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Sample records for drug-target interaction networks

  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. 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.

  3. 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

  4. 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

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

    PubMed Central

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

    2016-01-01

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

  6. 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

  7. 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.

  8. 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.

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

    PubMed Central

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

    2016-01-01

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

  10. 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.

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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

  19. 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

  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. 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. 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.

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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.

  9. 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

  10. 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.

  11. 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.

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

    PubMed Central

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

    2010-01-01

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

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

    PubMed

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

    2013-11-01

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

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

    PubMed

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

    2013-11-01

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

  15. 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.

  16. 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

  17. 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.

  18. 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

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

    PubMed

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

    2016-08-20

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

  20. 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

  1. 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

  2. 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.

  3. 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.

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

    PubMed Central

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

    2012-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2007-12-01

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

  6. 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

  7. 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

  8. 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

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

    PubMed Central

    2015-01-01

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

  10. 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.

  11. Drug interaction networks: an introduction to translational and clinical applications.

    PubMed

    Azuaje, Francisco

    2013-03-15

    This article introduces fundamental concepts to guide the analysis and interpretation of drug-target interaction networks. An overview of the generation and integration of interaction networks is followed by key strategies for extracting biologically meaningful information. The article highlights how this information can enable novel translational and clinically motivated applications. Important advances for the discovery of new treatments and for the detection of adverse drug effects are discussed. Examples of applications and findings originating from cardiovascular research are presented. The review ends with a discussion of crucial challenges and opportunities.

  12. Drug interaction networks: an introduction to translational and clinical applications.

    PubMed

    Azuaje, Francisco

    2013-03-15

    This article introduces fundamental concepts to guide the analysis and interpretation of drug-target interaction networks. An overview of the generation and integration of interaction networks is followed by key strategies for extracting biologically meaningful information. The article highlights how this information can enable novel translational and clinically motivated applications. Important advances for the discovery of new treatments and for the detection of adverse drug effects are discussed. Examples of applications and findings originating from cardiovascular research are presented. The review ends with a discussion of crucial challenges and opportunities. PMID:22977007

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

    PubMed

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

    2016-01-01

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

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

    PubMed

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

    2016-01-01

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

  15. 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.

  16. Chloride channels as drug targets

    PubMed Central

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

    2013-01-01

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

  17. 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

  18. 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

  19. 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.

  20. 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.

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. Histamine pharmacology and new CNS drug targets.

    PubMed

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

    2011-12-01

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

  7. Dynamic and interacting complex networks

    NASA Astrophysics Data System (ADS)

    Dickison, Mark E.

    This thesis employs methods of statistical mechanics and numerical simulations to study some aspects of dynamic and interacting complex networks. The mapping of various social and physical phenomena to complex networks has been a rich field in the past few decades. Subjects as broad as petroleum engineering, scientific collaborations, and the structure of the internet have all been analyzed in a network physics context, with useful and universal results. In the first chapter we introduce basic concepts in networks, including the two types of network configurations that are studied and the statistical physics and epidemiological models that form the framework of the network research, as well as covering various previously-derived results in network theory that are used in the work in the following chapters. In the second chapter we introduce a model for dynamic networks, where the links or the strengths of the links change over time. We solve the model by mapping dynamic networks to the problem of directed percolation, where the direction corresponds to the time evolution of the network. We show that the dynamic network undergoes a percolation phase transition at a critical concentration pc, that decreases with the rate r at which the network links are changed. The behavior near criticality is universal and independent of r. We find that for dynamic random networks fundamental laws are changed: i) The size of the giant component at criticality scales with the network size N for all values of r, rather than as N2/3 in static network, ii) In the presence of a broad distribution of disorder, the optimal path length between two nodes in a dynamic network scales as N1/2, compared to N1/3 in a static network. The third chapter consists of a study of the effect of quarantine on the propagation of epidemics on an adaptive network of social contacts. For this purpose, we analyze the susceptible-infected-recovered model in the presence of quarantine, where susceptible

  8. A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy.

    PubMed

    Srivas, Rohith; Shen, John Paul; Yang, Chih Cheng; Sun, Su Ming; Li, Jianfeng; Gross, Andrew M; Jensen, James; Licon, Katherine; Bojorquez-Gomez, Ana; Klepper, Kristin; Huang, Justin; Pekin, Daniel; Xu, Jia L; Yeerna, Huwate; Sivaganesh, Vignesh; Kollenstart, Leonie; van Attikum, Haico; Aza-Blanc, Pedro; Sobol, Robert W; Ideker, Trey

    2016-08-01

    An emerging therapeutic strategy for cancer is to induce selective lethality in a tumor by exploiting interactions between its driving mutations and specific drug targets. Here we use a multi-species approach to develop a resource of synthetic lethal interactions relevant to cancer therapy. First, we screen in yeast ∼169,000 potential interactions among orthologs of human tumor suppressor genes (TSG) and genes encoding drug targets across multiple genotoxic environments. Guided by the strongest signal, we evaluate thousands of TSG-drug combinations in HeLa cells, resulting in networks of conserved synthetic lethal interactions. Analysis of these networks reveals that interaction stability across environments and shared gene function increase the likelihood of observing an interaction in human cancer cells. Using these rules, we prioritize ∼10(5) human TSG-drug combinations for future follow-up. We validate interactions based on cell and/or patient survival, including topoisomerases with RAD17 and checkpoint kinases with BLM. PMID:27453043

  9. Dynamic interactions in neural networks

    SciTech Connect

    Arbib, M.A. ); Amari, S. )

    1989-01-01

    The study of neural networks is enjoying a great renaissance, both in computational neuroscience, the development of information processing models of living brains, and in neural computing, the use of neurally inspired concepts in the construction of intelligent machines. This volume presents models and data on the dynamic interactions occurring in the brain, and exhibits the dynamic interactions between research in computational neuroscience and in neural computing. The authors present current research, future trends and open problems.

  10. Interaction intimacy organizes networks of antagonistic interactions in different ways

    PubMed Central

    Pires, Mathias M.; Guimarães, Paulo R.

    2013-01-01

    Interaction intimacy, the degree of biological integration between interacting individuals, shapes the ecology and evolution of species interactions. A major question in ecology is whether interaction intimacy also shapes the way interactions are organized within communities. We combined analyses of network structure and food web models to test the role of interaction intimacy in determining patterns of antagonistic interactions, such as host–parasite, predator–prey and plant–herbivore interactions. Networks describing interactions with low intimacy were more connected, more nested and less modular than high-intimacy networks. Moreover, the performance of the models differed across networks with different levels of intimacy. All models reproduced well low-intimacy networks, whereas the more elaborate models were also capable of reproducing networks depicting interactions with higher levels of intimacy. Our results indicate the key role of interaction intimacy in organizing antagonisms, suggesting that greater interaction intimacy might be associated with greater complexity in the assembly rules shaping ecological networks. PMID:23015523

  11. Interaction intimacy organizes networks of antagonistic interactions in different ways.

    PubMed

    Pires, Mathias M; Guimarães, Paulo R

    2013-01-01

    Interaction intimacy, the degree of biological integration between interacting individuals, shapes the ecology and evolution of species interactions. A major question in ecology is whether interaction intimacy also shapes the way interactions are organized within communities. We combined analyses of network structure and food web models to test the role of interaction intimacy in determining patterns of antagonistic interactions, such as host-parasite, predator-prey and plant-herbivore interactions. Networks describing interactions with low intimacy were more connected, more nested and less modular than high-intimacy networks. Moreover, the performance of the models differed across networks with different levels of intimacy. All models reproduced well low-intimacy networks, whereas the more elaborate models were also capable of reproducing networks depicting interactions with higher levels of intimacy. Our results indicate the key role of interaction intimacy in organizing antagonisms, suggesting that greater interaction intimacy might be associated with greater complexity in the assembly rules shaping ecological networks.

  12. 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

  13. 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.

  14. Protein interaction networks from literature mining

    NASA Astrophysics Data System (ADS)

    Ihara, Sigeo

    2005-03-01

    The ability to accurately predict and understand physiological changes in the biological network system in response to disease or drug therapeutics is of crucial importance in life science. The extensive amount of gene expression data generated from even a single microarray experiment often proves difficult to fully interpret and comprehend the biological significance. An increasing knowledge of protein interactions stored in the PubMed database, as well as the advancement of natural language processing, however, makes it possible to construct protein interaction networks from the gene expression information that are essential for understanding the biological meaning. From the in house literature mining system we have developed, the protein interaction network for humans was constructed. By analysis based on the graph-theoretical characterization of the total interaction network in literature, we found that the network is scale-free and semantic long-ranged interactions (i.e. inhibit, induce) between proteins dominate in the total interaction network, reducing the degree exponent. Interaction networks generated based on scientific text in which the interaction event is ambiguously described result in disconnected networks. In contrast interaction networks based on text in which the interaction events are clearly stated result in strongly connected networks. The results of protein-protein interaction networks obtained in real applications from microarray experiments are discussed: For example, comparisons of the gene expression data indicative of either a good or a poor prognosis for acute lymphoblastic leukemia with MLL rearrangements, using our system, showed newly discovered signaling cross-talk.

  15. 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.

  16. 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

  17. 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

  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. PMID:26762852

  19. 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.

  20. 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

  1. 3D MI-DRAGON: new model for the reconstruction of US FDA drug- target network and theoretical-experimental studies of inhibitors of rasagiline derivatives for AChE.

    PubMed

    Prado-Prado, Francisco; García-Mera, Xerardo; Escobar, Manuel; Alonso, Nerea; Caamaño, Olga; Yañez, Matilde; González-Díaz, Humberto

    2012-01-01

    The number of neurodegenerative diseases has been increasing in recent years. Many of the drug candidates to be used in the treatment of neurodegenerative diseases present specific 3D structural features. An important protein in this sense is the acetylcholinesterase (AChE), which is the target of many Alzheimer's dementia drugs. Consequently, the prediction of Drug-Protein Interactions (DPIs/nDPIs) between new drug candidates and specific 3D structure and targets is of major importance. To this end, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out a rational DPIs prediction. Unfortunately, many previous QSAR models developed to predict DPIs take into consideration only 2D structural information and codify the activity against only one target. To solve this problem we can develop some 3D multi-target QSAR (3D mt-QSAR) models. In this study, using the 3D MI-DRAGON technique, we have introduced a new predictor for DPIs based on two different well-known software. We have used the MARCH-INSIDE (MI) and DRAGON software to calculate 3D structural parameters for drugs and targets respectively. Both classes of 3D parameters were used as input to train Artificial Neuronal Network (ANN) algorithms using as benchmark dataset the complex network (CN) made up of all DPIs between US FDA approved drugs and their targets. The entire dataset was downloaded from the DrugBank database. The best 3D mt-QSAR predictor found was an ANN of Multi-Layer Perceptron-type (MLP) with profile MLP 37:37-24-1:1. This MLP classifies correctly 274 out of 321 DPIs (Sensitivity = 85.35%) and 1041 out of 1190 nDPIs (Specificity = 87.48%), corresponding to training Accuracy = 87.03%. We have validated the model with external predicting series with Sensitivity = 84.16% (542/644 DPIs; Specificity = 87.51% (2039/2330 nDPIs) and Accuracy = 86.78%. The new CNs of DPIs reconstructed from US FDA can be used to explore large DPI databases in order to discover both new drugs

  2. Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing

    PubMed Central

    Udrescu, Lucreţia; Sbârcea, Laura; Topîrceanu, Alexandru; Iovanovici, Alexandru; Kurunczi, Ludovic; Bogdan, Paul; Udrescu, Mihai

    2016-01-01

    Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications. PMID:27599720

  3. Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing.

    PubMed

    Udrescu, Lucreţia; Sbârcea, Laura; Topîrceanu, Alexandru; Iovanovici, Alexandru; Kurunczi, Ludovic; Bogdan, Paul; Udrescu, Mihai

    2016-01-01

    Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications. PMID:27599720

  4. 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.

  5. A random interacting network model for complex networks.

    PubMed

    Goswami, Bedartha; Shekatkar, Snehal M; Rheinwalt, Aljoscha; Ambika, G; Kurths, Jürgen

    2015-01-01

    We propose a RAndom Interacting Network (RAIN) model to study the interactions between a pair of complex networks. The model involves two major steps: (i) the selection of a pair of nodes, one from each network, based on intra-network node-based characteristics, and (ii) the placement of a link between selected nodes based on the similarity of their relative importance in their respective networks. Node selection is based on a selection fitness function and node linkage is based on a linkage probability defined on the linkage scores of nodes. The model allows us to relate within-network characteristics to between-network structure. We apply the model to the interaction between the USA and Schengen airline transportation networks (ATNs). Our results indicate that two mechanisms: degree-based preferential node selection and degree-assortative link placement are necessary to replicate the observed inter-network degree distributions as well as the observed inter-network assortativity. The RAIN model offers the possibility to test multiple hypotheses regarding the mechanisms underlying network interactions. It can also incorporate complex interaction topologies. Furthermore, the framework of the RAIN model is general and can be potentially adapted to various real-world complex systems. PMID:26657032

  6. A random interacting network model for complex networks

    NASA Astrophysics Data System (ADS)

    Goswami, Bedartha; Shekatkar, Snehal M.; Rheinwalt, Aljoscha; Ambika, G.; Kurths, Jürgen

    2015-12-01

    We propose a RAndom Interacting Network (RAIN) model to study the interactions between a pair of complex networks. The model involves two major steps: (i) the selection of a pair of nodes, one from each network, based on intra-network node-based characteristics, and (ii) the placement of a link between selected nodes based on the similarity of their relative importance in their respective networks. Node selection is based on a selection fitness function and node linkage is based on a linkage probability defined on the linkage scores of nodes. The model allows us to relate within-network characteristics to between-network structure. We apply the model to the interaction between the USA and Schengen airline transportation networks (ATNs). Our results indicate that two mechanisms: degree-based preferential node selection and degree-assortative link placement are necessary to replicate the observed inter-network degree distributions as well as the observed inter-network assortativity. The RAIN model offers the possibility to test multiple hypotheses regarding the mechanisms underlying network interactions. It can also incorporate complex interaction topologies. Furthermore, the framework of the RAIN model is general and can be potentially adapted to various real-world complex systems.

  7. Network Physiology: How Organ Systems Dynamically Interact.

    PubMed

    Bartsch, Ronny P; Liu, Kang K L; Bashan, Amir; Ivanov, Plamen Ch

    2015-01-01

    We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems. PMID:26555073

  8. Network Physiology: How Organ Systems Dynamically Interact

    PubMed Central

    Bartsch, Ronny P.; Liu, Kang K. L.; Bashan, Amir; Ivanov, Plamen Ch.

    2015-01-01

    We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems. PMID:26555073

  9. A Network Synthesis Model for Generating Protein Interaction Network Families

    PubMed Central

    Sahraeian, Sayed Mohammad Ebrahim; Yoon, Byung-Jun

    2012-01-01

    In this work, we introduce a novel network synthesis model that can generate families of evolutionarily related synthetic protein–protein interaction (PPI) networks. Given an ancestral network, the proposed model generates the network family according to a hypothetical phylogenetic tree, where the descendant networks are obtained through duplication and divergence of their ancestors, followed by network growth using network evolution models. We demonstrate that this network synthesis model can effectively create synthetic networks whose internal and cross-network properties closely resemble those of real PPI networks. The proposed model can serve as an effective framework for generating comprehensive benchmark datasets that can be used for reliable performance assessment of comparative network analysis algorithms. Using this model, we constructed a large-scale network alignment benchmark, called NAPAbench, and evaluated the performance of several representative network alignment algorithms. Our analysis clearly shows the relative performance of the leading network algorithms, with their respective advantages and disadvantages. The algorithm and source code of the network synthesis model and the network alignment benchmark NAPAbench are publicly available at http://www.ece.tamu.edu/bjyoon/NAPAbench/. PMID:22912671

  10. Alignment-free protein interaction network comparison

    PubMed Central

    Ali, Waqar; Rito, Tiago; Reinert, Gesine; Sun, Fengzhu; Deane, Charlotte M.

    2014-01-01

    Motivation: Biological network comparison software largely relies on the concept of alignment where close matches between the nodes of two or more networks are sought. These node matches are based on sequence similarity and/or interaction patterns. However, because of the incomplete and error-prone datasets currently available, such methods have had limited success. Moreover, the results of network alignment are in general not amenable for distance-based evolutionary analysis of sets of networks. In this article, we describe Netdis, a topology-based distance measure between networks, which offers the possibility of network phylogeny reconstruction. Results: We first demonstrate that Netdis is able to correctly separate different random graph model types independent of network size and density. The biological applicability of the method is then shown by its ability to build the correct phylogenetic tree of species based solely on the topology of current protein interaction networks. Our results provide new evidence that the topology of protein interaction networks contains information about evolutionary processes, despite the lack of conservation of individual interactions. As Netdis is applicable to all networks because of its speed and simplicity, we apply it to a large collection of biological and non-biological networks where it clusters diverse networks by type. Availability and implementation: The source code of the program is freely available at http://www.stats.ox.ac.uk/research/proteins/resources. Contact: w.ali@stats.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25161230

  11. Explorers of the Universe: Interactive Electronic Network

    NASA Technical Reports Server (NTRS)

    Alvarez, Marino C.; Burks, Geoffrey; Busby, Michael R.; Cannon, Tiffani; Sotoohi, Goli; Wade, Montanez

    2000-01-01

    This paper details how the Interactive Electronic Network is being utilized by secondary and postsecondary students, and their teachers and professors, to facilitate learning and understanding. The Interactive Electronic Network is couched within the Explorers of the Universe web site in a restricted portion entitled Gateway.

  12. CABIN: Collective Analysis of Biological Interaction Networks

    SciTech Connect

    Singhal, Mudita; Domico, Kelly O.

    2007-06-01

    The importance of understanding biological interaction networks has fueled the development of numerous interaction data generation techniques, databases and prediction tools. However not all prediction tools and databases predict interactions with one hundred percent accuracy. Generation of high confidence interaction networks formulates the first step towards deciphering unknown protein functions, determining protein complexes and inventing drugs. The CABIN: Collective Analysis of Biological Interaction Networks software is an exploratory data analysis tool that enables analysis and integration of interactions evidence obtained from multiple sources, thereby increasing the confidence of computational predictions as well as validating experimental observations. CABIN has been written in JavaTM and is available as a plugin for Cytoscape – an open source network visualization tool.

  13. Interaction network among functional drug groups

    PubMed Central

    2013-01-01

    Background More attention has been being paid to combinatorial effects of drugs to treat complex diseases or to avoid adverse combinations of drug cocktail. Although drug interaction information has been increasingly accumulated, a novel approach like network-based method is needed to analyse that information systematically and intuitively Results Beyond focussing on drug-drug interactions, we examined interactions between functional drug groups. In this work, functional drug groups were defined based on the Anatomical Therapeutic Chemical (ATC) Classification System. We defined criteria whether two functional drug groups are related. Then we constructed the interaction network of drug groups. The resulting network provides intuitive interpretations. We further constructed another network based on interaction sharing ratio of the first network. Subsequent analysis of the networks showed that some features of drugs can be well described by this kind of interaction even for the case of structurally dissimilar drugs. Conclusion Our networks in this work provide intuitive insights into interactions among drug groups rather than those among single drugs. In addition, information on these interactions can be used as a useful source to describe mechanisms and features of drugs. PMID:24555875

  14. Estimation of intermolecular interactions in polymer networks

    SciTech Connect

    Subrananian, P.R.; Galiatsatos, V.

    1993-12-31

    Strain-birefringence measurements have been used to estimate intermolecular interactions in polymer networks. The intensity of the interaction has been quantified through a theoretical scheme recently proposed by Erman. The results show that these interactions diminish with decreasing molecular weight between cross-links and decreasing cross-link functionality.

  15. 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

  16. Interactivity vs. fairness in networked linux systems

    SciTech Connect

    Wu, Wenji; Crawford, Matt; /Fermilab

    2007-01-01

    In general, the Linux 2.6 scheduler can ensure fairness and provide excellent interactive performance at the same time. However, our experiments and mathematical analysis have shown that the current Linux interactivity mechanism tends to incorrectly categorize non-interactive network applications as interactive, which can lead to serious fairness or starvation issues. In the extreme, a single process can unjustifiably obtain up to 95% of the CPU! The root cause is due to the facts that: (1) network packets arrive at the receiver independently and discretely, and the 'relatively fast' non-interactive network process might frequently sleep to wait for packet arrival. Though each sleep lasts for a very short period of time, the wait-for-packet sleeps occur so frequently that they lead to interactive status for the process. (2) The current Linux interactivity mechanism provides the possibility that a non-interactive network process could receive a high CPU share, and at the same time be incorrectly categorized as 'interactive.' In this paper, we propose and test a possible solution to address the interactivity vs. fairness problems. Experiment results have proved the effectiveness of the proposed solution.

  17. Embryonic stem cells: protein interaction networks*

    PubMed Central

    Ng, Patricia Miang-Lon; Lufkin, Thomas

    2012-01-01

    Embryonic stem cells have the ability to differentiate into nearly all cell types. However, the molecular mechanism of its pluripotency is still unclear. Oct3/4, Sox2 and Nanog are important factors of pluripotency. Oct3/4 (hereafter referred to as Oct4), in particular, has been an irreplaceable factor in the induction of pluripotency in adult cells. Proteins interacting with Oct4 and Nanog have been identified via affinity purification and mass spectrometry. These data, together with iterative purifications of interacting proteins allowed a protein interaction network to be constructed. The network currently includes 77 transcription factors, all of which are interconnected in one network. In-depth studies of some of these transcription factors show that they all recruit the NuRD complex. Hence, transcription factor clustering and chromosomal remodeling are key mechanism used by embryonic stem cells. Studies using RNA interference suggest that more pluripotency genes are yet to be discovered via protein-protein interactions. More work is required to complete and curate the embryonic stem cell protein interaction network. Analysis of a saturated protein interaction network by system biology tools can greatly aid in the understanding of the embryonic stem cell pluripotency network. PMID:22639699

  18. Mutually-antagonistic interactions in baseball networks

    NASA Astrophysics Data System (ADS)

    Saavedra, Serguei; Powers, Scott; McCotter, Trent; Porter, Mason A.; Mucha, Peter J.

    2010-03-01

    We formulate the head-to-head matchups between Major League Baseball pitchers and batters from 1954 to 2008 as a bipartite network of mutually-antagonistic interactions. We consider both the full network and single-season networks, which exhibit structural changes over time. We find interesting structure in the networks and examine their sensitivity to baseball’s rule changes. We then study a biased random walk on the matchup networks as a simple and transparent way to (1) compare the performance of players who competed under different conditions and (2) include information about which particular players a given player has faced. We find that a player’s position in the network does not correlate with his placement in the random walker ranking. However, network position does have a substantial effect on the robustness of ranking placement to changes in head-to-head matchups.

  19. The dissimilarity of species interaction networks.

    PubMed

    Poisot, Timothée; Canard, Elsa; Mouillot, David; Mouquet, Nicolas; Gravel, Dominique

    2012-12-01

    In a context of global changes, and amidst the perpetual modification of community structure undergone by most natural ecosystems, it is more important than ever to understand how species interactions vary through space and time. The integration of biogeography and network theory will yield important results and further our understanding of species interactions. It has, however, been hampered so far by the difficulty to quantify variation among interaction networks. Here, we propose a general framework to study the dissimilarity of species interaction networks over time, space or environments, allowing both the use of quantitative and qualitative data. We decompose network dissimilarity into interactions and species turnover components, so that it is immediately comparable to common measures of β-diversity. We emphasise that scaling up β-diversity of community composition to the β-diversity of interactions requires only a small methodological step, which we foresee will help empiricists adopt this method. We illustrate the framework with a large dataset of hosts and parasites interactions and highlight other possible usages. We discuss a research agenda towards a biogeographical theory of species interactions. PMID:22994257

  20. 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.

  1. 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

  2. Pairwise alignment of protein interaction networks.

    PubMed

    Koyutürk, Mehmet; Kim, Yohan; Topkara, Umut; Subramaniam, Shankar; Szpankowski, Wojciech; Grama, Ananth

    2006-03-01

    With an ever-increasing amount of available data on protein-protein interaction (PPI) networks and research revealing that these networks evolve at a modular level, discovery of conserved patterns in these networks becomes an important problem. Although available data on protein-protein interactions is currently limited, recently developed algorithms have been shown to convey novel biological insights through employment of elegant mathematical models. The main challenge in aligning PPI networks is to define a graph theoretical measure of similarity between graph structures that captures underlying biological phenomena accurately. In this respect, modeling of conservation and divergence of interactions, as well as the interpretation of resulting alignments, are important design parameters. In this paper, we develop a framework for comprehensive alignment of PPI networks, which is inspired by duplication/divergence models that focus on understanding the evolution of protein interactions. We propose a mathematical model that extends the concepts of match, mismatch, and gap in sequence alignment to that of match, mismatch, and duplication in network alignment and evaluates similarity between graph structures through a scoring function that accounts for evolutionary events. By relying on evolutionary models, the proposed framework facilitates interpretation of resulting alignments in terms of not only conservation but also divergence of modularity in PPI networks. Furthermore, as in the case of sequence alignment, our model allows flexibility in adjusting parameters to quantify underlying evolutionary relationships. Based on the proposed model, we formulate PPI network alignment as an optimization problem and present fast algorithms to solve this problem. Detailed experimental results from an implementation of the proposed framework show that our algorithm is able to discover conserved interaction patterns very effectively, in terms of both accuracies and computational

  3. Systematic mapping of genetic interaction networks.

    PubMed

    Dixon, Scott J; Costanzo, Michael; Baryshnikova, Anastasia; Andrews, Brenda; Boone, Charles

    2009-01-01

    Genetic interactions influencing a phenotype of interest can be identified systematically using libraries of genetic tools that perturb biological systems in a defined manner. Systematic screens conducted in the yeast Saccharomyces cerevisiae have identified thousands of genetic interactions and provided insight into the global structure of biological networks. Techniques enabling systematic genetic interaction mapping have been extended to other single-celled organisms, the bacteria Escherichia coli and the yeast Schizosaccharomyces pombe, opening the way to comparative investigations of interaction networks. Genetic interaction screens in Caenorhabditis elegans, Drosophila melanogaster, and mammalian models are helping to improve our understanding of metazoan-specific signaling pathways. Together, our emerging knowledge of the genetic wiring diagrams of eukaryotic and prokaryotic cells is providing a new understanding of the relationship between genotype and phenotype.

  4. Random interactions in higher order neural networks

    NASA Technical Reports Server (NTRS)

    Baldi, Pierre; Venkatesh, Santosh S.

    1993-01-01

    Recurrent networks of polynomial threshold elements with random symmetric interactions are studied. Precise asymptotic estimates are derived for the expected number of fixed points as a function of the margin of stability. In particular, it is shown that there is a critical range of margins of stability (depending on the degree of polynomial interaction) such that the expected number of fixed points with margins below the critical range grows exponentially with the number of nodes in the network, while the expected number of fixed points with margins above the critical range decreases exponentially with the number of nodes in the network. The random energy model is also briefly examined and links with higher order neural networks and higher order spin glass models made explicit.

  5. 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

  6. 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

  7. Broadband networks for interactive telemedical applications

    NASA Astrophysics Data System (ADS)

    Graschew, Georgi; Roelofs, Theo A.; Rakowsky, Stefan; Schlag, Peter M.

    2002-08-01

    Using off-the-shelf hardware components and a specially developed high-end software communication system (WinVicos) satellite networks for interactive telemedicine have been designed and developed. These networks allow for various telemedical applications, like intraoperative teleconsultation, second opinioning, teleteaching, telementoring, etc.. Based on the successful GALENOS network, several projects are currently being realized: MEDASHIP (Medical Assistance for Ships); DELTASS (Disaster Emergency Logistic Telemedicine Advanced Satellites Systems) and EMISPHER (Euro-Mediterranean Internet-Satellite Platform for Health, medical Education and Research).

  8. 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

  9. A quantitative chaperone interaction network reveals the architecture of cellular protein homeostasis pathways

    PubMed Central

    Taipale, Mikko; Tucker, George; Peng, Jian; Krykbaeva, Irina; Lin, Zhen-Yuan; Larsen, Brett; Choi, Hyungwon; Berger, Bonnie; Gingras, Anne-Claude; Lindquist, Susan

    2014-01-01

    Chaperones are abundant cellular proteins that promote the folding and function of their substrate proteins (clients). In vivo, chaperones also associate with a large and diverse set of co-factors (co-chaperones) that regulate their specificity and function. However, how these co-chaperones regulate protein folding and whether they have chaperone-independent biological functions is largely unknown. We have combined mass spectrometry and quantitative high-throughput LUMIER assays to systematically characterize the chaperone/co-chaperone/client interaction network in human cells. We uncover hundreds of novel chaperone clients, delineate their participation in specific co-chaperone complexes, and establish a surprisingly distinct network of protein/protein interactions for co-chaperones. As a salient example of the power of such analysis, we establish that NUDC family co-chaperones specifically associate with structurally related but evolutionarily distinct β-propeller folds. We provide a framework for deciphering the proteostasis network, its regulation in development and disease, and expand the use of chaperones as sensors for drug/target engagement. PMID:25036637

  10. From networks of protein interactions to networks of functional dependencies

    PubMed Central

    2012-01-01

    Background As protein-protein interactions connect proteins that participate in either the same or different functions, networks of interacting and functionally annotated proteins can be converted into process graphs of inter-dependent function nodes (each node corresponding to interacting proteins with the same functional annotation). However, as proteins have multiple annotations, the process graph is non-redundant, if only proteins participating directly in a given function are included in the related function node. Results Reasoning that topological features (e.g., clusters of highly inter-connected proteins) might help approaching structured and non-redundant understanding of molecular function, an algorithm was developed that prioritizes inclusion of proteins into the function nodes that best overlap protein clusters. Specifically, the algorithm identifies function nodes (and their mutual relations), based on the topological analysis of a protein interaction network, which can be related to various biological domains, such as cellular components (e.g., peroxisome and cellular bud) or biological processes (e.g., cell budding) of the model organism S. cerevisiae. Conclusions The method we have described allows converting a protein interaction network into a non-redundant process graph of inter-dependent function nodes. The examples we have described show that the resulting graph allows researchers to formulate testable hypotheses about dependencies among functions and the underlying mechanisms. PMID:22607727

  11. 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.

  12. 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).

  13. Network Compression as a Quality Measure for Protein Interaction Networks

    PubMed Central

    Royer, Loic; Reimann, Matthias; Stewart, A. Francis; Schroeder, Michael

    2012-01-01

    With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients. PMID:22719828

  14. Network archaeology: uncovering ancient networks from present-day interactions.

    PubMed

    Navlakha, Saket; Kingsford, Carl

    2011-04-01

    What proteins interacted in a long-extinct ancestor of yeast? How have different members of a protein complex assembled together over time? Our ability to answer such questions has been limited by the unavailability of ancestral protein-protein interaction (PPI) networks. To overcome this limitation, we propose several novel algorithms to reconstruct the growth history of a present-day network. Our likelihood-based method finds a probable previous state of the graph by applying an assumed growth model backwards in time. This approach retains node identities so that the history of individual nodes can be tracked. Using this methodology, we estimate protein ages in the yeast PPI network that are in good agreement with sequence-based estimates of age and with structural features of protein complexes. Further, by comparing the quality of the inferred histories for several different growth models (duplication-mutation with complementarity, forest fire, and preferential attachment), we provide additional evidence that a duplication-based model captures many features of PPI network growth better than models designed to mimic social network growth. From the reconstructed history, we model the arrival time of extant and ancestral interactions and predict that complexes have significantly re-wired over time and that new edges tend to form within existing complexes. We also hypothesize a distribution of per-protein duplication rates, track the change of the network's clustering coefficient, and predict paralogous relationships between extant proteins that are likely to be complementary to the relationships inferred using sequence alone. Finally, we infer plausible parameters for the model, thereby predicting the relative probability of various evolutionary events. The success of these algorithms indicates that parts of the history of the yeast PPI are encoded in its present-day form. PMID:21533211

  15. Functional module identification in protein interaction networks by interaction patterns

    PubMed Central

    Wang, Yijie; Qian, Xiaoning

    2014-01-01

    Motivation: Identifying functional modules in protein–protein interaction (PPI) networks may shed light on cellular functional organization and thereafter underlying cellular mechanisms. Many existing module identification algorithms aim to detect densely connected groups of proteins as potential modules. However, based on this simple topological criterion of ‘higher than expected connectivity’, those algorithms may miss biologically meaningful modules of functional significance, in which proteins have similar interaction patterns to other proteins in networks but may not be densely connected to each other. A few blockmodel module identification algorithms have been proposed to address the problem but the lack of global optimum guarantee and the prohibitive computational complexity have been the bottleneck of their applications in real-world large-scale PPI networks. Results: In this article, we propose a novel optimization formulation LCP2 (low two-hop conductance sets) using the concept of Markov random walk on graphs, which enables simultaneous identification of both dense and sparse modules based on protein interaction patterns in given networks through searching for LCP2 by random walk. A spectral approximate algorithm SLCP2 is derived to identify non-overlapping functional modules. Based on a bottom-up greedy strategy, we further extend LCP2 to a new algorithm (greedy algorithm for LCP2) GLCP2 to identify overlapping functional modules. We compare SLCP2 and GLCP2 with a range of state-of-the-art algorithms on synthetic networks and real-world PPI networks. The performance evaluation based on several criteria with respect to protein complex prediction, high level Gene Ontology term prediction and especially sparse module detection, has demonstrated that our algorithms based on searching for LCP2 outperform all other compared algorithms. Availability and implementation: All data and code are available at http://www.cse.usf.edu/∼xqian/fmi/slcp2hop

  16. 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

  17. 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

  18. Interaction Energy Based Protein Structure Networks

    PubMed Central

    Vijayabaskar, M.S.; Vishveshwara, Saraswathi

    2010-01-01

    The three-dimensional structure of a protein is formed and maintained by the noncovalent interactions among the amino-acid residues of the polypeptide chain. These interactions can be represented collectively in the form of a network. So far, such networks have been investigated by considering the connections based on distances between the amino-acid residues. Here we present a method of constructing the structure network based on interaction energies among the amino-acid residues in the protein. We have investigated the properties of such protein energy-based networks (PENs) and have shown correlations to protein structural features such as the clusters of residues involved in stability, formation of secondary and super-secondary structural units. Further we demonstrate that the analysis of PENs in terms of parameters such as hubs and shortest paths can provide a variety of biologically important information, such as the residues crucial for stabilizing the folded units and the paths of communication between distal residues in the protein. Finally, the energy regimes for different levels of stabilization in the protein structure have clearly emerged from the PEN analysis. PMID:21112295

  19. 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

  20. The activation of interactive attentional networks.

    PubMed

    Xuan, Bin; Mackie, Melissa-Ann; Spagna, Alfredo; Wu, Tingting; Tian, Yanghua; Hof, Patrick R; Fan, Jin

    2016-04-01

    Attention can be conceptualized as comprising the functions of alerting, orienting, and executive control. Although the independence of these functions has been demonstrated, the neural mechanisms underlying their interactions remain unclear. Using the revised attention network test and functional magnetic resonance imaging, we examined cortical and subcortical activity related to these attentional functions and their interactions. Results showed that areas in the extended frontoparietal network (FPN), including dorsolateral prefrontal cortex, frontal eye fields (FEF), areas near and along the intraparietal sulcus, anterior cingulate and anterior insular cortices, basal ganglia, and thalamus were activated across multiple attentional functions. Specifically, the alerting function was associated with activation in the locus coeruleus (LC) in addition to regions in the FPN. The orienting functions were associated with activation in the superior colliculus (SC) and the FEF. The executive control function was mainly associated with activation of the FPN and cerebellum. The interaction effect of alerting by executive control was also associated with activation of the FPN, while the interaction effect of orienting validity by executive control was mainly associated with the activation in the pulvinar. The current findings demonstrate that cortical and specific subcortical areas play a pivotal role in the implementation of attentional functions and underlie their dynamic interactions.

  1. 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

  2. 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

  3. 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

  4. 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.

  5. 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.

  6. Network Understanding of Herb Medicine via Rapid Identification of Ingredient-Target Interactions

    PubMed Central

    Zhang, Hai-Ping; Pan, Jian-Bo; Zhang, Chi; Ji, Nan; Wang, Hao; Ji, Zhi-Liang

    2014-01-01

    Today, herb medicines have become the major source for discovery of novel agents in countermining diseases. However, many of them are largely under-explored in pharmacology due to the limitation of current experimental approaches. Therefore, we proposed a computational framework in this study for network understanding of herb pharmacology via rapid identification of putative ingredient-target interactions in human structural proteome level. A marketing anti-cancer herb medicine in China, Yadanzi (Brucea javanica), was chosen for mechanistic study. Total 7,119 ingredient-target interactions were identified for thirteen Yadanzi active ingredients. Among them, about 29.5% were estimated to have better binding affinity than their corresponding marketing drug-target interactions. Further Bioinformatics analyses suggest that simultaneous manipulation of multiple proteins in the MAPK signaling pathway and the phosphorylation process of anti-apoptosis may largely answer for Yadanzi against non-small cell lung cancers. In summary, our strategy provides an efficient however economic solution for systematic understanding of herbs' power. PMID:24429698

  7. Network understanding of herb medicine via rapid identification of ingredient-target interactions.

    PubMed

    Zhang, Hai-Ping; Pan, Jian-Bo; Zhang, Chi; Ji, Nan; Wang, Hao; Ji, Zhi-Liang

    2014-01-01

    Today, herb medicines have become the major source for discovery of novel agents in countermining diseases. However, many of them are largely under-explored in pharmacology due to the limitation of current experimental approaches. Therefore, we proposed a computational framework in this study for network understanding of herb pharmacology via rapid identification of putative ingredient-target interactions in human structural proteome level. A marketing anti-cancer herb medicine in China, Yadanzi (Brucea javanica), was chosen for mechanistic study. Total 7,119 ingredient-target interactions were identified for thirteen Yadanzi active ingredients. Among them, about 29.5% were estimated to have better binding affinity than their corresponding marketing drug-target interactions. Further Bioinformatics analyses suggest that simultaneous manipulation of multiple proteins in the MAPK signaling pathway and the phosphorylation process of anti-apoptosis may largely answer for Yadanzi against non-small cell lung cancers. In summary, our strategy provides an efficient however economic solution for systematic understanding of herbs' power.

  8. Network understanding of herb medicine via rapid identification of ingredient-target interactions.

    PubMed

    Zhang, Hai-Ping; Pan, Jian-Bo; Zhang, Chi; Ji, Nan; Wang, Hao; Ji, Zhi-Liang

    2014-01-01

    Today, herb medicines have become the major source for discovery of novel agents in countermining diseases. However, many of them are largely under-explored in pharmacology due to the limitation of current experimental approaches. Therefore, we proposed a computational framework in this study for network understanding of herb pharmacology via rapid identification of putative ingredient-target interactions in human structural proteome level. A marketing anti-cancer herb medicine in China, Yadanzi (Brucea javanica), was chosen for mechanistic study. Total 7,119 ingredient-target interactions were identified for thirteen Yadanzi active ingredients. Among them, about 29.5% were estimated to have better binding affinity than their corresponding marketing drug-target interactions. Further Bioinformatics analyses suggest that simultaneous manipulation of multiple proteins in the MAPK signaling pathway and the phosphorylation process of anti-apoptosis may largely answer for Yadanzi against non-small cell lung cancers. In summary, our strategy provides an efficient however economic solution for systematic understanding of herbs' power. PMID:24429698

  9. Network Understanding of Herb Medicine via Rapid Identification of Ingredient-Target Interactions

    NASA Astrophysics Data System (ADS)

    Zhang, Hai-Ping; Pan, Jian-Bo; Zhang, Chi; Ji, Nan; Wang, Hao; Ji, Zhi-Liang

    2014-01-01

    Today, herb medicines have become the major source for discovery of novel agents in countermining diseases. However, many of them are largely under-explored in pharmacology due to the limitation of current experimental approaches. Therefore, we proposed a computational framework in this study for network understanding of herb pharmacology via rapid identification of putative ingredient-target interactions in human structural proteome level. A marketing anti-cancer herb medicine in China, Yadanzi (Brucea javanica), was chosen for mechanistic study. Total 7,119 ingredient-target interactions were identified for thirteen Yadanzi active ingredients. Among them, about 29.5% were estimated to have better binding affinity than their corresponding marketing drug-target interactions. Further Bioinformatics analyses suggest that simultaneous manipulation of multiple proteins in the MAPK signaling pathway and the phosphorylation process of anti-apoptosis may largely answer for Yadanzi against non-small cell lung cancers. In summary, our strategy provides an efficient however economic solution for systematic understanding of herbs' power.

  10. Structure and Interactions in Neurofilament Networks

    NASA Astrophysics Data System (ADS)

    Jones, Jayna; Ojeda-Lopez, Miguel; Safinya, Cyrus

    2004-03-01

    Neurofilaments (NFs) are a major constituent of myelinated axons of nerve cells, which assemble from three subunit proteins of low, medium, and high molecular weight to form a 10 nm diameter rod with sidearms radiating from the center. The sidearm interactions impart structural stability and result in an oriented network of NFs running parallel to the axon. Over or under expression of NF subunits is related to abnormal NF-networks, which are known hallmarks of motor neuron diseases (ALS). Here, we reassemble NFs from subunit proteins purified from bovine spinal cord. We demonstrate the formation of the NF network in vitro where synchrotron x-ray scattering (SSRL) reveals a well-defined interfilament spacing while the defect structure in polarized optical microcopy shows the liquid crystalline nature. The spacing varies depending on subunit molar ratios and salt conditions and we relate this change to the mechanical stability of the lattice. This change in lattice spacing yields insight into the stabilizing interactions between the NF sidearms. Supported by NSF DMR- 0203755, CTS-0103516, and NIH GM-59288.

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

    PubMed

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

    2013-01-01

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

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

    PubMed Central

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

    2011-01-01

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

  13. 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.

  14. 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

  15. 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

  16. 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

  17. 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.

  18. Structure and Interactions in Neurofilament Networks

    NASA Astrophysics Data System (ADS)

    Jones, Jayna

    2005-03-01

    Neurofilaments (NFs) are a major constituent of nerve cell axons that assemble from three subunit proteins of low (NF-L), medium (NF-M), and high molecular weight (NF-H) to form a 10 nm diameter rod with radiating sidearms. The sidearm interactions result in an oriented network of NFs running parallel to the axon. Here, we reassemble NFs in vitro from varying weight ratios of two of the subunit proteins, NF-L and NF-M, purified from bovine spinal cord. We demonstrate the formation of the NF network where synchrotron x-ray scattering (SSRL) reveals a well-defined interfilament spacing, while the defect structure in polarized optical microcopy shows the liquid crystalline nature. The interfilament spacing varies depending on NF-M sidearm density and we relate this change to sidearm interactions. We show that at a low density of sidearms, repulsive forces dominate creating a lattice spacing that is regulated by the buffer volume. With an increasing sidearm density, the equilibrium interfilament spacing decreases as a result of competing repulsive and attractive forces. Supported by NIH GM-59288, NSF DMR- 0203755, & CTS-0404444.

  19. 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

  20. 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

  1. 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

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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

  9. 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.

  10. 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

  11. 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.

  12. 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

  13. 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

  14. 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

  15. Discrete network models of interacting nephrons

    NASA Astrophysics Data System (ADS)

    Moss, Rob; Kazmierczak, Ed; Kirley, Michael; Harris, Peter

    2009-11-01

    The kidney is one of the major organs involved in whole-body homeostasis, and exhibits many of the properties of a complex system. The functional unit of the kidney is the nephron, a complex, segmented tube into which blood plasma is filtered and its composition adjusted. Although the behaviour of individual nephrons can fluctuate widely and even chaotically, the behaviour of the kidney remains stable. In this paper, we investigate how the filtration rate of a multi-nephron system is affected by interactions between nephrons. We introduce a discrete-time multi-nephron network model. The tubular mechanisms that have the greatest effect on filtration rate are the transport of sodium and water, consequently our model attempts to capture these mechanisms. Multi-nephron systems also incorporate two competing coupling mechanisms-vascular and hemodynamic-that enforce in-phase and anti-phase synchronisations respectively. Using a two-nephron model, we demonstrate how changing the strength of the hemodynamic coupling mechanism and changing the arterial blood pressure have equivalent effects on the system. The same two-nephron system is then used to demonstrate the interactions that arise between the two coupling mechanisms. We conclude by arguing that our approach is scalable to large numbers of nephrons, based on the performance characteristics of the model.

  16. Evolution of biomolecular networks: lessons from metabolic and protein interactions.

    PubMed

    Yamada, Takuji; Bork, Peer

    2009-11-01

    Despite only becoming popular at the beginning of this decade, biomolecular networks are now frameworks that facilitate many discoveries in molecular biology. The nodes of these networks are usually proteins (specifically enzymes in metabolic networks), whereas the links (or edges) are their interactions with other molecules. These networks are made up of protein-protein interactions or enzyme-enzyme interactions through shared metabolites in the case of metabolic networks. Evolutionary analysis has revealed that changes in the nodes and links in protein-protein interaction and metabolic networks are subject to different selection pressures owing to distinct topological features. However, many evolutionary constraints can be uncovered only if temporal and spatial aspects are included in the network analysis.

  17. Dynamic interactions of proteins in complex networks

    SciTech Connect

    Appella, E.; Anderson, C.

    2009-10-01

    Recent advances in techniques such as NMR and EPR spectroscopy have enabled the elucidation of how proteins undergo structural changes to act in concert in complex networks. The three minireviews in this series highlight current findings and the capabilities of new methodologies for unraveling the dynamic changes controlling diverse cellular functions. They represent a sampling of the cutting-edge research presented at the 17th Meeting of Methods in Protein Structure Analysis, MPSA2008, in Sapporo, Japan, 26-29 August, 2008 (http://www.iapsap.bnl.gov). The first minireview, by Christensen and Klevit, reports on a structure-based yeast two-hybrid method for identifying E2 ubiquitin-conjugating enzymes that interact with the E3 BRCA1/BARD1 heterodimer ligase to generate either mono- or polyubiquitinated products. This method demonstrated for the first time that the BRCA1/BARD1 E3 can interact with 10 different E2 enzymes. Interestingly, the interaction with multiple E2 enzymes displayed unique ubiquitin-transfer properties, a feature expected to be common among other RING and U-box E3s. Further characterization of new E3 ligases and the E2 enzymes that interact with them will greatly enhance our understanding of ubiquitin transfer and facilitate studies of roles of ubiquitin and ubiquitin-like proteins in protein processing and trafficking. Stein et al., in the second minireview, describe recent progress in defining the binding specificity of different peptide-binding domains. The authors clearly point out that transient peptide interactions mediated by both post-translational modifications and disordered regions ensure a high level of specificity. They postulate that a regulatory code may dictate the number of combinations of domains and post-translational modifications needed to achieve the required level of interaction specificity. Moreover, recognition alone is not enough to obtain a stable complex, especially in a complex cellular environment. Increasing

  18. Multiple tipping points and optimal repairing in interacting networks

    NASA Astrophysics Data System (ADS)

    Majdandzic, Antonio; Braunstein, Lidia A.; Curme, Chester; Vodenska, Irena; Levy-Carciente, Sary; Eugene Stanley, H.; Havlin, Shlomo

    2016-03-01

    Systems composed of many interacting dynamical networks--such as the human body with its biological networks or the global economic network consisting of regional clusters--often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread and recovery. Here we develop a model for such systems and find a very rich phase diagram that becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions and two `forbidden' transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyse an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model.

  19. Reconstructing direct and indirect interactions in networked public goods game

    NASA Astrophysics Data System (ADS)

    Han, Xiao; Shen, Zhesi; Wang, Wen-Xu; Lai, Ying-Cheng; Grebogi, Celso

    2016-07-01

    Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system.

  20. Reconstructing direct and indirect interactions in networked public goods game.

    PubMed

    Han, Xiao; Shen, Zhesi; Wang, Wen-Xu; Lai, Ying-Cheng; Grebogi, Celso

    2016-07-22

    Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system.

  1. Reconstructing direct and indirect interactions in networked public goods game.

    PubMed

    Han, Xiao; Shen, Zhesi; Wang, Wen-Xu; Lai, Ying-Cheng; Grebogi, Celso

    2016-01-01

    Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system. PMID:27444774

  2. Reconstructing direct and indirect interactions in networked public goods game

    PubMed Central

    Han, Xiao; Shen, Zhesi; Wang, Wen-Xu; Lai, Ying-Cheng; Grebogi, Celso

    2016-01-01

    Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system. PMID:27444774

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. "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

  10. 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

  11. Phylogenetic analysis of modularity in protein interaction networks

    PubMed Central

    Erten, Sinan; Li, Xin; Bebek, Gurkan; Li, Jing; Koyutürk, Mehmet

    2009-01-01

    Background In systems biology, comparative analyses of molecular interactions across diverse species indicate that conservation and divergence of networks can be used to understand functional evolution from a systems perspective. A key characteristic of these networks is their modularity, which contributes significantly to their robustness, as well as adaptability. Consequently, analysis of modular network structures from a phylogenetic perspective may be useful in understanding the emergence, conservation, and diversification of functional modularity. Results In this paper, we propose a phylogenetic framework for analyzing network modules, with applications that extend well beyond network-based phylogeny reconstruction. Our approach is based on identification of modular network components from each network separately, followed by projection of these modules onto the networks of other species to compare different networks. Subsequently, we use the conservation of various modules in each network to assess the similarity between different networks. Compared to traditional methods that rely on topological comparisons, our approach has key advantages in (i) avoiding intractable graph comparison problems in comparative network analysis, (ii) accounting for noise and missing data through flexible treatment of network conservation, and (iii) providing insights on the evolution of biological systems through investigation of the evolutionary trajectories of network modules. We test our method, MOPHY, on synthetic data generated by simulation of network evolution, as well as existing protein-protein interaction data for seven diverse species. Comprehensive experimental results show that MOPHY is promising in reconstructing evolutionary histories of extant networks based on conservation of modularity, it is highly robust to noise, and outperforms existing methods that quantify network similarity in terms of conservation of network topology. Conclusion These results establish

  12. 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

  13. Protein-protein interaction networks (PPI) and complex diseases

    PubMed Central

    Safari-Alighiarloo, Nahid; Taghizadeh, Mohammad; Rezaei-Tavirani, Mostafa; Goliaei, Bahram

    2014-01-01

    The physical interaction of proteins which lead to compiling them into large densely connected networks is a noticeable subject to investigation. Protein interaction networks are useful because of making basic scientific abstraction and improving biological and biomedical applications. Based on principle roles of proteins in biological function, their interactions determine molecular and cellular mechanisms, which control healthy and diseased states in organisms. Therefore, such networks facilitate the understanding of pathogenic (and physiologic) mechanisms that trigger the onset and progression of diseases. Consequently, this knowledge can be translated into effective diagnostic and therapeutic strategies. Furthermore, the results of several studies have proved that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer and autoimmune disorders. Based on such relationship, a novel paradigm is suggested in order to confirm that the protein interaction networks can be the target of therapy for treatment of complex multi-genic diseases rather than individual molecules with disrespect the network. PMID:25436094

  14. 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.

  15. Multiple tipping points and optimal repairing in interacting networks

    PubMed Central

    Majdandzic, Antonio; Braunstein, Lidia A.; Curme, Chester; Vodenska, Irena; Levy-Carciente, Sary; Eugene Stanley, H.; Havlin, Shlomo

    2016-01-01

    Systems composed of many interacting dynamical networks—such as the human body with its biological networks or the global economic network consisting of regional clusters—often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread and recovery. Here we develop a model for such systems and find a very rich phase diagram that becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions and two ‘forbidden' transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyse an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model. PMID:26926803

  16. Socioeconomic networks with long-range interactions

    NASA Astrophysics Data System (ADS)

    Carvalho, Rui; Iori, Giulia

    2008-07-01

    We study a modified version of a model previously proposed by Jackson and Wolinsky to account for communication of information and allocation of goods in socioeconomic networks. In the model, the utility function of each node is given by a weighted sum of contributions from all accessible nodes. The weights, parametrized by the variable δ , decrease with distance. We introduce a growth mechanism where new nodes attach to the existing network preferentially by utility. By increasing δ , the network structure evolves from a power-law to an exponential degree distribution, passing through a regime characterized by shorter average path length, lower degree assortativity, and higher central point dominance. In the second part of the paper we compare different network structures in terms of the average utility received by each node. We show that power-law networks provide higher average utility than Poisson random networks. This provides a possible justification for the ubiquitousness of scale-free networks in the real world.

  17. The architecture of functional interaction networks in the retina.

    PubMed

    Ganmor, Elad; Segev, Ronen; Schneidman, Elad

    2011-02-23

    Sensory information is represented in the brain by the joint activity of large groups of neurons. Recent studies have shown that, although the number of possible activity patterns and underlying interactions is exponentially large, pairwise-based models give a surprisingly accurate description of neural population activity patterns. We explored the architecture of maximum entropy models of the functional interaction networks underlying the response of large populations of retinal ganglion cells, in adult tiger salamander retina, responding to natural and artificial stimuli. We found that we can further simplify these pairwise models by neglecting weak interaction terms or by relying on a small set of interaction strengths. Comparing network interactions under different visual stimuli, we show the existence of local network motifs in the interaction map of the retina. Our results demonstrate that the underlying interaction map of the retina is sparse and dominated by local overlapping interaction modules.

  18. 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

  19. Adenylating Enzymes in Mycobacterium tuberculosis as Drug Targets

    PubMed Central

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

    2013-01-01

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

  20. 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

  1. 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

  2. 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.

  3. Applied Graph-Mining Algorithms to Study Biomolecular Interaction Networks

    PubMed Central

    2014-01-01

    Protein-protein interaction (PPI) networks carry vital information on the organization of molecular interactions in cellular systems. The identification of functionally relevant modules in PPI networks is one of the most important applications of biological network analysis. Computational analysis is becoming an indispensable tool to understand large-scale biomolecular interaction networks. Several types of computational methods have been developed and employed for the analysis of PPI networks. Of these computational methods, graph comparison and module detection are the two most commonly used strategies. This review summarizes current literature on graph kernel and graph alignment methods for graph comparison strategies, as well as module detection approaches including seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods. Herein, we provide a comprehensive review of the major algorithms employed under each theme, including our recently published frequent subgraph method, for detecting functional modules commonly shared across multiple cancer PPI networks. PMID:24800226

  4. Guaranteeing global synchronization in networks with stochastic interactions

    NASA Astrophysics Data System (ADS)

    Klinglmayr, Johannes; Kirst, Christoph; Bettstetter, Christian; Timme, Marc

    2012-07-01

    We design the interactions between oscillators communicating via variably delayed pulse coupling to guarantee their synchronization on arbitrary network topologies. We identify a class of response functions and prove convergence to network-wide synchrony from arbitrary initial conditions. Synchrony is achieved if the pulse emission is unreliable or intentionally probabilistic. These results support the design of scalable, reliable and energy-efficient communication protocols for fully distributed synchronization as needed, e.g., in mobile phone networks, embedded systems, sensor networks and autonomously interacting swarm robots.

  5. Problem Solving Interactions on Electronic Networks.

    ERIC Educational Resources Information Center

    Waugh, Michael; And Others

    Arguing that electronic networking provides a medium which is qualitatively superior to the traditional classroom for conducting certain types of problem solving exercises, this paper details the Water Problem Solving Project, which was conducted on the InterCultural Learning Network in 1985 and 1986 with students from the United States, Mexico,…

  6. Gene Expression Correlations in Human Cancer Cell Lines Define Molecular Interaction Networks for Epithelial Phenotype

    PubMed Central

    Kohn, Kurt W.; Zeeberg, Barry M.; Reinhold, William C.; Pommier, Yves

    2014-01-01

    Using gene expression data to enhance our knowledge of control networks relevant to cancer biology and therapy is a challenging but urgent task. Based on the premise that genes that are expressed together in a variety of cell types are likely to functions together, we derived mutually correlated genes that function together in various processes in epithelial-like tumor cells. Expression-correlated genes were derived from data for the NCI-60 human tumor cell lines, as well as data from the Broad Institute’s CCLE cell lines. NCI-60 cell lines that selectively expressed a mutually correlated subset of tight junction genes served as a signature for epithelial-like cancer cells. Those signature cell lines served as a seed to derive other correlated genes, many of which had various other epithelial-related functions. Literature survey yielded molecular interaction and function information about those genes, from which molecular interaction maps were assembled. Many of the genes had epithelial functions unrelated to tight junctions, demonstrating that new function categories were elicited. The most highly correlated genes were implicated in the following epithelial functions: interactions at tight junctions (CLDN7, CLDN4, CLDN3, MARVELD3, MARVELD2, TJP3, CGN, CRB3, LLGL2, EPCAM, LNX1); interactions at adherens junctions (CDH1, ADAP1, CAMSAP3); interactions at desmosomes (PPL, PKP3, JUP); transcription regulation of cell-cell junction complexes (GRHL1 and 2); epithelial RNA splicing regulators (ESRP1 and 2); epithelial vesicle traffic (RAB25, EPN3, GRHL2, EHF, ADAP1, MYO5B); epithelial Ca(+2) signaling (ATP2C2, S100A14, BSPRY); terminal differentiation of epithelial cells (OVOL1 and 2, ST14, PRSS8, SPINT1 and 2); maintenance of apico-basal polarity (RAB25, LLGL2, EPN3). The findings provide a foundation for future studies to elucidate the functions of regulatory networks specific to epithelial-like cancer cells and to probe for anti-cancer drug targets. PMID:24940735

  7. Empirical evaluation of neutral interactions in host-parasite networks.

    PubMed

    Canard, E F; Mouquet, N; Mouillot, D; Stanko, M; Miklisova, D; Gravel, D

    2014-04-01

    While niche-based processes have been invoked extensively to explain the structure of interaction networks, recent studies propose that neutrality could also be of great importance. Under the neutral hypothesis, network structure would simply emerge from random encounters between individuals and thus would be directly linked to species abundance. We investigated the impact of species abundance distributions on qualitative and quantitative metrics of 113 host-parasite networks. We analyzed the concordance between neutral expectations and empirical observations at interaction, species, and network levels. We found that species abundance accurately predicts network metrics at all levels. Despite host-parasite systems being constrained by physiology and immunology, our results suggest that neutrality could also explain, at least partially, their structure. We hypothesize that trait matching would determine potential interactions between species, while abundance would determine their realization. PMID:24642492

  8. 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

  9. Interaction Networks: Generating High Level Hints Based on Network Community Clustering

    ERIC Educational Resources Information Center

    Eagle, Michael; Johnson, Matthew; Barnes, Tiffany

    2012-01-01

    We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals.…

  10. TCGA researchers identify potential drug targets, markers for leukemia risk

    Cancer.gov

    Investigators for The Cancer Genome Atlas (TCGA) Research Network have detailed and broadly classified the genomic alterations that frequently underlie the development of acute myeloid leukemia (AML), a deadly cancer of the blood and bone marrow. Their wo

  11. TCGA Bladder Cancer Study Reveals Potential Drug Targets - TCGA

    Cancer.gov

    Investigators with the TCGA Research Network have identified new potential therapeutic targets for a major form of bladder cancer, including important genes and pathways that are disrupted in the disease.

  12. Mathematically Designing a Local Interaction Algorithm for Decentralized Network Systems

    NASA Astrophysics Data System (ADS)

    Kubo, Takeshi; Hasegawa, Teruyuki; Hasegawa, Toru

    In the near future, decentralized network systems consisting of a huge number of sensor nodes are expected to play an important role. In such a network, each node should control itself by means of a local interaction algorithm. Although such local interaction algorithms improve system reliability, how to design a local interaction algorithm has become an issue. In this paper, we describe a local interaction algorithm in a partial differential equation (or PDE) and propose a new design method whereby a PDE is derived from the solution we desire. The solution is considered as a pattern of nodes' control values over the network each of which is used to control the node's behavior. As a result, nodes collectively provide network functions such as clustering, collision and congestion avoidance. In this paper, we focus on a periodic pattern comprising sinusoidal waves and derive the PDE whose solution exhibits such a pattern by exploiting the Fourier method.

  13. Ontology integration to identify protein complex in protein interaction networks

    PubMed Central

    2011-01-01

    Background Protein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of protein complexes detection algorithms. Methods We have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes. Results The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches. PMID:22165991

  14. Linking Classrooms of the Future through Interactive Telecommunications Network.

    ERIC Educational Resources Information Center

    Cisco, Ponney G.

    This document describes an interactive television (ITV) distance education network designed to service rural schools. Phase one of the network involved the installation of over 14 miles of fiber optic cable linking two high schools, a career center, and the University of Rio Grande; phase two will bring seven high schools in economically depressed…

  15. Development of Attention Networks and Their Interactions in Childhood

    ERIC Educational Resources Information Center

    Pozuelos, Joan P.; Paz-Alonso, Pedro M.; Castillo, Alejandro; Fuentes, Luis J.; Rueda, M. Rosario

    2014-01-01

    In the present study, we investigated developmental trajectories of alerting, orienting, and executive attention networks and their interactions over childhood. Two cross-sectional experiments were conducted with different samples of 6-to 12-year-old children using modified versions of the attention network task (ANT). In Experiment 1 (N = 106),…

  16. Percolation on networks with antagonistic and dependent interactions

    NASA Astrophysics Data System (ADS)

    Kotnis, Bhushan; Kuri, Joy

    2015-03-01

    Drawing inspiration from real world interacting systems, we study a system consisting of two networks that exhibit antagonistic and dependent interactions. By antagonistic and dependent interactions we mean that a proportion of functional nodes in a network cause failure of nodes in the other, while failure of nodes in the other results in failure of links in the first. In contrast to interdependent networks, which can exhibit first-order phase transitions, we find that the phase transitions in such networks are continuous. Our analysis shows that, compared to an isolated network, the system is more robust against random attacks. Surprisingly, we observe a region in the parameter space where the giant connected components of both networks start oscillating. Furthermore, we find that for Erdős-Rényi and scale-free networks the system oscillates only when the dependence and antagonism between the two networks are very high. We believe that this study can further our understanding of real world interacting systems.

  17. Instructional Technology: The Information Superhighway, the Internet, Interactive Video Networks.

    ERIC Educational Resources Information Center

    Odell, Kerry S.; And Others

    1994-01-01

    Includes "It Boggles the Mind" (Odell); "Merging Your Classroom onto the Information Superhighway" (Murphy); "The World's Largest Computer Network" (Fleck); "The Information Highway in Iowa" (Miller); "Interactive Video Networks in Secondary Schools" (Swan et al.); and "Upgrade to Humancentric Technology" (Berry). (JOW)

  18. Interacting Social Processes on Interconnected Networks

    PubMed Central

    Alvarez-Zuzek, Lucila G.; La Rocca, Cristian E.; Vazquez, Federico; Braunstein, Lidia A.

    2016-01-01

    We propose and study a model for the interplay between two different dynamical processes –one for opinion formation and the other for decision making– on two interconnected networks A and B. The opinion dynamics on network A corresponds to that of the M-model, where the state of each agent can take one of four possible values (S = −2,−1, 1, 2), describing its level of agreement on a given issue. The likelihood to become an extremist (S = ±2) or a moderate (S = ±1) is controlled by a reinforcement parameter r ≥ 0. The decision making dynamics on network B is akin to that of the Abrams-Strogatz model, where agents can be either in favor (S = +1) or against (S = −1) the issue. The probability that an agent changes its state is proportional to the fraction of neighbors that hold the opposite state raised to a power β. Starting from a polarized case scenario in which all agents of network A hold positive orientations while all agents of network B have a negative orientation, we explore the conditions under which one of the dynamics prevails over the other, imposing its initial orientation. We find that, for a given value of β, the two-network system reaches a consensus in the positive state (initial state of network A) when the reinforcement overcomes a crossover value r*(β), while a negative consensus happens for r < r*(β). In the r − β phase space, the system displays a transition at a critical threshold βc, from a coexistence of both orientations for β < βc to a dominance of one orientation for β > βc. We develop an analytical mean-field approach that gives an insight into these regimes and shows that both dynamics are equivalent along the crossover line (r*, β*). PMID:27689698

  19. Development of Novel Random Network Theory-Based Approaches to Identify Network Interactions among Nitrifying Bacteria

    SciTech Connect

    Shi, Cindy

    2015-07-17

    The interactions among different microbial populations in a community could play more important roles in determining ecosystem functioning than species numbers and their abundances, but very little is known about such network interactions at a community level. The goal of this project is to develop novel framework approaches and associated software tools to characterize the network interactions in microbial communities based on high throughput, large scale high-throughput metagenomics data and apply these approaches to understand the impacts of environmental changes (e.g., climate change, contamination) on network interactions among different nitrifying populations and associated microbial communities.

  20. Specialization for resistance in wild host-pathogen interaction networks

    PubMed Central

    Barrett, Luke G.; Encinas-Viso, Francisco; Burdon, Jeremy J.; Thrall, Peter H.

    2015-01-01

    Properties encompassed by host-pathogen interaction networks have potential to give valuable insight into the evolution of specialization and coevolutionary dynamics in host-pathogen interactions. However, network approaches have been rarely utilized in previous studies of host and pathogen phenotypic variation. Here we applied quantitative analyses to eight networks derived from spatially and temporally segregated host (Linum marginale) and pathogen (Melampsora lini) populations. First, we found that resistance strategies are highly variable within and among networks, corresponding to a spectrum of specialist and generalist resistance types being maintained within all networks. At the individual level, specialization was strongly linked to partial resistance, such that partial resistance was effective against a greater number of pathogens compared to full resistance. Second, we found that all networks were significantly nested. There was little support for the hypothesis that temporal evolutionary dynamics may lead to the development of nestedness in host-pathogen infection networks. Rather, the common patterns observed in terms of nestedness suggests a universal driver (or multiple drivers) that may be independent of spatial and temporal structure. Third, we found that resistance networks were significantly modular in two spatial networks, clearly reflecting spatial and ecological structure within one of the networks. We conclude that (1) overall patterns of specialization in the networks we studied mirror evolutionary trade-offs with the strength of resistance; (2) that specific network architecture can emerge under different evolutionary scenarios; and (3) network approaches offer great utility as a tool for probing the evolutionary and ecological genetics of host-pathogen interactions. PMID:26442074

  1. 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.

  2. Properties of interaction networks underlying the minority game

    NASA Astrophysics Data System (ADS)

    Caridi, Inés

    2014-11-01

    The minority game is a well-known agent-based model with no explicit interaction among its agents. However, it is known that they interact through the global magnitudes of the model and through their strategies. In this work we have attempted to formalize the implicit interactions among minority game agents as if they were links on a complex network. We have defined the link between two agents by quantifying the similarity between them. This link definition is based on the information of the instance of the game (the set of strategies assigned to each agent at the beginning) without any dynamic information on the game and brings about a static, unweighed and undirected network. We have analyzed the structure of the resulting network for different parameters, such as the number of agents (N ) and the agent's capacity to process information (m ), always taking into account games with two strategies per agent. In the region of crowd effects of the model, the resulting networks structure is a small-world network, whereas in the region where the behavior of the minority game is the same as in a game of random decisions, networks become a random network of Erdos-Renyi. The transition between these two types of networks is slow, without any peculiar feature of the network in the region of the coordination among agents. Finally, we have studied the resulting static networks for the full strategy minority game model, a maximal instance of the minority game in which all possible agents take part in the game. We have explicitly calculated the degree distribution of the full strategy minority game network and, on the basis of this analytical result, we have estimated the degree distribution of the minority game network, which is in accordance with computational results.

  3. Properties of interaction networks underlying the minority game.

    PubMed

    Caridi, Inés

    2014-11-01

    The minority game is a well-known agent-based model with no explicit interaction among its agents. However, it is known that they interact through the global magnitudes of the model and through their strategies. In this work we have attempted to formalize the implicit interactions among minority game agents as if they were links on a complex network. We have defined the link between two agents by quantifying the similarity between them. This link definition is based on the information of the instance of the game (the set of strategies assigned to each agent at the beginning) without any dynamic information on the game and brings about a static, unweighed and undirected network. We have analyzed the structure of the resulting network for different parameters, such as the number of agents (N) and the agent's capacity to process information (m), always taking into account games with two strategies per agent. In the region of crowd effects of the model, the resulting networks structure is a small-world network, whereas in the region where the behavior of the minority game is the same as in a game of random decisions, networks become a random network of Erdos-Renyi. The transition between these two types of networks is slow, without any peculiar feature of the network in the region of the coordination among agents. Finally, we have studied the resulting static networks for the full strategy minority game model, a maximal instance of the minority game in which all possible agents take part in the game. We have explicitly calculated the degree distribution of the full strategy minority game network and, on the basis of this analytical result, we have estimated the degree distribution of the minority game network, which is in accordance with computational results.

  4. NetworkAnalyst--integrative approaches for protein-protein interaction network analysis and visual exploration.

    PubMed

    Xia, Jianguo; Benner, Maia J; Hancock, Robert E W

    2014-07-01

    Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required--identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca. PMID:24861621

  5. Optimization of an interactive distributive computer network

    NASA Technical Reports Server (NTRS)

    Frederick, V.

    1985-01-01

    The activities under a cooperative agreement for the development of a computer network are briefly summarized. Research activities covered are: computer operating systems optimization and integration; software development and implementation of the IRIS (Infrared Imaging of Shuttle) Experiment; and software design, development, and implementation of the APS (Aerosol Particle System) Experiment.

  6. 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

  7. Modeling the dynamical interaction between epidemics on overlay networks

    NASA Astrophysics Data System (ADS)

    Marceau, Vincent; Noël, Pierre-André; Hébert-Dufresne, Laurent; Allard, Antoine; Dubé, Louis J.

    2011-08-01

    Epidemics seldom occur as isolated phenomena. Typically, two or more viral agents spread within the same host population and may interact dynamically with each other. We present a general model where two viral agents interact via an immunity mechanism as they propagate simultaneously on two networks connecting the same set of nodes. By exploiting a correspondence between the propagation dynamics and a dynamical process performing progressive network generation, we develop an analytical approach that accurately captures the dynamical interaction between epidemics on overlay networks. The formalism allows for overlay networks with arbitrary joint degree distribution and overlap. To illustrate the versatility of our approach, we consider a hypothetical delayed intervention scenario in which an immunizing agent is disseminated in a host population to hinder the propagation of an undesirable agent (e.g., the spread of preventive information in the context of an emerging infectious disease).

  8. Use of an Interactive Telecommunications Network to Deliver Inservice Education.

    ERIC Educational Resources Information Center

    Slaton, Deborah Bott; Lacefield, Warren E.

    1991-01-01

    A total of 155 educators in rural Kentucky participated in 4 inservice education sessions on learning disabilities and adaptive teaching, delivered via an interactive telecommunications network. Participants' reactions indicated that interactive television was an acceptable format for delivery of inservice education. (Author/JDD)

  9. Bilingual Lexical Interactions in an Unsupervised Neural Network Model

    ERIC Educational Resources Information Center

    Zhao, Xiaowei; Li, Ping

    2010-01-01

    In this paper we present an unsupervised neural network model of bilingual lexical development and interaction. We focus on how the representational structures of the bilingual lexicons can emerge, develop, and interact with each other as a function of the learning history. The results show that: (1) distinct representations for the two lexicons…

  10. 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.

  11. Major component analysis of dynamic networks of physiologic organ interactions

    NASA Astrophysics Data System (ADS)

    Liu, Kang K. L.; Bartsch, Ronny P.; Ma, Qianli D. Y.; Ivanov, Plamen Ch

    2015-09-01

    The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function.

  12. Network of immune-neuroendocrine interactions.

    PubMed Central

    Besedovsky, H; Sorkin, E

    1977-01-01

    In order to bring the self-regulated immune system into conformity with other body systems its functioning within the context of an immune-neuroendocrine network is proposed. This hypothesis is based on the existence of afferent--efferent pathways between immune and neuroendocrine structures. Major endocrine responses occur as a consequence of antigenic stimulation and changes in the electrical activity of the hypothalamus also take place; both of these alterations are temporally related to the immune response itself. This endocrine response has meaningful implications for immunoregulation and for immunospecificity. During ontogeny, there is also evidence for the operations of a complex network between the endocrine and immune system, a bidirectional interrelationship that may well affect each developmental stage of both functions. As sequels the functioning of the immune system and the outcome of this interrelation could be decisive in lymphoid cell homeostasis, self-tolerance, and could also have significant implications for pathology. PMID:849642

  13. 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.

  14. Modeling Human Dynamics of Face-to-Face Interaction Networks

    NASA Astrophysics Data System (ADS)

    Starnini, Michele; Baronchelli, Andrea; Pastor-Satorras, Romualdo

    2013-04-01

    Face-to-face interaction networks describe social interactions in human gatherings, and are the substrate for processes such as epidemic spreading and gossip propagation. The bursty nature of human behavior characterizes many aspects of empirical data, such as the distribution of conversation lengths, of conversations per person, or of interconversation times. Despite several recent attempts, a general theoretical understanding of the global picture emerging from data is still lacking. Here we present a simple model that reproduces quantitatively most of the relevant features of empirical face-to-face interaction networks. The model describes agents that perform a random walk in a two-dimensional space and are characterized by an attractiveness whose effect is to slow down the motion of people around them. The proposed framework sheds light on the dynamics of human interactions and can improve the modeling of dynamical processes taking place on the ensuing dynamical social networks.

  15. How do oncoprotein mutations rewire protein-protein interaction networks?

    PubMed

    Bowler, Emily H; Wang, Zhenghe; Ewing, Rob M

    2015-01-01

    The acquisition of mutations that activate oncogenes or inactivate tumor suppressors is a primary feature of most cancers. Mutations that directly alter protein sequence and structure drive the development of tumors through aberrant expression and modification of proteins, in many cases directly impacting components of signal transduction pathways and cellular architecture. Cancer-associated mutations may have direct or indirect effects on proteins and their interactions and while the effects of mutations on signaling pathways have been widely studied, how mutations alter underlying protein-protein interaction networks is much less well understood. Systematic mapping of oncoprotein protein interactions using proteomics techniques as well as computational network analyses is revealing how oncoprotein mutations perturb protein-protein interaction networks and drive the cancer phenotype. PMID:26325016

  16. Microbial interaction networks in soil and in silico

    NASA Astrophysics Data System (ADS)

    Vetsigian, Kalin

    2012-02-01

    Soil harbors a huge number of microbial species interacting through secretion of antibiotics and other chemicals. What patterns of species interactions allow for this astonishing biodiversity to be sustained, and how do these interactions evolve? I used a combined experimental-theoretical approach to tackle these questions. Focusing on bacteria from the genus Steptomyces, known for their diverse secondary metabolism, I isolated 64 natural strains from several individual grains of soil and systematically measured all pairwise interactions among them. Quantitative measurements on such scale were enabled by a novel experimental platform based on robotic handling, a custom scanner array and automatic image analysis. This unique platform allowed the simultaneous capturing of ˜15,000 time-lapse movies of growing colonies of each isolate on media conditioned by each of the other isolates. The data revealed a rich network of strong negative (inhibitory) and positive (stimulating) interactions. Analysis of this network and the phylogeny of the isolates, together with mathematical modeling of microbial communities, revealed that: 1) The network of interactions has three special properties: ``balance'', ``bi- modality'' and ``reciprocity''; 2) The interaction network is fast evolving; 3) Mathematical modeling explains how rapid evolution can give rise to the three special properties through an interplay between ecology and evolution. These properties are not a result of stable co-existence, but rather of continuous evolutionary turnover of strains with different production and resistance capabilities.

  17. Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction

    NASA Astrophysics Data System (ADS)

    Yeger-Lotem, Esti; Sattath, Shmuel; Kashtan, Nadav; Itzkovitz, Shalev; Milo, Ron; Pinter, Ron Y.; Alon, Uri; Margalit, Hanah

    2004-04-01

    Genes and proteins generate molecular circuitry that enables the cell to process information and respond to stimuli. A major challenge is to identify characteristic patterns in this network of interactions that may shed light on basic cellular mechanisms. Previous studies have analyzed aspects of this network, concentrating on either transcription-regulation or protein-protein interactions. Here we search for composite network motifs: characteristic network patterns consisting of both transcription-regulation and protein-protein interactions that recur significantly more often than in random networks. To this end we developed algorithms for detecting motifs in networks with two or more types of interactions and applied them to an integrated data set of protein-protein interactions and transcription regulation in Saccharomyces cerevisiae. We found a two-protein mixed-feedback loop motif, five types of three-protein motifs exhibiting coregulation and complex formation, and many motifs involving four proteins. Virtually all four-protein motifs consisted of combinations of smaller motifs. This study presents a basic framework for detecting the building blocks of networks with multiple types of interactions.

  18. 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

  19. Networked Interactive Video for Group Training

    ERIC Educational Resources Information Center

    Eary, John

    2008-01-01

    The National Computing Centre (NCC) has developed an interactive video training system for the Scottish Police College to help train police supervisory officers in crowd control at major spectator events, such as football matches. This approach involves technology-enhanced training in a group-learning environment, and may have significant impact…

  20. Unraveling novel broad-spectrum antibacterial targets in food and waterborne pathogens using comparative genomics and protein interaction network analysis.

    PubMed

    Jadhav, Ankush; Shanmugham, Buvaneswari; Rajendiran, Anjana; Pan, Archana

    2014-10-01

    Food and waterborne diseases are a growing concern in terms of human morbidity and mortality worldwide, even in the 21st century, emphasizing the need for new therapeutic interventions for these diseases. The current study aims at prioritizing broad-spectrum antibacterial targets, present in multiple food and waterborne bacterial pathogens, through a comparative genomics strategy coupled with a protein interaction network analysis. The pathways unique and common to all the pathogens under study (viz., methane metabolism, d-alanine metabolism, peptidoglycan biosynthesis, bacterial secretion system, two-component system, C5-branched dibasic acid metabolism), identified by comparative metabolic pathway analysis, were considered for the analysis. The proteins/enzymes involved in these pathways were prioritized following host non-homology analysis, essentiality analysis, gut flora non-homology analysis and protein interaction network analysis. The analyses revealed a set of promising broad-spectrum antibacterial targets, present in multiple food and waterborne pathogens, which are essential for bacterial survival, non-homologous to host and gut flora, and functionally important in the metabolic network. The identified broad-spectrum candidates, namely, integral membrane protein/virulence factor (MviN), preprotein translocase subunits SecB and SecG, carbon storage regulator (CsrA), and nitrogen regulatory protein P-II 1 (GlnB), contributed by the peptidoglycan pathway, bacterial secretion systems and two-component systems, were also found to be present in a wide range of other disease-causing bacteria. Cytoplasmic proteins SecG, CsrA and GlnB were considered as drug targets, while membrane proteins MviN and SecB were classified as vaccine targets. The identified broad-spectrum targets can aid in the design and development of antibacterial agents not only against food and waterborne pathogens but also against other pathogens. PMID:25128740

  1. Unraveling novel broad-spectrum antibacterial targets in food and waterborne pathogens using comparative genomics and protein interaction network analysis.

    PubMed

    Jadhav, Ankush; Shanmugham, Buvaneswari; Rajendiran, Anjana; Pan, Archana

    2014-10-01

    Food and waterborne diseases are a growing concern in terms of human morbidity and mortality worldwide, even in the 21st century, emphasizing the need for new therapeutic interventions for these diseases. The current study aims at prioritizing broad-spectrum antibacterial targets, present in multiple food and waterborne bacterial pathogens, through a comparative genomics strategy coupled with a protein interaction network analysis. The pathways unique and common to all the pathogens under study (viz., methane metabolism, d-alanine metabolism, peptidoglycan biosynthesis, bacterial secretion system, two-component system, C5-branched dibasic acid metabolism), identified by comparative metabolic pathway analysis, were considered for the analysis. The proteins/enzymes involved in these pathways were prioritized following host non-homology analysis, essentiality analysis, gut flora non-homology analysis and protein interaction network analysis. The analyses revealed a set of promising broad-spectrum antibacterial targets, present in multiple food and waterborne pathogens, which are essential for bacterial survival, non-homologous to host and gut flora, and functionally important in the metabolic network. The identified broad-spectrum candidates, namely, integral membrane protein/virulence factor (MviN), preprotein translocase subunits SecB and SecG, carbon storage regulator (CsrA), and nitrogen regulatory protein P-II 1 (GlnB), contributed by the peptidoglycan pathway, bacterial secretion systems and two-component systems, were also found to be present in a wide range of other disease-causing bacteria. Cytoplasmic proteins SecG, CsrA and GlnB were considered as drug targets, while membrane proteins MviN and SecB were classified as vaccine targets. The identified broad-spectrum targets can aid in the design and development of antibacterial agents not only against food and waterborne pathogens but also against other pathogens.

  2. 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

  3. Strategy selection in evolutionary game dynamics on group interaction networks.

    PubMed

    Tan, Shaolin; Feng, Shasha; Wang, Pei; Chen, Yao

    2014-11-01

    Evolutionary game theory provides an appropriate tool for investigating the competition and diffusion of behavioral traits in biological or social populations. A core challenge in evolutionary game theory is the strategy selection problem: Given two strategies, which one is favored by the population? Recent studies suggest that the answer depends not only on the payoff functions of strategies but also on the interaction structure of the population. Group interactions are one of the fundamental interactive modes within populations. This work aims to investigate the strategy selection problem in evolutionary game dynamics on group interaction networks. In detail, the strategy selection conditions are obtained for some typical networks with group interactions. Furthermore, the obtained conditions are applied to investigate selection between cooperation and defection in populations. The conditions for evolution of cooperation are derived for both the public goods game and volunteer's dilemma game. Numerical experiments validate the above analytical results.

  4. 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

  5. Speech networks at rest and in action: interactions between functional brain networks controlling speech production.

    PubMed

    Simonyan, Kristina; Fuertinger, Stefan

    2015-04-01

    Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network.

  6. 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.

  7. Topology and static response of interaction networks in molecular biology.

    PubMed

    Radulescu, Ovidiu; Lagarrigue, Sandrine; Siegel, Anne; Veber, Philippe; Le Borgne, Michel

    2006-02-22

    We introduce a mathematical framework describing static response of networks occurring in molecular biology. This formalism has many similarities with the Laplace-Kirchhoff equations for electrical networks. We introduce the concept of graph boundary and we show how the response of the biological networks to external perturbations can be related to the Dirichlet or Neumann problems for the corresponding equations on the interaction graph. Solutions to these two problems are given in terms of path moduli (measuring path rigidity with respect to the propagation of interaction along the graph). Path moduli are related to loop products in the interaction graph via generalized Mason-Coates formulae. We apply our results to two specific biological examples: the lactose operon and the genetic regulation of lipogenesis. Our applications show consistency with experimental results and in the case of lipogenesis check some hypothesis on the behaviour of hepatic fatty acids on fasting. PMID:16849230

  8. 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.

  9. Topology and static response of interaction networks in molecular biology

    PubMed Central

    Radulescu, Ovidiu; Lagarrigue, Sandrine; Siegel, Anne; Veber, Philippe; Le Borgne, Michel

    2005-01-01

    We introduce a mathematical framework describing static response of networks occurring in molecular biology. This formalism has many similarities with the Laplace–Kirchhoff equations for electrical networks. We introduce the concept of graph boundary and we show how the response of the biological networks to external perturbations can be related to the Dirichlet or Neumann problems for the corresponding equations on the interaction graph. Solutions to these two problems are given in terms of path moduli (measuring path rigidity with respect to the propagation of interaction along the graph). Path moduli are related to loop products in the interaction graph via generalized Mason–Coates formulae. We apply our results to two specific biological examples: the lactose operon and the genetic regulation of lipogenesis. Our applications show consistency with experimental results and in the case of lipogenesis check some hypothesis on the behaviour of hepatic fatty acids on fasting. PMID:16849230

  10. The evolution of generalized reciprocity on social interaction networks.

    PubMed

    van Doorn, Gerrit Sander; Taborsky, Michael

    2012-03-01

    Generalized reciprocity (help anyone, if helped by someone) is a minimal strategy capable of supporting cooperation between unrelated individuals. Its simplicity makes it an attractive model to explain the evolution of reciprocal altruism in animals that lack the information or cognitive skills needed for other types of reciprocity. Yet, generalized reciprocity is anonymous and thus defenseless against exploitation by defectors. Recognizing that animals hardly ever interact randomly, we investigate whether social network structure can mitigate this vulnerability. Our results show that heterogeneous interaction patterns strongly support the evolution of generalized reciprocity. The future probability of being rewarded for an altruistic act is inversely proportional to the average connectivity of the social network when cooperators are rare. Accordingly, sparse networks are conducive to the invasion of reciprocal altruism. Moreover, the evolutionary stability of cooperation is enhanced by a modular network structure. Communities of reciprocal altruists are protected against exploitation, because modularity increases the mean access time, that is, the average number of steps that it takes for a random walk on the network to reach a defector. Sparseness and community structure are characteristic properties of vertebrate social interaction patterns, as illustrated by network data from natural populations ranging from fish to primates.

  11. 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

  12. Graph spectral analysis of protein interaction network evolution.

    PubMed

    Thorne, Thomas; Stumpf, Michael P H

    2012-10-01

    We present an analysis of protein interaction network data via the comparison of models of network evolution to the observed data. We take a bayesian approach and perform posterior density estimation using an approximate bayesian computation with sequential Monte Carlo method. Our approach allows us to perform model selection over a selection of potential network growth models. The methodology we apply uses a distance defined in terms of graph spectra which captures the network data more naturally than previously used summary statistics such as the degree distribution. Furthermore, we include the effects of sampling into the analysis, to properly correct for the incompleteness of existing datasets, and have analysed the performance of our method under various degrees of sampling. We consider a number of models focusing not only on the biologically relevant class of duplication models, but also including models of scale-free network growth that have previously been claimed to describe such data. We find a preference for a duplication-divergence with linear preferential attachment model in the majority of the interaction datasets considered. We also illustrate how our method can be used to perform multi-model inference of network parameters to estimate properties of the full network from sampled data.

  13. Hazard interactions and interaction networks (cascades) within multi-hazard methodologies

    NASA Astrophysics Data System (ADS)

    Gill, Joel C.; Malamud, Bruce D.

    2016-08-01

    This paper combines research and commentary to reinforce the importance of integrating hazard interactions and interaction networks (cascades) into multi-hazard methodologies. We present a synthesis of the differences between multi-layer single-hazard approaches and multi-hazard approaches that integrate such interactions. This synthesis suggests that ignoring interactions between important environmental and anthropogenic processes could distort management priorities, increase vulnerability to other spatially relevant hazards or underestimate disaster risk. In this paper we proceed to present an enhanced multi-hazard framework through the following steps: (i) description and definition of three groups (natural hazards, anthropogenic processes and technological hazards/disasters) as relevant components of a multi-hazard environment, (ii) outlining of three types of interaction relationship (triggering, increased probability, and catalysis/impedance), and (iii) assessment of the importance of networks of interactions (cascades) through case study examples (based on the literature, field observations and semi-structured interviews). We further propose two visualisation frameworks to represent these networks of interactions: hazard interaction matrices and hazard/process flow diagrams. Our approach reinforces the importance of integrating interactions between different aspects of the Earth system, together with human activity, into enhanced multi-hazard methodologies. Multi-hazard approaches support the holistic assessment of hazard potential and consequently disaster risk. We conclude by describing three ways by which understanding networks of interactions contributes to the theoretical and practical understanding of hazards, disaster risk reduction and Earth system management. Understanding interactions and interaction networks helps us to better (i) model the observed reality of disaster events, (ii) constrain potential changes in physical and social vulnerability

  14. Community structure description in amino acid interaction networks.

    PubMed

    Gaci, Omar

    2011-03-01

    In this paper, we represent proteins by amino acid interaction networks. This is a graph whose vertices are the protein's amino acids and whose edges are the interactions between them. We begin by identifying the main topological properties of these interaction networks using graph theory measures. We observe that the amino acids interact specifically, according to their structural role, and depending on whether they participate or not in the secondary structure. Thus, certain amino acids tend to group together to form local clouds. Then, we study the formation of node aggregations through community structure detections. We observe that the composition of organizations confirms a specific aggregation between loops around a core composed of secondary.

  15. Evolutionary interaction networks of insect pathogenic fungi.

    PubMed

    Boomsma, Jacobus J; Jensen, Annette B; Meyling, Nicolai V; Eilenberg, Jørgen

    2014-01-01

    Lineages of insect pathogenic fungi are concentrated in three major clades: Hypocreales (several genera), Entomophthoromycota (orders Entomophthorales and Neozygitales), and Onygenales (genus Ascosphaera). Our review focuses on aspects of the evolutionary biology of these fungi that have remained underemphasized in previous reviews. To ensure integration with the better-known domains of insect pathology research, we followed a conceptual framework formulated by Tinbergen, asking complementary questions on mechanism, ontogeny, phylogeny, and adaptation. We aim to provide an introduction to the merits of evolutionary approaches for readers with a background in invertebrate pathology research and to make the insect pathogenic fungi more accessible as model systems for evolutionary biologists. We identify a number of questions in which fundamental research can offer novel insights into the evolutionary forces that have shaped host specialization and life-history traits such as spore number and size, somatic growth rate, toxin production, and interactions with host immune systems.

  16. Multiplicative interaction in network meta-analysis.

    PubMed

    Piepho, Hans-Peter; Madden, Laurence V; Williams, Emlyn R

    2015-02-20

    Meta-analysis of a set of clinical trials is usually conducted using a linear predictor with additive effects representing treatments and trials. Additivity is a strong assumption. In this paper, we consider models for two or more treatments that involve multiplicative terms for interaction between treatment and trial. Multiplicative models provide information on the sensitivity of each treatment effect relative to the trial effect. In developing these models, we make use of a two-way analysis-of-variance approach to meta-analysis and consider fixed or random trial effects. It is shown using two examples that models with multiplicative terms may fit better than purely additive models and provide insight into the nature of the trial effect. We also show how to model inconsistency using multiplicative terms.

  17. Ecological Networks: Structure, Interaction Strength, and Stability

    NASA Astrophysics Data System (ADS)

    Bhattacharyya, Samit; Sinha, Somdatta

    The fundamental building blocks of any ecosystem, the food webs, which are assemblages of species through various interconnections, provide a central concept in ecology. The study of a food web allows abstractions of the complexity and interconnectedness of natural communities that transcend the specific details of the underlying systems. For example, Fig. 1 shows a typical food web, where the species are connected through their feeding relationships. The top predator, Heliaster (starfish) feeds on many gastropods like Hexaplex, Morula, Cantharus, etc., some of whom predate on each other [129]. Interactions between species in a food web can be of many types, such as predation, competition, mutualism, commensalism, and ammensalism (see Section 1.1, Fig. 2).

  18. The Kinetochore Interaction Network (KIN) of ascomycetes

    PubMed Central

    Freitag, Michael

    2016-01-01

    Chromosome segregation relies on coordinated activity of a large assembly of proteins, the “Kinetochore Interaction Network” (KIN). How conserved the underlying mechanisms driving the epigenetic phenomenon of centromere and kinetochore assembly and maintenance are remains unclear, even though various eukaryotic models have been studied. More than 50 different proteins, many in multiple copies, comprise the KIN or are associated with fungal centromeres and kinetochores. Proteins isolated from immune sera recognized centromeric regions on chromosomes and were thus named centromere proteins (“CENPs”). CENP-A, sometimes called “centromere-specific H3” (CenH3), is incorporated into nucleosomes within or near centromeres. The “constitutive centromere-associated network” (CCAN) assembles on this specialized chromatin, likely based on specific interactions with and requiring presence of CENP-C. The outer kinetochore comprises the Knl1-Mis12-Ndc80 (“KMN”) protein complexes that connect the CCAN to spindles, accomplished by binding and stabilizing microtubules (MTs) and in the process generating load-bearing assemblies for chromatid segregation. In most fungi the Dam1/DASH complex connects the KMN complexes to MTs. Fungi present a rich resource to investigate mechanistic commonalities but also differences in kinetochore architecture. While ascomycetes have sets of CCAN and KMN proteins that are conserved with those of either budding yeast or metazoans, searching other major branches of the fungal kingdom revealed that CCAN proteins are poorly conserved at the primary sequence level. Several conserved binding motifs or domains within KMN complexes have been described recently, and these features of ascomycete KIN proteins are shared with most metazoan proteins. In addition, several ascomycete-specific domains have been identified here. PMID:26908646

  19. GINI: From ISH Images to Gene Interaction Networks

    PubMed Central

    Puniyani, Kriti; Xing, Eric P.

    2013-01-01

    Accurate inference of molecular and functional interactions among genes, especially in multicellular organisms such as Drosophila, often requires statistical analysis of correlations not only between the magnitudes of gene expressions, but also between their temporal-spatial patterns. The ISH (in-situ-hybridization)-based gene expression micro-imaging technology offers an effective approach to perform large-scale spatial-temporal profiling of whole-body mRNA abundance. However, analytical tools for discovering gene interactions from such data remain an open challenge due to various reasons, including difficulties in extracting canonical representations of gene activities from images, and in inference of statistically meaningful networks from such representations. In this paper, we present GINI, a machine learning system for inferring gene interaction networks from Drosophila embryonic ISH images. GINI builds on a computer-vision-inspired vector-space representation of the spatial pattern of gene expression in ISH images, enabled by our recently developed system; and a new multi-instance-kernel algorithm that learns a sparse Markov network model, in which, every gene (i.e., node) in the network is represented by a vector-valued spatial pattern rather than a scalar-valued gene intensity as in conventional approaches such as a Gaussian graphical model. By capturing the notion of spatial similarity of gene expression, and at the same time properly taking into account the presence of multiple images per gene via multi-instance kernels, GINI is well-positioned to infer statistically sound, and biologically meaningful gene interaction networks from image data. Using both synthetic data and a small manually curated data set, we demonstrate the effectiveness of our approach in network building. Furthermore, we report results on a large publicly available collection of Drosophila embryonic ISH images from the Berkeley Drosophila Genome Project, where GINI makes novel and

  20. Empirical temporal networks of face-to-face human interactions

    NASA Astrophysics Data System (ADS)

    Barrat, A.; Cattuto, C.; Colizza, V.; Gesualdo, F.; Isella, L.; Pandolfi, E.; Pinton, J.-F.; Ravà, L.; Rizzo, C.; Romano, M.; Stehlé, J.; Tozzi, A. E.; Van den Broeck, W.

    2013-09-01

    The ever increasing adoption of mobile technologies and ubiquitous services allows to sense human behavior at unprecedented level of details and scale. Wearable sensors, in particular, open up a new window on human mobility and proximity in a variety of indoor environments. Here we review stylized facts on the structural and dynamical properties of empirical networks of human face-to-face proximity, measured in three different real-world contexts: an academic conference, a hospital ward, and a museum exhibition. First, we discuss the structure of the aggregated contact networks, that project out the detailed ordering of contact events while preserving temporal heterogeneities in their weights. We show that the structural properties of aggregated networks highlight important differences and unexpected similarities across contexts, and discuss the additional complexity that arises from attributes that are typically associated with nodes in real-world interaction networks, such as role classes in hospitals. We then consider the empirical data at the finest level of detail, i.e., we consider time-dependent networks of face-to-face proximity between individuals. To gain insights on the effects that causal constraints have on spreading processes, we simulate the dynamics of a simple susceptible-infected model over the empirical time-resolved contact data. We show that the spreading pathways for the epidemic process are strongly affected by the temporal structure of the network data, and that the mere knowledge of static aggregated networks leads to erroneous conclusions about the transmission paths on the corresponding dynamical networks.

  1. Hazard Interactions and Interaction Networks (Cascades) within Multi-Hazard Methodologies

    NASA Astrophysics Data System (ADS)

    Gill, Joel; Malamud, Bruce D.

    2016-04-01

    Here we combine research and commentary to reinforce the importance of integrating hazard interactions and interaction networks (cascades) into multi-hazard methodologies. We present a synthesis of the differences between 'multi-layer single hazard' approaches and 'multi-hazard' approaches that integrate such interactions. This synthesis suggests that ignoring interactions could distort management priorities, increase vulnerability to other spatially relevant hazards or underestimate disaster risk. We proceed to present an enhanced multi-hazard framework, through the following steps: (i) describe and define three groups (natural hazards, anthropogenic processes and technological hazards/disasters) as relevant components of a multi-hazard environment; (ii) outline three types of interaction relationship (triggering, increased probability, and catalysis/impedance); and (iii) assess the importance of networks of interactions (cascades) through case-study examples (based on literature, field observations and semi-structured interviews). We further propose visualisation frameworks to represent these networks of interactions. Our approach reinforces the importance of integrating interactions between natural hazards, anthropogenic processes and technological hazards/disasters into enhanced multi-hazard methodologies. Multi-hazard approaches support the holistic assessment of hazard potential, and consequently disaster risk. We conclude by describing three ways by which understanding networks of interactions contributes to the theoretical and practical understanding of hazards, disaster risk reduction and Earth system management. Understanding interactions and interaction networks helps us to better (i) model the observed reality of disaster events, (ii) constrain potential changes in physical and social vulnerability between successive hazards, and (iii) prioritise resource allocation for mitigation and disaster risk reduction.

  2. Epidemic spreading in networks with nonrandom long-range interactions.

    PubMed

    Estrada, Ernesto; Kalala-Mutombo, Franck; Valverde-Colmeiro, Alba

    2011-09-01

    An "infection," understood here in a very broad sense, can be propagated through the network of social contacts among individuals. These social contacts include both "close" contacts and "casual" encounters among individuals in transport, leisure, shopping, etc. Knowing the first through the study of the social networks is not a difficult task, but having a clear picture of the network of casual contacts is a very hard problem in a society of increasing mobility. Here we assume, on the basis of several pieces of empirical evidence, that the casual contacts between two individuals are a function of their social distance in the network of close contacts. Then, we assume that we know the network of close contacts and infer the casual encounters by means of nonrandom long-range (LR) interactions determined by the social proximity of the two individuals. This approach is then implemented in a susceptible-infected-susceptible (SIS) model accounting for the spread of infections in complex networks. A parameter called "conductance" controls the feasibility of those casual encounters. In a zero conductance network only contagion through close contacts is allowed. As the conductance increases the probability of having casual encounters also increases. We show here that as the conductance parameter increases, the rate of propagation increases dramatically and the infection is less likely to die out. This increment is particularly marked in networks with scale-free degree distributions, where infections easily become epidemics. Our model provides a general framework for studying epidemic spreading in networks with arbitrary topology with and without casual contacts accounted for by means of LR interactions.

  3. Mining the Modular Structure of Protein Interaction Networks

    PubMed Central

    Furlong, Laura Inés; Chernomoretz, Ariel

    2015-01-01

    Background Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithms exist to infer topological network partitions. However, due to respective technical idiosyncrasies they might produce dissimilar modular decompositions of a given network. In this contribution, we aimed to analyze how alternative modular descriptions could condition the outcome of follow-up network biology analysis. Methodology We considered a human protein interaction network and two paradigmatic cluster recognition algorithms, namely: the Clauset-Newman-Moore and the infomap procedures. We analyzed to what extent both methodologies yielded different results in terms of granularity and biological congruency. In addition, taking into account Guimera’s cartographic role characterization of network nodes, we explored how the adoption of a given clustering methodology impinged on the ability to highlight relevant network meso-scale connectivity patterns. Results As a case study we considered a set of aging related proteins and showed that only the high-resolution modular description provided by infomap, could unveil statistically significant associations between them and inter/intra modular cartographic features. Besides reporting novel biological insights that could be gained from the discovered associations, our contribution warns against possible technical concerns that might affect the tools used to mine for interaction patterns in network biology studies. In particular our results suggested that sub-optimal partitions from the strict point of view of their modularity levels might still be worth being analyzed when meso-scale features were to be explored in connection with external source of biological knowledge. PMID:25856434

  4. 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

  5. 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.

  6. Ecological interaction and phylogeny, studying functionality on composed networks

    NASA Astrophysics Data System (ADS)

    Cruz, Claudia P. T.; Fonseca, Carlos Roberto; Corso, Gilberto

    2012-02-01

    We study a class of composed networks that are formed by two tree networks, TP and TA, whose end points touch each other through a bipartite network BPA. We explore this network using a functional approach. We are interested in how much the topology, or the structure, of TX (X=A or P) determines the links of BPA. This composed structure is a useful model in evolutionary biology, where TP and TA are the phylogenetic trees of plants and animals that interact in an ecological community. We make use of ecological networks of dispersion of fruits, which are formed by frugivorous animals and plants with fruits; the animals, usually birds, eat fruits and disperse their seeds. We analyse how the phylogeny of TX determines or is correlated with BPA using a Monte Carlo approach. We use the phylogenetic distance among elements that interact with a given species to construct an index κ that quantifies the influence of TX over BPA. The algorithm is based on the assumption that interaction matrices that follows a phylogeny of TX have a total phylogenetic distance smaller than the average distance of an ensemble of Monte Carlo realisations. We find that the effect of phylogeny of animal species is more pronounced in the ecological matrix than plant phylogeny.

  7. Optimizing a global alignment of protein interaction networks

    PubMed Central

    Chindelevitch, Leonid; Ma, Cheng-Yu; Liao, Chung-Shou; Berger, Bonnie

    2013-01-01

    Motivation: The global alignment of protein interaction networks is a widely studied problem. It is an important first step in understanding the relationship between the proteins in different species and identifying functional orthologs. Furthermore, it can provide useful insights into the species’ evolution. Results: We propose a novel algorithm, PISwap, for optimizing global pairwise alignments of protein interaction networks, based on a local optimization heuristic that has previously demonstrated its effectiveness for a variety of other intractable problems. PISwap can begin with different types of network alignment approaches and then iteratively adjust the initial alignments by incorporating network topology information, trading it off for sequence information. In practice, our algorithm efficiently refines other well-studied alignment techniques with almost no additional time cost. We also show the robustness of the algorithm to noise in protein interaction data. In addition, the flexible nature of this algorithm makes it suitable for different applications of network alignment. This algorithm can yield interesting insights into the evolutionary dynamics of related species. Availability: Our software is freely available for non-commercial purposes from our Web site, http://piswap.csail.mit.edu/. Contact: bab@csail.mit.edu or csliao@ie.nthu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24048352

  8. An integrated text mining framework for metabolic interaction network reconstruction.

    PubMed

    Patumcharoenpol, Preecha; Doungpan, Narumol; Meechai, Asawin; Shen, Bairong; Chan, Jonathan H; Vongsangnak, Wanwipa

    2016-01-01

    Text mining (TM) in the field of biology is fast becoming a routine analysis for the extraction and curation of biological entities (e.g., genes, proteins, simple chemicals) as well as their relationships. Due to the wide applicability of TM in situations involving complex relationships, it is valuable to apply TM to the extraction of metabolic interactions (i.e., enzyme and metabolite interactions) through metabolic events. Here we present an integrated TM framework containing two modules for the extraction of metabolic events (Metabolic Event Extraction module-MEE) and for the construction of a metabolic interaction network (Metabolic Interaction Network Reconstruction module-MINR). The proposed integrated TM framework performed well based on standard measures of recall, precision and F-score. Evaluation of the MEE module using the constructed Metabolic Entities (ME) corpus yielded F-scores of 59.15% and 48.59% for the detection of metabolic events for production and consumption, respectively. As for the testing of the entity tagger for Gene and Protein (GP) and metabolite with the test corpus, the obtained F-score was greater than 80% for the Superpathway of leucine, valine, and isoleucine biosynthesis. Mapping of enzyme and metabolite interactions through network reconstruction showed a fair performance for the MINR module on the test corpus with F-score >70%. Finally, an application of our integrated TM framework on a big-scale data (i.e., EcoCyc extraction data) for reconstructing a metabolic interaction network showed reasonable precisions at 69.93%, 70.63% and 46.71% for enzyme, metabolite and enzyme-metabolite interaction, respectively. This study presents the first open-source integrated TM framework for reconstructing a metabolic interaction network. This framework can be a powerful tool that helps biologists to extract metabolic events for further reconstruction of a metabolic interaction network. The ME corpus, test corpus, source code, and virtual

  9. Simulating market dynamics: interactions between consumer psychology and social networks.

    PubMed

    Janssen, Marco A; Jager, Wander

    2003-01-01

    Markets can show different types of dynamics, from quiet markets dominated by one or a few products, to markets with continual penetration of new and reintroduced products. In a previous article we explored the dynamics of markets from a psychological perspective using a multi-agent simulation model. The main results indicated that the behavioral rules dominating the artificial consumer's decision making determine the resulting market dynamics, such as fashions, lock-in, and unstable renewal. Results also show the importance of psychological variables like social networks, preferences, and the need for identity to explain the dynamics of markets. In this article we extend this work in two directions. First, we will focus on a more systematic investigation of the effects of different network structures. The previous article was based on Watts and Strogatz's approach, which describes the small-world and clustering characteristics in networks. More recent research demonstrated that many large networks display a scale-free power-law distribution for node connectivity. In terms of market dynamics this may imply that a small proportion of consumers may have an exceptional influence on the consumptive behavior of others (hubs, or early adapters). We show that market dynamics is a self-organized property depending on the interaction between the agents' decision-making process (heuristics), the product characteristics (degree of satisfaction of unit of consumption, visibility), and the structure of interactions between agents (size of network and hubs in a social network). PMID:14761255

  10. Topological Signatures of Species Interactions in Metabolic Networks

    PubMed Central

    Feldman, Marcus W.

    2009-01-01

    Abstract The topology of metabolic networks can provide insight not only into the metabolic processes that occur within each species, but also into interactions between different species. Here, we introduce a novel pair-wise, topology-based measure of biosynthetic support, reflecting the extent to which the nutritional requirements of one species could be satisfied by the biosynthetic capacity of another. To evaluate the biosynthetic support for a given pair of species, we use a graph-based algorithm to identify the set of exogenously acquired compounds in the metabolic network of the first species, and calculate the fraction of this set that occurs in the metabolic network of the second species. Reconstructing the metabolic network of 569 bacterial species and several eukaryotes, and calculating the biosynthetic support score for all bacterial-eukaryotic pairs, we show that this measure indeed reflects host-parasite interactions and facilitates a successful prediction of such interactions on a large-scale. Integrating this method with phylogenetic analysis and calculating the biosynthetic support of ancestral species in the Firmicutes division (as well as other bacterial divisions) further reveals a large-scale evolutionary trend of biosynthetic capacity loss in parasites. The inference of ecological features from genomic-based data presented here lays the foundations for an exciting “reverse ecology” framework for studying the complex web of interactions characterizing various ecosystems. PMID:19178139

  11. Characterizing interactions in online social networks during exceptional events

    NASA Astrophysics Data System (ADS)

    Omodei, Elisa; De Domenico, Manlio; Arenas, Alex

    2015-08-01

    Nowadays, millions of people interact on a daily basis on online social media like Facebook and Twitter, where they share and discuss information about a wide variety of topics. In this paper, we focus on a specific online social network, Twitter, and we analyze multiple datasets each one consisting of individuals' online activity before, during and after an exceptional event in terms of volume of the communications registered. We consider important events that occurred in different arenas that range from policy to culture or science. For each dataset, the users' online activities are modeled by a multilayer network in which each layer conveys a different kind of interaction, specifically: retweeting, mentioning and replying. This representation allows us to unveil that these distinct types of interaction produce networks with different statistical properties, in particular concerning the degree distribution and the clustering structure. These results suggests that models of online activity cannot discard the information carried by this multilayer representation of the system, and should account for the different processes generated by the different kinds of interactions. Secondly, our analysis unveils the presence of statistical regularities among the different events, suggesting that the non-trivial topological patterns that we observe may represent universal features of the social dynamics on online social networks during exceptional events.

  12. Brain Network Interactions in Auditory, Visual and Linguistic Processing

    ERIC Educational Resources Information Center

    Horwitz, Barry; Braun, Allen R.

    2004-01-01

    In the paper, we discuss the importance of network interactions between brain regions in mediating performance of sensorimotor and cognitive tasks, including those associated with language processing. Functional neuroimaging, especially PET and fMRI, provide data that are obtained essentially simultaneously from much of the brain, and thus are…

  13. Analysing Interactions in a Teacher Network Forum: A Sociometric Approach

    ERIC Educational Resources Information Center

    Lisboa, Eliana Santana; Coutinho, Clara Pereira

    2013-01-01

    This article presents the sociometric analysis of the interactions in a forum of a social network created for the professional development of Portuguese-speaking teachers. The main goal of the forum, which was titled Stricto Sensu, was to discuss the educational value of programmes that joined the distance learning model in Brazil. The empirical…

  14. Topological signatures of species interactions in metabolic networks.

    PubMed

    Borenstein, Elhanan; Feldman, Marcus W

    2009-02-01

    The topology of metabolic networks can provide insight not only into the metabolic processes that occur within each species, but also into interactions between different species. Here, we introduce a novel pair-wise, topology-based measure of biosynthetic support, reflecting the extent to which the nutritional requirements of one species could be satisfied by the biosynthetic capacity of another. To evaluate the biosynthetic support for a given pair of species, we use a graph-based algorithm to identify the set of exogenously acquired compounds in the metabolic network of the first species, and calculate the fraction of this set that occurs in the metabolic network of the second species. Reconstructing the metabolic network of 569 bacterial species and several eukaryotes, and calculating the biosynthetic support score for all bacterial-eukaryotic pairs, we show that this measure indeed reflects host-parasite interactions and facilitates a successful prediction of such interactions on a large-scale. Integrating this method with phylogenetic analysis and calculating the biosynthetic support of ancestral species in the Firmicutes division (as well as other bacterial divisions) further reveals a large-scale evolutionary trend of biosynthetic capacity loss in parasites. The inference of ecological features from genomic-based data presented here lays the foundations for an exciting "reverse ecology" framework for studying the complex web of interactions characterizing various ecosystems.

  15. The Three Attentional Networks: On Their Independence and Interactions

    ERIC Educational Resources Information Center

    Callejas, Alicia; Lupianez, Juan; Tudela, Pio

    2004-01-01

    The present investigation was aimed to the study of the three attentional networks (Alerting, Orienting, and Executive Function) and their interactions. A modification of the task developed by Fan, McCandliss, Sommer, Raz, and Posner (2002) was used, in which a cost and benefit paradigm was combined with a flanker task and an alerting signal. We…

  16. 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

  17. 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.

  18. 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

  19. 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.

  20. Visualizing Gene - Interactions within the Rice and Maize Network

    NASA Astrophysics Data System (ADS)

    Sampong, A.; Feltus, A.; Smith, M.

    2014-12-01

    The purpose of this research was to design a simpler visualization tool for comparing or viewing gene interaction graphs in systems biology. This visualization tool makes it possible and easier for a researcher to visualize the biological metadata of a plant and interact with the graph on a webpage. Currently available visualization software like Cytoscape and Walrus are difficult to interact with and do not scale effectively for large data sets, limiting the ability to visualize interactions within a biological system. The visualization tool developed is useful for viewing and interpreting the dataset of a gene interaction network. The graph layout drawn by this visualization tool is an improvement from the previous method of comparing lines of genes in two separate data files to, now having the ability to visually see the layout of the gene networks and how the two systems are related. The graph layout presented by the visualization tool draws a graph of the sample rice and maize gene networks, linking the common genes found in both plants and highlighting the functions served by common genes from each plant. The success of this visualization tool will enable Dr. Feltus to continue his investigations and draw conclusions on the biological evolution of the sorghum plant as well. REU Funded by NSF ACI Award 1359223 Vetria L. Byrd, PI

  1. 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

  2. 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.

  3. 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

  4. 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

  5. Digital Ecology: Coexistence and Domination among Interacting Networks

    NASA Astrophysics Data System (ADS)

    Kleineberg, Kaj-Kolja; Boguñá, Marián

    2015-05-01

    The overwhelming success of Web 2.0, within which online social networks are key actors, has induced a paradigm shift in the nature of human interactions. The user-driven character of Web 2.0 services has allowed researchers to quantify large-scale social patterns for the first time. However, the mechanisms that determine the fate of networks at the system level are still poorly understood. For instance, the simultaneous existence of multiple digital services naturally raises questions concerning which conditions these services can coexist under. Analogously to the case of population dynamics, the digital world forms a complex ecosystem of interacting networks. The fitness of each network depends on its capacity to attract and maintain users’ attention, which constitutes a limited resource. In this paper, we introduce an ecological theory of the digital world which exhibits stable coexistence of several networks as well as the dominance of an individual one, in contrast to the competitive exclusion principle. Interestingly, our theory also predicts that the most probable outcome is the coexistence of a moderate number of services, in agreement with empirical observations.

  6. 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

  7. 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.

  8. iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints.

    PubMed

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

    2013-11-21

    Many crucial functions in life, such as heartbeat, sensory transduction and central nervous system response, are controlled by cell signalings via various ion channels. Therefore, ion channels have become an excellent drug target, and study of ion channel-drug interaction networks is an important topic for drug development. However, it is both time-consuming and costly to determine whether a drug and a protein ion channel are interacting with each other in a cellular network by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (three-dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most protein ion channels are still unknown. With the avalanche of protein sequences generated in the post-genomic age, it is highly desirable to develop the sequence-based computational method to address this problem. To take up the challenge, we developed a new predictor called iCDI-PseFpt, in which the protein ion-channel sample is formulated by the PseAAC (pseudo amino acid composition) generated with the gray model theory, the drug compound by the 2D molecular fingerprint, and the operation engine is the fuzzy K-nearest neighbor algorithm. The overall success rate achieved by iCDI-PseFpt via the jackknife cross-validation was 87.27%, which is remarkably higher than that by any of the existing predictors in this area. As a user-friendly web-server, iCDI-PseFpt is freely accessible to the public at the website http://www.jci-bioinfo.cn/iCDI-PseFpt/. Furthermore, 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 the paper just for its integrity. It has not escaped our notice that the current approach can also be used to study other drug-target interaction networks.

  9. Auditing medical records accesses via healthcare interaction networks.

    PubMed

    Chen, You; Nyemba, Steve; Malin, Bradley

    2012-01-01

    Healthcare organizations are deploying increasingly complex clinical information systems to support patient care. Traditional information security practices (e.g., role-based access control) are embedded in enterprise-level systems, but are insufficient to ensure patient privacy. This is due, in part, to the dynamic nature of healthcare, which makes it difficult to predict which care providers need access to what and when. In this paper, we show that modeling operations at a higher level of granularity (e.g., the departmental level) are stable in the context of a relational network, which may enable more effective auditing strategies. We study three months of access logs from a large academic medical center to illustrate that departmental interaction networks exhibit certain invariants, such as the number, strength, and reciprocity of relationships. We further show that the relations extracted from the network can be leveraged to assess the extent to which a patient's care satisfies expected organizational behavior.

  10. Auditing Medical Records Accesses via Healthcare Interaction Networks

    PubMed Central

    Chen, You; Nyemba, Steve; Malin, Bradley

    2012-01-01

    Healthcare organizations are deploying increasingly complex clinical information systems to support patient care. Traditional information security practices (e.g., role-based access control) are embedded in enterprise-level systems, but are insufficient to ensure patient privacy. This is due, in part, to the dynamic nature of healthcare, which makes it difficult to predict which care providers need access to what and when. In this paper, we show that modeling operations at a higher level of granularity (e.g., the departmental level) are stable in the context of a relational network, which may enable more effective auditing strategies. We study three months of access logs from a large academic medical center to illustrate that departmental interaction networks exhibit certain invariants, such as the number, strength, and reciprocity of relationships. We further show that the relations extracted from the network can be leveraged to assess the extent to which a patient’s care satisfies expected organizational behavior. PMID:23304277

  11. Agreement dynamics on interaction networks with diverse topologies.

    PubMed

    Barrat, Alain; Baronchelli, Andrea; Dall'Asta, Luca; Loreto, Vittorio

    2007-06-01

    We review the behavior of a recently introduced model of agreement dynamics, called the "Naming Game." This model describes the self-organized emergence of linguistic conventions and the establishment of simple communication systems in a population of agents with pairwise local interactions. The mechanisms of convergence towards agreement strongly depend on the network of possible interactions between the agents. In particular, the mean-field case in which all agents communicate with all the others is not efficient, since a large temporary memory is requested for the agents. On the other hand, regular lattice topologies lead to a fast local convergence but to a slow global dynamics similar to coarsening phenomena. The embedding of the agents in a small-world network represents an interesting tradeoff: a local consensus is easily reached, while the long-range links allow to bypass coarsening-like convergence. We also consider alternative adaptive strategies which can lead to faster global convergence.

  12. Modeling of light-matter interactions with neural networks

    SciTech Connect

    Selle, Reimer; Vogt, Gerhard; Brixner, Tobias; Gerber, Gustav; Metzler, Richard; Kinzel, Wolfgang

    2007-08-15

    We show that a neural network (NN) can be used for automated generation of computer models of light-matter interaction. Nonlinear input-output maps are created for phase-shaped femtosecond laser pulses in the exemplary cases of second-harmonic generation and molecular fluorescence yield. Simulations and experiments demonstrate that the NN has the capability of generalizing and extrapolating beyond initial training data, by predicting the response of the investigated systems for arbitrary laser pulse shapes. Applications are envisioned in the area of quantum control, specifically for the interpolation and extrapolation of control maps and possibly as a tool for investigating control mechanisms. In a wider scope, neural networks might generally provide effective computer models for light-matter interactions for cases where ab initio calculations are intractable.

  13. Reconstruction and Application of Protein–Protein Interaction Network

    PubMed Central

    Hao, Tong; Peng, Wei; Wang, Qian; Wang, Bin; Sun, Jinsheng

    2016-01-01

    The protein-protein interaction network (PIN) is a useful tool for systematic investigation of the complex biological activities in the cell. With the increasing interests on the proteome-wide interaction networks, PINs have been reconstructed for many species, including virus, bacteria, plants, animals, and humans. With the development of biological techniques, the reconstruction methods of PIN are further improved. PIN has gradually penetrated many fields in biological research. In this work we systematically reviewed the development of PIN in the past fifteen years, with respect to its reconstruction and application of function annotation, subsystem investigation, evolution analysis, hub protein analysis, and regulation mechanism analysis. Due to the significant role of PIN in the in-depth exploration of biological process mechanisms, PIN will be preferred by more and more researchers for the systematic study of the protein systems in various kinds of organisms. PMID:27338356

  14. 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…

  15. 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.

  16. 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

  17. Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions.

    PubMed

    Wang, Xiang; Öngür, Dost; Auerbach, Randy P; Yao, Shuqiao

    2016-01-01

    Although it is generally accepted that cognitive factors contribute to the pathogenesis of major depressive disorder (MDD), there are missing links between behavioral and biological models of depression. Nevertheless, research employing neuroimaging technologies has elucidated some of the neurobiological mechanisms related to cognitive-vulnerability factors, especially from a whole-brain, dynamic perspective. In this review, we integrate well-established cognitive-vulnerability factors for MDD and corresponding neural mechanisms in intrinsic networks using a dual-process framework. We propose that the dynamic alteration and imbalance among the intrinsic networks, both in the resting-state and the rest-task transition stages, contribute to the development of cognitive vulnerability and MDD. Specifically, we propose that abnormally increased resting-state default mode network (DMN) activity and connectivity (mainly in anterior DMN regions) contribute to the development of cognitive vulnerability. Furthermore, when subjects confront negative stimuli in the period of rest-to-task transition, the following three kinds of aberrant network interactions have been identified as facilitators of vulnerability and dysphoric mood, each through a different cognitive mechanism: DMN dominance over the central executive network (CEN), an impaired salience network-mediated switching between the DMN and CEN, and ineffective CEN modulation of the DMN. This focus on interrelated networks and brain-activity changes between rest and task states provides a neural-system perspective for future research on cognitive vulnerability and resilience, and may potentially guide the development of new intervention strategies for MDD. PMID:27148911

  18. At the Intersection of Networks and Highly Interactive Online Games

    NASA Astrophysics Data System (ADS)

    Armitage, Grenville

    The game industry continues to evolves its techniques for extracting the most realistic 'immersion' experience for players given the vagaries on best-effort Internet service. A key challenge for service providers is understanding the characteristics of traffic imposed on networks by games, and their service quality requirements. Interactive online games are particularly susceptible to the side effects of other non-interactive (or delay- and loss-tolerant) traffic sharing next- generation access links. This creates challenges out toward the edges, where high-speed home LANs squeeze through broadband consumer access links to reach the Internet. In this chapter we identify a range of research work exploring many issues associated with the intersection of highly interactive games and the Internet, and hopefully stimulate some further thinking along these lines.

  19. Measuring Asymmetric Interactions in Resting State Brain Networks*

    PubMed Central

    Joshi, Anand A.; Salloum, Ronald; Bhushan, Chitresh; Leahy, Richard M.

    2015-01-01

    Directed graph representations of brain networks are increasingly being used in brain image analysis to indicate the direction and level of influence among brain regions. Most of the existing techniques for directed graph representations are based on time series analysis and the concept of causality, and use time lag information in the brain signals. These time lag-based techniques can be inadequate for functional magnetic resonance imaging (fMRI) signal analysis due to the limited time resolution of fMRI as well as the low frequency hemodynamic response. The aim of this paper is to present a novel measure of necessity that uses asymmetry in the joint distribution of brain activations to infer the direction and level of interaction among brain regions. We present a mathematical formula for computing necessity and extend this measure to partial necessity, which can potentially distinguish between direct and indirect interactions. These measures do not depend on time lag for directed modeling of brain interactions and therefore are more suitable for fMRI signal analysis. The necessity measures were used to analyze resting state fMRI data to determine the presence of hierarchy and asymmetry of brain interactions during resting state. We performed ROI-wise analysis using the proposed necessity measures to study the default mode network. The empirical joint distribution of the fMRI signals was determined using kernel density estimation, and was used for computation of the necessity and partial necessity measures. The significance of these measures was determined using a one-sided Wilcoxon rank-sum test. Our results are consistent with the hypothesis that the posterior cingulate cortex plays a central role in the default mode network. PMID:26221690

  20. Measuring Asymmetric Interactions in Resting State Brain Networks.

    PubMed

    Joshi, Anand A; Salloum, Ronald; Bhushan, Chitresh; Leahy, Richard M

    2015-01-01

    Directed graph representations of brain networks are increasingly being used to indicate the direction and level of influence among brain regions. Most of the existing techniques for directed graph representations are based on time series analysis and the concept of causality, and use time lag information in the brain signals. These time lag-based techniques can be inadequate for functional magnetic resonance imaging (fMRI) signal analysis due to the limited time resolution of fMRI as well as the low frequency hemodynamic response. The aim of this paper is to present a novel measure of necessity that uses asymmetry in the joint distribution of brain activations to infer the direction and level of interaction among brain regions. We present a mathematical formula for computing necessity and extend this measure to partial necessity, which can potentially distinguish between direct and indirect interactions. These measures do not depend on time lag for directed modeling of brain interactions and therefore are more suitable for fMRI signal analysis. The necessity measures were used to analyze resting state fMRI data to determine the presence of hierarchy and asymmetry of brain interactions during resting state. We performed ROI-wise analysis using the proposed necessity measures to study the default mode network. The empirical joint distribution of the fMRI signals was determined using kernel density estimation, and was used for computation of the necessity and partial necessity measures. The significance of these measures was determined using a one-sided Wilcoxon rank-sum test. Our results are consistent with the hypothesis that the posterior cingulate cortex plays a central role in the default mode network. PMID:26221690

  1. Plasticity of genetic interactions in metabolic networks of yeast.

    PubMed

    Harrison, Richard; Papp, Balázs; Pál, Csaba; Oliver, Stephen G; Delneri, Daniela

    2007-02-13

    Why are most genes dispensable? The impact of gene deletions may depend on the environment (plasticity), the presence of compensatory mechanisms (mutational robustness), or both. Here, we analyze the interaction between these two forces by exploring the condition-dependence of synthetic genetic interactions that define redundant functions and alternative pathways. We performed systems-level flux balance analysis of the yeast (Saccharomyces cerevisiae) metabolic network to identify genetic interactions and then tested the model's predictions with in vivo gene-deletion studies. We found that the majority of synthetic genetic interactions are restricted to certain environmental conditions, partly because of the lack of compensation under some (but not all) nutrient conditions. Moreover, the phylogenetic cooccurrence of synthetically interacting pairs is not significantly different from random expectation. These findings suggest that these gene pairs have at least partially independent functions, and, hence, compensation is only a byproduct of their evolutionary history. Experimental analyses that used multiple gene deletion strains not only confirmed predictions of the model but also showed that investigation of false predictions may both improve functional annotation within the model and also lead to the discovery of higher-order genetic interactions. Our work supports the view that functional redundancy may be more apparent than real, and it offers a unified framework for the evolution of environmental adaptation and mutational robustness. PMID:17284612

  2. Attractive interactions among intermediate filaments determine network mechanics in vitro.

    PubMed

    Pawelzyk, Paul; Mücke, Norbert; Herrmann, Harald; Willenbacher, Norbert

    2014-01-01

    Mechanical and structural properties of K8/K18 and vimentin intermediate filament (IF) networks have been investigated using bulk mechanical rheometry and optical microrheology including diffusing wave spectroscopy and multiple particle tracking. A high elastic modulus G0 at low protein concentration c, a weak concentration dependency of G0 (G0 ∼ c(0.5 ± 0.1)) and pronounced strain stiffening are found for these systems even without external crossbridgers. Strong attractive interactions among filaments are required to maintain these characteristic mechanical features, which have also been reported for various other IF networks. Filament assembly, the persistence length of the filaments and the network mesh size remain essentially unaffected when a nonionic surfactant is added, but strain stiffening is completely suppressed, G0 drops by orders of magnitude and exhibits a scaling G0 ∼ c(1.9 ± 0.2) in agreement with microrheological measurements and as expected for entangled networks of semi-flexible polymers. Tailless K8Δ/K18ΔT and various other tailless filament networks do not exhibit strain stiffening, but still show high G0 values. Therefore, two binding sites are proposed to exist in IF networks. A weaker one mediated by hydrophobic amino acid clusters in the central rod prevents stretched filaments between adjacent cross-links from thermal equilibration and thus provides the high G0 values. Another strong one facilitating strain stiffening is located in the tail domain with its high fraction of hydrophobic amino acid sequences. Strain stiffening is less pronounced for vimentin than for K8/K18 due to electrostatic repulsion forces partly compensating the strong attraction at filament contact points.

  3. Fractional Dynamics of Network Growth Constrained by Aging Node Interactions

    PubMed Central

    Safdari, Hadiseh; Zare Kamali, Milad; Shirazi, Amirhossein; Khalighi, Moein; Jafari, Gholamreza; Ausloos, Marcel

    2016-01-01

    In many social complex systems, in which agents are linked by non-linear interactions, the history of events strongly influences the whole network dynamics. However, a class of “commonly accepted beliefs” seems rarely studied. In this paper, we examine how the growth process of a (social) network is influenced by past circumstances. In order to tackle this cause, we simply modify the well known preferential attachment mechanism by imposing a time dependent kernel function in the network evolution equation. This approach leads to a fractional order Barabási-Albert (BA) differential equation, generalizing the BA model. Our results show that, with passing time, an aging process is observed for the network dynamics. The aging process leads to a decay for the node degree values, thereby creating an opposing process to the preferential attachment mechanism. On one hand, based on the preferential attachment mechanism, nodes with a high degree are more likely to absorb links; but, on the other hand, a node’s age has a reduced chance for new connections. This competitive scenario allows an increased chance for younger members to become a hub. Simulations of such a network growth with aging constraint confirm the results found from solving the fractional BA equation. We also report, as an exemplary application, an investigation of the collaboration network between Hollywood movie actors. It is undubiously shown that a decay in the dynamics of their collaboration rate is found, even including a sex difference. Such findings suggest a widely universal application of the so generalized BA model. PMID:27171424

  4. Fractional Dynamics of Network Growth Constrained by Aging Node Interactions.

    PubMed

    Safdari, Hadiseh; Zare Kamali, Milad; Shirazi, Amirhossein; Khalighi, Moein; Jafari, Gholamreza; Ausloos, Marcel

    2016-01-01

    In many social complex systems, in which agents are linked by non-linear interactions, the history of events strongly influences the whole network dynamics. However, a class of "commonly accepted beliefs" seems rarely studied. In this paper, we examine how the growth process of a (social) network is influenced by past circumstances. In order to tackle this cause, we simply modify the well known preferential attachment mechanism by imposing a time dependent kernel function in the network evolution equation. This approach leads to a fractional order Barabási-Albert (BA) differential equation, generalizing the BA model. Our results show that, with passing time, an aging process is observed for the network dynamics. The aging process leads to a decay for the node degree values, thereby creating an opposing process to the preferential attachment mechanism. On one hand, based on the preferential attachment mechanism, nodes with a high degree are more likely to absorb links; but, on the other hand, a node's age has a reduced chance for new connections. This competitive scenario allows an increased chance for younger members to become a hub. Simulations of such a network growth with aging constraint confirm the results found from solving the fractional BA equation. We also report, as an exemplary application, an investigation of the collaboration network between Hollywood movie actors. It is undubiously shown that a decay in the dynamics of their collaboration rate is found, even including a sex difference. Such findings suggest a widely universal application of the so generalized BA model. PMID:27171424

  5. Network of Interactions Between Ciliates and Phytoplankton During Spring

    PubMed Central

    Posch, Thomas; Eugster, Bettina; Pomati, Francesco; Pernthaler, Jakob; Pitsch, Gianna; Eckert, Ester M.

    2015-01-01

    The annually recurrent spring phytoplankton blooms in freshwater lakes initiate pronounced successions of planktonic ciliate species. Although there is considerable knowledge on the taxonomic diversity of these ciliates, their species-specific interactions with other microorganisms are still not well understood. Here we present the succession patterns of 20 morphotypes of ciliates during spring in Lake Zurich, Switzerland, and we relate their abundances to phytoplankton genera, flagellates, heterotrophic bacteria, and abiotic parameters. Interspecific relationships were analyzed by contemporaneous correlations and time-lagged co-occurrence and visualized as association networks. The contemporaneous network pointed to the pivotal role of distinct ciliate species (e.g., Balanion planctonicum, Rimostrombidium humile) as primary consumers of cryptomonads, revealed a clear overclustering of mixotrophic/omnivorous species, and highlighted the role of Halteria/Pelagohalteria as important bacterivores. By contrast, time-lagged statistical approaches (like local similarity analyses, LSA) proved to be inadequate for the evaluation of high-frequency sampling data. LSA led to a conspicuous inflation of significant associations, making it difficult to establish ecologically plausible interactions between ciliates and other microorganisms. Nevertheless, if adequate statistical procedures are selected, association networks can be powerful tools to formulate testable hypotheses about the autecology of only recently described ciliate species. PMID:26635757

  6. MiRTargetLink--miRNAs, Genes and Interaction Networks.

    PubMed

    Hamberg, Maarten; Backes, Christina; Fehlmann, Tobias; Hart, Martin; Meder, Benjamin; Meese, Eckart; Keller, Andreas

    2016-04-14

    Information on miRNA targeting genes is growing rapidly. For high-throughput experiments, but also for targeted analyses of few genes or miRNAs, easy analysis with concise representation of results facilitates the work of life scientists. We developed miRTargetLink, a tool for automating respective analysis procedures that are frequently applied. Input of the web-based solution is either a single gene or single miRNA, but also sets of genes or miRNAs, can be entered. Validated and predicted targets are extracted from databases and an interaction network is presented. Users can select whether predicted targets, experimentally validated targets with strong or weak evidence, or combinations of those are considered. Central genes or miRNAs are highlighted and users can navigate through the network interactively. To discover the most relevant biochemical processes influenced by the target network, gene set analysis and miRNA set analysis are integrated. As a showcase for miRTargetLink, we analyze targets of five cardiac miRNAs. miRTargetLink is freely available without restrictions at www.ccb.uni-saarland.de/mirtargetlink.

  7. miRTargetLink—miRNAs, Genes and Interaction Networks

    PubMed Central

    Hamberg, Maarten; Backes, Christina; Fehlmann, Tobias; Hart, Martin; Meder, Benjamin; Meese, Eckart; Keller, Andreas

    2016-01-01

    Information on miRNA targeting genes is growing rapidly. For high-throughput experiments, but also for targeted analyses of few genes or miRNAs, easy analysis with concise representation of results facilitates the work of life scientists. We developed miRTargetLink, a tool for automating respective analysis procedures that are frequently applied. Input of the web-based solution is either a single gene or single miRNA, but also sets of genes or miRNAs, can be entered. Validated and predicted targets are extracted from databases and an interaction network is presented. Users can select whether predicted targets, experimentally validated targets with strong or weak evidence, or combinations of those are considered. Central genes or miRNAs are highlighted and users can navigate through the network interactively. To discover the most relevant biochemical processes influenced by the target network, gene set analysis and miRNA set analysis are integrated. As a showcase for miRTargetLink, we analyze targets of five cardiac miRNAs. miRTargetLink is freely available without restrictions at www.ccb.uni-saarland.de/mirtargetlink. PMID:27089332

  8. Real-time hierarchically distributed processing network interaction simulation

    NASA Technical Reports Server (NTRS)

    Zimmerman, W. F.; Wu, C.

    1987-01-01

    The Telerobot Testbed is a hierarchically distributed processing system which is linked together through a standard, commercial Ethernet. Standard Ethernet systems are primarily designed to manage non-real-time information transfer. Therefore, collisions on the net (i.e., two or more sources attempting to send data at the same time) are managed by randomly rescheduling one of the sources to retransmit at a later time interval. Although acceptable for transmitting noncritical data such as mail, this particular feature is unacceptable for real-time hierarchical command and control systems such as the Telerobot. Data transfer and scheduling simulations, such as token ring, offer solutions to collision management, but do not appropriately characterize real-time data transfer/interactions for robotic systems. Therefore, models like these do not provide a viable simulation environment for understanding real-time network loading. A real-time network loading model is being developed which allows processor-to-processor interactions to be simulated, collisions (and respective probabilities) to be logged, collision-prone areas to be identified, and network control variable adjustments to be reentered as a means of examining and reducing collision-prone regimes that occur in the process of simulating a complete task sequence.

  9. Protozoan HSP90-heterocomplex: molecular interaction network and biological significance.

    PubMed

    Figueras, Maria J; Echeverria, Pablo C; Angel, Sergio O

    2014-05-01

    The HSP90 chaperone is a highly conserved protein from bacteria to higher eukaryotes. In eukaryotes, this chaperone participates in different large complexes, such as the HSP90 heterocomplex, which has important biological roles in cell homeostasis and differentiation. The HSP90-heterocomplex is also named the HSP90/HSP70 cycle because different co-chaperones (HIP, HSP40, HOP, p23, AHA1, immunophilins, PP5) participate in this complex by assembling sequentially, from the early to the mature complex. In this review, we analyze the conservation and relevance of HSP90 and the HSP90-heterocomplex in several protozoan parasites, with emphasis in Plasmodium spp., Toxoplasma spp., Leishmania spp. and Trypanosoma spp. In the last years, there has been an outburst of studies based on yeast two-hybrid methodology, co-immunoprecipitation-mass spectrometry and bioinformatics, which have generated a most comprehensive protein-protein interaction (PPI) network of HSP90 and its co-chaperones. This review analyzes the existing PPI networks of HSP90 and its co-chaperones of some protozoan parasites and discusses the usefulness of these powerful tools to analyze the biological role of the HSP90-heterocomplex in these parasites. The generation of a T. gondii HSP90 heterocomplex PPI network based on experimental data and a recent Plasmodium HSP90 heterocomplex PPI network are also included and discussed. As an example, the putative implication of nuclear transport and chromatin (histones and Sir2) as HSP90-heterocomplex interactors is here discussed.

  10. Protozoan HSP90-heterocomplex: molecular interaction network and biological significance.

    PubMed

    Figueras, Maria J; Echeverria, Pablo C; Angel, Sergio O

    2014-05-01

    The HSP90 chaperone is a highly conserved protein from bacteria to higher eukaryotes. In eukaryotes, this chaperone participates in different large complexes, such as the HSP90 heterocomplex, which has important biological roles in cell homeostasis and differentiation. The HSP90-heterocomplex is also named the HSP90/HSP70 cycle because different co-chaperones (HIP, HSP40, HOP, p23, AHA1, immunophilins, PP5) participate in this complex by assembling sequentially, from the early to the mature complex. In this review, we analyze the conservation and relevance of HSP90 and the HSP90-heterocomplex in several protozoan parasites, with emphasis in Plasmodium spp., Toxoplasma spp., Leishmania spp. and Trypanosoma spp. In the last years, there has been an outburst of studies based on yeast two-hybrid methodology, co-immunoprecipitation-mass spectrometry and bioinformatics, which have generated a most comprehensive protein-protein interaction (PPI) network of HSP90 and its co-chaperones. This review analyzes the existing PPI networks of HSP90 and its co-chaperones of some protozoan parasites and discusses the usefulness of these powerful tools to analyze the biological role of the HSP90-heterocomplex in these parasites. The generation of a T. gondii HSP90 heterocomplex PPI network based on experimental data and a recent Plasmodium HSP90 heterocomplex PPI network are also included and discussed. As an example, the putative implication of nuclear transport and chromatin (histones and Sir2) as HSP90-heterocomplex interactors is here discussed. PMID:24694366

  11. Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks

    PubMed Central

    Chambers, Brendan; MacLean, Jason N.

    2016-01-01

    Linking synaptic connectivity to dynamics is key to understanding information processing in neocortex. Circuit dynamics emerge from complex interactions of interconnected neurons, necessitating that links between connectivity and dynamics be evaluated at the network level. Here we map propagating activity in large neuronal ensembles from mouse neocortex and compare it to a recurrent network model, where connectivity can be precisely measured and manipulated. We find that a dynamical feature dominates statistical descriptions of propagating activity for both neocortex and the model: convergent clusters comprised of fan-in triangle motifs, where two input neurons are themselves connected. Fan-in triangles coordinate the timing of presynaptic inputs during ongoing activity to effectively generate postsynaptic spiking. As a result, paradoxically, fan-in triangles dominate the statistics of spike propagation even in randomly connected recurrent networks. Interplay between higher-order synaptic connectivity and the integrative properties of neurons constrains the structure of network dynamics and shapes the routing of information in neocortex. PMID:27542093

  12. Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks.

    PubMed

    Chambers, Brendan; MacLean, Jason N

    2016-08-01

    Linking synaptic connectivity to dynamics is key to understanding information processing in neocortex. Circuit dynamics emerge from complex interactions of interconnected neurons, necessitating that links between connectivity and dynamics be evaluated at the network level. Here we map propagating activity in large neuronal ensembles from mouse neocortex and compare it to a recurrent network model, where connectivity can be precisely measured and manipulated. We find that a dynamical feature dominates statistical descriptions of propagating activity for both neocortex and the model: convergent clusters comprised of fan-in triangle motifs, where two input neurons are themselves connected. Fan-in triangles coordinate the timing of presynaptic inputs during ongoing activity to effectively generate postsynaptic spiking. As a result, paradoxically, fan-in triangles dominate the statistics of spike propagation even in randomly connected recurrent networks. Interplay between higher-order synaptic connectivity and the integrative properties of neurons constrains the structure of network dynamics and shapes the routing of information in neocortex. PMID:27542093

  13. 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.

  14. 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.

  15. A physical interaction network of dengue virus and human proteins.

    PubMed

    Khadka, Sudip; Vangeloff, Abbey D; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J; Perera, Rushika; LaCount, Douglas J

    2011-12-01

    Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection.

  16. New perspectives in tracing vector-borne interaction networks.

    PubMed

    Gómez-Díaz, Elena; Figuerola, Jordi

    2010-10-01

    Disentangling trophic interaction networks in vector-borne systems has important implications in epidemiological and evolutionary studies. Molecular methods based on bloodmeal typing in vectors have been increasingly used to identify hosts. Although most molecular approaches benefit from good specificity and sensitivity, their temporal resolution is limited by the often rapid digestion of blood, and mixed bloodmeals still remain a challenge for bloodmeal identification in multi-host vector systems. Stable isotope analyses represent a novel complementary tool that can overcome some of these problems. The utility of these methods using examples from different vector-borne systems are discussed and the extents to which they are complementary and versatile are highlighted. There are excellent opportunities for progress in the study of vector-borne transmission networks resulting from the integration of both molecular and stable isotope approaches.

  17. 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

  18. 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

  19. 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

  20. 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

  1. Formation mechanism for a hybrid supramolecular network involving cooperative interactions.

    PubMed

    Mura, Manuela; Silly, Fabien; Burlakov, Victor; Castell, Martin R; Briggs, G Andrew D; Kantorovich, Lev N

    2012-04-27

    A novel mechanism of hybrid assembly of molecules on surfaces is proposed stemming from interactions between molecules and on-surface metal atoms which eventually got trapped inside the network pores. Based on state-of-the-art theoretical calculations, we find that the new mechanism relies on formation of molecule-metal atom pairs which, together with molecules themselves, participate in the assembly growth. Most remarkably, the dissociation of pairs is facilitated by a cooperative interaction involving many molecules. This new mechanism is illustrated on a low coverage Melamine hexagonal network on the Au(111) surface where multiple events of gold atoms trapping via a set of so-called "gate" transitions are found by kinetic Monte Carlo simulations based on transition rates obtained using ab initio density functional theory calculations and the nudged elastic band method. Simulated STM images of gold atoms trapped in the pores of the Melamine network predict that the atoms should appear as bright spots inside Melamine hexagons. No trapping was found at large Melamine coverages, however. These predictions have been supported by preliminary STM experiments which show bright spots inside Melamine hexagons at low Melamine coverages, while empty pores are mostly observed at large coverages. Therefore, we suggest that bright spots sometimes observed in the pores of molecular assemblies on metal surfaces may be attributed to trapped substrate metal atoms. We believe that this type of mechanism could be used for delivering adatom species of desired functionality (e.g., magnetic) into the pores of hydrogen-bonded networks serving as templates for their capture. PMID:22680886

  2. Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions

    PubMed Central

    Wang, Xiang; Öngür, Dost; Auerbach, Randy P.; Yao, Shuqiao

    2016-01-01

    Abstract Although it is generally accepted that cognitive factors contribute to the pathogenesis of major depressive disorder (MDD), there are missing links between behavioral and biological models of depression. Nevertheless, research employing neuroimaging technologies has elucidated some of the neurobiological mechanisms related to cognitive-vulnerability factors, especially from a whole-brain, dynamic perspective. In this review, we integrate well-established cognitive-vulnerability factors for MDD and corresponding neural mechanisms in intrinsic networks using a dual-process framework. We propose that the dynamic alteration and imbalance among the intrinsic networks, both in the resting-state and the rest-task transition stages, contribute to the development of cognitive vulnerability and MDD. Specifically, we propose that abnormally increased resting-state default mode network (DMN) activity and connectivity (mainly in anterior DMN regions) contribute to the development of cognitive vulnerability. Furthermore, when subjects confront negative stimuli in the period of rest-to-task transition, the following three kinds of aberrant network interactions have been identified as facilitators of vulnerability and dysphoric mood, each through a different cognitive mechanism: DMN dominance over the central executive network (CEN), an impaired salience network–mediated switching between the DMN and CEN, and ineffective CEN modulation of the DMN. This focus on interrelated networks and brain-activity changes between rest and task states provides a neural-system perspective for future research on cognitive vulnerability and resilience, and may potentially guide the development of new intervention strategies for MDD. PMID:27148911

  3. The Bilingual Language Interaction Network for Comprehension of Speech*

    PubMed Central

    Marian, Viorica

    2013-01-01

    During speech comprehension, bilinguals co-activate both of their languages, resulting in cross-linguistic interaction at various levels of processing. This interaction has important consequences for both the structure of the language system and the mechanisms by which the system processes spoken language. Using computational modeling, we can examine how cross-linguistic interaction affects language processing in a controlled, simulated environment. Here we present a connectionist model of bilingual language processing, the Bilingual Language Interaction Network for Comprehension of Speech (BLINCS), wherein interconnected levels of processing are created using dynamic, self-organizing maps. BLINCS can account for a variety of psycholinguistic phenomena, including cross-linguistic interaction at and across multiple levels of processing, cognate facilitation effects, and audio-visual integration during speech comprehension. The model also provides a way to separate two languages without requiring a global language-identification system. We conclude that BLINCS serves as a promising new model of bilingual spoken language comprehension. PMID:24363602

  4. The Bilingual Language Interaction Network for Comprehension of Speech.

    PubMed

    Shook, Anthony; Marian, Viorica

    2013-04-01

    During speech comprehension, bilinguals co-activate both of their languages, resulting in cross-linguistic interaction at various levels of processing. This interaction has important consequences for both the structure of the language system and the mechanisms by which the system processes spoken language. Using computational modeling, we can examine how cross-linguistic interaction affects language processing in a controlled, simulated environment. Here we present a connectionist model of bilingual language processing, the Bilingual Language Interaction Network for Comprehension of Speech (BLINCS), wherein interconnected levels of processing are created using dynamic, self-organizing maps. BLINCS can account for a variety of psycholinguistic phenomena, including cross-linguistic interaction at and across multiple levels of processing, cognate facilitation effects, and audio-visual integration during speech comprehension. The model also provides a way to separate two languages without requiring a global language-identification system. We conclude that BLINCS serves as a promising new model of bilingual spoken language comprehension. PMID:24363602

  5. 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.

  6. Visualization of protein interaction networks: problems and solutions

    PubMed Central

    2013-01-01

    Background Visualization concerns the representation of data visually and is an important task in scientific research. Protein-protein interactions (PPI) are discovered using either wet lab techniques, such mass spectrometry, or in silico predictions tools, resulting in large collections of interactions stored in specialized databases. The set of all interactions of an organism forms a protein-protein interaction network (PIN) and is an important tool for studying the behaviour of the cell machinery. Since graphic representation of PINs may highlight important substructures, e.g. protein complexes, visualization is more and more used to study the underlying graph structure of PINs. Although graphs are well known data structures, there are different open problems regarding PINs visualization: the high number of nodes and connections, the heterogeneity of nodes (proteins) and edges (interactions), the possibility to annotate proteins and interactions with biological information extracted by ontologies (e.g. Gene Ontology) that enriches the PINs with semantic information, but complicates their visualization. Methods In these last years many software tools for the visualization of PINs have been developed. Initially thought for visualization only, some of them have been successively enriched with new functions for PPI data management and PIN analysis. The paper analyzes the main software tools for PINs visualization considering four main criteria: (i) technology, i.e. availability/license of the software and supported OS (Operating System) platforms; (ii) interoperability, i.e. ability to import/export networks in various formats, ability to export data in a graphic format, extensibility of the system, e.g. through plug-ins; (iii) visualization, i.e. supported layout and rendering algorithms and availability of parallel implementation; (iv) analysis, i.e. availability of network analysis functions, such as clustering or mining of the graph, and the possibility to

  7. Global multiple protein-protein interaction network alignment by combining pairwise network alignments

    PubMed Central

    2015-01-01

    Background A wealth of protein interaction data has become available in recent years, creating an urgent need for powerful analysis techniques. In this context, the problem of finding biologically meaningful correspondences between different protein-protein interaction networks (PPIN) is of particular interest. The PPIN of a species can be compared with that of other species through the process of PPIN alignment. Such an alignment can provide insight into basic problems like species evolution and network component function determination, as well as translational problems such as target identification and elucidation of mechanisms of disease spread. Furthermore, multiple PPINs can be aligned simultaneously, expanding the analytical implications of the result. While there are several pairwise network alignment algorithms, few methods are capable of multiple network alignment. Results We propose SMAL, a MNA algorithm based on the philosophy of scaffold-based alignment. SMAL is capable of converting results from any global pairwise alignment algorithms into a MNA in linear time. Using this method, we have built multiple network alignments based on combining pairwise alignments from a number of publicly available (pairwise) network aligners. We tested SMAL using PPINs of eight species derived from the IntAct repository and employed a number of measures to evaluate performance. Additionally, as part of our experimental investigations, we compared the effectiveness of SMAL while aligning up to eight input PPINs, and examined the effect of scaffold network choice on the alignments. Conclusions A key advantage of SMAL lies in its ability to create MNAs through the use of pairwise network aligners for which native MNA implementations do not exist. Experiments indicate that the performance of SMAL was comparable to that of the native MNA implementation of established methods such as IsoRankN and SMETANA. However, in terms of computational time, SMAL was significantly faster

  8. 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

  9. 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.

  10. 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.

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

    PubMed

    Bhattacharya, Anindya; Biber, Knut

    2016-10-01

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

  12. Spatially-Interactive Biomolecular Networks Organized by Nucleic Acid Nanostructures

    PubMed Central

    Fu, Jinglin; Liu, Minghui; Liu, Yan; Yan, Hao

    2013-01-01

    Conspectus Living systems have evolved a variety of nanostructures to control the molecular interactions that mediate many functions including the recognition of targets by receptors, the binding of enzymes to substrates, and the regulation of enzymatic activity. Mimicking these structures outside of the cell requires methods that offer nanoscale control over the organization of individual network components. Advances in DNA nanotechnology have enabled the design and fabrication of sophisticated one-, two- and three-dimensional (1D, 2D and 3D) nanostructures that utilize spontaneous and sequence specific DNA hybridization. Compared to other self-assembling biopolymers, DNA nanostructures offer predictable and programmable interactions, and surface features to which other nanoparticles and bio-molecules can be precisely positioned. The ability to control the spatial arrangement of the components while constructing highly-organized networks will lead to various applications of these systems. For example, DNA nanoarrays with surface displays of molecular probes can sense noncovalent hybridization interactions with DNA, RNA, and proteins and covalent chemical reactions. DNA nanostructures can also align external molecules into well-defined arrays, which may improve the resolution of many structural determination methods, such as X-ray diffraction, cryo-EM, NMR, and super-resolution fluorescence. Moreover, by constraining target entities to specific conformations, self-assembled DNA nanostructures can serve as molecular rulers to evaluate conformation-dependent activities. This Account describes the most recent advances in the DNA nanostructure directed assembly of biomolecular networks and explores the possibility of applying this technology to other fields of study. Recently, several reports have demonstrated the DNA nanostructure directed assembly of spatially-interactive biomolecular networks. For example, researchers have constructed synthetic multi-enzyme cascades

  13. Chaos in generically coupled phase oscillator networks with nonpairwise interactions

    NASA Astrophysics Data System (ADS)

    Bick, Christian; Ashwin, Peter; Rodrigues, Ana

    2016-09-01

    The Kuramoto-Sakaguchi system of coupled phase oscillators, where interaction between oscillators is determined by a single harmonic of phase differences of pairs of oscillators, has very simple emergent dynamics in the case of identical oscillators that are globally coupled: there is a variational structure that means the only attractors are full synchrony (in-phase) or splay phase (rotating wave/full asynchrony) oscillations and the bifurcation between these states is highly degenerate. Here we show that nonpairwise coupling—including three and four-way interactions of the oscillator phases—that appears generically at the next order in normal-form based calculations can give rise to complex emergent dynamics in symmetric phase oscillator networks. In particular, we show that chaos can appear in the smallest possible dimension of four coupled phase oscillators for a range of parameter values.

  14. [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

  15. 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

  16. 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

  17. Phospho-tyrosine dependent protein–protein interaction network

    PubMed Central

    Grossmann, Arndt; Benlasfer, Nouhad; Birth, Petra; Hegele, Anna; Wachsmuth, Franziska; Apelt, Luise; Stelzl, Ulrich

    2015-01-01

    Post-translational protein modifications, such as tyrosine phosphorylation, regulate protein–protein interactions (PPIs) critical for signal processing and cellular phenotypes. We extended an established yeast two-hybrid system employing human protein kinases for the analyses of phospho-tyrosine (pY)-dependent PPIs in a direct experimental, large-scale approach. We identified 292 mostly novel pY-dependent PPIs which showed high specificity with respect to kinases and interacting proteins and validated a large fraction in co-immunoprecipitation experiments from mammalian cells. About one-sixth of the interactions are mediated by known linear sequence binding motifs while the majority of pY-PPIs are mediated by other linear epitopes or governed by alternative recognition modes. Network analysis revealed that pY-mediated recognition events are tied to a highly connected protein module dedicated to signaling and cell growth pathways related to cancer. Using binding assays, protein complementation and phenotypic readouts to characterize the pY-dependent interactions of TSPAN2 (tetraspanin 2) and GRB2 or PIK3R3 (p55γ), we exemplarily provide evidence that the two pY-dependent PPIs dictate cellular cancer phenotypes. PMID:25814554

  18. Analysing Health Professionals' Learning Interactions in an Online Social Network: A Longitudinal Study.

    PubMed

    Li, Xin; Verspoor, Karin; Gray, Kathleen; Barnett, Stephen

    2016-01-01

    This paper summarises a longitudinal analysis of learning interactions occurring over three years among health professionals in an online social network. The study employs the techniques of Social Network Analysis (SNA) and statistical modeling to identify the changes in patterns of interaction over time and test associated structural network effects. SNA results indicate overall low participation in the network, although some participants became active over time and even led discussions. In particular, the analysis has shown that a change of lead contributor results in a change in learning interaction and network structure. The analysis of structural network effects demonstrates that the interaction dynamics slow down over time, indicating that interactions in the network are more stable. The health professionals may be reluctant to share knowledge and collaborate in groups but were interested in building personal learning networks or simply seeking information. PMID:27440295

  19. A DNA Methylation Network Interaction Measure, and Detection of Network Oncomarkers

    PubMed Central

    Bartlett, Thomas E.; Olhede, Sofia C.; Zaikin, Alexey

    2014-01-01

    Epigenetic processes–including DNA methylation–are increasingly seen as having a fundamental role in chronic diseases like cancer. DNA methylation patterns offer a route to develop prognostic measures based directly on DNA measurements, rather than less-stable RNA measurements. A novel DNA methylation-based measure of the co-ordinated interactive behaviour of genes is developed, in a network context. It is shown that this measure reflects well the co-regulatory behaviour linked to gene expression (at the mRNA level) over the same network interactions. This measure, defined for pairs of genes in a single patient/sample, associates with overall survival outcome independent of known prognostic clinical features, in several independent data sets relating to different cancer types. In total, more than half a billion CpGs in over 1600 samples, taken from nine different cancer entities, are analysed. It is found that groups of gene-pair interactions which associate significantly with survival identify statistically significant subnetwork modules. Many of these subnetwork modules are shown to be biologically relevant by strong correlation with pre-defined gene sets, such as immune function, wound healing, mitochondrial function and MAP-kinase signalling. In particular, the wound healing module corresponds to an increase in co-ordinated interactive behaviour between genes for worse prognosis, and the immune module corresponds to a decrease in co-ordinated interactive behaviour between genes for worse prognosis. This measure has great potential for defining DNA-based cancer biomarkers. Such biomarkers could naturally be developed further, by drawing on the rapidly expanding knowledge base of network science. PMID:24400102

  20. Novel recurrent neural network for modelling biological networks: oscillatory p53 interaction dynamics.

    PubMed

    Ling, Hong; Samarasinghe, Sandhya; Kulasiri, Don

    2013-12-01

    Understanding the control of cellular networks consisting of gene and protein interactions and their emergent properties is a central activity of Systems Biology research. For this, continuous, discrete, hybrid, and stochastic methods have been proposed. Currently, the most common approach to modelling accurate temporal dynamics of networks is ordinary differential equations (ODE). However, critical limitations of ODE models are difficulty in kinetic parameter estimation and numerical solution of a large number of equations, making them more suited to smaller systems. In this article, we introduce a novel recurrent artificial neural network (RNN) that addresses above limitations and produces a continuous model that easily estimates parameters from data, can handle a large number of molecular interactions and quantifies temporal dynamics and emergent systems properties. This RNN is based on a system of ODEs representing molecular interactions in a signalling network. Each neuron represents concentration change of one molecule represented by an ODE. Weights of the RNN correspond to kinetic parameters in the system and can be adjusted incrementally during network training. The method is applied to the p53-Mdm2 oscillation system - a crucial component of the DNA damage response pathways activated by a damage signal. Simulation results indicate that the proposed RNN can successfully represent the behaviour of the p53-Mdm2 oscillation system and solve the parameter estimation problem with high accuracy. Furthermore, we presented a modified form of the RNN that estimates parameters and captures systems dynamics from sparse data collected over relatively large time steps. We also investigate the robustness of the p53-Mdm2 system using the trained RNN under various levels of parameter perturbation to gain a greater understanding of the control of the p53-Mdm2 system. Its outcomes on robustness are consistent with the current biological knowledge of this system. As more

  1. User-Centric Secure Cross-Site Interaction Framework for Online Social Networking Services

    ERIC Educational Resources Information Center

    Ko, Moo Nam

    2011-01-01

    Social networking service is one of major technological phenomena on Web 2.0. Hundreds of millions of users are posting message, photos, and videos on their profiles and interacting with other users, but the sharing and interaction are limited within the same social networking site. Although users can share some content on a social networking site…

  2. Topology of Protein Interaction Network Shapes Protein Abundances and Strengths of Their Functional and Nonspecific Interactions

    SciTech Connect

    Maslov, S.; Heo, M.; Shakhnovich, E.

    2011-03-08

    How do living cells achieve sufficient abundances of functional protein complexes while minimizing promiscuous nonfunctional interactions? Here we study this problem using a first-principle model of the cell whose phenotypic traits are directly determined from its genome through biophysical properties of protein structures and binding interactions in a crowded cellular environment. The model cell includes three independent prototypical pathways, whose topologies of protein-protein interaction (PPI) subnetworks are different, but whose contributions to the cell fitness are equal. Model cells evolve through genotypic mutations and phenotypic protein copy number variations. We found a strong relationship between evolved physical-chemical properties of protein interactions and their abundances due to a 'frustration' effect: Strengthening of functional interactions brings about hydrophobic interfaces, which make proteins prone to promiscuous binding. The balancing act is achieved by lowering concentrations of hub proteins while raising solubilities and abundances of functional monomers. On the basis of these principles we generated and analyzed a possible realization of the proteome-wide PPI network in yeast. In this simulation we found that high-throughput affinity capture-mass spectroscopy experiments can detect functional interactions with high fidelity only for high-abundance proteins while missing most interactions for low-abundance proteins.

  3. Interaction and localization diversities of global and local hubs in human protein-protein interaction networks.

    PubMed

    Kiran, M; Nagarajaram, H A

    2016-08-16

    Hubs, the highly connected nodes in protein-protein interaction networks (PPINs), are associated with several characteristic properties and are known to perform vital roles in cells. We defined two classes of hubs, global (housekeeping) and local (tissue-specific) hubs. These two categories of hubs are distinct from each other with respect to their abundance, structure and function. However, how distinct are the spatial expression pattern and other characteristics of their interacting partners is still not known. Our investigations revealed that the partners of the local hubs compared with those of global hubs are conserved across the tissues in which they are expressed. Partners of local hubs show diverse subcellular localizations as compared with the partners of global hubs. We examined the nature of interacting domains in both categories of hubs and found that they are promiscuous in global hubs but not so in local hubs. Deletion of some of the local and global hubs has an impact on the characteristic path length of the network indicating that those hubs are inter-modular in nature. Our present study has, therefore, shed further light on the characteristic features of the local and global hubs in human PPIN. This knowledge of different topological aspects of hubs with regard to their types and subtypes is essential as it helps in better understanding of roles of hub proteins in various cellular processes under various conditions including those caused by host-pathogen interactions and therefore useful in prioritizing targets for drug design and repositioning.

  4. Interaction and localization diversities of global and local hubs in human protein-protein interaction networks.

    PubMed

    Kiran, M; Nagarajaram, H A

    2016-08-16

    Hubs, the highly connected nodes in protein-protein interaction networks (PPINs), are associated with several characteristic properties and are known to perform vital roles in cells. We defined two classes of hubs, global (housekeeping) and local (tissue-specific) hubs. These two categories of hubs are distinct from each other with respect to their abundance, structure and function. However, how distinct are the spatial expression pattern and other characteristics of their interacting partners is still not known. Our investigations revealed that the partners of the local hubs compared with those of global hubs are conserved across the tissues in which they are expressed. Partners of local hubs show diverse subcellular localizations as compared with the partners of global hubs. We examined the nature of interacting domains in both categories of hubs and found that they are promiscuous in global hubs but not so in local hubs. Deletion of some of the local and global hubs has an impact on the characteristic path length of the network indicating that those hubs are inter-modular in nature. Our present study has, therefore, shed further light on the characteristic features of the local and global hubs in human PPIN. This knowledge of different topological aspects of hubs with regard to their types and subtypes is essential as it helps in better understanding of roles of hub proteins in various cellular processes under various conditions including those caused by host-pathogen interactions and therefore useful in prioritizing targets for drug design and repositioning. PMID:27400769

  5. Protein interaction network constructing based on text mining and reinforcement learning with application to prostate cancer.

    PubMed

    Zhu, Fei; Liu, Quan; Zhang, Xiaofang; Shen, Bairong

    2015-08-01

    Constructing interaction network from biomedical texts is a very important and interesting work. The authors take advantage of text mining and reinforcement learning approaches to establish protein interaction network. Considering the high computational efficiency of co-occurrence-based interaction extraction approaches and high precision of linguistic patterns approaches, the authors propose an interaction extracting algorithm where they utilise frequently used linguistic patterns to extract the interactions from texts and then find out interactions from extended unprocessed texts under the basic idea of co-occurrence approach, meanwhile they discount the interaction extracted from extended texts. They put forward a reinforcement learning-based algorithm to establish a protein interaction network, where nodes represent proteins and edges denote interactions. During the evolutionary process, a node selects another node and the attained reward determines which predicted interaction should be reinforced. The topology of the network is updated by the agent until an optimal network is formed. They used texts downloaded from PubMed to construct a prostate cancer protein interaction network by the proposed methods. The results show that their method brought out pretty good matching rate. Network topology analysis results also demonstrate that the curves of node degree distribution, node degree probability and probability distribution of constructed network accord with those of the scale-free network well. PMID:26243825

  6. Protein interaction network constructing based on text mining and reinforcement learning with application to prostate cancer.

    PubMed

    Zhu, Fei; Liu, Quan; Zhang, Xiaofang; Shen, Bairong

    2015-08-01

    Constructing interaction network from biomedical texts is a very important and interesting work. The authors take advantage of text mining and reinforcement learning approaches to establish protein interaction network. Considering the high computational efficiency of co-occurrence-based interaction extraction approaches and high precision of linguistic patterns approaches, the authors propose an interaction extracting algorithm where they utilise frequently used linguistic patterns to extract the interactions from texts and then find out interactions from extended unprocessed texts under the basic idea of co-occurrence approach, meanwhile they discount the interaction extracted from extended texts. They put forward a reinforcement learning-based algorithm to establish a protein interaction network, where nodes represent proteins and edges denote interactions. During the evolutionary process, a node selects another node and the attained reward determines which predicted interaction should be reinforced. The topology of the network is updated by the agent until an optimal network is formed. They used texts downloaded from PubMed to construct a prostate cancer protein interaction network by the proposed methods. The results show that their method brought out pretty good matching rate. Network topology analysis results also demonstrate that the curves of node degree distribution, node degree probability and probability distribution of constructed network accord with those of the scale-free network well.

  7. Structure and interactions in isotropic and liquid crystalline neurofilament networks

    NASA Astrophysics Data System (ADS)

    Jones, Jayna Bea

    2007-12-01

    Neurofilaments (NFs) are cytoskeletal proteins that are localized within nerve cells, which form long oriented bundles running the length of axons. While abnormal aggregations of these proteins have been implicated in several neurological disorders including Parkinson's disease and ALS, interfilament interactions in both the normal and diseased states are not well understood. In vivo, NFs are supramolecular structures composed of three subunit proteins of low (NF-L), medium (NF-M), and high molecular (NF-H) weight that assemble into a 10 nm diameter rod with radiating sidearms, forming a bottle-brush conformation. In this study we alter the subunit composition and probe the resulting networks with polarized microscopy and synchrotron small angle x-ray scattering (SAXS), in order to isolate the role of each subunit in interfilament interactions. By reassembling NFs in vitro from varying ratios of the subunit proteins, purified from bovine spinal cord, we form filaments with controlled subunit compositions. The resulting filaments, at a high volume fraction, are nematic liquid crystalline gels with a well defined spacing, determined with SAXS. Upon dilution the difference between the subunits is realized with NF-M grafted filaments being dominated by attractive interactions and remaining aligned, while those flanked with NF-H sidearms repel and become isotropic gels. Interplay between these forces is seen in the ternary system composed of all three subunit proteins (NF-LMH). The polyampholytic subunits have a charge distribution that varies along the length of the sidearm, which forms the brush layer, and the distribution is different for each subunit. The interfilament interactions are highly dependent on environmental conditions including salt concentration, pH, and osmotic pressure. Increasing ionic strength induces attractive interactions and a stabilization of the nematic phase in filaments that were repulsive at lower monovalent salt concentration. The

  8. Modeling attacker-defender interactions in information networks.

    SciTech Connect

    Collins, Michael Joseph

    2010-09-01

    The simplest conceptual model of cybersecurity implicitly views attackers and defenders as acting in isolation from one another: an attacker seeks to penetrate or disrupt a system that has been protected to a given level, while a defender attempts to thwart particular attacks. Such a model also views all non-malicious parties as having the same goal of preventing all attacks. But in fact, attackers and defenders are interacting parts of the same system, and different defenders have their own individual interests: defenders may be willing to accept some risk of successful attack if the cost of defense is too high. We have used game theory to develop models of how non-cooperative but non-malicious players in a network interact when there is a substantial cost associated with effective defensive measures. Although game theory has been applied in this area before, we have introduced some novel aspects of player behavior in our work, including: (1) A model of how players attempt to avoid the costs of defense and force others to assume these costs; (2) A model of how players interact when the cost of defending one node can be shared by other nodes; and (3) A model of the incentives for a defender to choose less expensive, but less effective, defensive actions.

  9. Phage-bacteria interaction network in human oral microbiome.

    PubMed

    Wang, Jinfeng; Gao, Yuan; Zhao, Fangqing

    2016-07-01

    Although increasing knowledge suggests that bacteriophages play important roles in regulating microbial ecosystems, phage-bacteria interaction in human oral cavities remains less understood. Here we performed a metagenomic analysis to explore the composition and variation of oral dsDNA phage populations and potential phage-bacteria interaction. A total of 1,711 contigs assembled with more than 100 Gb shotgun sequencing data were annotated to 104 phages based on their best BLAST matches against the NR database. Bray-Curtis dissimilarities demonstrated that both phage and bacterial composition are highly diverse between periodontally healthy samples but show a trend towards homogenization in diseased gingivae samples. Significantly, according to the CRISPR arrays that record infection relationship between bacteria and phage, we found certain oral phages were able to invade other bacteria besides their putative bacterial hosts. These cross-infective phages were positively correlated with commensal bacteria while were negatively correlated with major periodontal pathogens, suggesting possible connection between these phages and microbial community structure in oral cavities. By characterizing phage-bacteria interaction as networks rather than exclusively pairwise predator-prey relationships, our study provides the first insight into the participation of cross-infective phages in forming human oral microbiota.

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

    PubMed

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

    2014-01-01

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

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

    PubMed

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

    2014-01-01

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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. Interacting Bose gas, the logistic law, and complex networks

    NASA Astrophysics Data System (ADS)

    Sowa, A.

    2015-01-01

    We discuss a mathematical link between the Quantum Statistical Mechanics and the logistic growth and decay processes. It is based on an observation that a certain nonlinear operator evolution equation, which we refer to as the Logistic Operator Equation (LOE), provides an extension of the standard model of noninteracting bosons. We discuss formal solutions (asymptotic formulas) for a special calibration of the LOE, which sets it in the number-theoretic framework. This trick, in the tradition of Julia and Bost-Connes, makes it possible for us to tap into the vast resources of classical mathematics and, in particular, to construct explicit solutions of the LOE via the Dirichlet series. The LOE is applicable to a range of modeling and simulation tasks, from characterization of interacting boson systems to simulation of some complex man-made networks. The theoretical results enable numerical simulations, which, in turn, shed light at the unique complexities of the rich and multifaceted models resulting from the LOE.

  18. Properties of interacting 2D chiral tensor network states

    NASA Astrophysics Data System (ADS)

    Bradlyn, Barry; Dubail, Jerome; Read, Nicholas

    2015-03-01

    In a recent paper, Dubail and Read gave a construction for free fermion tensor network states(TNSs) in the chiral p + ip and ν = 1 Chern insulator topological phases in two dimensions, and gave a generalization to Laughlin-like states. However, on general principles these free fermion states must be ground states of gapless local Hamiltonians. In this talk, we address the issue of the energy gap in the interacting states, with a particular focus on the ν = 1 / 2 bosonic Laughlin-like TNS. Through a combination of analytic and numerical arguments, we will show that these states too have gapless local parent Hamiltonians. Nevertheless, we will explore to what degree they can be used as numerical approximations to gapped phases.

  19. Drug-Drug Interaction Extraction via Convolutional Neural Networks.

    PubMed

    Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong

    2016-01-01

    Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%. PMID:26941831

  20. Drug-Drug Interaction Extraction via Convolutional Neural Networks.

    PubMed

    Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong

    2016-01-01

    Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.

  1. 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.

  2. 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

  3. 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.

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

    PubMed

    Patel, Hershna; Kukol, Andreas

    2016-06-15

    Sequence variations in the binding sites of influenza A proteins are known to limit the effectiveness of current antiviral drugs. Clinically, this leads to increased rates of virus transmission and pathogenicity. Potential influenza A inhibitors are continually being discovered as a result of high-throughput cell based screening studies, whereas the application of computational tools to aid drug discovery has further increased the number of predicted inhibitors reported. This review brings together the aspects that relate to the identification of influenza A drug target sites and the findings from recent antiviral drug discovery strategies. PMID:27284062

  5. 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

  6. CombiMotif: A new algorithm for network motifs discovery in protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Luo, Jiawei; Li, Guanghui; Song, Dan; Liang, Cheng

    2014-12-01

    Discovering motifs in protein-protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMotif) that incorporates combinatorial techniques to count non-induced occurrences of subgraph topologies in the form of trees. The efficiency of our algorithm is demonstrated by comparing the obtained results with the current state-of-the art subgraph counting algorithms. We also show major differences between unicellular and multicellular organisms. The datasets and source code of CombiMotif are freely available upon request.

  7. Protein Interaction Networks Link Schizophrenia Risk Loci to Synaptic Function

    PubMed Central

    Schwarz, Emanuel; Izmailov, Rauf; Liò, Pietro; Meyer-Lindenberg, Andreas

    2016-01-01

    Schizophrenia is a severe and highly heritable psychiatric disorder affecting approximately 1% of the population. Genome-wide association studies have identified 108 independent genetic loci with genome-wide significance but their functional importance has yet to be elucidated. Here, we develop a novel strategy based on network analysis of protein–protein interactions (PPI) to infer biological function associated with variants most strongly linked to illness risk. We show that the schizophrenia loci are strongly linked to synaptic transmission (P FWE < .001) and ion transmembrane transport (P FWE = .03), but not to ontological categories previously found to be shared across psychiatric illnesses. We demonstrate that brain expression of risk-linked genes within the identified processes is strongly modulated during birth and identify a set of synaptic genes consistently changed across multiple brain regions of adult schizophrenia patients. These results suggest synaptic function as a developmentally determined schizophrenia process supported by the illness’s most associated genetic variants and their PPI networks. The implicated genes may be valuable targets for mechanistic experiments and future drug development approaches. PMID:27056717

  8. Ensemble transcript interaction networks: a case study on Alzheimer's disease.

    PubMed

    Armañanzas, Rubén; Larrañaga, Pedro; Bielza, Concha

    2012-10-01

    Systems biology techniques are a topic of recent interest within the neurological field. Computational intelligence (CI) addresses this holistic perspective by means of consensus or ensemble techniques ultimately capable of uncovering new and relevant findings. In this paper, we propose the application of a CI approach based on ensemble Bayesian network classifiers and multivariate feature subset selection to induce probabilistic dependences that could match or unveil biological relationships. The research focuses on the analysis of high-throughput Alzheimer's disease (AD) transcript profiling. The analysis is conducted from two perspectives. First, we compare the expression profiles of hippocampus subregion entorhinal cortex (EC) samples of AD patients and controls. Second, we use the ensemble approach to study four types of samples: EC and dentate gyrus (DG) samples from both patients and controls. Results disclose transcript interaction networks with remarkable structures and genes not directly related to AD by previous studies. The ensemble is able to identify a variety of transcripts that play key roles in other neurological pathologies. Classical statistical assessment by means of non-parametric tests confirms the relevance of the majority of the transcripts. The ensemble approach pinpoints key metabolic mechanisms that could lead to new findings in the pathogenesis and development of AD.

  9. Efficient quantum transport in disordered interacting many-body networks

    NASA Astrophysics Data System (ADS)

    Ortega, Adrian; Stegmann, Thomas; Benet, Luis

    2016-10-01

    The coherent transport of n fermions in disordered networks of l single-particle states connected by k -body interactions is studied. These networks are modeled by embedded Gaussian random matrix ensemble (EGE). The conductance bandwidth and the ensemble-averaged total current attain their maximal values if the system is highly filled n ˜l -1 and k ˜n /2 . For the cases k =1 and k =n the bandwidth is minimal. We show that for all parameters the transport is enhanced significantly whenever centrosymmetric embedded Gaussian ensemble (csEGE) are considered. In this case the transmission shows numerous resonances of perfect transport. Analyzing the transmission by spectral decomposition, we find that centrosymmetry induces strong correlations and enhances the extrema of the distributions. This suppresses destructive interference effects in the system and thus causes backscattering-free transmission resonances that enhance the overall transport. The distribution of the total current for the csEGE has a very large dominating peak for n =l -1 , close to the highest observed currents.

  10. Pleistocene megafaunal interaction networks became more vulnerable after human arrival.

    PubMed

    Pires, Mathias M; Koch, Paul L; Fariña, Richard A; de Aguiar, Marcus A M; dos Reis, Sérgio F; Guimarães, Paulo R

    2015-09-01

    The end of the Pleistocene was marked by the extinction of almost all large land mammals worldwide except in Africa. Although the debate on Pleistocene extinctions has focused on the roles of climate change and humans, the impact of perturbations depends on properties of ecological communities, such as species composition and the organization of ecological interactions. Here, we combined palaeoecological and ecological data, food-web models and community stability analysis to investigate if differences between Pleistocene and modern mammalian assemblages help us understand why the megafauna died out in the Americas while persisting in Africa. We show Pleistocene and modern assemblages share similar network topology, but differences in richness and body size distributions made Pleistocene communities significantly more vulnerable to the effects of human arrival. The structural changes promoted by humans in Pleistocene networks would have increased the likelihood of unstable dynamics, which may favour extinction cascades in communities facing extrinsic perturbations. Our findings suggest that the basic aspects of the organization of ecological communities may have played an important role in major extinction events in the past. Knowledge of community-level properties and their consequences to dynamics may be critical to understand past and future extinctions. PMID:26336175

  11. Ensemble transcript interaction networks: a case study on Alzheimer's disease.

    PubMed

    Armañanzas, Rubén; Larrañaga, Pedro; Bielza, Concha

    2012-10-01

    Systems biology techniques are a topic of recent interest within the neurological field. Computational intelligence (CI) addresses this holistic perspective by means of consensus or ensemble techniques ultimately capable of uncovering new and relevant findings. In this paper, we propose the application of a CI approach based on ensemble Bayesian network classifiers and multivariate feature subset selection to induce probabilistic dependences that could match or unveil biological relationships. The research focuses on the analysis of high-throughput Alzheimer's disease (AD) transcript profiling. The analysis is conducted from two perspectives. First, we compare the expression profiles of hippocampus subregion entorhinal cortex (EC) samples of AD patients and controls. Second, we use the ensemble approach to study four types of samples: EC and dentate gyrus (DG) samples from both patients and controls. Results disclose transcript interaction networks with remarkable structures and genes not directly related to AD by previous studies. The ensemble is able to identify a variety of transcripts that play key roles in other neurological pathologies. Classical statistical assessment by means of non-parametric tests confirms the relevance of the majority of the transcripts. The ensemble approach pinpoints key metabolic mechanisms that could lead to new findings in the pathogenesis and development of AD. PMID:22281045

  12. Module organization and variance in protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Lin, Chun-Yu; Lee, Tsai-Ling; Chiu, Yi-Yuan; Lin, Yi-Wei; Lo, Yu-Shu; Lin, Chih-Ta; Yang, Jinn-Moon

    2015-03-01

    A module is a group of closely related proteins that act in concert to perform specific biological functions through protein-protein interactions (PPIs) that occur in time and space. However, the underlying module organization and variance remain unclear. In this study, we collected module templates to infer respective module families, including 58,041 homologous modules in 1,678 species, and PPI families using searches of complete genomic database. We then derived PPI evolution scores and interface evolution scores to describe the module elements, including core and ring components. Functions of core components were highly correlated with those of essential genes. In comparison with ring components, core proteins/PPIs were conserved across multiple species. Subsequently, protein/module variance of PPI networks confirmed that core components form dynamic network hubs and play key roles in various biological functions. Based on the analyses of gene essentiality, module variance, and gene co-expression, we summarize the observations of module organization and variance as follows: 1) a module consists of core and ring components; 2) core components perform major biological functions and collaborate with ring components to execute certain functions in some cases; 3) core components are more conserved and essential during organizational changes in different biological states or conditions.

  13. Pleistocene megafaunal interaction networks became more vulnerable after human arrival

    PubMed Central

    Pires, Mathias M.; Koch, Paul L.; Fariña, Richard A.; de Aguiar, Marcus A. M.; dos Reis, Sérgio F.; Guimarães, Paulo R.

    2015-01-01

    The end of the Pleistocene was marked by the extinction of almost all large land mammals worldwide except in Africa. Although the debate on Pleistocene extinctions has focused on the roles of climate change and humans, the impact of perturbations depends on properties of ecological communities, such as species composition and the organization of ecological interactions. Here, we combined palaeoecological and ecological data, food-web models and community stability analysis to investigate if differences between Pleistocene and modern mammalian assemblages help us understand why the megafauna died out in the Americas while persisting in Africa. We show Pleistocene and modern assemblages share similar network topology, but differences in richness and body size distributions made Pleistocene communities significantly more vulnerable to the effects of human arrival. The structural changes promoted by humans in Pleistocene networks would have increased the likelihood of unstable dynamics, which may favour extinction cascades in communities facing extrinsic perturbations. Our findings suggest that the basic aspects of the organization of ecological communities may have played an important role in major extinction events in the past. Knowledge of community-level properties and their consequences to dynamics may be critical to understand past and future extinctions. PMID:26336175

  14. Pleistocene megafaunal interaction networks became more vulnerable after human arrival.

    PubMed

    Pires, Mathias M; Koch, Paul L; Fariña, Richard A; de Aguiar, Marcus A M; dos Reis, Sérgio F; Guimarães, Paulo R

    2015-09-01

    The end of the Pleistocene was marked by the extinction of almost all large land mammals worldwide except in Africa. Although the debate on Pleistocene extinctions has focused on the roles of climate change and humans, the impact of perturbations depends on properties of ecological communities, such as species composition and the organization of ecological interactions. Here, we combined palaeoecological and ecological data, food-web models and community stability analysis to investigate if differences between Pleistocene and modern mammalian assemblages help us understand why the megafauna died out in the Americas while persisting in Africa. We show Pleistocene and modern assemblages share similar network topology, but differences in richness and body size distributions made Pleistocene communities significantly more vulnerable to the effects of human arrival. The structural changes promoted by humans in Pleistocene networks would have increased the likelihood of unstable dynamics, which may favour extinction cascades in communities facing extrinsic perturbations. Our findings suggest that the basic aspects of the organization of ecological communities may have played an important role in major extinction events in the past. Knowledge of community-level properties and their consequences to dynamics may be critical to understand past and future extinctions.

  15. Statistical Approaches for the Construction and Interpretation of Human Protein-Protein Interaction Network

    PubMed Central

    Hu, Yang; Zhang, Ying; Ren, Jun

    2016-01-01

    The overall goal is to establish a reliable human protein-protein interaction network and develop computational tools to characterize a protein-protein interaction (PPI) network and the role of individual proteins in the context of the network topology and their expression status. A novel and unique feature of our approach is that we assigned confidence measure to each derived interacting pair and account for the confidence in our network analysis. We integrated experimental data to infer human PPI network. Our model treated the true interacting status (yes versus no) for any given pair of human proteins as a latent variable whose value was not observed. The experimental data were the manifestation of interacting status, which provided evidence as to the likelihood of the interaction. The confidence of interactions would depend on the strength and consistency of the evidence.

  16. Statistical Approaches for the Construction and Interpretation of Human Protein-Protein Interaction Network.

    PubMed

    Hu, Yang; Zhang, Ying; Ren, Jun; Wang, Yadong; Wang, Zhenzhen; Zhang, Jun

    2016-01-01

    The overall goal is to establish a reliable human protein-protein interaction network and develop computational tools to characterize a protein-protein interaction (PPI) network and the role of individual proteins in the context of the network topology and their expression status. A novel and unique feature of our approach is that we assigned confidence measure to each derived interacting pair and account for the confidence in our network analysis. We integrated experimental data to infer human PPI network. Our model treated the true interacting status (yes versus no) for any given pair of human proteins as a latent variable whose value was not observed. The experimental data were the manifestation of interacting status, which provided evidence as to the likelihood of the interaction. The confidence of interactions would depend on the strength and consistency of the evidence. PMID:27648447

  17. Statistical Approaches for the Construction and Interpretation of Human Protein-Protein Interaction Network

    PubMed Central

    Hu, Yang; Zhang, Ying; Ren, Jun

    2016-01-01

    The overall goal is to establish a reliable human protein-protein interaction network and develop computational tools to characterize a protein-protein interaction (PPI) network and the role of individual proteins in the context of the network topology and their expression status. A novel and unique feature of our approach is that we assigned confidence measure to each derived interacting pair and account for the confidence in our network analysis. We integrated experimental data to infer human PPI network. Our model treated the true interacting status (yes versus no) for any given pair of human proteins as a latent variable whose value was not observed. The experimental data were the manifestation of interacting status, which provided evidence as to the likelihood of the interaction. The confidence of interactions would depend on the strength and consistency of the evidence. PMID:27648447

  18. Detecting phylogenetic signal in mutualistic interaction networks using a Markov process model

    PubMed Central

    Minoarivelo, H. O.; Hui, C.; Terblanche, J. S.; Pond, S. L. Kosakovsky; Scheffler, K.

    2014-01-01

    Ecological interaction networks, such as those describing the mutualistic interactions between plants and their pollinators or between plants and their frugivores, exhibit non-random structural properties that cannot be explained by simple models of network formation. One factor affecting the formation and eventual structure of such a network is its evolutionary history. We argue that this, in many cases, is closely linked to the evolutionary histories of the species involved in the interactions. Indeed, empirical studies of interaction networks along with the phylogenies of the interacting species have demonstrated significant associations between phylogeny and network structure. To date, however, no generative model explaining the way in which the evolution of individual species affects the evolution of interaction networks has been proposed. We present a model describing the evolution of pairwise interactions as a branching Markov process, drawing on phylogenetic models of molecular evolution. Using knowledge of the phylogenies of the interacting species, our model yielded a significantly better fit to 21% of a set of plant – pollinator and plant – frugivore mutualistic networks. This highlights the importance, in a substantial minority of cases, of inheritance of interaction patterns without excluding the potential role of ecological novelties in forming the current network architecture. We suggest that our model can be used as a null model for controlling evolutionary signals when evaluating the role of other factors in shaping the emergence of ecological networks. PMID:25294947

  19. How antibiotics kill bacteria: from targets to networks

    PubMed Central

    Kohanski, Michael A; Dwyer, Daniel J; Collins, James J

    2010-01-01

    Preface Antibiotic drug-target interactions, and their respective direct effects, are generally well-characterized. In contrast, the bacterial responses to antibiotic drug treatments that contribute to cell death are not as well understood and have proven to be quite complex, involving multiple genetic and biochemical pathways. Here, we review the multi-layered effects of drug-target interactions, including the essential cellular processes inhibited by bactericidal antibiotics and the associated cellular response mechanisms that contribute to killing by bactericidal antibiotics. We also discuss new insights into these mechanisms that have been revealed through the study of biological networks, and describe how these insights, together with related developments in synthetic biology, may be exploited to create novel antibacterial therapies. PMID:20440275

  20. Protein-protein interaction network analysis of cirrhosis liver disease

    PubMed Central

    Safaei, Akram; Rezaei Tavirani, Mostafa; Arefi Oskouei, Afsaneh; Zamanian Azodi, Mona; Mohebbi, Seyed Reza; Nikzamir, Abdol Rahim

    2016-01-01

    Aim: Evaluation of biological characteristics of 13 identified proteins of patients with cirrhotic liver disease is the main aim of this research. Background: In clinical usage, liver biopsy remains the gold standard for diagnosis of hepatic fibrosis. Evaluation and confirmation of liver fibrosis stages and severity of chronic diseases require a precise and noninvasive biomarkers. Since the early detection of cirrhosis is a clinical problem, achieving a sensitive, specific and predictive novel method based on biomarkers is an important task. Methods: Essential analysis, such as gene ontology (GO) enrichment and protein-protein interactions (PPI) was undergone EXPASy, STRING Database and DAVID Bioinformatics Resources query. Results: Based on GO analysis, most of proteins are located in the endoplasmic reticulum lumen, intracellular organelle lumen, membrane-enclosed lumen, and extracellular region. The relevant molecular functions are actin binding, metal ion binding, cation binding and ion binding. Cell adhesion, biological adhesion, cellular amino acid derivative, metabolic process and homeostatic process are the related processes. Protein-protein interaction network analysis introduced five proteins (fibroblast growth factor receptor 4, tropomyosin 4, tropomyosin 2 (beta), lectin, Lectin galactoside-binding soluble 3 binding protein and apolipoprotein A-I) as hub and bottleneck proteins. Conclusion: Our result indicates that regulation of lipid metabolism and cell survival are important biological processes involved in cirrhosis disease. More investigation of above mentioned proteins will provide a better understanding of cirrhosis disease. PMID:27099671

  1. Opinion dynamics on interacting networks: media competition and social influence.

    PubMed

    Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

    2014-01-01

    The inner dynamics of the multiple actors of the informations systems - i.e, T.V., newspapers, blogs, social network platforms, - play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist. PMID:24861995

  2. Opinion dynamics on interacting networks: media competition and social influence

    PubMed Central

    Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

    2014-01-01

    The inner dynamics of the multiple actors of the informations systems – i.e, T.V., newspapers, blogs, social network platforms, – play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist. PMID:24861995

  3. Opinion dynamics on interacting networks: media competition and social influence.

    PubMed

    Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

    2014-05-27

    The inner dynamics of the multiple actors of the informations systems - i.e, T.V., newspapers, blogs, social network platforms, - play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist.

  4. Opinion dynamics on interacting networks: media competition and social influence

    NASA Astrophysics Data System (ADS)

    Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

    2014-05-01

    The inner dynamics of the multiple actors of the informations systems - i.e, T.V., newspapers, blogs, social network platforms, - play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist.

  5. Network synchronization landscape reveals compensatory structures, quantization, and the positive effect of negative interactions

    PubMed Central

    Nishikawa, Takashi; Motter, Adilson E.

    2010-01-01

    Synchronization, in which individual dynamical units keep in pace with each other in a decentralized fashion, depends both on the dynamical units and on the properties of the interaction network. Yet, the role played by the network has resisted comprehensive characterization within the prevailing paradigm that interactions facilitating pairwise synchronization also facilitate collective synchronization. Here we challenge this paradigm and show that networks with best complete synchronization, least coupling cost, and maximum dynamical robustness, have arbitrary complexity but quantized total interaction strength, which constrains the allowed number of connections. It stems from this characterization that negative interactions as well as link removals can be used to systematically improve and optimize synchronization properties in both directed and undirected networks. These results extend the recently discovered compensatory perturbations in metabolic networks to the realm of oscillator networks and demonstrate why “less can be more” in network synchronization. PMID:20489183

  6. 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

  7. 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

  8. 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

  9. 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.

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

    PubMed Central

    Sakane, Fumio; Mizuno, Satoru; Komenoi, Suguru

    2016-01-01

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

  11. 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

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

    PubMed

    Sakane, Fumio; Mizuno, Satoru; Komenoi, Suguru

    2016-01-01

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

  13. 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

  14. 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.

  15. 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.

  16. Finding Collaborators: Toward Interactive Discovery Tools for Research Network Systems

    PubMed Central

    Schleyer, Titus K; Becich, Michael J; Hochheiser, Harry

    2014-01-01

    Background Research networking systems hold great promise for helping biomedical scientists identify collaborators with the expertise needed to build interdisciplinary teams. Although efforts to date have focused primarily on collecting and aggregating information, less attention has been paid to the design of end-user tools for using these collections to identify collaborators. To be effective, collaborator search tools must provide researchers with easy access to information relevant to their collaboration needs. Objective The aim was to study user requirements and preferences for research networking system collaborator search tools and to design and evaluate a functional prototype. Methods Paper prototypes exploring possible interface designs were presented to 18 participants in semistructured interviews aimed at eliciting collaborator search needs. Interview data were coded and analyzed to identify recurrent themes and related software requirements. Analysis results and elements from paper prototypes were used to design a Web-based prototype using the D3 JavaScript library and VIVO data. Preliminary usability studies asked 20 participants to use the tool and to provide feedback through semistructured interviews and completion of the System Usability Scale (SUS). Results Initial interviews identified consensus regarding several novel requirements for collaborator search tools, including chronological display of publication and research funding information, the need for conjunctive keyword searches, and tools for tracking candidate collaborators. Participant responses were positive (SUS score: mean 76.4%, SD 13.9). Opportunities for improving the interface design were identified. Conclusions Interactive, timeline-based displays that support comparison of researcher productivity in funding and publication have the potential to effectively support searching for collaborators. Further refinement and longitudinal studies may be needed to better understand the

  17. Functional features and protein network of human sperm-egg interaction.

    PubMed

    Sabetian, Soudabeh; Shamsir, Mohd Shahir; Abu Naser, Mohammed

    2014-12-01

    Elucidation of the sperm-egg interaction at the molecular level is one of the unresolved problems in sexual reproduction, and understanding the molecular mechanism is crucial in solving problems in infertility and failed in vitro fertilization (IVF). Many molecular interactions in the form of protein-protein interactions (PPIs) mediate the sperm-egg membrane interaction. Due to the complexity of the problem such as difficulties in analyzing in vivo membrane PPIs, many efforts have failed to comprehensively elucidate the fusion mechanism and the molecular interactions that mediate sperm-egg membrane fusion. The main purpose of this study was to reveal possible protein interactions and associated molecular function during sperm-egg interaction using a protein interaction network approach. Different databases have been used to construct the human sperm-egg interaction network. The constructed network revealed new interactions. These included CD151 and CD9 in human oocyte that interact with CD49 in sperm, and CD49 and ITGA4 in sperm that interact with CD63 and CD81, respectively, in the oocyte. These results showed that the different integrins in sperm may be involved in human sperm-egg interaction. It was also suggested that sperm ADAM2 plays a role as a protein candidate involved in sperm-egg membrane interaction by interacting with CD9 in the oocyte. Interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, and manganese ion transmembrane transport activity are the major molecular functions in sperm-egg interaction protein network. The disease association analysis indicated that sperm-egg interaction defects are also reflected in other disease networks such as cardiovascular, hematological, and breast cancer diseases. By analyzing the network, we identified the major molecular functions and disease association genes in sperm-egg interaction protein. Further experimental studies will be required to confirm the significance of these new

  18. Functional features and protein network of human sperm-egg interaction.

    PubMed

    Sabetian, Soudabeh; Shamsir, Mohd Shahir; Abu Naser, Mohammed

    2014-12-01

    Elucidation of the sperm-egg interaction at the molecular level is one of the unresolved problems in sexual reproduction, and understanding the molecular mechanism is crucial in solving problems in infertility and failed in vitro fertilization (IVF). Many molecular interactions in the form of protein-protein interactions (PPIs) mediate the sperm-egg membrane interaction. Due to the complexity of the problem such as difficulties in analyzing in vivo membrane PPIs, many efforts have failed to comprehensively elucidate the fusion mechanism and the molecular interactions that mediate sperm-egg membrane fusion. The main purpose of this study was to reveal possible protein interactions and associated molecular function during sperm-egg interaction using a protein interaction network approach. Different databases have been used to construct the human sperm-egg interaction network. The constructed network revealed new interactions. These included CD151 and CD9 in human oocyte that interact with CD49 in sperm, and CD49 and ITGA4 in sperm that interact with CD63 and CD81, respectively, in the oocyte. These results showed that the different integrins in sperm may be involved in human sperm-egg interaction. It was also suggested that sperm ADAM2 plays a role as a protein candidate involved in sperm-egg membrane interaction by interacting with CD9 in the oocyte. Interleukin-4 receptor activity, receptor signaling protein tyrosine kinase activity, and manganese ion transmembrane transport activity are the major molecular functions in sperm-egg interaction protein network. The disease association analysis indicated that sperm-egg interaction defects are also reflected in other disease networks such as cardiovascular, hematological, and breast cancer diseases. By analyzing the network, we identified the major molecular functions and disease association genes in sperm-egg interaction protein. Further experimental studies will be required to confirm the significance of these new

  19. The virome: a missing component of biological interaction networks in health and disease.

    PubMed

    Handley, Scott A

    2016-01-01

    Host-associated viral populations, viromes, have been understudied relative to their contribution to human physiology. Viruses interact with host gene networks, influencing both health and disease. Analysis of host gene networks in the absence of virome analysis risks missing important network information. PMID:27037032

  20. 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

  1. 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.

  2. Interaction in agent-based economics: A survey on the network approach

    NASA Astrophysics Data System (ADS)

    Bargigli, Leonardo; Tedeschi, Gabriele

    2014-04-01

    In this paper we aim to introduce the reader to some basic concepts and instruments used in a wide range of economic networks models. In particular, we adopt the theory of random networks as the main tool to describe the relationship between the organization of interaction among individuals within different components of the economy and overall aggregate behavior. The focus is on the ways in which economic agents interact and the possible consequences of their interaction on the system. We show that network models are able to introduce complex phenomena in economic systems by allowing for the endogenous evolution of networks.

  3. Antagonistic interaction networks are structured independently of latitude and host guild.

    PubMed

    Morris, Rebecca J; Gripenberg, Sofia; Lewis, Owen T; Roslin, Tomas

    2014-03-01

    An increase in species richness with decreasing latitude is a prominent pattern in nature. However, it remains unclear whether there are corresponding latitudinal gradients in the properties of ecological interaction networks. We investigated the structure of 216 quantitative antagonistic networks comprising insect hosts and their parasitoids, drawn from 28 studies from the High Arctic to the tropics. Key metrics of network structure were strongly affected by the size of the interaction matrix (i.e. the total number of interactions documented between individuals) and by the taxonomic diversity of the host taxa involved. After controlling for these sampling effects, quantitative networks showed no consistent structural patterns across latitude and host guilds, suggesting that there may be basic rules for how sets of antagonists interact with resource species. Furthermore, the strong association between network size and structure implies that many apparent spatial and temporal variations in network structure may prove to be artefacts. PMID:24354432

  4. Topology association analysis in weighted protein interaction network for gene prioritization

    NASA Astrophysics Data System (ADS)

    Wu, Shunyao; Shao, Fengjing; Zhang, Qi; Ji, Jun; Xu, Shaojie; Sun, Rencheng; Sun, Gengxin; Du, Xiangjun; Sui, Yi

    2016-11-01

    Although lots of algorithms for disease gene prediction have been proposed, the weights of edges are rarely taken into account. In this paper, the strengths of topology associations between disease and essential genes are analyzed in weighted protein interaction network. Empirical analysis demonstrates that compared to other genes, disease genes are weakly connected with essential genes in protein interaction network. Based on this finding, a novel global distance measurement for gene prioritization with weighted protein interaction network is proposed in this paper. Positive and negative flow is allocated to disease and essential genes, respectively. Additionally network propagation model is extended for weighted network. Experimental results on 110 diseases verify the effectiveness and potential of the proposed measurement. Moreover, weak links play more important role than strong links for gene prioritization, which is meaningful to deeply understand protein interaction network.

  5. Antagonistic interaction networks are structured independently of latitude and host guild.

    PubMed

    Morris, Rebecca J; Gripenberg, Sofia; Lewis, Owen T; Roslin, Tomas

    2014-03-01

    An increase in species richness with decreasing latitude is a prominent pattern in nature. However, it remains unclear whether there are corresponding latitudinal gradients in the properties of ecological interaction networks. We investigated the structure of 216 quantitative antagonistic networks comprising insect hosts and their parasitoids, drawn from 28 studies from the High Arctic to the tropics. Key metrics of network structure were strongly affected by the size of the interaction matrix (i.e. the total number of interactions documented between individuals) and by the taxonomic diversity of the host taxa involved. After controlling for these sampling effects, quantitative networks showed no consistent structural patterns across latitude and host guilds, suggesting that there may be basic rules for how sets of antagonists interact with resource species. Furthermore, the strong association between network size and structure implies that many apparent spatial and temporal variations in network structure may prove to be artefacts.

  6. Spreading information in a network of interacting neighbours.

    PubMed

    Halupka, Konrad

    2014-01-01

    Dispersed individuals can coordinate the onset of life history events, like reproduction or migration, on a large (population) spatial scale. However, the mechanism of this synchronisation has not yet been identified. In many species signals produced by one individual stimulate signalling activity of immediate neighbours. I propose that such local focuses of signalling could transform into waves propagating in space. This hypothesis predicts that signalling self-organizes into bursts, because neighbours tend to enter activity and refractory periods together. Temporal characteristics of such pulses should be more similar in locations proximate to one another than in distant ones. Finally, denser populations should produce relatively more complex wave patterns, since the number of propagating waves is proportional to the number of individuals. These predictions were tested in an analysis of time series of numbers of territorial songs in chaffinches, Fringilla coelebs, and the results supported the hypothesis. Time series of singing activity had memory of their past states: they were autoregressive processes with spectra in which low frequency oscillations predominated. The degree of similarity in two synchronously sampled time series, measured as a Euclidean distance between their spectra, decreased with the increasing physical distance of sampling spots and the number of signalling males. It is concluded that networks of interacting neighbours may integrate populations synchronising life cycles of dispersed individuals. PMID:25036106

  7. Spreading Information in a Network of Interacting Neighbours

    PubMed Central

    Halupka, Konrad

    2014-01-01

    Dispersed individuals can coordinate the onset of life history events, like reproduction or migration, on a large (population) spatial scale. However, the mechanism of this synchronisation has not yet been identified. In many species signals produced by one individual stimulate signalling activity of immediate neighbours. I propose that such local focuses of signalling could transform into waves propagating in space. This hypothesis predicts that signalling self-organizes into bursts, because neighbours tend to enter activity and refractory periods together. Temporal characteristics of such pulses should be more similar in locations proximate to one another than in distant ones. Finally, denser populations should produce relatively more complex wave patterns, since the number of propagating waves is proportional to the number of individuals. These predictions were tested in an analysis of time series of numbers of territorial songs in chaffinches, Fringilla coelebs, and the results supported the hypothesis. Time series of singing activity had memory of their past states: they were autoregressive processes with spectra in which low frequency oscillations predominated. The degree of similarity in two synchronously sampled time series, measured as a Euclidean distance between their spectra, decreased with the increasing physical distance of sampling spots and the number of signalling males. It is concluded that networks of interacting neighbours may integrate populations synchronising life cycles of dispersed individuals. PMID:25036106

  8. 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

  9. 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.

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

    PubMed Central

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

    2012-01-01

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

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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

  18. 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

  19. 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

  20. [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

  1. 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.

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

    PubMed

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

    2014-01-27

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

  3. 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

  4. 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.

  5. 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

  6. Evaluating the Spatio-Temporal Factors that Structure Network Parameters of Plant-Herbivore Interactions

    PubMed Central

    López-Carretero, Antonio; Díaz-Castelazo, Cecilia; Boege, Karina; Rico-Gray, Víctor

    2014-01-01

    Despite the dynamic nature of ecological interactions, most studies on species networks offer static representations of their structure, constraining our understanding of the ecological mechanisms involved in their spatio-temporal stability. This is the first study to evaluate plant-herbivore interaction networks on a small spatio-temporal scale. Specifically, we simultaneously assessed the effect of host plant availability, habitat complexity and seasonality on the structure of plant-herbivore networks in a coastal tropical ecosystem. Our results revealed that changes in the host plant community resulting from seasonality and habitat structure are reflected not only in the herbivore community, but also in the emergent properties (network parameters) of the plant-herbivore interaction network such as connectance, selectiveness and modularity. Habitat conditions and periods that are most stressful favored the presence of less selective and susceptible herbivore species, resulting in increased connectance within networks. In contrast, the high degree of selectivennes (i.e. interaction specialization) and modularity of the networks under less stressful conditions was promoted by the diversification in resource use by herbivores. By analyzing networks at a small spatio-temporal scale we identified the ecological factors structuring this network such as habitat complexity and seasonality. Our research offers new evidence on the role of abiotic and biotic factors in the variation of the properties of species interaction networks. PMID:25340790

  7. Simulated evolution of protein-protein interaction networks with realistic topology.

    PubMed

    Peterson, G Jack; Pressé, Steve; Peterson, Kristin S; Dill, Ken A

    2012-01-01

    We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein's neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution.

  8. Simulated evolution of protein-protein interaction networks with realistic topology.

    PubMed

    Peterson, G Jack; Pressé, Steve; Peterson, Kristin S; Dill, Ken A

    2012-01-01

    We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein's neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution. PMID:22768057

  9. PathBLAST: a tool for alignment of protein interaction networks.

    PubMed

    Kelley, Brian P; Yuan, Bingbing; Lewitter, Fran; Sharan, Roded; Stockwell, Brent R; Ideker, Trey

    2004-07-01

    PathBLAST is a network alignment and search tool for comparing protein interaction networks across species to identify protein pathways and complexes that have been conserved by evolution. The basic method searches for high-scoring alignments between pairs of protein interaction paths, for which proteins of the first path are paired with putative orthologs occurring in the same order in the second path. This technique discriminates between true- and false-positive interactions and allows for functional annotation of protein interaction pathways based on similarity to the network of another, well-characterized species. PathBLAST is now available at http://www.pathblast.org/ as a web-based query. In this implementation, the user specifies a short protein interaction path for query against a target protein-protein interaction network selected from a network database. PathBLAST returns a ranked list of matching paths from the target network along with a graphical view of these paths and the overlap among them. Target protein-protein interaction networks are currently available for Helicobacter pylori, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. Just as BLAST enables rapid comparison of protein sequences between genomes, tools such as PathBLAST are enabling comparative genomics at the network level.

  10. Dominating Biological Networks

    PubMed Central

    Milenković, Tijana; Memišević, Vesna; Bonato, Anthony; Pržulj, Nataša

    2011-01-01

    Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI) networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of “biologically central (BC)” genes (i.e., their protein products), such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network. To help resolve this debate, we design new network-based approaches and apply them to get new insight into biological function and disease. We hypothesize that BC genes have a topologically central (TC) role in the human PPI network. We propose two different concepts of topological centrality. We design a new centrality measure to capture complex wirings of proteins in the network that identifies as TC those proteins that reside in dense extended network neighborhoods. Also, we use the notion of domination and find dominating sets (DSs) in the PPI network, i.e., sets of proteins such that every protein is either in the DS or is a neighbor of the DS. Clearly, a DS has a TC role, as it enables efficient communication between different network parts. We find statistically significant enrichment in BC genes of TC nodes and outperform the existing methods indicating that genes involved in key biological processes occupy topologically complex and dense regions of the network and correspond to its “spine” that connects all other network parts and can thus pass cellular signals efficiently throughout the network. To our knowledge, this is the first study that explores domination in the context of PPI networks. PMID:21887225

  11. The structure of legume–rhizobium interaction networks and their response to tree invasions

    PubMed Central

    Le Roux, Johannes J.; Mavengere, Natasha R.; Ellis, Allan G.

    2016-01-01

    Establishing mutualistic interactions in novel environments is important for the successful establishment of some non-native plant species. These associations may, in turn, impact native species interaction networks as non-natives become dominant in their new environments. Using phylogenetic and ecological interaction network approaches we provide the first report of the structure of belowground legume–rhizobium interaction networks and how they change along a gradient of invasion (uninvaded, semi invaded and heavily invaded sites) by Australian Acacia species in South Africa’s Cape Floristic Region. We found that native and invasive legumes interact with distinct rhizobial lineages, most likely due to phylogenetic uniqueness of native and invasive host plants. Moreover, legume–rhizobium interaction networks are not nested, but significantly modular with high levels of specialization possibly as a result of legume–rhizobium co-evolution. Although network topology remained constant across the invasion gradient, composition of bacterial communities associated with native legumes changed dramatically as acacias increasingly dominated the landscape. In stark contrast to aboveground interaction networks (e.g. pollination and seed dispersal) we show that invasive legumes do not infiltrate existing native legume–rhizobium networks but rather form novel modules. This absence of mutualist overlap between native and invasive legumes suggests the importance of co-invading rhizobium–acacia species complexes for Acacia invasion success, and argues against a ubiquitous role for the formation and evolutionary refinement of novel interactions. PMID:27255514

  12. The structure of legume-rhizobium interaction networks and their response to tree invasions.

    PubMed

    Le Roux, Johannes J; Mavengere, Natasha R; Ellis, Allan G

    2016-01-01

    Establishing mutualistic interactions in novel environments is important for the successful establishment of some non-native plant species. These associations may, in turn, impact native species interaction networks as non-natives become dominant in their new environments. Using phylogenetic and ecological interaction network approaches we provide the first report of the structure of belowground legume-rhizobium interaction networks and how they change along a gradient of invasion (uninvaded, semi invaded and heavily invaded sites) by Australian Acacia species in South Africa's Cape Floristic Region. We found that native and invasive legumes interact with distinct rhizobial lineages, most likely due to phylogenetic uniqueness of native and invasive host plants. Moreover, legume-rhizobium interaction networks are not nested, but significantly modular with high levels of specialization possibly as a result of legume-rhizobium co-evolution. Although network topology remained constant across the invasion gradient, composition of bacterial communities associated with native legumes changed dramatically as acacias increasingly dominated the landscape. In stark contrast to aboveground interaction networks (e.g. pollination and seed dispersal) we show that invasive legumes do not infiltrate existing native legume-rhizobium networks but rather form novel modules. This absence of mutualist overlap between native and invasive legumes suggests the importance of co-invading rhizobium-acacia species complexes for Acacia invasion success, and argues against a ubiquitous role for the formation and evolutionary refinement of novel interactions.

  13. An inter-species protein-protein interaction network across vast evolutionary distance.

    PubMed

    Zhong, Quan; Pevzner, Samuel J; Hao, Tong; Wang, Yang; Mosca, Roberto; Menche, Jörg; Taipale, Mikko; Taşan, Murat; Fan, Changyu; Yang, Xinping; Haley, Patrick; Murray, Ryan R; Mer, Flora; Gebreab, Fana; Tam, Stanley; MacWilliams, Andrew; Dricot, Amélie; Reichert, Patrick; Santhanam, Balaji; Ghamsari, Lila; Calderwood, Michael A; Rolland, Thomas; Charloteaux, Benoit; Lindquist, Susan; Barabási, Albert-László; Hill, David E; Aloy, Patrick; Cusick, Michael E; Xia, Yu; Roth, Frederick P; Vidal, Marc

    2016-04-22

    In cellular systems, biophysical interactions between macromolecules underlie a complex web of functional interactions. How biophysical and functional networks are coordinated, whether all biophysical interactions correspond to functional interactions, and how such biophysical-versus-functional network coordination is shaped by evolutionary forces are all largely unanswered questions. Here, we investigate these questions using an "inter-interactome" approach. We systematically probed the yeast and human proteomes for interactions between proteins from these two species and functionally characterized the resulting inter-interactome network. After a billion years of evolutionary divergence, the yeast and human proteomes are still capable of forming a biophysical network with properties that resemble those of intra-species networks. Although substantially reduced relative to intra-species networks, the levels of functional overlap in the yeast-human inter-interactome network uncover significant remnants of co-functionality widely preserved in the two proteomes beyond human-yeast homologs. Our data support evolutionary selection against biophysical interactions between proteins with little or no co-functionality. Such non-functional interactions, however, represent a reservoir from which nascent functional interactions may arise.

  14. The structure of legume-rhizobium interaction networks and their response to tree invasions.

    PubMed

    Le Roux, Johannes J; Mavengere, Natasha R; Ellis, Allan G

    2016-01-01

    Establishing mutualistic interactions in novel environments is important for the successful establishment of some non-native plant species. These associations may, in turn, impact native species interaction networks as non-natives become dominant in their new environments. Using phylogenetic and ecological interaction network approaches we provide the first report of the structure of belowground legume-rhizobium interaction networks and how they change along a gradient of invasion (uninvaded, semi invaded and heavily invaded sites) by Australian Acacia species in South Africa's Cape Floristic Region. We found that native and invasive legumes interact with distinct rhizobial lineages, most likely due to phylogenetic uniqueness of native and invasive host plants. Moreover, legume-rhizobium interaction networks are not nested, but significantly modular with high levels of specialization possibly as a result of legume-rhizobium co-evolution. Although network topology remained constant across the invasion gradient, composition of bacterial communities associated with native legumes changed dramatically as acacias increasingly dominated the landscape. In stark contrast to aboveground interaction networks (e.g. pollination and seed dispersal) we show that invasive legumes do not infiltrate existing native legume-rhizobium networks but rather form novel modules. This absence of mutualist overlap between native and invasive legumes suggests the importance of co-invading rhizobium-acacia species complexes for Acacia invasion success, and argues against a ubiquitous role for the formation and evolutionary refinement of novel interactions. PMID:27255514

  15. An antibiotic target ranking and prioritization pipeline combining sequence, structure and network-based approaches exemplified for Serratia marcescens.

    PubMed

    Gupta, Shishir K; Gross, Roy; Dandekar, Thomas

    2016-10-10

    We investigate a drug target screening pipeline comparing sequence, structure and network-based criteria for prioritization. Serratia marcescens, an opportunistic pathogen, serves as test case. We rank according to (i) availability of three dimensional structures and lead compounds, (ii) not occurring in man and general sequence conservation information, and (iii) network information on the importance of the protein (conserved protein-protein interactions; metabolism; reported to be an essential gene in other organisms). We identify 45 potential anti-microbial drug targets in S. marcescens with KdsA involved in LPS biosynthesis as top candidate drug target. LpxC and FlgB are further top-ranked targets identified by interactome analysis not suggested before for S. marcescens. Pipeline, targets and complementarity of the three approaches are evaluated by available experimental data and genetic evidence and against other antibiotic screening pipelines. This supports reliable drug target identification and prioritization for infectious agents (bacteria, parasites, fungi) by these bundled complementary criteria.

  16. A Network Approach to Rare Disease Modeling

    NASA Astrophysics Data System (ADS)

    Ghiassian, Susan; Rabello, Sabrina; Sharma, Amitabh; Wiest, Olaf; Barabasi, Albert-Laszlo

    2011-03-01

    Network approaches have been widely used to better understand different areas of natural and social sciences. Network Science had a particularly great impact on the study of biological systems. In this project, using biological networks, candidate drugs as a potential treatment of rare diseases were identified. Developing new drugs for more than 2000 rare diseases (as defined by ORPHANET) is too expensive and beyond expectation. Disease proteins do not function in isolation but in cooperation with other interacting proteins. Research on FDA approved drugs have shown that most of the drugs do not target the disease protein but a protein which is 2 or 3 steps away from the disease protein in the Protein-Protein Interaction (PPI) network. We identified the already known drug targets in the disease gene's PPI subnetwork (up to the 3rd neighborhood) and among them those in the same sub cellular compartment and higher coexpression coefficient with the disease gene are expected to be stronger candidates. Out of 2177 rare diseases, 1092 were found not to have any drug target. Using the above method, we have found the strongest candidates among the rest in order to further experimental validations.

  17. Top-down network analysis characterizes hidden termite-termite interactions.

    PubMed

    Campbell, Colin; Russo, Laura; Marins, Alessandra; DeSouza, Og; Schönrogge, Karsten; Mortensen, David; Tooker, John; Albert, Réka; Shea, Katriona

    2016-09-01

    The analysis of ecological networks is generally bottom-up, where networks are established by observing interactions between individuals. Emergent network properties have been indicated to reflect the dominant mode of interactions in communities that might be mutualistic (e.g., pollination) or antagonistic (e.g., host-parasitoid communities). Many ecological communities, however, comprise species interactions that are difficult to observe directly. Here, we propose that a comparison of the emergent properties from detail-rich reference communities with known modes of interaction can inform our understanding of detail-sparse focal communities. With this top-down approach, we consider patterns of coexistence between termite species that live as guests in mounds built by other host termite species as a case in point. Termite societies are extremely sensitive to perturbations, which precludes determining the nature of their interactions through direct observations. We perform a literature review to construct two networks representing termite mound cohabitation in a Brazilian savanna and in the tropical forest of Cameroon. We contrast the properties of these cohabitation networks with a total of 197 geographically diverse mutualistic plant-pollinator and antagonistic host-parasitoid networks. We analyze network properties for the networks, perform a principal components analysis (PCA), and compute the Mahalanobis distance of the termite networks to the cloud of mutualistic and antagonistic networks to assess the extent to which the termite networks overlap with the properties of the reference networks. Both termite networks overlap more closely with the mutualistic plant-pollinator communities than the antagonistic host-parasitoid communities, although the Brazilian community overlap with mutualistic communities is stronger. The analysis raises the hypothesis that termite-termite cohabitation networks may be overall mutualistic. More broadly, this work provides support

  18. Top-down network analysis characterizes hidden termite-termite interactions.

    PubMed

    Campbell, Colin; Russo, Laura; Marins, Alessandra; DeSouza, Og; Schönrogge, Karsten; Mortensen, David; Tooker, John; Albert, Réka; Shea, Katriona

    2016-09-01

    The analysis of ecological networks is generally bottom-up, where networks are established by observing interactions between individuals. Emergent network properties have been indicated to reflect the dominant mode of interactions in communities that might be mutualistic (e.g., pollination) or antagonistic (e.g., host-parasitoid communities). Many ecological communities, however, comprise species interactions that are difficult to observe directly. Here, we propose that a comparison of the emergent properties from detail-rich reference communities with known modes of interaction can inform our understanding of detail-sparse focal communities. With this top-down approach, we consider patterns of coexistence between termite species that live as guests in mounds built by other host termite species as a case in point. Termite societies are extremely sensitive to perturbations, which precludes determining the nature of their interactions through direct observations. We perform a literature review to construct two networks representing termite mound cohabitation in a Brazilian savanna and in the tropical forest of Cameroon. We contrast the properties of these cohabitation networks with a total of 197 geographically diverse mutualistic plant-pollinator and antagonistic host-parasitoid networks. We analyze network properties for the networks, perform a principal components analysis (PCA), and compute the Mahalanobis distance of the termite networks to the cloud of mutualistic and antagonistic networks to assess the extent to which the termite networks overlap with the properties of the reference networks. Both termite networks overlap more closely with the mutualistic plant-pollinator communities than the antagonistic host-parasitoid communities, although the Brazilian community overlap with mutualistic communities is stronger. The analysis raises the hypothesis that termite-termite cohabitation networks may be overall mutualistic. More broadly, this work provides support

  19. 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

  20. Deciphering microbial interactions and detecting keystone species with co-occurrence networks.

    PubMed

    Berry, David; Widder, Stefanie

    2014-01-01

    Co-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members. While this approach has potential to reveal ecological processes, it has been insufficiently validated due to the technical limitations inherent in studying complex microbial ecosystems. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics. We then construct co-occurrence networks and evaluate how well networks reveal the underlying interactions and how experimental and ecological parameters can affect network inference and interpretation. We find that co-occurrence networks can recapitulate interaction networks under certain conditions, but that they lose interpretability when the effects of habitat filtering become significant. We demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of hub species that engage in many interactions. We also identify topological features associated with keystone species in co-occurrence networks. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets.

  1. Deciphering microbial interactions and detecting keystone species with co-occurrence networks.

    PubMed

    Berry, David; Widder, Stefanie

    2014-01-01

    Co-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members. While this approach has potential to reveal ecological processes, it has been insufficiently validated due to the technical limitations inherent in studying complex microbial ecosystems. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics. We then construct co-occurrence networks and evaluate how well networks reveal the underlying interactions and how experimental and ecological parameters can affect network inference and interpretation. We find that co-occurrence networks can recapitulate interaction networks under certain conditions, but that they lose interpretability when the effects of habitat filtering become significant. We demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of hub species that engage in many interactions. We also identify topological features associated with keystone species in co-occurrence networks. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets. PMID:24904535

  2. Novel Visualizations and Interactions for Social Networks Exploration

    NASA Astrophysics Data System (ADS)

    Riche, Nathalie Henry; Fekete, Jean-Daniel

    In the last decade, the popularity of social networking applications has dramatically increased. Social networks are collection of persons or organizations connected by relations. Members of Facebook listed as friends or persons connected by family ties in genealogical trees are examples of social networks. Today's web surfers are often part of many online social networks: they communicate in groups or forums on topics of interests, exchange emails with their friends and colleagues, express their ideas on public blogs, share videos on YouTube, exchange and comment photos on Flickr, participate to the edition of the online encyclopedia Wikipedia or contribute to daily news by collaborating to Wikinews or Agoravox.

  3. A novel interacting multiple model based network intrusion detection scheme

    NASA Astrophysics Data System (ADS)

    Xin, Ruichi; Venkatasubramanian, Vijay; Leung, Henry

    2006-04-01

    In today's information age, information and network security are of primary importance to any organization. Network intrusion is a serious threat to security of computers and data networks. In internet protocol (IP) based network, intrusions originate in different kinds of packets/messages contained in the open system interconnection (OSI) layer 3 or higher layers. Network intrusion detection and prevention systems observe the layer 3 packets (or layer 4 to 7 messages) to screen for intrusions and security threats. Signature based methods use a pre-existing database that document intrusion patterns as perceived in the layer 3 to 7 protocol traffics and match the incoming traffic for potential intrusion attacks. Alternately, network traffic data can be modeled and any huge anomaly from the established traffic pattern can be detected as network intrusion. The latter method, also known as anomaly based detection is gaining popularity for its versatility in learning new patterns and discovering new attacks. It is apparent that for a reliable performance, an accurate model of the network data needs to be established. In this paper, we illustrate using collected data that network traffic is seldom stationary. We propose the use of multiple models to accurately represent the traffic data. The improvement in reliability of the proposed model is verified by measuring the detection and false alarm rates on several datasets.

  4. 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.

  5. Interactive effects of elevation, species richness and extreme climatic events on plant-pollinator networks.

    PubMed

    Hoiss, Bernhard; Krauss, Jochen; Steffan-Dewenter, Ingolf

    2015-11-01

    Plant-pollinator interactions are essential for the functioning of terrestrial ecosystems, but are increasingly affected by global change. The risks to such mutualistic interactions from increasing temperature and more frequent extreme climatic events such as drought or advanced snow melt are assumed to depend on network specialization, species richness, local climate and associated parameters such as the amplitude of extreme events. Even though elevational gradients provide valuable model systems for climate change and are accompanied by changes in species richness, responses of plant-pollinator networks to climatic extreme events under different environmental and biotic conditions are currently unknown. Here, we show that elevational climatic gradients, species richness and experimentally simulated extreme events interactively change the structure of mutualistic networks in alpine grasslands. We found that the degree of specialization in plant-pollinator networks (H2') decreased with elevation. Nonetheless, network specialization increased after advanced snow melt at high elevations, whereas changes in network specialization after drought were most pronounced at sites with low species richness. Thus, changes in network specialization after extreme climatic events depended on climatic context and were buffered by high species richness. In our experiment, only generalized plant-pollinator networks changed in their degree of specialization after climatic extreme events. This indicates that contrary to our assumptions, network generalization may not always foster stability of mutualistic interaction networks.

  6. DyNet: visualization and analysis of dynamic molecular interaction networks

    PubMed Central

    Goenawan, Ivan H.; Lynn, David J.

    2016-01-01

    Summary: The ability to experimentally determine molecular interactions on an almost proteome-wide scale under different conditions is enabling researchers to move from static to dynamic network analysis, uncovering new insights into how interaction networks are physically rewired in response to different stimuli and in disease. Dynamic interaction data presents a special challenge in network biology. Here, we present DyNet, a Cytoscape application that provides a range of functionalities for the visualization, real-time synchronization and analysis of large multi-state dynamic molecular interaction networks enabling users to quickly identify and analyze the most ‘rewired’ nodes across many network states. Availability and Implementation: DyNet is available at the Cytoscape (3.2+) App Store (http://apps.cytoscape.org/apps/dynet). Contact: david.lynn@sahmri.com. Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:27153624

  7. Pin-Align: A New Dynamic Programming Approach to Align Protein-Protein Interaction Networks

    PubMed Central

    2014-01-01

    To date, few tools for aligning protein-protein interaction networks have been suggested. These tools typically find conserved interaction patterns using various local or global alignment algorithms. However, the improvement of the speed, scalability, simplification, and accuracy of network alignment tools is still the target of new researches. In this paper, we introduce Pin-Align, a new tool for local alignment of protein-protein interaction networks. Pin-Align accuracy is tested on protein interaction networks from IntAct, DIP, and the Stanford Network Database and the results are compared with other well-known algorithms. It is shown that Pin-Align has higher sensitivity and specificity in terms of KEGG Ortholog groups. PMID:25435900

  8. Identification and evolution of structurally dominant nodes in protein-protein interaction networks.

    PubMed

    Wang, Pei; Yu, Xinghuo; Lü, Jinhu

    2014-02-01

    It is well known that protein-protein interaction (PPI) networks are typical evolving complex networks. Identification of important nodes has been an emerging popular topic in complex networks. Many indexes have been proposed to measure the importance of nodes in complex networks, such as degree, closeness, betweenness, k-shell, clustering coefficient, semi-local centrality, eigenvector centrality. Based on multivariate statistical analysis, through integrating the above indexes and further considering the appearances of nodes in network motifs, this paper aims at developing a new measure to characterize the structurally dominant proteins (SDP) in PPI networks. Moreover, we will further investigate the evolution of the defined dominant nodes in temporal evolving real-world and artificial PPI networks. Our results indicate that the constructed artificial networks have some similar statistical properties as those of the real-world evolving networks. In this case, the artificial PPI networks can be used to further investigate the above evolution characteristics of the real-world evolving networks. Simulation results reveal that SDP in the yeast PPI networks are evolutionary conserved, however, the undominant nodes evolve rapidly. Furthermore, PPI networks are very robust against random mutations, while fragile yet with certain robustness to targeted mutations on SDP. Our investigations shed some light on the future applications of the evolving characteristics of bio-molecular networks, such as reengineering of particular networks for technological, synthetic or pharmacological purposes. PMID:24681922

  9. Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism.

    PubMed

    Corominas, Roser; Yang, Xinping; Lin, Guan Ning; Kang, Shuli; Shen, Yun; Ghamsari, Lila; Broly, Martin; Rodriguez, Maria; Tam, Stanley; Trigg, Shelly A; Fan, Changyu; Yi, Song; Tasan, Murat; Lemmens, Irma; Kuang, Xingyan; Zhao, Nan; Malhotra, Dheeraj; Michaelson, Jacob J; Vacic, Vladimir; Calderwood, Michael A; Roth, Frederick P; Tavernier, Jan; Horvath, Steve; Salehi-Ashtiani, Kourosh; Korkin, Dmitry; Sebat, Jonathan; Hill, David E; Hao, Tong; Vidal, Marc; Iakoucheva, Lilia M

    2014-04-11

    Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases.

  10. Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism

    PubMed Central

    Corominas, Roser; Yang, Xinping; Lin, Guan Ning; Kang, Shuli; Shen, Yun; Ghamsari, Lila; Broly, Martin; Rodriguez, Maria; Tam, Stanley; Trigg, Shelly A.; Fan, Changyu; Yi, Song; Tasan, Murat; Lemmens, Irma; Kuang, Xingyan; Zhao, Nan; Malhotra, Dheeraj; Michaelson, Jacob J.; Vacic, Vladimir; Calderwood, Michael A.; Roth, Frederick P.; Tavernier, Jan; Horvath, Steve; Salehi-Ashtiani, Kourosh; Korkin, Dmitry; Sebat, Jonathan; Hill, David E.; Hao, Tong; Vidal, Marc; Iakoucheva, Lilia M.

    2014-01-01

    Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases. PMID:24722188

  11. 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

  12. Limitation of degree information for analyzing the interaction evolution in online social networks

    NASA Astrophysics Data System (ADS)

    Shang, Ke-Ke; Yan, Wei-Sheng; Xu, Xiao-Ke

    2014-04-01

    Previously many studies on online social networks simply analyze the static topology in which the friend relationship once established, then the links and nodes will not disappear, but this kind of static topology may not accurately reflect temporal interactions on online social services. In this study, we define four types of users and interactions in the interaction (dynamic) network. We found that active, disappeared, new and super nodes (users) have obviously different strength distribution properties and this result also can be revealed by the degree characteristics of the unweighted interaction and friendship (static) networks. However, the active, disappeared, new and super links (interactions) only can be reflected by the strength distribution in the weighted interaction network. This result indicates the limitation of the static topology data on analyzing social network evolutions. In addition, our study uncovers the approximately stable statistics for the dynamic social network in which there are a large variation for users and interaction intensity. Our findings not only verify the correctness of our definitions, but also helped to study the customer churn and evaluate the commercial value of valuable customers in online social networks.

  13. Teaching Heat Exchanger Network Synthesis Using Interactive Microcomputer Graphics.

    ERIC Educational Resources Information Center

    Dixon, Anthony G.

    1987-01-01

    Describes the Heat Exchanger Network Synthesis (HENS) program used at Worcester Polytechnic Institute (Massachusetts) as an aid to teaching the energy integration step in process design. Focuses on the benefits of the computer graphics used in the program to increase the speed of generating and changing networks. (TW)

  14. Social Network Analysis to Examine Interaction Patterns in Knowledge Building Communities

    ERIC Educational Resources Information Center

    Philip, Donald N.

    2010-01-01

    This paper describes use of social network analysis to examine student interaction patterns in a Grade 5/6 Knowledge Building class. The analysis included face-to-face interactions and interactions in the Knowledge Forum[R] Knowledge Building environment. It is argued that sociogram data are useful to reveal group processes; in sociological terms,…

  15. Modularity, pollination systems, and interaction turnover in plant-pollinator networks across space.

    PubMed

    Carstensen, Daniel W; Sabatino, Malena; Morellato, Leonor Patricia C

    2016-05-01

    Mutualistic interaction networks have been shown to be structurally conserved over space and time while pairwise interactions show high variability. In such networks, modularity is the division of species into compartments, or modules, where species within modules share more interactions with each other than they do with species from other modules. Such a modular structure is common in mutualistic networks and several evolutionary and ecological mechanisms have been proposed as underlying drivers. One prominent explanation is the existence of pollination syndromes where flowers tend to attract certain pollinators as determined by a set of traits. We investigate the modularity of seven community level plant-pollinator networks sampled in rupestrian grasslands, or campos rupestres, in SE Brazil. Defining pollination systems as corresponding groups of flower syndromes and pollinator functional groups, we test the two hypotheses that (1) interacting species from the same pollination system are more often assigned to the same module than interacting species from different pollination systems and; that (2) interactions between species from the same pollination system are more consistent across space than interactions between species from different pollination systems. Specifically we ask (1) whether networks are consistently modular across space; (2) whether interactions among species of the same pollination system occur more often inside modules, compared to interactions among species of different pollination systems, and finally; (3) whether the spatial variation in interaction identity, i.e., spatial interaction rewiring, is affected by trait complementarity among species as indicated by pollination systems. We confirm that networks are consistently modular across space and that interactions within pollination systems principally occur inside modules. Despite a strong tendency, we did not find a significant effect of pollination systems on the spatial consistency of

  16. Modularity, pollination systems, and interaction turnover in plant-pollinator networks across space.

    PubMed

    Carstensen, Daniel W; Sabatino, Malena; Morellato, Leonor Patricia C

    2016-05-01

    Mutualistic interaction networks have been shown to be structurally conserved over space and time while pairwise interactions show high variability. In such networks, modularity is the division of species into compartments, or modules, where species within modules share more interactions with each other than they do with species from other modules. Such a modular structure is common in mutualistic networks and several evolutionary and ecological mechanisms have been proposed as underlying drivers. One prominent explanation is the existence of pollination syndromes where flowers tend to attract certain pollinators as determined by a set of traits. We investigate the modularity of seven community level plant-pollinator networks sampled in rupestrian grasslands, or campos rupestres, in SE Brazil. Defining pollination systems as corresponding groups of flower syndromes and pollinator functional groups, we test the two hypotheses that (1) interacting species from the same pollination system are more often assigned to the same module than interacting species from different pollination systems and; that (2) interactions between species from the same pollination system are more consistent across space than interactions between species from different pollination systems. Specifically we ask (1) whether networks are consistently modular across space; (2) whether interactions among species of the same pollination system occur more often inside modules, compared to interactions among species of different pollination systems, and finally; (3) whether the spatial variation in interaction identity, i.e., spatial interaction rewiring, is affected by trait complementarity among species as indicated by pollination systems. We confirm that networks are consistently modular across space and that interactions within pollination systems principally occur inside modules. Despite a strong tendency, we did not find a significant effect of pollination systems on the spatial consistency of

  17. Assessing group interaction with social language network analysis.

    SciTech Connect

    Pennebaker, James; Scholand, Andrew Joseph; Tausczik, Yla R.

    2010-04-01

    In this paper we discuss a new methodology, social language network analysis (SLNA), that combines tools from social language processing and network analysis to assess socially situated working relationships within a group. Specifically, SLNA aims to identify and characterize the nature of working relationships by processing artifacts generated with computer-mediated communication systems, such as instant message texts or emails. Because social language processing is able to identify psychological, social, and emotional processes that individuals are not able to fully mask, social language network analysis can clarify and highlight complex interdependencies between group members, even when these relationships are latent or unrecognized.

  18. Assessing Group Interaction with Social Language Network Analysis

    NASA Astrophysics Data System (ADS)

    Scholand, Andrew J.; Tausczik, Yla R.; Pennebaker, James W.

    In this paper we discuss a new methodology, social language network analysis (SLNA), that combines tools from social language processing and network analysis to assess socially situated working relationships within a group. Specifically, SLNA aims to identify and characterize the nature of working relationships by processing artifacts generated with computer-mediated communication systems, such as instant message texts or emails. Because social language processing is able to identify psychological, social, and emotional processes that individuals are not able to fully mask, social language network analysis can clarify and highlight complex interdependencies between group members, even when these relationships are latent or unrecognized.

  19. Molecular Principles of Gene Fusion Mediated Rewiring of Protein Interaction Networks in Cancer.

    PubMed

    Latysheva, Natasha S; Oates, Matt E; Maddox, Louis; Flock, Tilman; Gough, Julian; Buljan, Marija; Weatheritt, Robert J; Babu, M Madan

    2016-08-18

    Gene fusions are common cancer-causing mutations, but the molecular principles by which fusion protein products affect interaction networks and cause disease are not well understood. Here, we perform an integrative analysis of the structural, interactomic, and regulatory properties of thousands of putative fusion proteins. We demonstrate that genes that form fusions (i.e., parent genes) tend to be highly connected hub genes, whose protein products are enriched in structured and disordered interaction-mediating features. Fusion often results in the loss of these parental features and the depletion of regulatory sites such as post-translational modifications. Fusion products disproportionately connect proteins that did not previously interact in the protein interaction network. In this manner, fusion products can escape cellular regulation and constitutively rewire protein interaction networks. We suggest that the deregulation of central, interaction-prone proteins may represent a widespread mechanism by which fusion proteins alter the topology of cellular signaling pathways and promote cancer. PMID:27540857

  20. Molecular Principles of Gene Fusion Mediated Rewiring of Protein Interaction Networks in Cancer.

    PubMed

    Latysheva, Natasha S; Oates, Matt E; Maddox, Louis; Flock, Tilman; Gough, Julian; Buljan, Marija; Weatheritt, Robert J; Babu, M Madan

    2016-08-18

    Gene fusions are common cancer-causing mutations, but the molecular principles by which fusion protein products affect interaction networks and cause disease are not well understood. Here, we perform an integrative analysis of the structural, interactomic, and regulatory properties of thousands of putative fusion proteins. We demonstrate that genes that form fusions (i.e., parent genes) tend to be highly connected hub genes, whose protein products are enriched in structured and disordered interaction-mediating features. Fusion often results in the loss of these parental features and the depletion of regulatory sites such as post-translational modifications. Fusion products disproportionately connect proteins that did not previously interact in the protein interaction network. In this manner, fusion products can escape cellular regulation and constitutively rewire protein interaction networks. We suggest that the deregulation of central, interaction-prone proteins may represent a widespread mechanism by which fusion proteins alter the topology of cellular signaling pathways and promote cancer.

  1. [The study on the characters of membrane protein interaction and its network based on integrated intelligence method].

    PubMed

    Shen, Yizhen; Ding, Yongsheng; Hao, Kuangrong

    2011-08-01

    Membrane protein and its interaction network have become a novel research direction in bioinformatics. In this paper, a novel membrane protein interaction network simulator is proposed for system biology studies by integrated intelligence method including spectrum analysis, fuzzy K-Nearest Neighbor(KNN) algorithm and so on. We consider biological system as a set of active computational components interacting with each other and with the external environment. Then we can use the network simulator to construct membrane protein interaction networks. Based on the proposed approach, we found that the membrane protein interaction network almost has some dynamic and collective characteristics, such as small-world network, scale free distributing, and hierarchical module structure. These properties are similar to those of other extensively studied protein interaction networks. The present studies on the characteristics of the membrane protein interaction network will be valuable for its relatively biological and medical studies. PMID:21936357

  2. 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.

  3. Frequency specific interactions of MEG resting state activity within and across brain networks as revealed by the multivariate interaction measure.

    PubMed

    Marzetti, L; Della Penna, S; Snyder, A Z; Pizzella, V; Nolte, G; de Pasquale, F; Romani, G L; Corbetta, M

    2013-10-01

    Resting state networks (RSNs) are sets of brain regions exhibiting temporally coherent activity fluctuations in the absence of imposed task structure. RSNs have been extensively studied with fMRI in the infra-slow frequency range (nominally <10(-1)Hz). The topography of fMRI RSNs reflects stationary temporal correlation over minutes. However, neuronal communication occurs on a much faster time scale, at frequencies nominally in the range of 10(0)-10(2)Hz. We examined phase-shifted interactions in the delta (2-3.5 Hz), theta (4-7 Hz), alpha (8-12 Hz) and beta (13-30 Hz) frequency bands of resting-state source space MEG signals. These analyses were conducted between nodes of the dorsal attention network (DAN), one of the most robust RSNs, and between the DAN and other networks. Phase shifted interactions were mapped by the multivariate interaction measure (MIM), a measure of true interaction constructed from the maximization of imaginary coherency in the virtual channels comprised of voxel signals in source space. Non-zero-phase interactions occurred between homologous left and right hemisphere regions of the DAN in the delta and alpha frequency bands. Even stronger non-zero-phase interactions were detected between networks. Visual regions bilaterally showed phase-shifted interactions in the alpha band with regions of the DAN. Bilateral somatomotor regions interacted with DAN nodes in the beta band. These results demonstrate the existence of consistent, frequency specific phase-shifted interactions on a millisecond time scale between cortical regions within RSN as well as across RSNs. PMID:23631996

  4. 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.

  5. 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.

  6. 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.

  7. 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.

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

    PubMed Central

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

    2016-01-01

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

  9. 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

  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. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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.

  17. 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

  18. 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.

  19. Leveraging structure determination with fragment screening for infectious disease drug targets: MECP synthase from Burkholderia pseudomallei

    SciTech Connect

    Begley, Darren W.; Hartley, Robert C.; Davies, Douglas R.; Edwards, Thomas E.; Leonard, Jess T.; Abendroth, Jan; Burris, Courtney A.; Bhandari, Janhavi; Myler, Peter J.; Staker, Bart L.; Stewart, Lance J.

    2011-09-28

    As part of the Seattle Structural Genomics Center for Infectious Disease, we seek to enhance structural genomics with ligand-bound structure data which can serve as a blueprint for structure-based drug design. We have adapted fragment-based screening methods to our structural genomics pipeline to generate multiple ligand-bound structures of high priority drug targets from pathogenic organisms. In this study, we report fragment screening methods and structure determination results for 2C-methyl-D-erythritol-2,4-cyclo-diphosphate (MECP) synthase from Burkholderia pseudomallei, the gram-negative bacterium which causes melioidosis. Screening by nuclear magnetic resonance spectroscopy as well as crystal soaking followed by X-ray diffraction led to the identification of several small molecules which bind this enzyme in a critical metabolic pathway. A series of complex structures obtained with screening hits reveal distinct binding pockets and a range of small molecules which form complexes with the target. Additional soaks with these compounds further demonstrate a subset of fragments to only bind the protein when present in specific combinations. This ensemble of fragment-bound complexes illuminates several characteristics of MECP synthase, including a previously unknown binding surface external to the catalytic active site. These ligand-bound structures now serve to guide medicinal chemists and structural biologists in rational design of novel inhibitors for this enzyme.

  20. 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

  1. 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.

  2. Structural diversity effects of multilayer networks on the threshold of interacting epidemics

    NASA Astrophysics Data System (ADS)

    Wang, Weihong; Chen, MingMing; Min, Yong; Jin, Xiaogang

    2016-02-01

    Foodborne diseases always spread through multiple vectors (e.g. fresh vegetables and fruits) and reveal that multilayer network could spread fatal pathogen with complex interactions. In this paper, first, we use a "top-down analysis framework that depends on only two distributions to describe a random multilayer network with any number of layers. These two distributions are the overlaid degree distribution and the edge-type distribution of the multilayer network. Second, based on the two distributions, we adopt three indicators of multilayer network diversity to measure the correlation between network layers, including network richness, likeness, and evenness. The network richness is the number of layers forming the multilayer network. The network likeness is the degree of different layers sharing the same edge. The network evenness is the variance of the number of edges in every layer. Third, based on a simple epidemic model, we analyze the influence of network diversity on the threshold of interacting epidemics with the coexistence of collaboration and competition. Our work extends the "top-down" analysis framework to deal with the more complex epidemic situation and more diversity indicators and quantifies the trade-off between thresholds of inter-layer collaboration and intra-layer transmission.

  3. Flexible foraging shapes the topology of plant-pollinator interaction networks.

    PubMed

    Spiesman, Brian J; Gratton, Claudio

    2016-06-01

    In plant-pollinator networks, foraging choices by pollinators help form the connecting links between species. Flexible foraging should therefore play an important role in defining network topology. Factors such as morphological trait complementarity limit a pollinator's pool of potential floral resources, but which potential resource species are actually utilized at a location depends on local environmental and ecological context. Pollinators can be highly flexible foragers, but the effect of this flexibility on network topology remains unclear. To understand how flexible foraging affects network topology, we examined differences between sets of locally realized interactions and corresponding sets of potential interactions within 25 weighted plant-pollinator networks in two different regions of the United States. We examined two possible mechanisms for flexible foraging effects on realized networks: (1) preferential targeting of higher-density plant resources, which should increase network nestedness, and (2) context-dependent resource partitioning driven by interspecific competition, which should increase modularity and complementary specialization. We found that flexible foraging has strong effects on realized network topology. Realized connectance was much lower than connectance based on potential interactions, indicating a local narrowing of diet breadth. Moreover, the foraging choices pollinators made, which particular plant species to visit and at what rates, resulted in networks that were significantly less nested and significantly more modular and specialized than their corresponding networks of potential interactions. Preferentially foraging on locally abundant resources was not a strong driver of the realization of potential interactions. However, the degree of modularity and complementary specialization both increased with the number of competing pollinator species and with niche availability. We therefore conclude that flexible foraging affects realized

  4. Flexible foraging shapes the topology of plant-pollinator interaction networks.

    PubMed

    Spiesman, Brian J; Gratton, Claudio

    2016-06-01

    In plant-pollinator networks, foraging choices by pollinators help form the connecting links between species. Flexible foraging should therefore play an important role in defining