a network-based computational method, which is called lncRNA-protein interaction prediction based on Heterogeneous Network Model (LPIHN), to predict the potential lncRNA-protein interactions. First, we construct a heterogeneous network by integrating the lncRNA-lncRNA similarity network, lncRNA-protein interaction network, and protein-protein interaction (PPI) network. Then, a random walk with restart is implemented on the heterogeneous network to infer novel lncRNA-protein interactions. The leave-one-out cross validation test shows that our approach can achieve an AUC value of 96.0%. Some lncRNA-protein interactions predicted by our method have been confirmed in recent research or database, indicating the efficiency of LPIHN to predict novel lncRNA-protein interactions. PMID:26839884

  19. A comprehensive comparison of general RNA–RNA interaction prediction methods

    PubMed Central

    Lai, Daniel; Meyer, Irmtraud M.

    2016-01-01

    RNA–RNA interactions are fast emerging as a major functional component in many newly discovered non-coding RNAs. Basepairing is believed to be a major contributor to the stability of these intermolecular interactions, much like intramolecular basepairs formed in RNA secondary structure. As such, using algorithms similar to those for predicting RNA secondary structure, computational methods have been recently developed for the prediction of RNA–RNA interactions. We provide the first comprehensive comparison comprising 14 methods that predict general intermolecular basepairs. To evaluate these, we compile an extensive data set of 54 experimentally confirmed fungal snoRNA–rRNA interactions and 102 bacterial sRNA–mRNA interactions. We test the performance accuracy of all methods, evaluating the effects of tool settings, sequence length, and multiple sequence alignment usage and quality. Our results show that—unlike for RNA secondary structure prediction—the overall best performing tools are non-comparative energy-based tools utilizing accessibility information that predict short interactions on this data set. Furthermore, we find that maintaining high accuracy across biologically different data sets and increasing input lengths remains a huge challenge, causing implications for de novo transcriptome-wide searches. Finally, we make our interaction data set publicly available for future development and benchmarking efforts. PMID:26673718

  20. PETcofold: predicting conserved interactions and structures of two multiple alignments of RNA sequences

    PubMed Central

    Seemann, Stefan E.; Richter, Andreas S.; Gesell, Tanja; Backofen, Rolf; Gorodkin, Jan

    2011-01-01

    Motivation: Predicting RNA–RNA interactions is essential for determining the function of putative non-coding RNAs. Existing methods for the prediction of interactions are all based on single sequences. Since comparative methods have already been useful in RNA structure determination, we assume that conserved RNA–RNA interactions also imply conserved function. Of these, we further assume that a non-negligible amount of the existing RNA–RNA interactions have also acquired compensating base changes throughout evolution. We implement a method, PETcofold, that can take covariance information in intra-molecular and inter-molecular base pairs into account to predict interactions and secondary structures of two multiple alignments of RNA sequences. Results: PETcofold's ability to predict RNA–RNA interactions was evaluated on a carefully curated dataset of 32 bacterial small RNAs and their targets, which was manually extracted from the literature. For evaluation of both RNA–RNA interaction and structure prediction, we were able to extract only a few high-quality examples: one vertebrate small nucleolar RNA and four bacterial small RNAs. For these we show that the prediction can be improved by our comparative approach. Furthermore, PETcofold was evaluated on controlled data with phylogenetically simulated sequences enriched for covariance patterns at the interaction sites. We observed increased performance with increased amounts of covariance. Availability: The program PETcofold is available as source code and can be downloaded from http://rth.dk/resources/petcofold. Contact: gorodkin@rth.dk; backofen@informatik.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21088024

  1. Predicting physiologically relevant SH3 domain mediated protein–protein interactions in yeast

    PubMed Central

    Jain, Shobhit; Bader, Gary D.

    2016-01-01

    Motivation: Many intracellular signaling processes are mediated by interactions involving peptide recognition modules such as SH3 domains. These domains bind to small, linear protein sequence motifs which can be identified using high-throughput experimental screens such as phage display. Binding motif patterns can then be used to computationally predict protein interactions mediated by these domains. While many protein–protein interaction prediction methods exist, most do not work with peptide recognition module mediated interactions or do not consider many of the known constraints governing physiologically relevant interactions between two proteins. Results: A novel method for predicting physiologically relevant SH3 domain-peptide mediated protein–protein interactions in S. cerevisae using phage display data is presented. Like some previous similar methods, this method uses position weight matrix models of protein linear motif preference for individual SH3 domains to scan the proteome for potential hits and then filters these hits using a range of evidence sources related to sequence-based and cellular constraints on protein interactions. The novelty of this approach is the large number of evidence sources used and the method of combination of sequence based and protein pair based evidence sources. By combining different peptide and protein features using multiple Bayesian models we are able to predict high confidence interactions with an overall accuracy of 0.97. Availability and implementation: Domain-Motif Mediated Interaction Prediction (DoMo-Pred) command line tool and all relevant datasets are available under GNU LGPL license for download from http://www.baderlab.org/Software/DoMo-Pred. The DoMo-Pred command line tool is implemented using Python 2.7 and C ++. Contact: gary.bader@utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26861823

  2. Role of evolutionary information in prediction of aromatic-backbone NH interactions in proteins.

    PubMed

    Kaur, Harpreet; Raghava, G P S

    2004-04-23

    In this study, an attempt has been made to develop a neural network-based method for predicting segments in proteins containing aromatic-backbone NH (Ar-NH) interactions using multiple sequence alignment. We have analyzed 3121 segments seven residues long containing Ar-NH interactions, extracted from 2298 non-redundant protein structures where no two proteins have more than 25% sequence identity. Two consecutive feed-forward neural networks with a single hidden layer have been trained with standard back-propagation as learning algorithm. The performance of the method improves from 0.12 to 0.15 in terms of Matthews correlation coefficient (MCC) value when evolutionary information (multiple alignment obtained from PSI-BLAST) is used as input instead of a single sequence. The performance of the method further improves from MCC 0.15 to 0.20 when secondary structure information predicted by PSIPRED is incorporated in the prediction. The final network yields an overall prediction accuracy of 70.1% and an MCC of 0.20 when tested by five-fold cross-validation. Overall the performance is 15.2% higher than the random prediction. The method consists of two neural networks: (i) a sequence-to-structure network which predicts the aromatic residues involved in Ar-NH interaction from multiple alignment of protein sequences and (ii) a structure-to structure network where the input consists of the output obtained from the first network and predicted secondary structure. Further, the actual position of the donor residue within the 'potential' predicted fragment has been predicted using a separate sequence-to-structure neural network. Based on the present study, a server Ar_NHPred has been developed which predicts Ar-NH interaction in a given amino acid sequence. The web server Ar_NHPred is available at and (mirror site).

  3. Predicting catalyst-support interactions between metal nanoparticles and amorphous silica supports

    NASA Astrophysics Data System (ADS)

    Ewing, Christopher S.; Veser, Götz; McCarthy, Joseph J.; Lambrecht, Daniel S.; Johnson, J. Karl

    2016-10-01

    Metal-support interactions significantly affect the stability and activity of supported catalytic nanoparticles (NPs), yet there is no simple and reliable method for estimating NP-support interactions, especially for amorphous supports. We present an approach for rapid prediction of catalyst-support interactions between Pt NPs and amorphous silica supports for NPs of various sizes and shapes. We use density functional theory calculations of 13 atom Pt clusters on model amorphous silica supports to determine linear correlations relating catalyst properties to NP-support interactions. We show that these correlations can be combined with fast discrete element method simulations to predict adhesion energy and NP net charge for NPs of larger sizes and different shapes. Furthermore, we demonstrate that this approach can be successfully transferred to Pd, Au, Ni, and Fe NPs. This approach can be used to quickly screen stability and net charge transfer and leads to a better fundamental understanding of catalyst-support interactions.

  4. Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases

    PubMed Central

    Stephens, Christopher R.; Heau, Joaquín Giménez; González, Camila; Ibarra-Cerdeña, Carlos N.; Sánchez-Cordero, Victor; González-Salazar, Constantino

    2009-01-01

    Networks offer a powerful tool for understanding and visualizing inter-species ecological and evolutionary interactions. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for this methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease - Leishmaniasis. This data mining methodology allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases. PMID:19478956

  5. Identification of New Drug Targets and Resistance Mechanisms in Mycobacterium tuberculosis

    PubMed Central

    Ioerger, Thomas R.; O’Malley, Theresa; Liao, Reiling; Guinn, Kristine M.; Hickey, Mark J.; Mohaideen, Nilofar; Murphy, Kenan C.; Boshoff, Helena I. M.; Mizrahi, Valerie; Rubin, Eric J.; Sassetti, Christopher M.; Barry, Clifton E.; Sherman, David R.; Parish, Tanya; Sacchettini, James C.

    2013-01-01

    Identification of new drug targets is vital for the advancement of drug discovery against Mycobacterium tuberculosis, especially given the increase of resistance worldwide to first- and second-line drugs. Because traditional target-based screening has largely proven unsuccessful for antibiotic discovery, we have developed a scalable platform for target identification in M. tuberculosis that is based on whole-cell screening, coupled with whole-genome sequencing of resistant mutants and recombineering to confirm. The method yields targets paired with whole-cell active compounds, which can serve as novel scaffolds for drug development, molecular tools for validation, and/or as ligands for co-crystallization. It may also reveal other information about mechanisms of action, such as activation or efflux. Using this method, we identified resistance-linked genes for eight compounds with anti-tubercular activity. Four of the genes have previously been shown to be essential: AspS, aspartyl-tRNA synthetase, Pks13, a polyketide synthase involved in mycolic acid biosynthesis, MmpL3, a membrane transporter, and EccB3, a component of the ESX-3 type VII secretion system. AspS and Pks13 represent novel targets in protein translation and cell-wall biosynthesis. Both MmpL3 and EccB3 are involved in membrane transport. Pks13, AspS, and EccB3 represent novel candidates not targeted by existing TB drugs, and the availability of whole-cell active inhibitors greatly increases their potential for drug discovery. PMID:24086479

  6. Chemical and Genetic Validation of the Statin Drug Target to Treat the Helminth Disease, Schistosomiasis

    PubMed Central

    Rojo-Arreola, Liliana; Long, Thavy; Asarnow, Dan; Suzuki, Brian M.; Singh, Rahul; Caffrey, Conor R.

    2014-01-01

    The mevalonate pathway is essential in eukaryotes and responsible for a diversity of fundamental synthetic activities. 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) is the rate-limiting enzyme in the pathway and is targeted by the ubiquitous statin drugs to treat hypercholesterolemia. Independent reports have indicated the cidal effects of statins against the flatworm parasite, S. mansoni, and the possibility that SmHMGR is a useful drug target to develop new statin-based anti-schistosome therapies. For six commercially available statins, we demonstrate concentration- and time-dependent killing of immature (somule) and adult S. mansoni in vitro at sub-micromolar and micromolar concentrations, respectively. Cidal activity trends with statin lipophilicity whereby simvastatin and pravastatin are the most and least active, respectively. Worm death is preventable by excess mevalonate, the product of HMGR. Statin activity against somules was quantified both manually and automatically using a new, machine learning-based automated algorithm with congruent results. In addition, to chemical targeting, RNA interference (RNAi) of HMGR also kills somules in vitro and, again, lethality is blocked by excess mevalonate. Further, RNAi of HMGR of somules in vitro subsequently limits parasite survival in a mouse model of infection by up to 80%. Parasite death, either via statins or specific RNAi of HMGR, is associated with activation of apoptotic caspase activity. Together, our genetic and chemical data confirm that S. mansoni HMGR is an essential gene and the relevant target of statin drugs. We discuss our findings in context of a potential drug development program and the desired product profile for a new schistosomiasis drug. PMID:24489942

  7. A tale of two drug targets: the evolutionary history of BACE1 and BACE2

    PubMed Central

    Southan, Christopher; Hancock, John M.

    2013-01-01

    The beta amyloid (APP) cleaving enzyme (BACE1) has been a drug target for Alzheimer's Disease (AD) since 1999 with lead inhibitors now entering clinical trials. In 2011, the paralog, BACE2, became a new target for type II diabetes (T2DM) having been identified as a TMEM27 secretase regulating pancreatic β cell function. However, the normal roles of both enzymes are unclear. This study outlines their evolutionary history and new opportunities for functional genomics. We identified 30 homologs (UrBACEs) in basal phyla including Placozoans, Cnidarians, Choanoflagellates, Porifera, Echinoderms, Annelids, Mollusks and Ascidians (but not Ecdysozoans). UrBACEs are predominantly single copy, show 35–45% protein sequence identity with mammalian BACE1, are ~100 residues longer than cathepsin paralogs with an aspartyl protease domain flanked by a signal peptide and a C-terminal transmembrane domain. While multiple paralogs in Trichoplax and Monosiga pre-date the nervous system, duplication of the UrBACE in fish gave rise to BACE1 and BACE2 in the vertebrate lineage. The latter evolved more rapidly as the former maintained the emergent neuronal role. In mammals, Ka/Ks for BACE2 is higher than BACE1 but low ratios for both suggest purifying selection. The 5' exons show higher Ka/Ks than the catalytic section. Model organism genomes show the absence of certain BACE human substrates when the UrBACE is present. Experiments could thus reveal undiscovered substrates and roles. The human protease double-target status means that evolutionary trajectories and functional shifts associated with different substrates will have implications for the development of clinical candidates for both AD and T2DM. A rational basis for inhibition specificity ratios and assessing target-related side effects will be facilitated by a more complete picture of BACE1 and BACE2 functions informed by their evolutionary context. PMID:24381583

  8. 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

  9. 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.

  10. 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

  11. 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

  12. Voltage-Gated Proton Channels as Novel Drug Targets: From NADPH Oxidase Regulation to Sperm Biology

    PubMed Central

    Demaurex, Nicolas; Krause, Karl-Heinz

    2015-01-01

    Abstract Significance: Voltage-gated proton channels are increasingly implicated in cellular proton homeostasis. Proton currents were originally identified in snail neurons less than 40 years ago, and subsequently shown to play an important auxiliary role in the functioning of reactive oxygen species (ROS)-generating nicotinamide adenine dinucleotide phosphate (NADPH) oxidases. Molecular identification of voltage-gated proton channels was achieved less than 10 years ago. Interestingly, so far, only one gene coding for voltage-gated proton channels has been identified, namely hydrogen voltage-gated channel 1 (HVCN1), which codes for the HV1 proton channel protein. Over the last years, the first picture of putative physiological functions of HV1 has been emerging. Recent Advances: The best-studied role remains charge and pH compensation during the respiratory burst of the phagocyte NADPH oxidase (NOX). Strong evidence for a role of HV1 is also emerging in sperm biology, but the relationship with the sperm NOX5 remains unclear. Probably in many instances, HV1 functions independently of NOX: for example in snail neurons, basophils, osteoclasts, and cancer cells. Critical Issues: Generally, ion channels are good drug targets; however, this feature has so far not been exploited for HV1, and hitherto no inhibitors compatible with clinical use exist. However, there are emerging indications for HV1 inhibitors, ranging from diseases with a strong activation of the phagocyte NOX (e.g., stroke) to infertility, osteoporosis, and cancer. Future Directions: Clinically useful HV1-active drugs should be developed and might become interesting drugs of the future. Antioxid. Redox Signal. 23, 490–513. PMID:24483328

  13. Utilizing Chemical Genomics to Identify Cytochrome b as a Novel Drug Target for Chagas Disease

    PubMed Central

    Khare, Shilpi; Roach, Steven L.; Barnes, S. Whitney; Hoepfner, Dominic; Walker, John R.; Chatterjee, Arnab K.; Neitz, R. Jeffrey; Arkin, Michelle R.; McNamara, Case W.; Ballard, Jaime; Lai, Yin; Fu, Yue; Molteni, Valentina; Yeh, Vince; McKerrow, James H.; Glynne, Richard J.; Supek, Frantisek

    2015-01-01

    Unbiased phenotypic screens enable identification of small molecules that inhibit pathogen growth by unanticipated mechanisms. These small molecules can be used as starting points for drug discovery programs that target such mechanisms. A major challenge of the approach is the identification of the cellular targets. Here we report GNF7686, a small molecule inhibitor of Trypanosoma cruzi, the causative agent of Chagas disease, and identification of cytochrome b as its target. Following discovery of GNF7686 in a parasite growth inhibition high throughput screen, we were able to evolve a GNF7686-resistant culture of T. cruzi epimastigotes. Clones from this culture bore a mutation coding for a substitution of leucine by phenylalanine at amino acid position 197 in cytochrome b. Cytochrome b is a component of complex III (cytochrome bc1) in the mitochondrial electron transport chain and catalyzes the transfer of electrons from ubiquinol to cytochrome c by a mechanism that utilizes two distinct catalytic sites, QN and QP. The L197F mutation is located in the QN site and confers resistance to GNF7686 in both parasite cell growth and biochemical cytochrome b assays. Additionally, the mutant cytochrome b confers resistance to antimycin A, another QN site inhibitor, but not to strobilurin or myxothiazol, which target the QP site. GNF7686 represents a promising starting point for Chagas disease drug discovery as it potently inhibits growth of intracellular T. cruzi amastigotes with a half maximal effective concentration (EC50) of 0.15 µM, and is highly specific for T. cruzi cytochrome b. No effect on the mammalian respiratory chain or mammalian cell proliferation was observed with up to 25 µM of GNF7686. Our approach, which combines T. cruzi chemical genetics with biochemical target validation, can be broadly applied to the discovery of additional novel drug targets and drug leads for Chagas disease. PMID:26186534

  14. 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

  15. A tale of two drug targets: the evolutionary history of BACE1 and BACE2.

    PubMed

    Southan, Christopher; Hancock, John M

    2013-01-01

    The beta amyloid (APP) cleaving enzyme (BACE1) has been a drug target for Alzheimer's Disease (AD) since 1999 with lead inhibitors now entering clinical trials. In 2011, the paralog, BACE2, became a new target for type II diabetes (T2DM) having been identified as a TMEM27 secretase regulating pancreatic β cell function. However, the normal roles of both enzymes are unclear. This study outlines their evolutionary history and new opportunities for functional genomics. We identified 30 homologs (UrBACEs) in basal phyla including Placozoans, Cnidarians, Choanoflagellates, Porifera, Echinoderms, Annelids, Mollusks and Ascidians (but not Ecdysozoans). UrBACEs are predominantly single copy, show 35-45% protein sequence identity with mammalian BACE1, are ~100 residues longer than cathepsin paralogs with an aspartyl protease domain flanked by a signal peptide and a C-terminal transmembrane domain. While multiple paralogs in Trichoplax and Monosiga pre-date the nervous system, duplication of the UrBACE in fish gave rise to BACE1 and BACE2 in the vertebrate lineage. The latter evolved more rapidly as the former maintained the emergent neuronal role. In mammals, Ka/Ks for BACE2 is higher than BACE1 but low ratios for both suggest purifying selection. The 5' exons show higher Ka/Ks than the catalytic section. Model organism genomes show the absence of certain BACE human substrates when the UrBACE is present. Experiments could thus reveal undiscovered substrates and roles. The human protease double-target status means that evolutionary trajectories and functional shifts associated with different substrates will have implications for the development of clinical candidates for both AD and T2DM. A rational basis for inhibition specificity ratios and assessing target-related side effects will be facilitated by a more complete picture of BACE1 and BACE2 functions informed by their evolutionary context.

  16. Transient receptor potential (TRP) channels as drug targets for diseases of the digestive system

    PubMed Central

    Holzer, Peter

    2011-01-01

    Approximately 20 of the 30 mammalian transient receptor potential (TRP) channel subunits are expressed by specific neurons and cells within the alimentary canal. They subserve important roles in taste, chemesthesis, mechanosensation, pain and hyperalgesia and contribute to the regulation of gastrointestinal motility, absorptive and secretory processes, blood flow, and mucosal homeostasis. In a cellular perspective, TRP channels operate either as primary detectors of chemical and physical stimuli, as secondary transducers of ionotropic or metabotropic receptors, or as ion transport channels. The polymodal sensory function of TRPA1, TRPM5, TRPM8, TRPP2, TRPV1, TRPV3 and TRPV4 enables the digestive system to survey its physical and chemical environment, which is relevant to all processes of digestion. TRPV5 and TRPV6 as well as TRPM6 and TRPM7 contribute to the absorption of Ca2+ and Mg2+, respectively. TRPM7 participates in intestinal pacemaker activity, and TRPC4 transduces muscarinic acetylcholine receptor activation to smooth muscle contraction. Changes in TRP channel expression or function are associated with a variety of diseases/disorders of the digestive system, notably gastro-esophageal reflux disease, inflammatory bowel disease, pain and hyperalgesia in heartburn, functional dyspepsia and irritable bowel syndrome, cholera, hypomagnesemia with secondary hypocalcemia, infantile hypertrophic pyloric stenosis, esophageal, gastrointestinal and pancreatic cancer, and polycystic liver disease. These implications identify TRP channels as promising drug targets for the management of a number of gastrointestinal pathologies. As a result, major efforts are put into the development of selective TRP channel agonists and antagonists and the assessment of their therapeutic potential. PMID:21420431

  17. Discovery of novel vaccine candidates and drug targets against visceral leishmaniasis using proteomics and transcriptomics.

    PubMed

    Kumari, Shraddha; Kumar, Awanish; Samant, Mukesh; Singh, Neeloo; Dube, Anuradha

    2008-11-01

    Among the three clinical forms (cutaneous, mucosal and visceral) of leishmaniasis visceral (VL) one is the most devastating type caused by the invasion of the reticuloendothelial system of human by Leishmania donovani, L. infantum and L. chagasi. India and Sudan account for about half the world's burden of VL. Current control strategy is based on chemotherapy, which is difficult to administer, expensive and becoming ineffective due to the emergence of drug resistance. An understanding of resistance mechanism(s) operating in clinical isolates might provide additional leads for the development of new drugs. Further, due to the lack of fully effective treatment the search for novel immune targets is also needed. So far, no vaccine exists for VL despite indications of naturally developing immunity. Therefore, an urgent need for new and effective leishmanicidal agents and for this identification of novel drug and vaccine targets is imperative. The availability of the complete genome sequence of Leishmania has revolutionised many areas of leishmanial research and facilitated functional genomic studies as well as provided a wide range of novel targets for drug designing. Most notably, proteomics and transcriptomics have become important tools in gaining increased understanding of the biology of Leishmania to be explored on a global scale, thus accelerating the pace of discovery of vaccine/drug targets. In addition, these approaches provide the information regarding genes and proteins that are expressed and under which conditions. This review provides a comprehensive view about those proteins/genes identified using proteomics and transcriptomic tools for the development of vaccine/drug against VL.

  18. The role of intermolecular interactions in the prediction of the phase equilibria of carbon dioxide hydrates.

    PubMed

    Costandy, Joseph; Michalis, Vasileios K; Tsimpanogiannis, Ioannis N; Stubos, Athanassios K; Economou, Ioannis G

    2015-09-01

    The direct phase coexistence methodology was used to predict the three-phase equilibrium conditions of carbon dioxide hydrates. Molecular dynamics simulations were performed in the isobaric-isothermal ensemble for the determination of the three-phase coexistence temperature (T3) of the carbon dioxide-water system, at pressures in the range of 200-5000 bar. The relative importance of the water-water and water-guest interactions in the prediction of T3 is investigated. The water-water interactions were modeled through the use of TIP4P/Ice and TIP4P/2005 force fields. The TraPPE force field was used for carbon dioxide, and the water-guest interactions were probed through the modification of the cross-interaction Lennard-Jones energy parameter between the oxygens of the unlike molecules. It was found that when using the classic Lorentz-Berthelot combining rules, both models fail to predict T3 accurately. In order to rectify this problem, the water-guest interaction parameters were optimized, based on the solubility of carbon dioxide in water. In this case, it is shown that the prediction of T3 is limited only by the accuracy of the water model in predicting the melting temperature of ice. PMID:26342376

  19. The role of intermolecular interactions in the prediction of the phase equilibria of carbon dioxide hydrates.

    PubMed

    Costandy, Joseph; Michalis, Vasileios K; Tsimpanogiannis, Ioannis N; Stubos, Athanassios K; Economou, Ioannis G

    2015-09-01

    The direct phase coexistence methodology was used to predict the three-phase equilibrium conditions of carbon dioxide hydrates. Molecular dynamics simulations were performed in the isobaric-isothermal ensemble for the determination of the three-phase coexistence temperature (T3) of the carbon dioxide-water system, at pressures in the range of 200-5000 bar. The relative importance of the water-water and water-guest interactions in the prediction of T3 is investigated. The water-water interactions were modeled through the use of TIP4P/Ice and TIP4P/2005 force fields. The TraPPE force field was used for carbon dioxide, and the water-guest interactions were probed through the modification of the cross-interaction Lennard-Jones energy parameter between the oxygens of the unlike molecules. It was found that when using the classic Lorentz-Berthelot combining rules, both models fail to predict T3 accurately. In order to rectify this problem, the water-guest interaction parameters were optimized, based on the solubility of carbon dioxide in water. In this case, it is shown that the prediction of T3 is limited only by the accuracy of the water model in predicting the melting temperature of ice.

  20. The role of intermolecular interactions in the prediction of the phase equilibria of carbon dioxide hydrates

    NASA Astrophysics Data System (ADS)

    Costandy, Joseph; Michalis, Vasileios K.; Tsimpanogiannis, Ioannis N.; Stubos, Athanassios K.; Economou, Ioannis G.

    2015-09-01

    The direct phase coexistence methodology was used to predict the three-phase equilibrium conditions of carbon dioxide hydrates. Molecular dynamics simulations were performed in the isobaric-isothermal ensemble for the determination of the three-phase coexistence temperature (T3) of the carbon dioxide-water system, at pressures in the range of 200-5000 bar. The relative importance of the water-water and water-guest interactions in the prediction of T3 is investigated. The water-water interactions were modeled through the use of TIP4P/Ice and TIP4P/2005 force fields. The TraPPE force field was used for carbon dioxide, and the water-guest interactions were probed through the modification of the cross-interaction Lennard-Jones energy parameter between the oxygens of the unlike molecules. It was found that when using the classic Lorentz-Berthelot combining rules, both models fail to predict T3 accurately. In order to rectify this problem, the water-guest interaction parameters were optimized, based on the solubility of carbon dioxide in water. In this case, it is shown that the prediction of T3 is limited only by the accuracy of the water model in predicting the melting temperature of ice.

  1. Interactions of odorants with olfactory receptors and other preprocessing mechanisms: how complex and difficult to predict?

    PubMed

    Rospars, Jean-Pierre

    2013-05-01

    In this issue of Chemical Senses, Münch et al. present a thorough analysis of how mixtures of odorants interact with olfactory receptors (ORs) borne by olfactory receptor neurons (ORNs). Using fruit fly ORNs expressing the receptor OR22a, they provide a clear example of mixture interaction and confirm that the response of an ORN to a binary mixture can be sometimes predicted quantitatively knowing the ORN responses to its components as shown previously in rat ORNs. The prediction is based on a nonlinear model that assumes a classical 2-step activation of the OR and competition of the 2 odorants in the mixture for the same binding site. Can this success be generalized to all odorant-receptor pairs? This would be an encouraging perspective, especially for the fragrance and flavor industries, as it would permit the prediction of all mixtures. To address this question, I outline its conceptual framework and discuss the variety of mixture interactions found so far. In accordance with the effects described in the study of other receptors, several kinds of mixture interactions have been found that are not easily predictable. The relative importance of the predictable and less predictable effects thus appears as a major issue for future developments.

  2. 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

  3. 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

  4. COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS

    PubMed Central

    REGENBOGEN, SAM; WILKINS, ANGELA D.; LICHTARGE, OLIVIER

    2015-01-01

    Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses. PMID:26776170

  5. Bacteria-polymeric membrane interactions: atomic force microscopy and XDLVO predictions.

    PubMed

    Thwala, Justice M; Li, Minghua; Wong, Mavis C Y; Kang, Seoktae; Hoek, Eric M V; Mamba, Bhekie B

    2013-11-12

    Atomic force microscopy (AFM) in conjunction with a bioprobe developed using a polydopamine wet adhesive was used to directly measure the adhesive force between bacteria and different polymeric membrane surfaces. Bacterial cells of Pseudomonas putida and Bacillus subtilis were immobilized onto the tip of a standard AFM cantilever, and force measurements made using the modified cantilever on various membranes. Interaction forces measured with the bacterial probe were compared, qualitatively, to predictions by the extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory with steric interactions included. The XDLVO theory predicted attractive interactions between low energy hydrophobic membranes with high energy hydrophilic bacterium (P. putida). It also predicted a shallow primary maximum with the most hydrophilic bacterium, B. subtilis . Discrepancies between predictions using the XDLVO theory and theory require involvement of factors such as bridging effects. Differences in interaction between P. putida and B. subtilis are attributed to acid-base interactions and steric interactions. P. putida is Gram negative with lipopolysaccharides present in the outer cell membrane. A variation in forces of adhesion for bacteria on polymeric membranes studied was interpreted in terms of hydrophilicity and interfacial surface potential calculated from physicochemical properties.

  6. Interaction Network Estimation: Predicting Problem-Solving Diversity in Interactive Environments

    ERIC Educational Resources Information Center

    Eagle, Michael; Hicks, Drew; Barnes, Tiffany

    2015-01-01

    Intelligent tutoring systems and computer aided learning environments aimed at developing problem solving produce large amounts of transactional data which make it a challenge for both researchers and educators to understand how students work within the environment. Researchers have modeled student-tutor interactions using complex networks in…

  7. Prediction of BVI noise patterns and correlation with wake interaction locations

    NASA Technical Reports Server (NTRS)

    Marcolini, Michael A.; Martin, Ruth M.; Lorber, Peter F.; Egolf, T. A.

    1992-01-01

    High resolution fluctuating airloads data were acquired during a test of a contemporary design United Technologies model rotor in the Duits-Nederlandse Windtunnel (DNW). The airloads are used as input to the noise prediction program WOPWOP, in order to predict the blade-vortex interaction (BVI) noise field on a large plane below the rotor. Trends of predicted advancing and retreating side BVI noise levels and directionality as functions of flight condition are presented. The measured airloads have been analyzed to determine the BVI locations on the blade surface, and are used to interpret the predicted BVI noise radiation patterns. Predicted BVI locations are obtained using the free wake model in CAMRAD/JA, the UTRC Generalized Forward Flight Distorted Wake Model, and the UTRC FREEWAKE analysis. These predicted BVI locations are compared with those obtained from the measured pressure data.

  8. Choosing negative examples for the prediction of protein-protein interactions

    PubMed Central

    Ben-Hur, Asa; Noble, William Stafford

    2006-01-01

    The protein-protein interaction networks of even well-studied model organisms are sketchy at best, highlighting the continued need for computational methods to help direct experimentalists in the search for novel interactions. This need has prompted the development of a number of methods for predicting protein-protein interactions based on various sources of data and methodologies. The common method for choosing negative examples for training a predictor of protein-protein interactions is based on annotations of cellular localization, and the observation that pairs of proteins that have different localization patterns are unlikely to interact. While this method leads to high quality sets of non-interacting proteins, we find that this choice can lead to biased estimates of prediction accuracy, because the constraints placed on the distribution of the negative examples makes the task easier. The effects of this bias are demonstrated in the context of both sequence-based and non-sequence based features used for predicting protein-protein interactions. PMID:16723005

  9. 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

  10. Computing with evidence part II: an evidential approach to predicting metabolic drug-drug interactions

    PubMed Central

    Boyce, Richard; Collins, Carol; Horn, John; Kalet, Ira

    2009-01-01

    We describe a novel experiment that we conducted with the Drug Interaction Knowledge-base (DIKB) to determine which combinations of evidence enable a rule-based theory of metabolic drug-drug interactions to make the most optimal set of predictions. The focus of the experiment was a group of 16 drugs including six members of the HMG-CoA-reductase inhibitor family (statins). The experiment helped identify evidence-use strategies that enabled the DIKB to predict significantly more interactions present in a validation set than the most rigorous strategy developed by drug experts with no loss of accuracy. The best-performing strategies included evidence types that would normally be of lesser predictive value but that are often more accessible than more rigorous types. Our experimental methods represent a new approach to leveraging the available scientific evidence within a domain where important evidence is often missing or of questionable value for supporting important assertions. PMID:19539050

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

    PubMed Central

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

    2016-01-01

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

  12. Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects.

    PubMed

    Zhang, Ping; Wang, Fei; Hu, Jianying; Sorrentino, Robert

    2015-01-01

    Drug-drug interaction (DDI) is an important topic for public health, and thus attracts attention from both academia and industry. Here we hypothesize that clinical side effects (SEs) provide a human phenotypic profile and can be translated into the development of computational models for predicting adverse DDIs. We propose an integrative label propagation framework to predict DDIs by integrating SEs extracted from package inserts of prescription drugs, SEs extracted from FDA Adverse Event Reporting System, and chemical structures from PubChem. Experimental results based on hold-out validation demonstrated the effectiveness of the proposed algorithm. In addition, the new algorithm also ranked drug information sources based on their contributions to the prediction, thus not only confirming that SEs are important features for DDI prediction but also paving the way for building more reliable DDI prediction models by prioritizing multiple data sources. By applying the proposed algorithm to 1,626 small-molecule drugs which have one or more SE profiles, we obtained 145,068 predicted DDIs. The predicted DDIs will help clinicians to avoid hazardous drug interactions in their prescriptions and will aid pharmaceutical companies to design large-scale clinical trial by assessing potentially hazardous drug combinations. All data sets and predicted DDIs are available at http://astro.temple.edu/~tua87106/ddi.html. PMID:26196247

  13. Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects.

    PubMed

    Zhang, Ping; Wang, Fei; Hu, Jianying; Sorrentino, Robert

    2015-01-01

    Drug-drug interaction (DDI) is an important topic for public health, and thus attracts attention from both academia and industry. Here we hypothesize that clinical side effects (SEs) provide a human phenotypic profile and can be translated into the development of computational models for predicting adverse DDIs. We propose an integrative label propagation framework to predict DDIs by integrating SEs extracted from package inserts of prescription drugs, SEs extracted from FDA Adverse Event Reporting System, and chemical structures from PubChem. Experimental results based on hold-out validation demonstrated the effectiveness of the proposed algorithm. In addition, the new algorithm also ranked drug information sources based on their contributions to the prediction, thus not only confirming that SEs are important features for DDI prediction but also paving the way for building more reliable DDI prediction models by prioritizing multiple data sources. By applying the proposed algorithm to 1,626 small-molecule drugs which have one or more SE profiles, we obtained 145,068 predicted DDIs. The predicted DDIs will help clinicians to avoid hazardous drug interactions in their prescriptions and will aid pharmaceutical companies to design large-scale clinical trial by assessing potentially hazardous drug combinations. All data sets and predicted DDIs are available at http://astro.temple.edu/~tua87106/ddi.html.

  14. Multi-level machine learning prediction of protein-protein interactions in Saccharomyces cerevisiae.

    PubMed

    Zubek, Julian; Tatjewski, Marcin; Boniecki, Adam; Mnich, Maciej; Basu, Subhadip; Plewczynski, Dariusz

    2015-01-01

    Accurate identification of protein-protein interactions (PPI) is the key step in understanding proteins' biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein-protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB) database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein-protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC). Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent).

  15. Multi-level machine learning prediction of protein–protein interactions in Saccharomyces cerevisiae

    PubMed Central

    Zubek, Julian; Tatjewski, Marcin; Boniecki, Adam; Mnich, Maciej; Basu, Subhadip

    2015-01-01

    Accurate identification of protein–protein interactions (PPI) is the key step in understanding proteins’ biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein–protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB) database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein–protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC). Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent). PMID:26157620

  16. Multi-level machine learning prediction of protein-protein interactions in Saccharomyces cerevisiae.

    PubMed

    Zubek, Julian; Tatjewski, Marcin; Boniecki, Adam; Mnich, Maciej; Basu, Subhadip; Plewczynski, Dariusz

    2015-01-01

    Accurate identification of protein-protein interactions (PPI) is the key step in understanding proteins' biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein-protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB) database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein-protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC). Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent). PMID:26157620

  17. Maintenance factors in coercive mother-child interactions: the compliance and predictability hypotheses.

    PubMed Central

    Wahler, R G; Dumas, J E

    1986-01-01

    Two stimulus control processes by which some parent-child dyads occasionally escalate their aversive exchanges into progressively more coercive interactions are described. The compliance hypothesis suggests that aversive actions have instructional properties for the dyad and that parent compliance with such child instructions maintains behavior chains of increasing aversiveness. The predictability hypothesis suggests that social interactions are most likely to function as aversive stimuli in the dyad when delivered in unpredictable fashion by either party and that responses instrumental in reducing dyadic unpredictability maintain aversive behavior chains. Expectations derived from both hypotheses are evaluated in a series of correlational analyses of mother-child interactions obtained in extended baseline observations of three dyads seeking psychological help for severe interactional problems. Results provide tentative support for the predictability hypothesis and suggest important avenues of further research. PMID:3710944

  18. Predictive Integration of Gene Ontology-Driven Similarity and Functional Interactions

    PubMed Central

    Wang, Haiying; Zheng, Huiru; Bodenreider, Olivier; Chesneau, Alban

    2015-01-01

    There is a need to develop methods to automatically incorporate prior knowledge to support the prediction and validation of novel functional associations. One such important source is represented by the Gene Ontology (GO)™ and the many model organism databases of gene products annotated to the GO. We investigated quantitative relationships between the GO-driven similarity of genes and their functional interactions by analyzing different types of associations in Saccharomyces cerevisiae and Caenorhabditis elegans. Interacting genes exhibited significantly higher levels of GO-driven similarity (GOS) in comparison to random pairs of genes used as a surrogate for negative interactions. The Biological Process hierarchy provides more reliable results for co-regulatory and protein-protein interactions. GOS represent a relevant resource to support prediction of functional networks in combination with other resources. PMID:25698910

  19. String-theory-based predictions for nonhydrodynamic collective modes in strongly interacting Fermi gases

    NASA Astrophysics Data System (ADS)

    Bantilan, H.; Brewer, J. T.; Ishii, T.; Lewis, W. E.; Romatschke, P.

    2016-09-01

    Very different strongly interacting quantum systems such as Fermi gases, quark-gluon plasmas formed in high-energy ion collisions, and black holes studied theoretically in string theory are known to exhibit quantitatively similar damping of hydrodynamic modes. It is not known if such similarities extend beyond the hydrodynamic limit. Do nonhydrodynamic collective modes in Fermi gases with strong interactions also match those from string theory calculations? In order to answer this question, we use calculations based on string theory to make predictions for modes outside the hydrodynamic regime in trapped Fermi gases. These predictions are amenable to direct testing with current state-of-the-art cold atom experiments.

  20. 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

  1. PAIRpred: partner-specific prediction of interacting residues from sequence and structure.

    PubMed

    Minhas, Fayyaz ul Amir Afsar; Geiss, Brian J; Ben-Hur, Asa

    2014-07-01

    We present a novel partner-specific protein-protein interaction site prediction method called PAIRpred. Unlike most existing machine learning binding site prediction methods, PAIRpred uses information from both proteins in a protein complex to predict pairs of interacting residues from the two proteins. PAIRpred captures sequence and structure information about residue pairs through pairwise kernels that are used for training a support vector machine classifier. As a result, PAIRpred presents a more detailed model of protein binding, and offers state of the art accuracy in predicting binding sites at the protein level as well as inter-protein residue contacts at the complex level. We demonstrate PAIRpred's performance on Docking Benchmark 4.0 and recent CAPRI targets. We present a detailed performance analysis outlining the contribution of different sequence and structure features, together with a comparison to a variety of existing interface prediction techniques. We have also studied the impact of binding-associated conformational change on prediction accuracy and found PAIRpred to be more robust to such structural changes than existing schemes. As an illustration of the potential applications of PAIRpred, we provide a case study in which PAIRpred is used to analyze the nature and specificity of the interface in the interaction of human ISG15 protein with NS1 protein from influenza A virus. Python code for PAIRpred is available at http://combi.cs.colostate.edu/supplements/pairpred/. PMID:24243399

  2. Prognostic and Predictive Values and Statistical Interactions in the Era of Targeted Treatment.

    PubMed

    Satagopan, Jaya M; Iasonos, Alexia; Zhou, Qin

    2015-11-01

    The current era of targeted treatment has accelerated the interest in studying gene-treatment, gene-gene, and gene-environment interactions using statistical models in the health sciences. Interactions are incorporated into models as product terms of risk factors. The statistical significance of interactions is traditionally examined using a likelihood ratio test (LRT). Epidemiological and clinical studies also evaluate interactions in order to understand the prognostic and predictive values of genetic factors. However, it is not clear how different types and magnitudes of interaction effects are related to prognostic and predictive values. The contribution of interaction to prognostic values can be examined via improvements in the area under the receiver operating characteristic curve due to the inclusion of interaction terms in the model (ΔAUC). We develop a resampling based approach to test the significance of this improvement and show that it is equivalent to LRT. Predictive values provide insights into whether carriers of genetic factors benefit from specific treatment or preventive interventions relative to noncarriers, under some definition of treatment benefit. However, there is no unique definition of the term treatment benefit. We show that ΔAUC and relative excess risk due to interaction (RERI) measure predictive values under two specific definitions of treatment benefit. We investigate the properties of LRT, ΔAUC, and RERI using simulations. We illustrate these approaches using published melanoma data to understand the benefits of possible intervention on sun exposure in relation to the MC1R gene. The goal is to evaluate possible interventions on sun exposure in relation to MC1R. PMID:26349638

  3. Predicting protein-protein interactions in unbalanced data using the primary structure of proteins

    PubMed Central

    2010-01-01

    Background Elucidating protein-protein interactions (PPIs) is essential to constructing protein interaction networks and facilitating our understanding of the general principles of biological systems. Previous studies have revealed that interacting protein pairs can be predicted by their primary structure. Most of these approaches have achieved satisfactory performance on datasets comprising equal number of interacting and non-interacting protein pairs. However, this ratio is highly unbalanced in nature, and these techniques have not been comprehensively evaluated with respect to the effect of the large number of non-interacting pairs in realistic datasets. Moreover, since highly unbalanced distributions usually lead to large datasets, more efficient predictors are desired when handling such challenging tasks. Results This study presents a method for PPI prediction based only on sequence information, which contributes in three aspects. First, we propose a probability-based mechanism for transforming protein sequences into feature vectors. Second, the proposed predictor is designed with an efficient classification algorithm, where the efficiency is essential for handling highly unbalanced datasets. Third, the proposed PPI predictor is assessed with several unbalanced datasets with different positive-to-negative ratios (from 1:1 to 1:15). This analysis provides solid evidence that the degree of dataset imbalance is important to PPI predictors. Conclusions Dealing with data imbalance is a key issue in PPI prediction since there are far fewer interacting protein pairs than non-interacting ones. This article provides a comprehensive study on this issue and develops a practical tool that achieves both good prediction performance and efficiency using only protein sequence information. PMID:20361868

  4. Prognostic and Predictive Values and Statistical Interactions in the Era of Targeted Treatment.

    PubMed

    Satagopan, Jaya M; Iasonos, Alexia; Zhou, Qin

    2015-11-01

    The current era of targeted treatment has accelerated the interest in studying gene-treatment, gene-gene, and gene-environment interactions using statistical models in the health sciences. Interactions are incorporated into models as product terms of risk factors. The statistical significance of interactions is traditionally examined using a likelihood ratio test (LRT). Epidemiological and clinical studies also evaluate interactions in order to understand the prognostic and predictive values of genetic factors. However, it is not clear how different types and magnitudes of interaction effects are related to prognostic and predictive values. The contribution of interaction to prognostic values can be examined via improvements in the area under the receiver operating characteristic curve due to the inclusion of interaction terms in the model (ΔAUC). We develop a resampling based approach to test the significance of this improvement and show that it is equivalent to LRT. Predictive values provide insights into whether carriers of genetic factors benefit from specific treatment or preventive interventions relative to noncarriers, under some definition of treatment benefit. However, there is no unique definition of the term treatment benefit. We show that ΔAUC and relative excess risk due to interaction (RERI) measure predictive values under two specific definitions of treatment benefit. We investigate the properties of LRT, ΔAUC, and RERI using simulations. We illustrate these approaches using published melanoma data to understand the benefits of possible intervention on sun exposure in relation to the MC1R gene. The goal is to evaluate possible interventions on sun exposure in relation to MC1R.

  5. Is racial bias malleable? Whites' lay theories of racial bias predict divergent strategies for interracial interactions.

    PubMed

    Neel, Rebecca; Shapiro, Jenessa R

    2012-07-01

    How do Whites approach interracial interactions? We argue that a previously unexamined factor-beliefs about the malleability of racial bias-guides Whites' strategies for difficult interracial interactions. We predicted and found that those who believe racial bias is malleable favor learning-oriented strategies such as taking the other person's perspective and trying to learn why an interaction is challenging, whereas those who believe racial bias is fixed favor performance-oriented strategies such as overcompensating in the interaction and trying to end the interaction as quickly as possible. Four studies support these predictions. Whether measured (Studies 1, 3, and 4) or manipulated (Study 2), beliefs that racial bias is fixed versus malleable yielded these divergent strategies for difficult interracial interactions. Furthermore, beliefs about the malleability of racial bias are distinct from related constructs (e.g., prejudice and motivations to respond without prejudice; Studies 1, 3, and 4) and influence self-reported (Studies 1-3) and actual (Study 4) strategies in imagined (Studies 1-2) and real (Studies 3-4) interracial interactions. Together, these findings demonstrate that beliefs about the malleability of racial bias influence Whites' approaches to and strategies within interracial interactions. PMID:22564011

  6. IntApop: a web service for predicting apoptotic protein interactions in humans.

    PubMed

    Zhou, Nan; Zhang, Jinchun; Feng, Ling; Lu, Bangmin; Wang, Zijie; Sun, Rong; Wu, Chuanfang; Bao, Jinku

    2013-12-01

    Apoptosis, a type of cell death, is necessary for maintaining tissue homeostasis and removing malignant cells. Interrupted apoptosis process contributes to carcinogenesis, developmental defects, autoimmune diseases and neurological disorders. Due to the complexity of the process, the molecular dynamics and relative interactions of individual proteins responsible for the activation or inhibition of apoptosis should be researched systematically. In this study, we integrate known protein interactions from databases DIP, IntAct, MINT, HPRD and BioGRID by Naïve Bayes classifier. The receiver operation characteristic (ROC) curve with the area under the ROC curve (AUC) of 0.797 indicates it has a good performance in prediction. Then, we predict the global human apoptotic protein interactions network. Within it, we not only identify the already known interactions of caspases (caspase-8/-10, caspase-9, caspase-3/-6/-7) and Bcl-2 family, but also reveal that Bid can interact with casein kinases (CSK21/22/2B, KC1A, KC1E); both of B2LA1 and B2CL2 can interact with Bid, Bax and Bak; caspase-8 interacts with autophagic proteins (MLP3B, MLP3A and LRRk2). Consequently, we make an initial step to develop the web service IntApop that provides an appropriate platform for apoptosis researchers, systems biologists and translational clinician scientists to predict apoptotic protein interactions in human. In addition, the interaction network can be visualized online, making it a widely applicable systems biology tool for apoptosis and cancer researchers.

  7. On Acoustic Source Specification for Rotor-Stator Interaction Noise Prediction

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

    This paper describes the use of measured source data to assess the effects of acoustic source specification on rotor-stator interaction noise predictions. Specifically, the acoustic propagation and radiation portions of a recently developed coupled computational approach are used to predict tonal rotor-stator interaction noise from a benchmark configuration. In addition to the use of full measured data, randomization of source mode relative phases is also considered for specification of the acoustic source within the computational approach. Comparisons with sideline noise measurements are performed to investigate the effects of various source descriptions on both inlet and exhaust predictions. The inclusion of additional modal source content is shown to have a much greater influence on the inlet results. Reasonable agreement between predicted and measured levels is achieved for the inlet, as well as the exhaust when shear layer effects are taken into account. For the number of trials considered, phase randomized predictions follow statistical distributions similar to those found in previous statistical source investigations. The shape of the predicted directivity pattern relative to measurements also improved with phase randomization, having predicted levels generally within one standard deviation of the measured levels.

  8. The Frequency-Predictability Interaction in Reading: It Depends Where You're Coming from

    ERIC Educational Resources Information Center

    Hand, Christopher J.; Miellet, Sebastien; O'Donnell, Patrick J.; Sereno, Sara C.

    2010-01-01

    A word's frequency of occurrence and its predictability from a prior context are key factors determining how long the eyes remain on that word in normal reading. Past reaction-time and eye movement research can be distinguished by whether these variables, when combined, produce interactive or additive results, respectively. Our study addressed…

  9. Developing algorithms for predicting protein-protein interactions of homology modeled proteins.

    SciTech Connect

    Martin, Shawn Bryan; Sale, Kenneth L.; Faulon, Jean-Loup Michel; Roe, Diana C.

    2006-01-01

    The goal of this project was to examine the protein-protein docking problem, especially as it relates to homology-based structures, identify the key bottlenecks in current software tools, and evaluate and prototype new algorithms that may be developed to improve these bottlenecks. This report describes the current challenges in the protein-protein docking problem: correctly predicting the binding site for the protein-protein interaction and correctly placing the sidechains. Two different and complementary approaches are taken that can help with the protein-protein docking problem. The first approach is to predict interaction sites prior to docking, and uses bioinformatics studies of protein-protein interactions to predict theses interaction site. The second approach is to improve validation of predicted complexes after docking, and uses an improved scoring function for evaluating proposed docked poses, incorporating a solvation term. This scoring function demonstrates significant improvement over current state-of-the art functions. Initial studies on both these approaches are promising, and argue for full development of these algorithms.

  10. Network centrality and seasonality interact to predict lice load in a social primate

    PubMed Central

    Duboscq, Julie; Romano, Valeria; Sueur, Cédric; MacIntosh, Andrew J.J.

    2016-01-01

    Lice are socially-transmitted ectoparasites. Transmission depends upon their host’s degree of contact with conspecifics. While grooming facilitates ectoparasite transmission via body contact, it also constrains their spread through parasite removal. We investigated relations between parasite burden and sociality in female Japanese macaques following two opposing predictions: i) central females in contact/grooming networks harbour more lice, related to their numerous contacts; ii) central females harbour fewer lice, related to receiving more grooming. We estimated lice load non-invasively using the conspicuous louse egg-picking behaviour performed by macaques during grooming. We tested for covariation in several centrality measures and lice load, controlling for season, female reproductive state and dominance rank. Results show that the interaction between degree centrality (number of partners) and seasonality predicted lice load: females interacting with more partners had fewer lice than those interacting with fewer partners in winter and summer, whereas there was no relationship between lice load and centrality in spring and fall. This is counter to the prediction that increased contact leads to greater louse burden but fits the prediction that social grooming limits louse burden. Interactions between environmental seasonality and both parasite and host biology appeared to mediate the role of social processes in louse burden. PMID:26915589

  11. Testing Predictions of the Interactive Activation Model in Recovery from Aphasia after Treatment

    ERIC Educational Resources Information Center

    Jokel, Regina; Rochon, Elizabeth; Leonard, Carol

    2004-01-01

    This paper presents preliminary results of pre- and post-treatment error analysis from an aphasic patient with anomia. The Interactive Activation (IA) model of word production (Dell, Schwartz, Martin, Saffran, & Gagnon, 1997) is utilized to make predictions about the anticipated changes on a picture naming task and to explain emerging patterns.…

  12. Interactions of Team Mental Models and Monitoring Behaviors Predict Team Performance in Simulated Anesthesia Inductions

    ERIC Educational Resources Information Center

    Burtscher, Michael J.; Kolbe, Michaela; Wacker, Johannes; Manser, Tanja

    2011-01-01

    In the present study, we investigated how two team mental model properties (similarity vs. accuracy) and two forms of monitoring behavior (team vs. systems) interacted to predict team performance in anesthesia. In particular, we were interested in whether the relationship between monitoring behavior and team performance was moderated by team…

  13. Parenting and Child "DRD4" Genotype Interact to Predict Children's Early Emerging Effortful Control

    ERIC Educational Resources Information Center

    Smith, Heather J.; Sheikh, Haroon I.; Dyson, Margaret W.; Olino, Thomas M.; Laptook, Rebecca S.; Durbin, C. Emily; Hayden, Elizabeth P.; Singh, Shiva M.; Klein, Daniel N.

    2012-01-01

    Effortful control (EC), or the trait-like capacity to regulate dominant responses, has important implications for children's development. Although genetic factors and parenting likely influence EC, few studies have examined whether they interact to predict its development. This study examined whether the "DRD4" exon III variable number tandem…

  14. Subjective Stress and Coping Resources Interact To Predict Blood Pressure Reactivity in Black College Students.

    ERIC Educational Resources Information Center

    Clark, Rodney

    2003-01-01

    Examined the effects of subjective stress and coping resources on blood pressure reactivity among black college students. The interactive effects of subjective stress and coping resources predicted diastolic blood pressure reactivity. Higher levels of problem-focused coping related to more marked diastolic blood pressure changes under conditions…

  15. Predicting Young Children's Externalizing Problems: Interactions among Effortful Control, Parenting, and Child Gender

    ERIC Educational Resources Information Center

    Karreman, Annemiek; van Tuijl, Cathy; van Aken, Marcel A. G.; Dekovi, Maja

    2009-01-01

    This study investigated interactions between observed temperamental effortful control and observed parenting in the prediction of externalizing problems. Child gender effects on these relations were examined. The relations were examined concurrently when the child was 3 years old and longitudinally at 4.5 years. The sample included 89 two-parent…

  16. Contextual Predictive Factors of Child Sexual Abuse: The Role of Parent-Child Interaction

    ERIC Educational Resources Information Center

    Ramirez, Clemencia; Pinzon-Rondon, Angela Maria; Botero, Juan Carlos

    2011-01-01

    Objectives: To determine the prevalence of child sexual abuse in the Colombian coasts, as well as to assess the role of parent-child interactions on its occurrence and to identify factors from different environmental levels that predict it. Methods: This cross-sectional study explores the results of 1,089 household interviews responded by mothers.…

  17. Understanding how children’s engagement and teachers’ interactions combine to predict school readiness

    PubMed Central

    Williford, Amanda P.; Maier, Michelle F.; Downer, Jason T.; Pianta, Robert C.; Howes, Carolee

    2015-01-01

    This study examined the quality of preschool classroom experiences through the combination of teachers’ interactions at the classroom level and children’s individual patterns of engagement in predicting children’s gains in school readiness. A sample of 605 children and 309 teachers participated. The quality of children’s engagement and teacher interactions was directly observed in the classroom setting, and direct assessments of children’s school readiness skills were obtained in the fall and again in the spring. The quality of teacher interactions was associated with gains across all school readiness skills. The effect of children’s individual classroom engagement on their gains in school readiness skills (specifically phonological awareness and expressive vocabulary) was moderated by classroom level teacher interactions. The results suggest that if teachers provide highly responsive interactions at the classroom level, children may develop more equitable school readiness skills regardless of their individual engagement patterns. PMID:26722137

  18. [Study of decision tree in the application of predicting protein-protein interactions].

    PubMed

    Guo, Xiaolong; Jiang, Yan; Qui, Lu

    2013-10-01

    Proteins are the final executive actor of cell viability and function. Protein-protein interactions determine the complexity of the organism. Research on the protein interactions can help us understand the function of the protein at the molecular level, learn the cell growth, development, differentiation, apoptosis and understand biological regulation mechanisms and other activities. They are essential for understanding the pathologies of diseases and helpful in the prevention and treatment of diseases, as well as in the development of new drugs. In this paper, we employ the single decision-tree classification model to predict protein-protein interactions in the yeast. The original data came from the existing literature. Using software Clementine, this paper analyzes how these attributes affect the accuracy of the model by adjusting the predicted attributes. The result shows that a single decision tree is a good classification model and it has higher accuracy compared to those in the previous researches.

  19. A Modified Nonlinear Damage Accumulation Model for Fatigue Life Prediction Considering Load Interaction Effects

    PubMed Central

    Huang, Hong-Zhong; Yuan, Rong

    2014-01-01

    Many structures are subjected to variable amplitude loading in engineering practice. The foundation of fatigue life prediction under variable amplitude loading is how to deal with the fatigue damage accumulation. A nonlinear fatigue damage accumulation model to consider the effects of load sequences was proposed in earlier literature, but the model cannot consider the load interaction effects, and sometimes it makes a major error. A modified nonlinear damage accumulation model is proposed in this paper to account for the load interaction effects. Experimental data of two metallic materials are used to validate the proposed model. The agreement between the model prediction and experimental data is observed, and the predictions by proposed model are more possibly in accordance with experimental data than that by primary model and Miner's rule. Comparison between the predicted cumulative damage by the proposed model and an existing model shows that the proposed model predictions can meet the accuracy requirement of the engineering project and it can be used to predict the fatigue life of welded aluminum alloy joint of Electric Multiple Units (EMU); meanwhile, the accuracy of approximation can be obtained from the proposed model though more simple computing process and less material parameters calling for extensive testing than the existing model. PMID:24574866

  20. Dynamic circadian protein-protein interaction networks predict temporal organization of cellular functions.

    PubMed

    Wallach, Thomas; Schellenberg, Katja; Maier, Bert; Kalathur, Ravi Kiran Reddy; Porras, Pablo; Wanker, Erich E; Futschik, Matthias E; Kramer, Achim

    2013-03-01

    Essentially all biological processes depend on protein-protein interactions (PPIs). Timing of such interactions is crucial for regulatory function. Although circadian (~24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner. PMID:23555304

  1. MULTIPROSPECTOR: an algorithm for the prediction of protein-protein interactions by multimeric threading.

    PubMed

    Lu, Long; Lu, Hui; Skolnick, Jeffrey

    2002-11-15

    In this postgenomic era, the ability to identify protein-protein interactions on a genomic scale is very important to assist in the assignment of physiological function. Because of the increasing number of solved structures involving protein complexes, the time is ripe to extend threading to the prediction of quaternary structure. In this spirit, a multimeric threading approach has been developed. The approach is comprised of two phases. In the first phase, traditional threading on a single chain is applied to generate a set of potential structures for the query sequences. In particular, we use our recently developed threading algorithm, PROSPECTOR. Then, for those proteins whose template structures are part of a known complex, we rethread on both partners in the complex and now include a protein-protein interfacial energy. To perform this analysis, a database of multimeric protein structures has been constructed, the necessary interfacial pairwise potentials have been derived, and a set of empirical indicators to identify true multimers based on the threading Z-score and the magnitude of the interfacial energy have been established. The algorithm has been tested on a benchmark set comprised of 40 homodimers, 15 heterodimers, and 69 monomers that were scanned against a protein library of 2478 structures that comprise a representative set of structures in the Protein Data Bank. Of these, the method correctly recognized and assigned 36 homodimers, 15 heterodimers, and 65 monomers. This protocol was applied to identify partners and assign quaternary structures of proteins found in the yeast database of interacting proteins. Our multimeric threading algorithm correctly predicts 144 interacting proteins, compared to the 56 (26) cases assigned by PSI-BLAST using a (less) permissive E-value of 1 (0.01). Next, all possible pairs of yeast proteins have been examined. Predictions (n = 2865) of protein-protein interactions are made; 1138 of these 2865 interactions have

  2. PRECISE: a Database of Predicted and Consensus Interaction Sites in Enzymes.

    PubMed

    Sheu, Shu-Hsien; Lancia, David R; Clodfelter, Karl H; Landon, Melissa R; Vajda, Sandor

    2005-01-01

    PRECISE (Predicted and Consensus Interaction Sites in Enzymes) is a database of interactions between the amino acid residues of an enzyme and its ligands (substrate and transition state analogs, cofactors, inhibitors and products). It is available online at http://precise.bu.edu/. In the current version, all information on interactions is extracted from the enzyme-ligand complexes in the Protein Data Bank (PDB) by performing the following steps: (i) clustering homologous enzyme chains such that, in each cluster, the proteins have the same EC number and all sequences are similar; (ii) selecting a representative chain for each cluster; (iii) selecting ligand types; (iv) finding non-bonded interactions and hydrogen bonds; and (v) summing the interactions for all chains within the cluster. The output of the search is the color-coded sequence of the representative. The colors indicate the total number of interactions found at each amino acid position in all chains of the cluster. Clicking on a residue displays a detailed list of interactions for that residue. Optional filters allow restricting the output to selected chains in the cluster, to non-bonded or hydrogen bonding interactions, and to selected ligand types. The binding site information is essential for understanding and altering substrate specificity and for the design of enzyme inhibitors.

  3. Out-of-sample predictions from plant-insect food webs: robustness to missing and erroneous trophic interaction records.

    PubMed

    Pearse, Ian S; Altermatt, Florian

    2015-10-01

    With increasing biotic introductions, there is a great need for predictive tools to anticipate which new trophic interactions will develop and which will not. Phylogenetic constraint of interactions in both native and novel food webs can make some novel interactions predictable. However, many food webs are sparsely sampled, or may include inaccurate interactions. In such cases, it is unclear whether modeling methods are still useful to anticipate novel interactions. We ran bootstrap simulations of host-use models on a Lepidoptera-plant data set to remove native trophic records or add erroneous records in order to observe the effect of missing or erroneous data on the prediction of interactions with novel plants. We found that the model was robust to a large amount of missing interaction records, but lost predictive power with the addition of relatively few erroneous interaction records. The loss of predictive power with missing records was due to inaccuracy in estimating phylogenetic distance between native and novel hosts. Removal of interaction records proportionally to their encounter frequency in the field had little effect on the loss of predictive power. Host-use models may have immediate value for predicting novel interactions from large, but sparsely sampled databases of trophic interactions.

  4. Comprehensive prediction of drug-protein interactions and side effects for the human proteome

    PubMed Central

    Zhou, Hongyi; Gao, Mu; Skolnick, Jeffrey

    2015-01-01

    Identifying unexpected drug-protein interactions is crucial for drug repurposing. We develop a comprehensive proteome scale approach that predicts human protein targets and side effects of drugs. For drug-protein interaction prediction, FINDSITEcomb, whose average precision is ~30% and recall ~27%, is employed. For side effect prediction, a new method is developed with a precision of ~57% and a recall of ~24%. Our predictions show that drugs are quite promiscuous, with the average (median) number of human targets per drug of 329 (38), while a given protein interacts with 57 drugs. The result implies that drug side effects are inevitable and existing drugs may be useful for repurposing, with only ~1,000 human proteins likely causing serious side effects. A killing index derived from serious side effects has a strong correlation with FDA approved drugs being withdrawn. Therefore, it provides a pre-filter for new drug development. The methodology is free to the academic community on the DR. PRODIS (DRugome, PROteome, and DISeasome) webserver at http://cssb.biology.gatech.edu/dr.prodis/. DR. PRODIS provides protein targets of drugs, drugs for a given protein target, associated diseases and side effects of drugs, as well as an interface for the virtual target screening of new compounds. PMID:26057345

  5. Probability weighted ensemble transfer learning for predicting interactions between HIV-1 and human proteins.

    PubMed

    Mei, Suyu

    2013-01-01

    Reconstruction of host-pathogen protein interaction networks is of great significance to reveal the underlying microbic pathogenesis. However, the current experimentally-derived networks are generally small and should be augmented by computational methods for less-biased biological inference. From the point of view of computational modelling, data scarcity, data unavailability and negative data sampling are the three major problems for host-pathogen protein interaction networks reconstruction. In this work, we are motivated to address the three concerns and propose a probability weighted ensemble transfer learning model for HIV-human protein interaction prediction (PWEN-TLM), where support vector machine (SVM) is adopted as the individual classifier of the ensemble model. In the model, data scarcity and data unavailability are tackled by homolog knowledge transfer. The importance of homolog knowledge is measured by the ROC-AUC metric of the individual classifiers, whose outputs are probability weighted to yield the final decision. In addition, we further validate the assumption that only the homolog knowledge is sufficient to train a satisfactory model for host-pathogen protein interaction prediction. Thus the model is more robust against data unavailability with less demanding data constraint. As regards with negative data construction, experiments show that exclusiveness of subcellular co-localized proteins is unbiased and more reliable than random sampling. Last, we conduct analysis of overlapped predictions between our model and the existing models, and apply the model to novel host-pathogen PPIs recognition for further biological research.

  6. The role of radiation-dynamics interaction in regional numerical weather prediction

    NASA Technical Reports Server (NTRS)

    Chang, Chia-Bo

    1988-01-01

    The role of radiation-dynamics interaction in regional numerical weather prediction of severe storm environment and mesoscale convective systems over the United States is researched. Based upon the earlier numerical model simulation experiments, it is believed that such interaction can have a profound impact on the dynamics and thermodynamics of regional weather systems. The research will be carried out using real-data model forecast experiments performed on the Cray-X/MP computer. The forecasting system to be used is a comprehensive mesoscale prediction system which includes analysis and initialization, the dynamic model, and the post-forecast diagnosis codes. The model physics are currently undergoing many improvements in parameterizing radiation processes in the model atmosphere. The forecast experiments in conjunction with in-depth model verification and diagnosis are aimed at a quantitative understanding of the interaction between atmospheric radiation and regional dynamical processes in mesoscale models as well as in nature. Thus, significant advances in regional numerical weather prediction can be made. Results shall also provide valuable information for observational designs in the area of remote sensing techniques to study the characteristics of air-land thermal interaction and moist processes under various atmospheric conditions.

  7. Protein function prediction using neighbor relativity in protein-protein interaction network.

    PubMed

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

    There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network.

  8. Predicting metal effects and interactions on light output in bacteria (Photobacterium phosphoreum) using coordination chemistry data

    SciTech Connect

    McCloskey, J.T.; Newman, M.C.

    1994-12-31

    Previous studies have shown correlations between Log of the negative solubility product for the metal hydroxide (Log -KsoMOH) and bioaccumulation of metals. The concentration of metal at which light output was reduced 50% (EC-50) in a marine bacteria (Photobacterium phosphoreum) was determined for several metals using Microtox{reg_sign}. Median affect concentrations were corrected for the amount of free ion using MINTEQ and graphed against Log -KsoMOH. A negative relationship was found between 15 minute EC50`s for all metals tested and Log -KsoMOH. The interaction between metal pairs were then determined to see if the interaction between metals could be predicted from Log -KsoMOH. In general, metals with similar values of Log KsoMOH interacted more strongly than metals with very different values. The authors conclude that metal effects (EC-50) could be predicted based on the Log -KsoMOH and that for metals with similar mechanisms of toxicity, the interaction between metals was also predictable based on Log -KsoMOH.

  9. Conflict and love: predicting newlywed marital outcomes from two interaction contexts.

    PubMed

    Graber, Elana C; Laurenceau, Jean-Philippe; Miga, Erin; Chango, Joanna; Coan, James

    2011-08-01

    Research on marital interaction has focused primarily on couples in conflict contexts to understand better processes associated with concurrent and longitudinal outcomes such as marital stability and quality. Although this work has consistently revealed particular emotions (e.g., contempt) or behavioral sequences (e.g., demand/withdraw) predictive of later marital distress, it largely has neglected to take positive contexts into consideration. The present longitudinal study begins to address this gap in the literature by directly comparing newlywed behaviors from a conflict-resolution interaction with those from a love-paradigm interaction to predict relationship satisfaction and divorce proneness approximately 15 months later. Results showed that actor and partner negative (contempt) and positive (affection) emotions elicited in both positive (i.e., love) and negative (i.e., conflict) interaction contexts emerged as unique predictors of relationship quality and stability for both husbands and wives. Moreover, using a linear growth model, the temporal course of positive emotion during the love context, but not the conflict context, was predictive of later relationship satisfaction. Implications for future marital research and intervention are discussed.

  10. Abdominal obesity and chronic stress interact to predict blunted cardiovascular reactivity.

    PubMed

    Singh, Kulwinder; Shen, Biing-Jiun

    2013-10-01

    Abdominal obesity and chronic stress have independent effects on cardiac autonomic regulation, and may also interact to influence cardiovascular reactivity. In addition to main effects, we hypothesized that abdominal obesity and chronic stress would interact and predict blunted cardiovascular reactivity. One hundred and twenty-two undergraduate students engaged in two stressful laboratory tasks while cardiovascular activity was assessed. Results indicated that higher abdominal obesity significantly predicted blunted systolic blood pressure (SBP) and mean arterial pressure (MAP) change, while chronic stress was not directly associated with any measure of cardiovascular reactivity. Furthermore, there was a significant interaction between abdominal obesity and chronic stress on SBP and MAP change such that among participants with higher chronic stress, higher abdominal obesity was significantly associated with reduced SBP and MAP reactivity. In addition, body-mass index (BMI), a measure of overall obesity, also had both main and interaction effects with chronic stress to predict blunted cardiovascular reactivity. These results suggest that abdominally obese individuals may incur difficulty in mounting appropriately-sized cardiovascular responses during acute stress, particularly when under high levels of chronic stress.

  11. Mapping Plant Interactomes Using Literature Curated and Predicted Protein–Protein Interaction Data Sets[W

    PubMed Central

    Lee, KiYoung; Thorneycroft, David; Achuthan, Premanand; Hermjakob, Henning; Ideker, Trey

    2010-01-01

    Most cellular processes are enabled by cohorts of interacting proteins that form dynamic networks within the plant proteome. The study of these networks can provide insight into protein function and provide new avenues for research. This article informs the plant science community of the currently available sources of protein interaction data and discusses how they can be useful to researchers. Using our recently curated IntAct Arabidopsis thaliana protein–protein interaction data set as an example, we discuss potentials and limitations of the plant interactomes generated to date. In addition, we present our efforts to add value to the interaction data by using them to seed a proteome-wide map of predicted protein subcellular locations. PMID:20371643

  12. Learning to Predict miRNA-mRNA Interactions from AGO CLIP Sequencing and CLASH Data

    PubMed Central

    Lu, Yuheng; Leslie, Christina S.

    2016-01-01

    Recent technologies like AGO CLIP sequencing and CLASH enable direct transcriptome-wide identification of AGO binding and miRNA target sites, but the most widely used miRNA target prediction algorithms do not exploit these data. Here we use discriminative learning on AGO CLIP and CLASH interactions to train a novel miRNA target prediction model. Our method combines two SVM classifiers, one to predict miRNA-mRNA duplexes and a second to learn a binding model of AGO’s local UTR sequence preferences and positional bias in 3’UTR isoforms. The duplex SVM model enables the prediction of non-canonical target sites and more accurately resolves miRNA interactions from AGO CLIP data than previous methods. The binding model is trained using a multi-task strategy to learn context-specific and common AGO sequence preferences. The duplex and common AGO binding models together outperform existing miRNA target prediction algorithms on held-out binding data. Open source code is available at https://bitbucket.org/leslielab/chimiric. PMID:27438777

  13. Learning to Predict miRNA-mRNA Interactions from AGO CLIP Sequencing and CLASH Data.

    PubMed

    Lu, Yuheng; Leslie, Christina S

    2016-07-01

    Recent technologies like AGO CLIP sequencing and CLASH enable direct transcriptome-wide identification of AGO binding and miRNA target sites, but the most widely used miRNA target prediction algorithms do not exploit these data. Here we use discriminative learning on AGO CLIP and CLASH interactions to train a novel miRNA target prediction model. Our method combines two SVM classifiers, one to predict miRNA-mRNA duplexes and a second to learn a binding model of AGO's local UTR sequence preferences and positional bias in 3'UTR isoforms. The duplex SVM model enables the prediction of non-canonical target sites and more accurately resolves miRNA interactions from AGO CLIP data than previous methods. The binding model is trained using a multi-task strategy to learn context-specific and common AGO sequence preferences. The duplex and common AGO binding models together outperform existing miRNA target prediction algorithms on held-out binding data. Open source code is available at https://bitbucket.org/leslielab/chimiric. PMID:27438777

  14. Predicting protein-RNA interaction amino acids using random forest based on submodularity subset selection.

    PubMed

    Pan, Xiaoyong; Zhu, Lin; Fan, Yong-Xian; Yan, Junchi

    2014-11-13

    Protein-RNA interaction plays a very crucial role in many biological processes, such as protein synthesis, transcription and post-transcription of gene expression and pathogenesis of disease. Especially RNAs always function through binding to proteins. Identification of binding interface region is especially useful for cellular pathways analysis and drug design. In this study, we proposed a novel approach for binding sites identification in proteins, which not only integrates local features and global features from protein sequence directly, but also constructed a balanced training dataset using sub-sampling based on submodularity subset selection. Firstly we extracted local features and global features from protein sequence, such as evolution information and molecule weight. Secondly, the number of non-interaction sites is much more than interaction sites, which leads to a sample imbalance problem, and hence biased machine learning model with preference to non-interaction sites. To better resolve this problem, instead of previous randomly sub-sampling over-represented non-interaction sites, a novel sampling approach based on submodularity subset selection was employed, which can select more representative data subset. Finally random forest were trained on optimally selected training subsets to predict interaction sites. Our result showed that our proposed method is very promising for predicting protein-RNA interaction residues, it achieved an accuracy of 0.863, which is better than other state-of-the-art methods. Furthermore, it also indicated the extracted global features have very strong discriminate ability for identifying interaction residues from random forest feature importance analysis.

  15. Flow structure generated by perpendicular blade vortex interaction and implications for helicopter noise predictions

    NASA Technical Reports Server (NTRS)

    Devenport, William J.; Glegg, Stewart A. L.

    1994-01-01

    Activities carried out in support of research on flow structure generated by perpendicular blade vortex interaction and implications for helicopter noise prediction are summarized. Progress in the following areas is described: (1) construction of 8 inch-chord NACA 0012 full-span blade; (2) Acquisition of two full-span blades; (3) preparation for hot wire measurements; (4) related work on a modified Betz's theory; and (5) work related to helicopter noise prediction. In addition, a list of publications based on the results of prior experimentation is presented.

  16. The use of in vitro methods to predict in vivo pharmacokinetics and drug interactions.

    PubMed

    Bachmann, K A; Ghosh, R

    2001-09-01

    With the dramatic change underway in the process of drug discovery and development it has become increasingly important to define, both qualitatively and quantitatively, the dispositional features of new chemical entities (NCEs) as early in the process as possible. To that end strategies have emerged that are designed to enable reasonable predictions about a NCE's absorption from the gastrointestinal tract, systemic bioavailability and likelihood for significant pre-systemic clearance, character of metabolic processing both within the gastrointestinal tract and the liver, in vivo pharmacokinetics (PK), and likelihood for clinically significant interactions with other drugs. To some extent these strategies have embraced interspecies allometric scaling in which findings in animals are extrapolated to predict outcomes in humans. However, a greater emphasis in recent years has been placed on predicting human PK and the likelihood of clinically significant drug-drug interactions for NCEs solely from in vitro experiments. These general strategies have been methodologically streamlined so that hundreds or even thousands of experiments on a given NCE can be conducted within several days. Dispositional data from these pre-clinical experiments is useful for rapidly identifying potential marketing advantages for NCEs, and for screening out those substances that should not be placed into more expensive and labor-intensive animal experiments or brought to clinical trial. The key issue in these strategies is the accuracy with which pre-clinical findings predict clinical outcomes. Based largely on retrospective analyses the current state of the art exhibits a high percentage of useful predictions. However, there are many examples in which the prediction of either human PK or clinical drug-drug interactions from pre-clinical data has failed. The reasons for inaccurate predictions are manifold, and may include the actual in vitro methodology used, inappropriate model selection, and

  17. Computational approaches for prediction of pathogen-host protein-protein interactions.

    PubMed

    Nourani, Esmaeil; Khunjush, Farshad; Durmuş, Saliha

    2015-01-01

    Infectious diseases are still among the major and prevalent health problems, mostly because of the drug resistance of novel variants of pathogens. Molecular interactions between pathogens and their hosts are the key parts of the infection mechanisms. Novel antimicrobial therapeutics to fight drug resistance is only possible in case of a thorough understanding of pathogen-host interaction (PHI) systems. Existing databases, which contain experimentally verified PHI data, suffer from scarcity of reported interactions due to the technically challenging and time consuming process of experiments. These have motivated many researchers to address the problem by proposing computational approaches for analysis and prediction of PHIs. The computational methods primarily utilize sequence information, protein structure and known interactions. Classic machine learning techniques are used when there are sufficient known interactions to be used as training data. On the opposite case, transfer and multitask learning methods are preferred. Here, we present an overview of these computational approaches for predicting PHI systems, discussing their weakness and abilities, with future directions. PMID:25759684

  18. Rotor Wake/Stator Interaction Noise Prediction Code Technical Documentation and User's Manual

    NASA Technical Reports Server (NTRS)

    Topol, David A.; Mathews, Douglas C.

    2010-01-01

    This report documents the improvements and enhancements made by Pratt & Whitney to two NASA programs which together will calculate noise from a rotor wake/stator interaction. The code is a combination of subroutines from two NASA programs with many new features added by Pratt & Whitney. To do a calculation V072 first uses a semi-empirical wake prediction to calculate the rotor wake characteristics at the stator leading edge. Results from the wake model are then automatically input into a rotor wake/stator interaction analytical noise prediction routine which calculates inlet aft sound power levels for the blade-passage-frequency tones and their harmonics, along with the complex radial mode amplitudes. The code allows for a noise calculation to be performed for a compressor rotor wake/stator interaction, a fan wake/FEGV interaction, or a fan wake/core stator interaction. This report is split into two parts, the first part discusses the technical documentation of the program as improved by Pratt & Whitney. The second part is a user's manual which describes how input files are created and how the code is run.

  19. Combining many interaction networks to predict gene function and analyze gene lists.

    PubMed

    Mostafavi, Sara; Morris, Quaid

    2012-05-01

    In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems.

  20. Heat shock protein 90 as a drug target against protozoan infections: biochemical characterization of HSP90 from Plasmodium falciparum and Trypanosoma evansi and evaluation of its inhibitor as a candidate drug.

    PubMed

    Pallavi, Rani; Roy, Nainita; Nageshan, Rishi Kumar; Talukdar, Pinaki; Pavithra, Soundara Raghavan; Reddy, Raghunath; Venketesh, S; Kumar, Rajender; Gupta, Ashok Kumar; Singh, Raj Kumar; Yadav, Suresh Chandra; Tatu, Utpal

    2010-12-01

    Using a pharmacological inhibitor of Hsp90 in cultured malarial parasite, we have previously implicated Plasmodium falciparum Hsp90 (PfHsp90) as a drug target against malaria. In this study, we have biochemically characterized PfHsp90 in terms of its ATPase activity and interaction with its inhibitor geldanamycin (GA) and evaluated its potential as a drug target in a preclinical mouse model of malaria. In addition, we have explored the potential of Hsp90 inhibitors as drugs for the treatment of Trypanosoma infection in animals. Our studies with full-length PfHsp90 showed it to have the highest ATPase activity of all known Hsp90s; its ATPase activity was 6 times higher than that of human Hsp90. Also, GA brought about more robust inhibition of PfHsp90 ATPase activity as compared with human Hsp90. Mass spectrometric analysis of PfHsp90 expressed in P. falciparum identified a site of acetylation that overlapped with Aha1 and p23 binding domain, suggesting its role in modulating Hsp90 multichaperone complex assembly. Indeed, treatment of P. falciparum cultures with a histone deacetylase inhibitor resulted in a partial dissociation of PfHsp90 complex. Furthermore, we found a well known, semisynthetic Hsp90 inhibitor, namely 17-(allylamino)-17-demethoxygeldanamycin, to be effective in attenuating parasite growth and prolonging survival in a mouse model of malaria. We also characterized GA binding to Hsp90 from another protozoan parasite, namely Trypanosoma evansi. We found 17-(allylamino)-17-demethoxygeldanamycin to potently inhibit T. evansi growth in a mouse model of trypanosomiasis. In all, our biochemical characterization, drug interaction, and animal studies supported Hsp90 as a drug target and its inhibitor as a potential drug against protozoan diseases.

  1. 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

  2. Sequence-based prediction of protein-protein interaction sites with L1-logreg classifier.

    PubMed

    Dhole, Kaustubh; Singh, Gurdeep; Pai, Priyadarshini P; Mondal, Sukanta

    2014-05-01

    Protein-protein interactions are of central importance for virtually every process in a living cell. Information about the interaction sites in proteins improves our understanding of disease mechanisms and can provide the basis for new therapeutic approaches. Since a multitude of unique residue-residue contacts facilitate the interactions, protein-protein interaction sites prediction has become one of the most important and challenging problems of computational biology. Although much progress in this field has been reported, this problem is yet to be satisfactorily solved. Here, a novel method (LORIS: L1-regularized LOgistic Regression based protein-protein Interaction Sites predictor) is proposed, that identifies interaction residues, using sequence features and is implemented via the L1-logreg classifier. Results show that LORIS is not only quite effective, but also, performs better than existing state-of-the art methods. LORIS, available as standalone package, can be useful for facilitating drug-design and targeted mutation related studies, which require a deeper knowledge of protein interactions sites. PMID:24486250

  3. Computationally predicting protein-RNA interactions using only positive and unlabeled examples.

    PubMed

    Cheng, Zhanzhan; Zhou, Shuigeng; Guan, Jihong

    2015-06-01

    Protein-RNA interactions (PRIs) are considerably important in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulations of gene expression to the active defense of host against virus. With the development of high throughput technology, large amounts of PRI information is available for computationally predicting unknown PRIs. In recent years, a number of computational methods for predicting PRIs have been developed in the literature, which usually artificially construct negative samples based on verified nonredundant datasets of PRIs to train classifiers. However, such negative samples are not real negative samples, some even may be unknown positive samples. Consequently, the classifiers trained with such training datasets cannot achieve satisfactory prediction performance. In this paper, we propose a novel method PRIPU that employs biased-support vector machine (SVM) for predicting Protein-RNA Interactions using only Positive and Unlabeled examples. To the best of our knowledge, this is the first work that predicts PRIs using only positive and unlabeled samples. We first collect known PRIs as our benchmark datasets and extract sequence-based features to represent each PRI. To reduce the dimension of feature vectors for lowering computational cost, we select a subset of features by a filter-based feature selection method. Then, biased-SVM is employed to train prediction models with different PRI datasets. To evaluate the new method, we also propose a new performance measure called explicit positive recall (EPR), which is specifically suitable for the task of learning positive and unlabeled data. Experimental results over three datasets show that our method not only outperforms four existing methods, but also is able to predict unknown PRIs. Source code, datasets and related documents of PRIPU are available at: http://admis.fudan.edu.cn/projects/pripu.htm . PMID:25790785

  4. New models and predictions for Brownian coagulation of non-interacting spheres.

    PubMed

    Kelkar, Aniruddha V; Dong, Jiannan; Franses, Elias I; Corti, David S

    2013-01-01

    The classical steady-state Smoluchowski model for Brownian coagulation is evaluated using Brownian Dynamics Simulations (BDS) as a benchmark. The predictions of this approach compare favorably with the results of BDS only in the dilute limit, that is, for volume fractions of φ≤5×10(-4). From the solution of the more general unsteady-state diffusion equation, a new model for coagulation is developed. The resulting coagulation rate constant is time-dependent and approaches the steady-state limit only at large times. Moreover, in contrast to the Smoluchowski model, this rate constant depends on the particle size, with the transient effects becoming more significant at larger sizes. The predictions of the unsteady-state model agree well with the BDS results up to volume fractions of about φ=0.1, at which the aggregation half-time predicted by the Smoluchowski model is five times that of the BDS. A new procedure to extract the aggregation rate constant from simulation results based on this model is presented. The choice of the rate constant kernel used in the population balance equations for complete aggregation is also evaluated. Extension of the new model to a variable rate constant kernel leads to increased accuracy of the predictions, especially for φ≤5×10(-3). This size-dependence of the rate constant kernel affects particularly the predictions for initially polydisperse sphere systems. In addition, the model is extended to account in a novel way for both short-range viscous two-particle interactions and long-range many-particle Hydrodynamic Interactions (HI). Predictions including HI agree best with the BDS results. The new models presented here offer accurate and computationally less-intensive predictions of the coagulation dynamics while also accounting for hydrodynamic coupling. PMID:23036339

  5. 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

  6. The Generalization of Attachment Representations to New Social Situations: Predicting Behavior during Initial Interactions with Strangers

    PubMed Central

    Feeney, Brooke C.; Cassidy, Jude; Ramos-Marcuse, Fatima

    2008-01-01

    The idea that attachment representations are generalized to new social situations and guide behavior with unfamiliar others is central to attachment theory. However, research regarding this important theoretical postulate has been lacking in adolescence and adulthood, as most research has focused on establishing the influence of attachment representations on close relationship dynamics. Thus, the goal of this investigation was to examine the extent to which attachment representations are predictive of adolescents’ initial behavior when meeting and interacting with new peers. High school adolescents (N = 135) participated with unfamiliar peers from another school in two social support interactions that were videotaped and coded by independent observers. Results indicated that attachment representations (assessed through interview and self-report measures) were predictive of behaviors exhibited during the discussions. Theoretical implications of results and contributions to existing literature are discussed. PMID:19025297

  7. Direct-heating containment vessel interaction code (DHCVIC) and prediction of SNL SURTSEY test DCH-1

    SciTech Connect

    Ginsberg, T.; Tutu, N.

    1986-01-01

    High-pressure melt ejection from pressurized water reactor (PWR) vessels has been identified as a severe core-accident scenario which could potentially lead to early containment failure. Melt ejection, followed by dispersal of the melt by high-velocity steam in the cavity beneath the PWR vessel could, according to this scenario, lead to rapid transfer of energy from the melt droplets to the containment atmosphere. This paper describes DHCVIC, an integrated model of the thermal, chemical, and hydrodynamic interactions which are postulated to take place during high-pressure melt ejection sequences. The model, which characterizes interactions occurring within the reactor cavity, as well as in the containment vessel (or building), is applied to prediction of the Sandia National Laboratory (SNL) SURTSEY Test DCH-1 and a (post-test) prediction of that test is made.

  8. Infant Attachment Security and Early Childhood Behavioral Inhibition Interact to Predict Adolescent Social Anxiety Symptoms

    PubMed Central

    Lewis-Morrarty, Erin; Degnan, Kathryn A.; Chronis-Tuscano, Andrea; Pine, Daniel S.; Henderson, Heather A.; Fox, Nathan A.

    2014-01-01

    Insecure attachment and behavioral inhibition (BI) increase risk for internalizing problems, but few longitudinal studies have examined their interaction in predicting adolescent anxiety. This study included 165 adolescents (ages 14-17 years) selected based on their reactivity to novelty at 4 months. Infant attachment was assessed with the Strange Situation. Multi-method BI assessments were conducted across childhood. Adolescents and their parents independently reported on anxiety. The interaction of attachment and BI significantly predicted adolescent anxiety symptoms, such that BI and anxiety were only associated among adolescents with histories of insecure attachment. Exploratory analyses revealed that this effect was driven by insecure-resistant attachment and that the association between BI and social anxiety was significant only for insecure males. Clinical implications are discussed. PMID:25522059

  9. Saccharomyces cerevisiae Genetics Predicts Candidate Therapeutic Genetic Interactions at the Mammalian Replication Fork

    PubMed Central

    van Pel, Derek M.; Stirling, Peter C.; Minaker, Sean W.; Sipahimalani, Payal; Hieter, Philip

    2013-01-01

    The concept of synthetic lethality has gained popularity as a rational guide for predicting chemotherapeutic targets based on negative genetic interactions between tumor-specific somatic mutations and a second-site target gene. One hallmark of most cancers that can be exploited by chemotherapies is chromosome instability (CIN). Because chromosome replication, maintenance, and segregation represent conserved and cell-essential processes, they can be modeled effectively in simpler eukaryotes such as Saccharomyces cerevisiae. Here we analyze and extend genetic networks of CIN cancer gene orthologs in yeast, focusing on essential genes. This identifies hub genes and processes that are candidate targets for synthetic lethal killing of cancer cells with defined somatic mutations. One hub process in these networks is DNA replication. A nonessential, fork-associated scaffold, CTF4, is among the most highly connected genes. As Ctf4 lacks enzymatic activity, potentially limiting its development as a therapeutic target, we exploited its function as a physical interaction hub to rationally predict synthetic lethal interactions between essential Ctf4-binding proteins and CIN cancer gene orthologs. We then validated a subset of predicted genetic interactions in a human colorectal cancer cell line, showing that siRNA-mediated knockdown of MRE11A sensitizes cells to depletion of various replication fork-associated proteins. Overall, this work describes methods to identify, predict, and validate in cancer cells candidate therapeutic targets for tumors with known somatic mutations in CIN genes using data from yeast. We affirm not only replication stress but also the targeting of DNA replication fork proteins themselves as potential targets for anticancer therapeutic development. PMID:23390603

  10. Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms

    PubMed Central

    Jian, Jhih-Wei; Elumalai, Pavadai; Pitti, Thejkiran; Wu, Chih Yuan; Tsai, Keng-Chang; Chang, Jeng-Yih; Peng, Hung-Pin; Yang, An-Suei

    2016-01-01

    Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites. PMID:27513851

  11. Genetic variants and their interactions in disease risk prediction – machine learning and network perspectives

    PubMed Central

    2013-01-01

    A central challenge in systems biology and medical genetics is to understand how interactions among genetic loci contribute to complex phenotypic traits and human diseases. While most studies have so far relied on statistical modeling and association testing procedures, machine learning and predictive modeling approaches are increasingly being applied to mining genotype-phenotype relationships, also among those associations that do not necessarily meet statistical significance at the level of individual variants, yet still contributing to the combined predictive power at the level of variant panels. Network-based analysis of genetic variants and their interaction partners is another emerging trend by which to explore how sub-network level features contribute to complex disease processes and related phenotypes. In this review, we describe the basic concepts and algorithms behind machine learning-based genetic feature selection approaches, their potential benefits and limitations in genome-wide setting, and how physical or genetic interaction networks could be used as a priori information for providing improved predictive power and mechanistic insights into the disease networks. These developments are geared toward explaining a part of the missing heritability, and when combined with individual genomic profiling, such systems medicine approaches may also provide a principled means for tailoring personalized treatment strategies in the future. PMID:23448398

  12. Variability and Predictability of Land-Atmosphere Interactions: Observational and Modeling Studies

    NASA Technical Reports Server (NTRS)

    Roads, John; Oglesby, Robert; Marshall, Susan; Robertson, Franklin R.

    2002-01-01

    The overall goal of this project is to increase our understanding of seasonal to interannual variability and predictability of atmosphere-land interactions. The project objectives are to: 1. Document the low frequency variability in land surface features and associated water and energy cycles from general circulation models (GCMs), observations and reanalysis products. 2. Determine what relatively wet and dry years have in common on a region-by-region basis and then examine the physical mechanisms that may account for a significant portion of the variability. 3. Develop GCM experiments to examine the hypothesis that better knowledge of the land surface enhances long range predictability. This investigation is aimed at evaluating and predicting seasonal to interannual variability for selected regions emphasizing the role of land-atmosphere interactions. Of particular interest are the relationships between large, regional and local scales and how they interact to account for seasonal and interannual variability, including extreme events such as droughts and floods. North and South America, including the Global Energy and Water Cycle Experiment Continental International Project (GEWEX GCIP), MacKenzie, and LBA basins, are currently being emphasized. We plan to ultimately generalize and synthesize to other land regions across the globe, especially those pertinent to other GEWEX projects.

  13. Talking Less during Social Interactions Predicts Enjoyment: A Mobile Sensing Pilot Study

    PubMed Central

    Sandstrom, Gillian M.; Tseng, Vincent Wen-Sheng; Costa, Jean; Okeke, Fabian; Choudhury, Tanzeem; Dunn, Elizabeth W.

    2016-01-01

    Can we predict which conversations are enjoyable without hearing the words that are spoken? A total of 36 participants used a mobile app, My Social Ties, which collected data about 473 conversations that the participants engaged in as they went about their daily lives. We tested whether conversational properties (conversation length, rate of turn taking, proportion of speaking time) and acoustical properties (volume, pitch) could predict enjoyment of a conversation. Surprisingly, people enjoyed their conversations more when they spoke a smaller proportion of the time. This pilot study demonstrates how conversational properties of social interactions can predict psychologically meaningful outcomes, such as how much a person enjoys the conversation. It also illustrates how mobile phones can provide a window into everyday social experiences and well-being. PMID:27438475

  14. Talking Less during Social Interactions Predicts Enjoyment: A Mobile Sensing Pilot Study.

    PubMed

    Sandstrom, Gillian M; Tseng, Vincent Wen-Sheng; Costa, Jean; Okeke, Fabian; Choudhury, Tanzeem; Dunn, Elizabeth W

    2016-01-01

    Can we predict which conversations are enjoyable without hearing the words that are spoken? A total of 36 participants used a mobile app, My Social Ties, which collected data about 473 conversations that the participants engaged in as they went about their daily lives. We tested whether conversational properties (conversation length, rate of turn taking, proportion of speaking time) and acoustical properties (volume, pitch) could predict enjoyment of a conversation. Surprisingly, people enjoyed their conversations more when they spoke a smaller proportion of the time. This pilot study demonstrates how conversational properties of social interactions can predict psychologically meaningful outcomes, such as how much a person enjoys the conversation. It also illustrates how mobile phones can provide a window into everyday social experiences and well-being. PMID:27438475

  15. Interaction of natural products with cell survival and signaling pathways in the biochemical elucidation of drug targets in cancer.

    PubMed

    Qurishi, Yasrib; Hamid, Abid; Majeed, Rabiya; Hussain, Aashiq; Qazi, Asif K; Ahmed, Mudassier; Zargar, Mohmmad Afzal; Singh, Shashank Kumar; Saxena, Ajit Kumar

    2011-08-01

    The use of natural products with therapeutic properties is as ancient as human civilization and for a long time mineral, plant and animal products were the main sources of drugs. Worldwide sales of medicinal plants, crude extracts and finished products amounted to US$15 billion in 1999 and it increased to $23 billion in 2002. More interestingly, the influence of natural products upon anticancer drug discovery and design cannot be underestimated. Approximately 60% of all drugs in clinical trials are either a natural product, compounds derived from natural products or contain pharmacophores derived from active natural products. Thus, even today, in the presence of massive numbers of agents from combinatorial libraries, compounds from natural sources are still in the forefront of cancer chemotherapeutics as sources of active drug types, as well as being involved in drug discovery in diseases such as microbial and parasitic infections and the control of cholesterol/lipids, among other functions.

  16. Children's cortisol and salivary alpha-amylase interact to predict attention bias to threatening stimuli.

    PubMed

    Ursache, Alexandra; Blair, Clancy

    2015-01-01

    Physiological responses to threat occur through both the autonomic nervous system (ANS) and the hypothalamic pituitary adrenal (HPA) axis. Activity in these systems can be measured through salivary alpha-amylase (sAA) and salivary cortisol, respectively. Theoretical work and empirical studies have suggested the importance of examining the coordination of these systems in relation to cognitive functioning and behavior problems. Less is known, however, about whether these systems interactively predict more automatic aspects of attention processing such as attention toward emotionally salient threatening stimuli. We used a dot probe task to assess attention bias toward threatening stimuli in 347 kindergarten children. Cortisol and sAA were assayed from saliva samples collected prior to children's participation in assessments on a subsequent day. Using regression analyses, we examined relations of sAA and cortisol to attention bias. Results indicate that cortisol and sAA interact in predicting attention bias. Higher levels of cortisol predicted greater bias toward threat for children who had high levels of sAA, but predicted greater bias away from threat for children who had low levels of sAA. These results suggest that greater symmetry in HPA and ANS functioning is associated with greater reliance on automatic attention processes in the face of threat.

  17. Computational prediction of heme-binding residues by exploiting residue interaction network.

    PubMed

    Liu, Rong; Hu, Jianjun

    2011-01-01

    Computational identification of heme-binding residues is beneficial for predicting and designing novel heme proteins. Here we proposed a novel method for heme-binding residue prediction by exploiting topological properties of these residues in the residue interaction networks derived from three-dimensional structures. Comprehensive analysis showed that key residues located in heme-binding regions are generally associated with the nodes with higher degree, closeness and betweenness, but lower clustering coefficient in the network. HemeNet, a support vector machine (SVM) based predictor, was developed to identify heme-binding residues by combining topological features with existing sequence and structural features. The results showed that incorporation of network-based features significantly improved the prediction performance. We also compared the residue interaction networks of heme proteins before and after heme binding and found that the topological features can well characterize the heme-binding sites of apo structures as well as those of holo structures, which led to reliable performance improvement as we applied HemeNet to predicting the binding residues of proteins in the heme-free state. HemeNet web server is freely accessible at http://mleg.cse.sc.edu/hemeNet/. PMID:21991319

  18. Finding friends and enemies in an enemies-only network: A graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions

    PubMed Central

    Qi, Yan; Suhail, Yasir; Lin, Yu-yi; Boeke, Jef D.; Bader, Joel S.

    2008-01-01

    The yeast synthetic lethal genetic interaction network contains rich information about underlying pathways and protein complexes as well as new genetic interactions yet to be discovered. We have developed a graph diffusion kernel as a unified framework for inferring complex/pathway membership analogous to “friends” and genetic interactions analogous to “enemies” from the genetic interaction network. When applied to the Saccharomyces cerevisiae synthetic lethal genetic interaction network, we can achieve a precision around 50% with 20% to 50% recall in the genome-wide prediction of new genetic interactions, supported by experimental validation. The kernels show significant improvement over previous best methods for predicting genetic interactions and protein co-complex membership from genetic interaction data. PMID:18832443

  19. Personality interacts with implicit affect to predict performance in analytic versus holistic processing.

    PubMed

    Kazén, Miguel; Kuhl, Julius; Quirin, Markus

    2015-06-01

    Both theoretical approaches and empirical evidence suggest that negative affect fosters analytic processing, whereas positive affect fosters holistic processing, but these effects are inconsistent. We aim to show that (a) differences in affect regulation abilities ("action orientation") and (b) implicit more so than self-reported affect assessment need to be considered to advance our understanding of these processes. Forty participants were asked to verify whether a word was correctly or incorrectly spelled to measure analytic processing, as well as to intuitively assess whether sets of three words were coherent (remote associates task) to measure holistic processing. As expected, implicit but not explicit negative affect interacted with low action orientation ("state orientation") to predict higher d' performance in word spelling, whereas implicit but not explicit positive affect interacted with high action orientation to predict higher d' performance in coherence judgments for word triads. Results are interpreted according to personality systems interaction theory. These findings suggest that affect and affect changes should be measured explicitly and implicitly to investigate affect-cognition interactions. Moreover, they suggest that good affect regulators benefit from positive affect for holistic processing, whereas bad affect regulators benefit from negative affect for analytical processing. PMID:24725069

  20. Proteomics characterization of the cytotoxicity mechanism of ganoderic acid D and computer-automated estimation of the possible drug target network.

    PubMed

    Yue, Qing-Xi; Cao, Zhi-Wei; Guan, Shu-Hong; Liu, Xiao-Hui; Tao, Lin; Wu, Wan-Ying; Li, Yi-Xue; Yang, Peng-Yuan; Liu, Xuan; Guo, De-An

    2008-05-01

    Triterpenes isolated from Ganoderma lucidum could inhibit the growth of numerous cancer cell lines and were thought to be the basis of the anticancer effects of G. lucidum. Ganoderic acid D (GAD) is one of the major components in Ganoderma triterpenes. GAD treatment for 48 h inhibited the proliferation of HeLa human cervical carcinoma cells with an IC(50) value of 17.3 +/- 0.3 microM. Flow cytometric analysis and DNA fragmentation analysis indicated that GAD induced G(2)/M cell cycle arrest and apoptosis. To identify the cellular targets of GAD, two-dimensional gel electrophoresis was performed, and proteins altered in expressional level after GAD exposure of cells were identified by MALDI-TOF MS/MS. The regulation of proteins was also confirmed by Western blotting. The cytotoxic effect of GAD was associated with regulated expression of 21 proteins. Furthermore these possible GAD target-related proteins were evaluated by an in silico drug target searching program, INVDOCK. The INVDOCK analysis results suggested that GAD could bind six isoforms of 14-3-3 protein family, annexin A5, and aminopeptidase B. The direct binding affinity of GAD toward 14-3-3 zeta was confirmed in vitro using surface plasmon resonance biosensor analysis. In addition, the intensive study of functional association among these 21 proteins revealed that 14 of them were closely related in the protein-protein interaction network. They had been found to either interact with each other directly or associate with each other via only one intermediate protein from previous protein-protein interaction experimental results. When the network was expanded to a further interaction outward, all 21 proteins could be included into one network. In this way, the possible network associated with GAD target-related proteins was constructed, and the possible contribution of these proteins to the cytotoxicity of GAD is discussed in this report.

  1. Positive affect predicts avoidance goals in social interaction anxiety: testing a hierarchical model of social goals.

    PubMed

    Trew, Jennifer L; Alden, Lynn E

    2012-01-01

    Models of self-regulation suggest that social goals may contribute to interpersonal and affective difficulties, yet little research has addressed this issue in the context of social anxiety. The present studies evaluated a hierarchical model of approach and avoidance in the context of social interaction anxiety, with affect as a mediating factor in the relationship between motivational tendencies and social goals. This model was refined in one undergraduate sample (N = 186) and cross-validated in a second sample (N = 195). The findings support hierarchical relationships between motivational tendencies, social interaction anxiety, affect, and social goals, with higher positive affect predicting fewer avoidance goals in both samples. Implications for the treatment of social interaction anxiety are discussed. PMID:22489603

  2. Depressive symptoms in early marriage: predictions from relationship confidence and negative marital interaction.

    PubMed

    Whitton, Sarah W; Olmos-Gallo, P Antonio; Stanley, Scott M; Prado, Lydia M; Kline, Galena H; St Peters, Michelle; Markman, Howard J

    2007-06-01

    The authors proposed a model of depressive symptoms in early marriage in which relationship confidence, defined as perceived couple-level efficacy to manage conflicts and maintain a healthy relationship, mediates the effect of negative marital interactions on depressive symptoms. The model was tested in a sample of 139 couples assessed prior to marriage and 1 year later. As predicted, relationship confidence demonstrated simple negative associations with negative marital interaction and depressive symptoms for all participants. Longitudinal path analyses supported the mediational model for women only. In women but not men, negative marital interaction indirectly had an impact on depressive symptoms through the mediator of relationship confidence. Findings suggest that relationship confidence may be important to understanding links between marital distress and depressive symptoms, especially in women.

  3. Prediction of thermodynamic instabilities of protein solutions from simple protein-protein interactions

    NASA Astrophysics Data System (ADS)

    D'Agostino, Tommaso; Solana, José Ramón; Emanuele, Antonio

    2013-10-01

    Statistical thermodynamics of protein solutions is often studied in terms of simple, microscopic models of particles interacting via pairwise potentials. Such modelling can reproduce the short range structure of protein solutions at equilibrium and predict thermodynamics instabilities of these systems. We introduce a square well model of effective protein-protein interaction that embeds the solvent’s action. We modify an existing model [45] by considering a well depth having an explicit dependence on temperature, i.e. an explicit free energy character, thus encompassing the statistically relevant configurations of solvent molecules around proteins. We choose protein solutions exhibiting demixing upon temperature decrease (lysozyme, enthalpy driven) and upon temperature increase (haemoglobin, entropy driven). We obtain satisfactory fits of spinodal curves for both the two proteins without adding any mean field term, thus extending the validity of the original model. Our results underline the solvent role in modulating or stretching the interaction potential.

  4. Qualities of Peer Relations on Social Networking Websites: Predictions from Negative Mother-Teen Interactions.

    PubMed

    Szwedo, David E; Mikami, Amori Yee; Allen, Joseph P

    2011-09-01

    This study examined associations between characteristics of teenagers' relationships with their mothers and their later socializing behavior and peer relationship quality online. At age 13, teenagers and their mothers participated in an interaction in which mothers' and adolescents' behavior undermining autonomy and relatedness was observed, and indicators of teens' depressive symptoms and social anxiety were assessed. At age 20, youth self-reported on their online behaviors, youths' social networking webpages were observationally coded to assess peer relationship quality online, and symptoms of depression and social anxiety were reassessed. Results suggested that problematic mother-teen relationships were predictive of youths' later preference for online communication and greater likelihood of forming a friendship with someone met online, yet poorer quality in online relationships. Findings are discussed within a developmental framework suggesting the importance of considering youths' family interactions during early adolescence as predictors of future online socializing behavior and online interactions with peers. PMID:21860584

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

    PubMed Central

    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

  6. Data-Driven Prediction and Design of bZIP Coiled-Coil Interactions

    PubMed Central

    Potapov, Vladimir; Kaplan, Jenifer B.; Keating, Amy E.

    2015-01-01

    Selective dimerization of the basic-region leucine-zipper (bZIP) transcription factors presents a vivid example of how a high degree of interaction specificity can be achieved within a family of structurally similar proteins. The coiled-coil motif that mediates homo- or hetero-dimerization of the bZIP proteins has been intensively studied, and a variety of methods have been proposed to predict these interactions from sequence data. In this work, we used a large quantitative set of 4,549 bZIP coiled-coil interactions to develop a predictive model that exploits knowledge of structurally conserved residue-residue interactions in the coiled-coil motif. Our model, which expresses interaction energies as a sum of interpretable residue-pair and triplet terms, achieves a correlation with experimental binding free energies of R = 0.68 and significantly out-performs other scoring functions. To use our model in protein design applications, we devised a strategy in which synthetic peptides are built by assembling 7-residue native-protein heptad modules into new combinations. An integer linear program was used to find the optimal combination of heptads to bind selectively to a target human bZIP coiled coil, but not to target paralogs. Using this approach, we designed peptides to interact with the bZIP domains from human JUN, XBP1, ATF4 and ATF5. Testing more than 132 candidate protein complexes using a fluorescence resonance energy transfer assay confirmed the formation of tight and selective heterodimers between the designed peptides and their targets. This approach can be used to make inhibitors of native proteins, or to develop novel peptides for applications in synthetic biology or nanotechnology. PMID:25695764

  7. Development of a protein-ligand-binding site prediction method based on interaction energy and sequence conservation.

    PubMed

    Tsujikawa, Hiroto; Sato, Kenta; Wei, Cao; Saad, Gul; Sumikoshi, Kazuya; Nakamura, Shugo; Terada, Tohru; Shimizu, Kentaro

    2016-09-01

    We present a new method for predicting protein-ligand-binding sites based on protein three-dimensional structure and amino acid conservation. This method involves calculation of the van der Waals interaction energy between a protein and many probes placed on the protein surface and subsequent clustering of the probes with low interaction energies to identify the most energetically favorable locus. In addition, it uses amino acid conservation among homologous proteins. Ligand-binding sites were predicted by combining the interaction energy and the amino acid conservation score. The performance of our prediction method was evaluated using a non-redundant dataset of 348 ligand-bound and ligand-unbound protein structure pairs, constructed by filtering entries in a ligand-binding site structure database, LigASite. Ligand-bound structure prediction (bound prediction) indicated that 74.0 % of predicted ligand-binding sites overlapped with real ligand-binding sites by over 25 % of their volume. Ligand-unbound structure prediction (unbound prediction) indicated that 73.9 % of predicted ligand-binding residues overlapped with real ligand-binding residues. The amino acid conservation score improved the average prediction accuracy by 17.0 and 17.6 points for the bound and unbound predictions, respectively. These results demonstrate the effectiveness of the combined use of the interaction energy and amino acid conservation in the ligand-binding site prediction. PMID:27400687

  8. PiDNA: Predicting protein-DNA interactions with structural models.

    PubMed

    Lin, Chih-Kang; Chen, Chien-Yu

    2013-07-01

    Predicting binding sites of a transcription factor in the genome is an important, but challenging, issue in studying gene regulation. In the past decade, a large number of protein-DNA co-crystallized structures available in the Protein Data Bank have facilitated the understanding of interacting mechanisms between transcription factors and their binding sites. Recent studies have shown that both physics-based and knowledge-based potential functions can be applied to protein-DNA complex structures to deliver position weight matrices (PWMs) that are consistent with the experimental data. To further use the available structural models, the proposed Web server, PiDNA, aims at first constructing reliable PWMs by applying an atomic-level knowledge-based scoring function on numerous in silico mutated complex structures, and then using the PWM constructed by the structure models with small energy changes to predict the interaction between proteins and DNA sequences. With PiDNA, the users can easily predict the relative preference of all the DNA sequences with limited mutations from the native sequence co-crystallized in the model in a single run. More predictions on sequences with unlimited mutations can be realized by additional requests or file uploading. Three types of information can be downloaded after prediction: (i) the ranked list of mutated sequences, (ii) the PWM constructed by the favourable mutated structures, and (iii) any mutated protein-DNA complex structure models specified by the user. This study first shows that the constructed PWMs are similar to the annotated PWMs collected from databases or literature. Second, the prediction accuracy of PiDNA in detecting relatively high-specificity sites is evaluated by comparing the ranked lists against in vitro experiments from protein-binding microarrays. Finally, PiDNA is shown to be able to select the experimentally validated binding sites from 10,000 random sites with high accuracy. With PiDNA, the users can

  9. Proteins and Their Interacting Partners: An Introduction to Protein–Ligand Binding Site Prediction Methods

    PubMed Central

    Roche, Daniel Barry; Brackenridge, Danielle Allison; McGuffin, Liam James

    2015-01-01

    Elucidating the biological and biochemical roles of proteins, and subsequently determining their interacting partners, can be difficult and time consuming using in vitro and/or in vivo methods, and consequently the majority of newly sequenced proteins will have unknown structures and functions. However, in silico methods for predicting protein–ligand binding sites and protein biochemical functions offer an alternative practical solution. The characterisation of protein–ligand binding sites is essential for investigating new functional roles, which can impact the major biological research spheres of health, food, and energy security. In this review we discuss the role in silico methods play in 3D modelling of protein–ligand binding sites, along with their role in predicting biochemical functionality. In addition, we describe in detail some of the key alternative in silico prediction approaches that are available, as well as discussing the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated Model EvaluatiOn (CAMEO) projects, and their impact on developments in the field. Furthermore, we discuss the importance of protein function prediction methods for tackling 21st century problems. PMID:26694353

  10. Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods.

    PubMed

    Roche, Daniel Barry; Brackenridge, Danielle Allison; McGuffin, Liam James

    2015-12-15

    Elucidating the biological and biochemical roles of proteins, and subsequently determining their interacting partners, can be difficult and time consuming using in vitro and/or in vivo methods, and consequently the majority of newly sequenced proteins will have unknown structures and functions. However, in silico methods for predicting protein-ligand binding sites and protein biochemical functions offer an alternative practical solution. The characterisation of protein-ligand binding sites is essential for investigating new functional roles, which can impact the major biological research spheres of health, food, and energy security. In this review we discuss the role in silico methods play in 3D modelling of protein-ligand binding sites, along with their role in predicting biochemical functionality. In addition, we describe in detail some of the key alternative in silico prediction approaches that are available, as well as discussing the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated Model EvaluatiOn (CAMEO) projects, and their impact on developments in the field. Furthermore, we discuss the importance of protein function prediction methods for tackling 21st century problems.

  11. Implication from the predicted docked interaction of sigma H and exploration of its interaction with RNA polymerase in Mycobacterium tuberculosis.

    PubMed

    Gupta, Aayatti Mallick; Bhattacharya, Simanti; Bagchi, Angshuman; Mandal, Sukhendu

    2015-01-01

    M. tuberculosis is adapted to remain active in the extreme environmental condition due to the presence of atypical sigma factors commonly called extra cytoplasmic function (ECF) sigma factors. Among the 13 sigma factors of M. tuberculosis, 10 are regarded as the ECF sigma factor that exerts their attributes in various stress response. Therefore it is of interest to describe the structural prediction of one of the ECF sigma factors, sigma H (SigH), involved in oxidative and heat stress having interaction with the β׳ subunit of M. tuberculosis. RNA polymerase (Mtb-RNAP). The model of Mtb-SigH was build using the commercial package of Discovery Studio version 2.5 from Accelerys (San Diego, CA, USA) containing the inbuilt MODELER module and that of β׳ subunit of Mtb-RNAP using Phyre Server. Further, the protein models were docked using the fully automated web tool ClusPro (cluspro.bu.edu/login.php). Mtb-SigH is a triple helical structure having a putative DNA-binding site and the β׳ subunit of MtbRNAP consists of 18-beta sheets and 22 helices. The SigH-Mtb-RNAP β׳ interaction studies showed that Arg26, Gln19 andAsp18, residues of SigH protein are involved in binding with Arg137, Gln140, Arg152, Asn133 and Asp144 of β׳ subunit of Mtb-RNAP. The predicted model helps to explore the molecular mechanism in the control of gene regulation with a novel unique target for potential new generation inhibitor.

  12. Computational prediction of molecular pathogen-host interactions based on dual transcriptome data

    PubMed Central

    Schulze, Sylvie; Henkel, Sebastian G.; Driesch, Dominik; Guthke, Reinhard; Linde, Jörg

    2015-01-01

    Inference of inter-species gene regulatory networks based on gene expression data is an important computational method to predict pathogen-host interactions (PHIs). Both the experimental setup and the nature of PHIs exhibit certain characteristics. First, besides an environmental change, the battle between pathogen and host leads to a constantly changing environment and thus complex gene expression patterns. Second, there might be a delay until one of the organisms reacts. Third, toward later time points only one organism may survive leading to missing gene expression data of the other organism. Here, we account for PHI characteristics by extending NetGenerator, a network inference tool that predicts gene regulatory networks from gene expression time series data. We tested multiple modeling scenarios regarding the stimuli functions of the interaction network based on a benchmark example. We show that modeling perturbation of a PHI network by multiple stimuli better represents the underlying biological phenomena. Furthermore, we utilized the benchmark example to test the influence of missing data points on the inference performance. Our results suggest that PHI network inference with missing data is possible, but we recommend to provide complete time series data. Finally, we extended the NetGenerator tool to incorporate gene- and time point specific variances, because complex PHIs may lead to high variance in expression data. Sample variances are directly considered in the objective function of NetGenerator and indirectly by testing the robustness of interactions based on variance dependent disturbance of gene expression values. We evaluated the method of variance incorporation on dual RNA sequencing (RNA-Seq) data of Mus musculus dendritic cells incubated with Candida albicans and proofed our method by predicting previously verified PHIs as robust interactions. PMID:25705211

  13. Computational prediction of molecular pathogen-host interactions based on dual transcriptome data.

    PubMed

    Schulze, Sylvie; Henkel, Sebastian G; Driesch, Dominik; Guthke, Reinhard; Linde, Jörg

    2015-01-01

    Inference of inter-species gene regulatory networks based on gene expression data is an important computational method to predict pathogen-host interactions (PHIs). Both the experimental setup and the nature of PHIs exhibit certain characteristics. First, besides an environmental change, the battle between pathogen and host leads to a constantly changing environment and thus complex gene expression patterns. Second, there might be a delay until one of the organisms reacts. Third, toward later time points only one organism may survive leading to missing gene expression data of the other organism. Here, we account for PHI characteristics by extending NetGenerator, a network inference tool that predicts gene regulatory networks from gene expression time series data. We tested multiple modeling scenarios regarding the stimuli functions of the interaction network based on a benchmark example. We show that modeling perturbation of a PHI network by multiple stimuli better represents the underlying biological phenomena. Furthermore, we utilized the benchmark example to test the influence of missing data points on the inference performance. Our results suggest that PHI network inference with missing data is possible, but we recommend to provide complete time series data. Finally, we extended the NetGenerator tool to incorporate gene- and time point specific variances, because complex PHIs may lead to high variance in expression data. Sample variances are directly considered in the objective function of NetGenerator and indirectly by testing the robustness of interactions based on variance dependent disturbance of gene expression values. We evaluated the method of variance incorporation on dual RNA sequencing (RNA-Seq) data of Mus musculus dendritic cells incubated with Candida albicans and proofed our method by predicting previously verified PHIs as robust interactions.

  14. Prediction of Drug Clearance and Drug-Drug Interactions in Microscale Cultures of Human Hepatocytes.

    PubMed

    Lin, Christine; Shi, Julianne; Moore, Amanda; Khetani, Salman R

    2016-01-01

    Accurate prediction of in vivo hepatic drug clearance using in vitro assays is important to properly estimate clinical dosing regimens. Clearance of low-turnover compounds is especially difficult to predict using short-lived suspensions of unpooled primary human hepatocytes (PHHs) and functionally declining PHH monolayers. Micropatterned cocultures (MPCCs) of PHHs and 3T3-J2 fibroblasts have been shown previously to display major liver functions for several weeks in vitro. In this study, we first characterized long-term activities of major cytochrome P450 enzymes in MPCCs created from unpooled cryopreserved PHH donors. MPCCs were then used to predict the clearance of 26 drugs that exhibit a wide range of turnover rates in vivo (0.05-19.5 ml/min per kilogram). MPCCs predicted 73, 92, and 96% of drug clearance values for all tested drugs within 2-fold, 3-fold, and 4-fold of in vivo values, respectively. There was good correlation (R(2) = 0.94, slope = 1.05) of predictions between the two PHH donors. On the other hand, suspension hepatocytes and conventional monolayers created from the same donor had significantly reduced predictive capacity (i.e., 30-50% clearance values within 4-fold of in vivo), and were not able to metabolize several drugs. Finally, we modulated drug clearance in MPCCs by inducing or inhibiting P450s. Rifampin-mediated CYP3A4 induction increased midazolam clearance by 73%, while CYP3A4 inhibition with ritonavir decreased midazolam clearance by 79%. Similarly, quinidine-mediated CYP2D6 inhibition reduced clearance of dextromethorphan and desipramine by 71 and 22%, respectively. In conclusion, MPCCs created using cryopreserved unpooled PHHs can be used for drug clearance predictions and to model drug-drug interactions. PMID:26452722

  15. Prediction of protein-protein interactions with clustered amino acids and weighted sparse representation.

    PubMed

    Huang, Qiaoying; You, Zhuhong; Zhang, Xiaofeng; Zhou, Yong

    2015-01-01

    With the completion of the Human Genome Project, bioscience has entered into the era of the genome and proteome. Therefore, protein-protein interactions (PPIs) research is becoming more and more important. Life activities and the protein-protein interactions are inseparable, such as DNA synthesis, gene transcription activation, protein translation, etc. Though many methods based on biological experiments and machine learning have been proposed, they all spent a long time to learn and obtained an imprecise accuracy. How to efficiently and accurately predict PPIs is still a big challenge. To take up such a challenge, we developed a new predictor by incorporating the reduced amino acid alphabet (RAAA) information into the general form of pseudo-amino acid composition (PseAAC) and with the weighted sparse representation-based classification (WSRC). The remarkable advantages of introducing the reduced amino acid alphabet is being able to avoid the notorious dimensionality disaster or overfitting problem in statistical prediction. Additionally, experiments have proven that our method achieved good performance in both a low- and high-dimensional feature space. Among all of the experiments performed on the PPIs data of Saccharomyces cerevisiae, the best one achieved 90.91% accuracy, 94.17% sensitivity, 87.22% precision and a 83.43% Matthews correlation coefficient (MCC) value. In order to evaluate the prediction ability of our method, extensive experiments are performed to compare with the state-of-the-art technique, support vector machine (SVM). The achieved results show that the proposed approach is very promising for predicting PPIs, and it can be a helpful supplement for PPIs prediction. PMID:25984606

  16. Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model.

    PubMed

    Lopez-Cruz, Marco; Crossa, Jose; Bonnett, David; Dreisigacker, Susanne; Poland, Jesse; Jannink, Jean-Luc; Singh, Ravi P; Autrique, Enrique; de los Campos, Gustavo

    2015-04-01

    Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype × environment interaction(G×E). Several authors have proposed extensions of the single-environment GS model that accommodate G×E using either covariance functions or environmental covariates. In this study, we model G×E using a marker × environment interaction (M×E) GS model; the approach is conceptually simple and can be implemented with existing GS software. We discuss how the model can be implemented by using an explicit regression of phenotypes on markers or using co-variance structures (a genomic best linear unbiased prediction-type model). We used the M×E model to analyze three CIMMYT wheat data sets (W1, W2, and W3), where more than 1000 lines were genotyped using genotyping-by-sequencing and evaluated at CIMMYT's research station in Ciudad Obregon, Mexico, under simulated environmental conditions that covered different irrigation levels, sowing dates and planting systems. We compared the M×E model with a stratified (i.e., within-environment) analysis and with a standard (across-environment) GS model that assumes that effects are constant across environments (i.e., ignoring G×E). The prediction accuracy of the M×E model was substantially greater of that of an across-environment analysis that ignores G×E. Depending on the prediction problem, the M×E model had either similar or greater levels of prediction accuracy than the stratified analyses. The M×E model decomposes marker effects and genomic values into components that are stable across environments (main effects) and others that are environment-specific (interactions). Therefore, in principle, the interaction model could shed light over which variants have effects that are stable across environments and which ones are responsible for G×E. The data set and the scripts required to reproduce the analysis are

  17. Increased Prediction Accuracy in Wheat Breeding Trials Using a Marker × Environment Interaction Genomic Selection Model

    PubMed Central

    Lopez-Cruz, Marco; Crossa, Jose; Bonnett, David; Dreisigacker, Susanne; Poland, Jesse; Jannink, Jean-Luc; Singh, Ravi P.; Autrique, Enrique; de los Campos, Gustavo

    2015-01-01

    Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype × environment interaction(G×E). Several authors have proposed extensions of the single-environment GS model that accommodate G×E using either covariance functions or environmental covariates. In this study, we model G×E using a marker × environment interaction (M×E) GS model; the approach is conceptually simple and can be implemented with existing GS software. We discuss how the model can be implemented by using an explicit regression of phenotypes on markers or using co-variance structures (a genomic best linear unbiased prediction-type model). We used the M×E model to analyze three CIMMYT wheat data sets (W1, W2, and W3), where more than 1000 lines were genotyped using genotyping-by-sequencing and evaluated at CIMMYT’s research station in Ciudad Obregon, Mexico, under simulated environmental conditions that covered different irrigation levels, sowing dates and planting systems. We compared the M×E model with a stratified (i.e., within-environment) analysis and with a standard (across-environment) GS model that assumes that effects are constant across environments (i.e., ignoring G×E). The prediction accuracy of the M×E model was substantially greater of that of an across-environment analysis that ignores G×E. Depending on the prediction problem, the M×E model had either similar or greater levels of prediction accuracy than the stratified analyses. The M×E model decomposes marker effects and genomic values into components that are stable across environments (main effects) and others that are environment-specific (interactions). Therefore, in principle, the interaction model could shed light over which variants have effects that are stable across environments and which ones are responsible for G×E. The data set and the scripts required to reproduce the analysis

  18. The influence of source-receiver interaction on the numerical prediction of railway induced vibrations

    NASA Astrophysics Data System (ADS)

    Coulier, P.; Lombaert, G.; Degrande, G.

    2014-06-01

    The numerical prediction of vibrations in buildings due to railway traffic is a complicated problem where wave propagation in the soil couples the source (railway tunnel or track) and the receiver (building). This through-soil coupling is often neglected in state-of-the-art numerical models in order to reduce the computational cost. In this paper, the effect of this simplifying assumption on the accuracy of numerical predictions is investigated. A coupled finite element-boundary element methodology is employed to analyze the interaction between a building and a railway tunnel at depth or a ballasted track at the surface of a homogeneous halfspace, respectively. Three different soil types are considered. It is demonstrated that the dynamic axle loads can be calculated with reasonable accuracy using an uncoupled strategy in which through-soil coupling is disregarded. If the transfer functions from source to receiver are considered, however, large local variations in terms of vibration insertion gain are induced by source-receiver interaction, reaching up to 10 dB and higher, although the overall wave field is only moderately affected. A global quantification of the significance of through-soil coupling is made, based on the mean vibrational energy entering a building. This approach allows assessing the common assumption in seismic engineering that source-receiver interaction can be neglected if the distance between source and receiver is sufficiently large compared to the wavelength of waves in the soil. It is observed that the interaction between a source at depth and a receiver mainly affects the power flow distribution if the distance between source and receiver is smaller than the dilatational wavelength in the soil. Interaction effects for a railway track at grade are observed if the source-receiver distance is smaller than six Rayleigh wavelengths. A similar trend is revealed if the passage of a freight train is considered. The overall influence of dynamic

  19. Harsh Parenting and Fearfulness in Toddlerhood Interact to Predict Amplitudes of Preschool Error-Related Negativity

    PubMed Central

    Brooker, Rebecca J.; Buss, Kristin A.

    2014-01-01

    Temperamentally fearful children are at increased risk for the development of anxiety problems relative to less-fearful children. This risk is even greater when early environments include high levels of harsh parenting behaviors. However, the mechanisms by which harsh parenting may impact fearful children’s risk for anxiety problems are largely unknown. Recent neuroscience work has suggested that punishment is associated with exaggerated error-related negativity (ERN), an event-related potential linked to performance monitoring, even after the threat of punishment is removed. In the current study, we examined the possibility that harsh parenting interacts with fearfulness, impacting anxiety risk via neural processes of performance monitoring. We found that greater fearfulness and harsher parenting at 2 years of age predicted greater fearfulness and greater ERN amplitudes at age 4. Supporting the role of cognitive processes in this association, greater fearfulness and harsher parenting also predicted less efficient neural processing during preschool. This study provides initial evidence that performance monitoring may be a candidate process by which early parenting interacts with fearfulness to predict risk for anxiety problems. PMID:24721466

  20. Harsh parenting and fearfulness in toddlerhood interact to predict amplitudes of preschool error-related negativity.

    PubMed

    Brooker, Rebecca J; Buss, Kristin A

    2014-07-01

    Temperamentally fearful children are at increased risk for the development of anxiety problems relative to less-fearful children. This risk is even greater when early environments include high levels of harsh parenting behaviors. However, the mechanisms by which harsh parenting may impact fearful children's risk for anxiety problems are largely unknown. Recent neuroscience work has suggested that punishment is associated with exaggerated error-related negativity (ERN), an event-related potential linked to performance monitoring, even after the threat of punishment is removed. In the current study, we examined the possibility that harsh parenting interacts with fearfulness, impacting anxiety risk via neural processes of performance monitoring. We found that greater fearfulness and harsher parenting at 2 years of age predicted greater fearfulness and greater ERN amplitudes at age 4. Supporting the role of cognitive processes in this association, greater fearfulness and harsher parenting also predicted less efficient neural processing during preschool. This study provides initial evidence that performance monitoring may be a candidate process by which early parenting interacts with fearfulness to predict risk for anxiety problems.

  1. Needles: Toward Large-Scale Genomic Prediction with Marker-by-Environment Interaction.

    PubMed

    De Coninck, Arne; De Baets, Bernard; Kourounis, Drosos; Verbosio, Fabio; Schenk, Olaf; Maenhout, Steven; Fostier, Jan

    2016-05-01

    Genomic prediction relies on genotypic marker information to predict the agronomic performance of future hybrid breeds based on trial records. Because the effect of markers may vary substantially under the influence of different environmental conditions, marker-by-environment interaction effects have to be taken into account. However, this may lead to a dramatic increase in the computational resources needed for analyzing large-scale trial data. A high-performance computing solution, called Needles, is presented for handling such data sets. Needles is tailored to the particular properties of the underlying algebraic framework by exploiting a sparse matrix formalism where suited and by utilizing distributed computing techniques to enable the use of a dedicated computing cluster. It is demonstrated that large-scale analyses can be performed within reasonable time frames with this framework. Moreover, by analyzing simulated trial data, it is shown that the effects of markers with a high environmental interaction can be predicted more accurately when more records per environment are available in the training data. The availability of such data and their analysis with Needles also may lead to the discovery of highly contributing QTL in specific environmental conditions. Such a framework thus opens the path for plant breeders to select crops based on these QTL, resulting in hybrid lines with optimized agronomic performance in specific environmental conditions.

  2. Characterization of EPPIN's semenogelin I binding site: a contraceptive drug target.

    PubMed

    Silva, Erick J R; Hamil, Katherine G; Richardson, Richard T; O'Rand, Michael G

    2012-09-01

    Epididymal protease inhibitor (EPPIN) is found on the surface of spermatozoa and works as a central hub for a sperm surface protein complex (EPPIN protein complex [EPC]) that inhibits sperm motility on the binding of semenogelin I (SEMG1) during ejaculation. Here, we identify EPPIN's amino acids involved in the interactions within the EPC and demonstrate that EPPIN's sequence C102-P133 contains the major binding site for SEMG1. Within the same region, the sequence F117-P133 binds the EPC-associated protein lactotransferrin (LTF). We show that residues Cys102, Tyr107, and Phe117 in the EPPIN C-terminus are required for SEMG1 binding. Additionally, residues Tyr107 and Phe117 are critically involved in the interaction between EPPIN and LTF. Our findings demonstrate that EPPIN is a key player in the protein-protein interactions within the EPC. Target identification is an important step toward the development of a novel male contraceptive, and the functionality of EPPIN's residues Cys102, Tyr107, and Phe117 offers novel opportunities for contraceptive compounds that inhibit sperm motility by targeting this region of the molecule.

  3. Alanine-scanning mutagenesis of the predicted rRNA-binding domain of ErmC' redefines the substrate-binding site and suggests a model for protein-RNA interactions.

    PubMed

    Maravić, Gordana; Bujnicki, Janusz M; Feder, Marcin; Pongor, Sándor; Flögel, Mirna

    2003-08-15

    The Erm family of adenine-N(6) methyltransferases (MTases) is responsible for the development of resistance to macrolide-lincosamide-streptogramin B antibiotics through the methylation of 23S ribosomal RNA. Hence, these proteins are important potential drug targets. Despite the availability of the NMR and crystal structures of two members of the family (ErmAM and ErmC', respectively) and extensive studies on the RNA substrate, the substrate-binding site and the amino acids involved in RNA recognition by the Erm MTases remain unknown. It has been proposed that the small C-terminal domain functions as a target-binding module, but this prediction has not been tested experimentally. We have undertaken structure-based mutational analysis of 13 charged or polar residues located on the predicted rRNA-binding surface of ErmC' with the aim to identify the area of protein-RNA interactions. The results of in vivo and in vitro analyses of mutant protein suggest that the key RNA-binding residues are located not in the small domain, but in the large catalytic domain, facing the cleft between the two domains. Based on the mutagenesis data, a preliminary three-dimensional model of ErmC' complexed with the minimal substrate was constructed. The identification of the RNA-binding site of ErmC' may be useful for structure-based design of novel drugs that do not necessarily bind to the cofactor-binding site common to many S-adenosyl-L- methionine-dependent MTases, but specifically block the substrate-binding site of MTases from the Erm family. PMID:12907737

  4. Alanine-scanning mutagenesis of the predicted rRNA-binding domain of ErmC' redefines the substrate-binding site and suggests a model for protein-RNA interactions.

    PubMed

    Maravić, Gordana; Bujnicki, Janusz M; Feder, Marcin; Pongor, Sándor; Flögel, Mirna

    2003-08-15

    The Erm family of adenine-N(6) methyltransferases (MTases) is responsible for the development of resistance to macrolide-lincosamide-streptogramin B antibiotics through the methylation of 23S ribosomal RNA. Hence, these proteins are important potential drug targets. Despite the availability of the NMR and crystal structures of two members of the family (ErmAM and ErmC', respectively) and extensive studies on the RNA substrate, the substrate-binding site and the amino acids involved in RNA recognition by the Erm MTases remain unknown. It has been proposed that the small C-terminal domain functions as a target-binding module, but this prediction has not been tested experimentally. We have undertaken structure-based mutational analysis of 13 charged or polar residues located on the predicted rRNA-binding surface of ErmC' with the aim to identify the area of protein-RNA interactions. The results of in vivo and in vitro analyses of mutant protein suggest that the key RNA-binding residues are located not in the small domain, but in the large catalytic domain, facing the cleft between the two domains. Based on the mutagenesis data, a preliminary three-dimensional model of ErmC' complexed with the minimal substrate was constructed. The identification of the RNA-binding site of ErmC' may be useful for structure-based design of novel drugs that do not necessarily bind to the cofactor-binding site common to many S-adenosyl-L- methionine-dependent MTases, but specifically block the substrate-binding site of MTases from the Erm family.

  5. Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions

    PubMed Central

    Tewarie, P.; Bright, M.G.; Hillebrand, A.; Robson, S.E.; Gascoyne, L.E.; Morris, P.G.; Meier, J.; Van Mieghem, P.; Brookes, M.J.

    2016-01-01

    Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typically measured using haemodynamic signals) and electrophysiology has been explored using functional Magnetic Resonance Imaging (fMRI) and magnetoencephalography (MEG). Significant progress has been made, with similar spatial structure observable in both modalities. However, there is a pressing need to understand this relationship beyond simple visual similarity of RSN patterns. Here, we introduce a mathematical model to predict fMRI-based RSNs using MEG. Our unique model, based upon a multivariate Taylor series, incorporates both phase and amplitude based MEG connectivity metrics, as well as linear and non-linear interactions within and between neural oscillations measured in multiple frequency bands. We show that including non-linear interactions, multiple frequency bands and cross-frequency terms significantly improves fMRI network prediction. This shows that fMRI connectivity is not only the result of direct electrophysiological connections, but is also driven by the overlap of connectivity profiles between separate regions. Our results indicate that a complete understanding of the electrophysiological basis of RSNs goes beyond simple frequency-specific analysis, and further exploration of non-linear and cross-frequency interactions will shed new light on distributed network connectivity, and its perturbation in pathology. PMID:26827811

  6. Predicting metabolic pathways of small molecules and enzymes based on interaction information of chemicals and proteins.

    PubMed

    Gao, Yu-Fei; Chen, Lei; Cai, Yu-Dong; Feng, Kai-Yan; Huang, Tao; Jiang, Yang

    2012-01-01

    Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.

  7. Predicting relative toxicity and interactions of divalent metal ions: Microtox{reg_sign} bioluminescence assay

    SciTech Connect

    Newman, M.C.; McCloskey, J.T.

    1996-03-01

    Both relative toxicity and interactions between paired metal ions were predicted with least-squares linear regression and various ion characteristics. Microtox{reg_sign} 15 min EC50s (expressed as free ion) for Ca(II), Cd(II), Cu(II), Hg(II), Mg(II), Mn(II), Ni(II), Pb(II), and Zn(II) were most effectively modeled with the constant for the first hydrolysis (K{sub H} for M{sup n+} + H{sub 2}O {yields} MOH{sup a{minus}1} + H{sup +}) although other ion characteristics were also significant in regression models. The {vert_bar}log K{sub H}{vert_bar} is correlated with metal ion affinity to intermediate ligands such as many biochemical functional groups with O donor atoms. Further, ordination of metals according to ion characteristics, e.g., {vert_bar}log K{sub H}{vert_bar} facilitated prediction of paired metal interactions. Pairing metals with strong tendencies to complex with intermediate or soft ligands such as those with O or S donor atoms resulted in strong interactions.

  8. Prediction of allosteric sites and mediating interactions through bond-to-bond propensities

    NASA Astrophysics Data System (ADS)

    Amor, B. R. C.; Schaub, M. T.; Yaliraki, S. N.; Barahona, M.

    2016-08-01

    Allostery is a fundamental mechanism of biological regulation, in which binding of a molecule at a distant location affects the active site of a protein. Allosteric sites provide targets to fine-tune protein activity, yet we lack computational methodologies to predict them. Here we present an efficient graph-theoretical framework to reveal allosteric interactions (atoms and communication pathways strongly coupled to the active site) without a priori information of their location. Using an atomistic graph with energy-weighted covalent and weak bonds, we define a bond-to-bond propensity quantifying the non-local effect of instantaneous bond fluctuations propagating through the protein. Significant interactions are then identified using quantile regression. We exemplify our method with three biologically important proteins: caspase-1, CheY, and h-Ras, correctly predicting key allosteric interactions, whose significance is additionally confirmed against a reference set of 100 proteins. The almost-linear scaling of our method renders it suitable for high-throughput searches for candidate allosteric sites.

  9. Prediction of allosteric sites and mediating interactions through bond-to-bond propensities

    PubMed Central

    Amor, B. R. C.; Schaub, M. T.; Yaliraki, S. N.; Barahona, M.

    2016-01-01

    Allostery is a fundamental mechanism of biological regulation, in which binding of a molecule at a distant location affects the active site of a protein. Allosteric sites provide targets to fine-tune protein activity, yet we lack computational methodologies to predict them. Here we present an efficient graph-theoretical framework to reveal allosteric interactions (atoms and communication pathways strongly coupled to the active site) without a priori information of their location. Using an atomistic graph with energy-weighted covalent and weak bonds, we define a bond-to-bond propensity quantifying the non-local effect of instantaneous bond fluctuations propagating through the protein. Significant interactions are then identified using quantile regression. We exemplify our method with three biologically important proteins: caspase-1, CheY, and h-Ras, correctly predicting key allosteric interactions, whose significance is additionally confirmed against a reference set of 100 proteins. The almost-linear scaling of our method renders it suitable for high-throughput searches for candidate allosteric sites. PMID:27561351

  10. Predicting first onset of depression in young girls: Interaction of diurnal cortisol and negative life events.

    PubMed

    LeMoult, Joelle; Ordaz, Sarah J; Kircanski, Katharina; Singh, Manpreet K; Gotlib, Ian H

    2015-11-01

    Interactions between biological vulnerability and environmental adversity are central to the pathophysiology of depression. Given evidence that the hypothalamic-pituitary-adrenal (HPA) axis influences biological responses to environmental events, in the current longitudinal study the authors examined HPA-axis functioning, negative life events, and their interaction as predictors of the first onset of depression. At baseline, girls ages 9 to 14 years provided saliva samples to assess levels of diurnal cortisol production, quantified by total cortisol production (area under the curve with respect to ground; AUCg) and the cortisol awakening response (CAR). The authors then followed these participants until they reached age 18 in order to assess their subsequent experience of negative life events and the onset of a depressive episode. They found that the influence of negative life events on the subsequent onset of depression depended on HPA-axis functioning at baseline. Specifically, negative life events predicted the onset of depression in girls with higher levels of AUCg, but not in girls with lower levels of AUCg. In contrast, CAR did not predict the onset of depression either alone or in interaction with negative life events. These findings suggest that elevated total cortisol production in daily life potentiates susceptibility to environmental adversity and signals the need for early intervention. PMID:26595472

  11. Predicting First Onset of Depression in Young Girls: Interaction of Diurnal Cortisol and Negative Life Events

    PubMed Central

    LeMoult, Joelle; Ordaz, Sarah J.; Kircanski, Katharina; Singh, Manpreet K.; Gotlib, Ian H.

    2015-01-01

    Interactions between biological vulnerability and environmental adversity are central to the pathophysiology of depression. Given evidence that the hypothalamic-pituitary-adrenal (HPA) axis influences biological responses to environmental events, in the current longitudinal study we examined HPA-axis functioning, negative life events, and their interaction as predictors of the first onset of depression. At baseline, girls ages 9 to 14 years provided saliva samples to assess levels of diurnal cortisol production, quantified by total cortisol production (area under the curve with respect to ground; AUCg) and the cortisol awakening response (CAR). We then followed these participants until they reached age 18 in order to assess their subsequent experience of negative life events and the onset of a depressive episode. We found that the influence of negative life events on the subsequent onset of depression depended on HPA-axis functioning at baseline. Specifically, negative life events predicted the onset of depression in girls with higher levels AUCg, but not in girls with lower levels of AUCg. In contrast, CAR did not predict the onset of depression either alone or in interaction with negative life events. These findings suggest that elevated total cortisol production in daily life potentiates susceptibility to environmental adversity and signals the need for early intervention. PMID:26595472

  12. Dengue serotype immune-interactions and their consequences for vaccine impact predictions.

    PubMed

    Lourenço, José; Recker, Mario

    2016-09-01

    Dengue is one of the most important and wide-spread viral infections affecting human populations. The last few decades have seen a dramatic increase in the global burden of dengue, with the virus now being endemic or near-endemic in over 100 countries world-wide. A recombinant tetravalent vaccine candidate (CYD-TDV) has recently completed Phase III clinical efficacy trials in South East Asia and Latin America and has been licensed for use in several countries. The trial results showed moderate-to-high efficacies in protection against clinical symptoms and hospitalisation but with so far unknown effects on transmission and infections per se. Model-based predictions about the vaccine's short- or long-term impact on the burden of dengue are therefore subject to a considerable degree of uncertainty. Furthermore, different immune interactions between dengue's serotypes have frequently been evoked by modelling studies to underlie dengue's oscillatory dynamics in disease incidence and serotype prevalence. Here we show how model assumptions regarding immune interactions in the form of antibody-dependent enhancement, temporary cross-immunity and the number of infections required to develop full immunity can significantly affect the predicted outcome of a dengue vaccination campaign. Our results thus re-emphasise the important gap in our current knowledge concerning the effects of previous exposure on subsequent dengue infections and further suggest that intervention impact studies should be critically evaluated by their underlying assumptions about serotype immune-interactions. PMID:27663790

  13. Prediction of allosteric sites and mediating interactions through bond-to-bond propensities.

    PubMed

    Amor, B R C; Schaub, M T; Yaliraki, S N; Barahona, M

    2016-01-01

    Allostery is a fundamental mechanism of biological regulation, in which binding of a molecule at a distant location affects the active site of a protein. Allosteric sites provide targets to fine-tune protein activity, yet we lack computational methodologies to predict them. Here we present an efficient graph-theoretical framework to reveal allosteric interactions (atoms and communication pathways strongly coupled to the active site) without a priori information of their location. Using an atomistic graph with energy-weighted covalent and weak bonds, we define a bond-to-bond propensity quantifying the non-local effect of instantaneous bond fluctuations propagating through the protein. Significant interactions are then identified using quantile regression. We exemplify our method with three biologically important proteins: caspase-1, CheY, and h-Ras, correctly predicting key allosteric interactions, whose significance is additionally confirmed against a reference set of 100 proteins. The almost-linear scaling of our method renders it suitable for high-throughput searches for candidate allosteric sites. PMID:27561351

  14. Quantitative imaging for development of companion diagnostics to drugs targeting HGF/MET

    PubMed Central

    Huang, Fangjin; Ma, Zhaoxuan; Pollan, Sara; Yuan, Xiaopu; Swartwood, Steven; Gertych, Arkadiusz; Rodriguez, Maria; Mallick, Jayati; Bhele, Sanica; Guindi, Maha; Dhall, Deepti; Walts, Ann E; Bose, Shikha; de Peralta Venturina, Mariza; Marchevsky, Alberto M; Luthringer, Daniel J; Feller, Stephan M; Berman, Benjamin; Freeman, Michael R; Alvord, W Gregory; Vande Woude, George; Amin, Mahul B

    2016-01-01

    Abstract The limited clinical success of anti‐HGF/MET drugs can be attributed to the lack of predictive biomarkers that adequately select patients for treatment. We demonstrate here that quantitative digital imaging of formalin fixed paraffin embedded tissues stained by immunohistochemistry can be used to measure signals from weakly staining antibodies and provides new opportunities to develop assays for detection of MET receptor activity. To establish a biomarker panel of MET activation, we employed seven antibodies measuring protein expression in the HGF/MET pathway in 20 cases and up to 80 cores from 18 human cancer types. The antibodies bind to epitopes in the extra (EC)‐ and intracellular (IC) domains of MET (MET4EC, SP44_METIC, D1C2_METIC), to MET‐pY1234/pY1235, a marker of MET kinase activation, as well as to HGF, pSFK or pMAPK. Expression of HGF was determined in tumour cells (T_HGF) as well as in stroma surrounding cancer (St_HGF). Remarkably, MET4EC correlated more strongly with pMET (r = 0.47) than SP44_METIC (r = 0.21) or D1C2_METIC (r = 0.08) across 18 cancer types. In addition, correlation coefficients of pMET and T_HGF (r = 0.38) and pMET and pSFK (r = 0.56) were high. Prediction models of MET activation reveal cancer‐type specific differences in performance of MET4EC, SP44_METIC and anti‐HGF antibodies. Thus, we conclude that assays to predict the response to HGF/MET inhibitors require a cancer‐type specific antibody selection and should be developed in those cancer types in which they are employed clinically. PMID:27785366

  15. 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

  16. HIPK2 is a new drug target for anti-fibrosis therapy in kidney disease

    PubMed Central

    Nugent, Melinda M.; Lee, Kyung; He, John Cijiang

    2015-01-01

    In vitro and animal studies continue to elucidate the mechanisms of fibrosis and have led to advancements in treatment for idiopathic pulmonary fibrosis and cirrhosis, but the search for treatments for renal fibrosis has been more disappointing. Here, we will discuss homeodomain-interacting-protein kinase 2 (HIPK2), a novel regulator of fibrosis that acts upstream of major fibrosis signaling pathways. Its key role in renal fibrosis has been validated in vitro and in several murine models of chronic kidney diseases (CKD). PMID:25972814

  17. Direct heating containment vessel interactions code (DHCVIC) and prediction of SNL ''SURTSEY'' test DCH-1

    SciTech Connect

    Ginsberg, T.; Tutu, N.

    1986-01-01

    High-pressure melt ejection from PWR vessels has been identified as a severe core accident scenario which could potentially lead to ''early'' containment failure. Melt ejection, followed by dispersal of the melt by high velocity steam in the cavity beneath the PWR vessel could, according to this scenario, lead to rapid transfer of energy from the melt droplets to the containment atmosphere. This paper describes DHCVIC, an integrated model of the thermal, chemical and hydrodynamic interactions which are postulated to take place during high-pressure melt ejection sequences. The model, which characterizes vessel (or building), is applied to prediction of the Sandia National Laboratory ''SURTSEY'' Test DCH-1 and a (post-test) prediction of that test is made.

  18. Personality Moderates the Interaction between Positive and Negative Daily Events Predicting Negative Affect and Stress

    PubMed Central

    Longua, Julie; DeHart, Tracy; Tennen, Howard; Armeli, Stephen

    2009-01-01

    A 30-day diary study examined personality moderators (neuroticism and extraversion) of the interaction between positive and negative daily events predicting daily negative affect and night-time stress. Multilevel analyses revealed positive daily events buffered the effect of negative daily events on negative affect for individuals low in neuroticism and individuals high in extraversion, but not for individuals high in neuroticism or individuals low in extraversion. Positive daily events also buffered the effect of negative daily events on that night’s stress, but only for participants low in neuroticism. As such, this research linked today’s events to tonight’s stressfulness. This study advances our understanding of how neuroticism and extraversion influence within-person associations between positive and negative events predicting negative affect and stress. PMID:20161239

  19. Ligand Similarity Complements Sequence, Physical Interaction, and Co-Expression for Gene Function Prediction.

    PubMed

    O'Meara, Matthew J; Ballouz, Sara; Shoichet, Brian K; Gillis, Jesse

    2016-01-01

    The expansion of protein-ligand annotation databases has enabled large-scale networking of proteins by ligand similarity. These ligand-based protein networks, which implicitly predict the ability of neighboring proteins to bind related ligands, may complement biologically-oriented gene networks, which are used to predict functional or disease relevance. To quantify the degree to which such ligand-based protein associations might complement functional genomic associations, including sequence similarity, physical protein-protein interactions, co-expression, and disease gene annotations, we calculated a network based on the Similarity Ensemble Approach (SEA: sea.docking.org), where protein neighbors reflect the similarity of their ligands. We also measured the similarity with functional genomic networks over a common set of 1,131 genes, and found that the networks had only small overlaps, which were significant only due to the large scale of the data. Consistent with the view that the networks contain different information, combining them substantially improved Molecular Function prediction within GO (from AUROC~0.63-0.75 for the individual data modalities to AUROC~0.8 in the aggregate). We investigated the boost in guilt-by-association gene function prediction when the networks are combined and describe underlying properties that can be further exploited. PMID:27467773

  20. Ligand Similarity Complements Sequence, Physical Interaction, and Co-Expression for Gene Function Prediction

    PubMed Central

    Shoichet, Brian K.; Gillis, Jesse

    2016-01-01

    The expansion of protein-ligand annotation databases has enabled large-scale networking of proteins by ligand similarity. These ligand-based protein networks, which implicitly predict the ability of neighboring proteins to bind related ligands, may complement biologically-oriented gene networks, which are used to predict functional or disease relevance. To quantify the degree to which such ligand-based protein associations might complement functional genomic associations, including sequence similarity, physical protein-protein interactions, co-expression, and disease gene annotations, we calculated a network based on the Similarity Ensemble Approach (SEA: sea.docking.org), where protein neighbors reflect the similarity of their ligands. We also measured the similarity with functional genomic networks over a common set of 1,131 genes, and found that the networks had only small overlaps, which were significant only due to the large scale of the data. Consistent with the view that the networks contain different information, combining them substantially improved Molecular Function prediction within GO (from AUROC~0.63–0.75 for the individual data modalities to AUROC~0.8 in the aggregate). We investigated the boost in guilt-by-association gene function prediction when the networks are combined and describe underlying properties that can be further exploited. PMID:27467773

  1. The effects of reading comprehension and launch site on frequency-predictability interactions during paragraph reading.

    PubMed

    Whitford, Veronica; Titone, Debra

    2014-01-01

    We used eye movement measures of paragraph reading to examine whether word frequency and predictability interact during the earliest stages of lexical processing, with a specific focus on whether these effects are modulated by individual differences in reading comprehension or launch site (i.e., saccade length between the prior and currently fixated word--a proxy for the amount of parafoveal word processing). The joint impact of frequency and predictability on reading will elucidate whether these variables additively or multiplicatively affect the earliest stages of lexical access, which, in turn, has implications for computational models of eye movements during reading. Linear mixed effects models revealed additive effects during both early- and late-stage reading, where predictability effects were comparable for low- and high-frequency words. Moreover, less cautious readers (e.g., readers who engaged in skimming, scanning, mindless reading) demonstrated smaller frequency effects than more cautious readers. Taken together, our findings suggest that during extended reading, frequency and predictability exert additive influences on lexical and postlexical processing, and that individual differences in reading comprehension modulate sensitivity to the effects of word frequency.

  2. Predicting potential responses to future climate in an alpine ungulate: interspecific interactions exceed climate effects.

    PubMed

    Mason, Tom H E; Stephens, Philip A; Apollonio, Marco; Willis, Stephen G

    2014-12-01

    The altitudinal shifts of many montane populations are lagging behind climate change. Understanding habitual, daily behavioural rhythms, and their climatic and environmental influences, could shed light on the constraints on long-term upslope range-shifts. In addition, behavioural rhythms can be affected by interspecific interactions, which can ameliorate or exacerbate climate-driven effects on ecology. Here, we investigate the relative influences of ambient temperature and an interaction with domestic sheep (Ovis aries) on the altitude use and activity budgets of a mountain ungulate, the Alpine chamois (Rupicapra rupicapra). Chamois moved upslope when it was hotter but this effect was modest compared to that of the presence of sheep, to which they reacted by moving 89-103 m upslope, into an entirely novel altitudinal range. Across the European Alps, a range-shift of this magnitude corresponds to a 46% decrease in the availability of suitable foraging habitat. This highlights the importance of understanding how factors such as competition and disturbance shape a given species' realised niche when predicting potential future responses to change. Furthermore, it exposes the potential for manipulations of species interactions to ameliorate the impacts of climate change, in this case by the careful management of livestock. Such manipulations could be particularly appropriate for species where competition or disturbance already strongly restricts their available niche. Our results also reveal the potential role of behavioural flexibility in responses to climate change. Chamois reduced their activity when it was warmer, which could explain their modest altitudinal migrations. Considering this behavioural flexibility, our model predicts a small 15-30 m upslope shift by 2100 in response to climate change, less than 4% of the altitudinal shift that would be predicted using a traditional species distribution model-type approach (SDM), which assumes that species' behaviour

  3. Modeling microbial communities: current, developing, and future technologies for predicting microbial community interaction.

    PubMed

    Larsen, Peter; Hamada, Yuki; Gilbert, Jack

    2012-07-31

    Never has there been a greater opportunity for investigating microbial communities. Not only are the profound effects of microbial ecology on every aspect of Earth's geochemical cycles beginning to be understood, but also the analytical and computational tools for investigating microbial Earth are undergoing a rapid revolution. This environmental microbial interactome, the system of interactions between the microbiome and the environment, has shaped the planet's past and will undoubtedly continue to do so in the future. We review recent approaches for modeling microbial community structures and the interactions of microbial populations with their environments. Different modeling approaches consider the environmental microbial interactome from different aspects, and each provides insights to different facets of microbial ecology. We discuss the challenges and opportunities for the future of microbial modeling and describe recent advances in microbial community modeling that are extending current descriptive technologies into a predictive science.

  4. Theranostics meets traditional Chinese medicine: rational prediction of drug-herb interactions.

    PubMed

    Hu, Miao; Fan, Lan; Zhou, Hong-Hao; Tomlinson, Brian

    2012-11-01

    Herbal medicines including traditional Chinese medicine are becoming increasingly more popular worldwide. However, there is considerable potential for interaction between herbal components and drugs, as all herbal medicines contain a combination of potentially biologically active compounds possessing various inherent pharmacological activities, and the components of herbal products consumed are eliminated from the body by the same mechanisms that remove drugs. Indeed, many so-called conventional drugs are derived f