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Sample records for drug-target interaction prediction

  1. Predicting Drug-Target Interactions Using Drug-Drug Interactions

    PubMed Central

    Kim, Shinhyuk; Jin, Daeyong; Lee, Hyunju

    2013-01-01

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

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

    PubMed

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

    2011-11-01

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

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

    PubMed

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

    2015-09-09

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

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

    PubMed

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

    2016-01-01

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

  5. Toward more realistic drug-target interaction predictions.

    PubMed

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

    2015-03-01

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

  6. Predicting drug-target interactions using restricted Boltzmann machines

    PubMed Central

    Wang, Yuhao; Zeng, Jianyang

    2013-01-01

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

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

    PubMed

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

    2017-04-07

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

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

    PubMed

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

    2013-12-23

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

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

    PubMed

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

    2016-07-01

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

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed

    Heiskanen, Marja A; Aittokallio, Tero

    2013-04-05

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

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

    PubMed

    Zhang, Shao-Wu; Yan, Xiao-Ying

    2017-01-01

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

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

    PubMed

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

    2010-11-01

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

  14. 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.

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

    PubMed Central

    Keum, Jongsoo; Nam, Hojung

    2017-01-01

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

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

    PubMed Central

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

    2012-01-01

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

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

    PubMed Central

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

    2017-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

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

    PubMed Central

    2016-01-01

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

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

    PubMed

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

    2016-11-21

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

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

    PubMed Central

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

    2010-01-01

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

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

    PubMed

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

    2016-02-25

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

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2016-11-14

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

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

    PubMed

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

    2017-06-19

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

  6. 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

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

    PubMed

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

    2017-09-11

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

  8. 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 .

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

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

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

    PubMed

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

    2015-07-15

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

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

    PubMed

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

    2017-09-28

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

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

    PubMed

    Feng, Yanghe; Wang, Qi; Wang, Tengjiao

    2017-01-01

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

  13. DRUG TARGET PREDICTIONS BASED ON HETEROGENEOUS GRAPH INFERENCE

    PubMed Central

    Wang, Wenhui; Yang, Sen; Li, JING

    2013-01-01

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

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

    PubMed

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

    2017-07-01

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

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

    PubMed

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

    2017-08-14

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

  16. 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.

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

    PubMed Central

    2015-01-01

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

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

    PubMed

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

    2015-01-01

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

  19. Protein-protein interactions as drug targets.

    PubMed

    Skwarczynska, Malgorzata; Ottmann, Christian

    2015-01-01

    Modulation of protein-protein interactions (PPIs) is becoming increasingly important in drug discovery and chemical biology. While a few years ago this 'target class' was deemed to be largely undruggable an impressing number of publications and success stories now show that targeting PPIs with small, drug-like molecules indeed is a feasible approach. Here, we summarize the current state of small-molecule inhibition and stabilization of PPIs and review the active molecules from a structural and medicinal chemistry angle, especially focusing on the key examples of iNOS, LFA-1 and 14-3-3.

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

    PubMed

    Chen, Lei; Zeng, Wei-Ming

    2013-03-01

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

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

    PubMed

    Huang, Guohua; Yan, Fengxia; Tan, Duoduo

    2016-11-14

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

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

    PubMed

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

    2012-09-15

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

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2013-10-25

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

  5. Weighted feature value based Drug Target Protein prediction.

    PubMed

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

    2008-01-01

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

  6. Computational Drug Target Screening through Protein Interaction Profiles

    PubMed Central

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

    2016-01-01

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

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

    PubMed Central

    2010-01-01

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

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

    PubMed

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

    2014-07-01

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

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

    PubMed

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

    2011-05-23

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

  10. 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

  11. The RAS-Effector Interaction as a Drug Target.

    PubMed

    Keeton, Adam B; Salter, E Alan; Piazza, Gary A

    2017-01-15

    About a third of all human cancers harbor mutations in one of the K-, N-, or HRAS genes that encode an abnormal RAS protein locked in a constitutively activated state to drive malignant transformation and tumor growth. Despite more than three decades of intensive research aimed at the discovery of RAS-directed therapeutics, there are no FDA-approved drugs that are broadly effective against RAS-driven cancers. Although RAS proteins are often said to be "undruggable," there is mounting evidence suggesting it may be feasible to develop direct inhibitors of RAS proteins. Here, we review this evidence with a focus on compounds capable of inhibiting the interaction of RAS proteins with their effectors that transduce the signals of RAS and that drive and sustain malignant transformation and tumor growth. These reports of direct-acting RAS inhibitors provide valuable insight for further discovery and development of clinical candidates for RAS-driven cancers involving mutations in RAS genes or otherwise activated RAS proteins. Cancer Res; 77(2); 221-6. ©2017 AACR.

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

    PubMed

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

    2010-01-01

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

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

    PubMed

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

    2017-09-14

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

  14. 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.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed Central

    2010-01-01

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

  17. Data on overlapping brain disorders and emerging drug targets in human Dopamine Receptors Interaction Network.

    PubMed

    Podder, Avijit; Latha, N

    2017-06-01

    Intercommunication of Dopamine Receptors (DRs) with their associate protein partners is crucial to maintain regular brain function in human. Majority of the brain disorders arise due to malfunctioning of such communication process. Hence, contributions of genetic factors, as well as phenotypic indications for various neurological and psychiatric disorders are often attributed as sharing in nature. In our earlier research article entitled "Human Dopamine Receptors Interaction Network (DRIN): a systems biology perspective on topology, stability and functionality of the network" (Podder et al., 2014) [1], we had depicted a holistic interaction map of human Dopamine Receptors. Given emphasis on the topological parameters, we had characterized the functionality along with the vulnerable properties of the network. In support of this, we hereby provide an additional data highlighting the genetic overlapping of various brain disorders in the network. The data indicates the sharing nature of disease genes for various neurological and psychiatric disorders in dopamine receptors connecting protein-protein interactions network. The data also indicates toward an alternative approach to prioritize proteins for overlapping brain disorders as valuable drug targets in the network.

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

    PubMed

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

    2016-05-03

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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

    Firman, Keith; Evans, Luke; Youell, James

    2012-07-16

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

  1. Detection of real-time dynamics of drug-target interactions by ultralong nanowalls.

    PubMed

    Menzel, Andreas; Gübeli, Raphael J; Güder, Firat; Weber, Wilfried; Zacharias, Margit

    2013-11-07

    Detecting drug-target interactions in real-time is a powerful approach for drug discovery and analytics. We show here for the first time the ultra fast electrical real-time detection and quantification of antibiotics using a novel biohybrid nanosensor. The biomolecular sensing is performed on ultralong (mm range) high aspect ratio nanowall (50 nm width) surfaces functionalized with operator DNA tetO which is specifically bound by the sensor protein TetR. This sensor protein is released from the operator DNA in a dose dependent manner by exposing the device functionalized with this bound DNA-protein complex to tetracycline antibiotics. As a result, the electrical conductance is accordingly modulated by these surface net charge changes. The switching mechanism of sensor proteins attached at the functionalized surfaces and releasing them again by antibiotics is demonstrated. With the here presented device the detection limit is below the limits of prevailing detection methods. Moreover, the study is extended to detect antibiotic residues in spiked organic milk from cows far below the maximum residual level of the European Union. In spiked milk samples a detection limit for tetracycline concentrations in the 100 fM level was achieved. The nanowall devices are fabricated by atomic layer deposition-based spacer lithography on full wafer scale which is a simple approach capable for mass production.

  2. Understanding of known drug-target interactions in the catalytic pocket of neuraminidase subtype N1.

    PubMed

    Malaisree, Maturos; Rungrotmongkol, Thanyada; Decha, Panita; Intharathep, Pathumwadee; Aruksakunwong, Ornjira; Hannongbua, Supot

    2008-06-01

    To provide detailed information and insight into the drug-target interaction, structure, solvation, and dynamic and thermodynamic properties, the three known-neuraminidase inhibitors-oseltamivir (OTV), zanamivir (ZNV), and peramivir (PRV)-embedded in the catalytic site of neuraminidase (NA) subtype N1 were studied using molecular dynamics simulations. In terms of ligand conformation, there were major differences in the structures of the guanidinium and the bulky groups. The atoms of the guanidinium group of PRV were observed to form many more hydrogen bonds with the surrounded residues and were much less solvated by water molecules, in comparison with the other two inhibitors. Consequently, D151 lying on the 150-loop (residues 147-152) of group-1 neuraminidase (N1, N4, N5, and N8) was considerably shifted to form direct hydrogen bonds with the --OH group of the PRV, which was located rather far from the 150-loop. For the bulky group, direct hydrogen bonds were detected only between the hydrophilic side chain of ZNV and residues R224, E276, and E277 of N1 with rather weak binding, 20-70% occupation. This is not the case for OTV and PRV, in which flexibility and steric effects due to the hydrophobic side chain lead to the rearrangement of the surrounded residues, that is, the negatively charged side chain of E276 was shifted and rotated to form hydrogen bonds with the positively charged moiety of R224. Taking into account all the ligand-enzyme interaction data, the gas phase MM interaction energy of -282.2 kcal/mol as well as the binding free energy (DeltaG(binding)) of -227.4 kcal/mol for the PRV-N1 are significantly lower than those of the other inhibitors. The ordering of DeltaG(binding) of PRV < ZNV < OTV agrees well with the ordering of experimental IC(50) value. (c) 2008 Wiley-Liss, Inc.

  3. 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

  4. MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development

    PubMed Central

    Moran, Josue D.; Giuste, Felipe O.; Du, Yuhong; Ivanov, Andrei A.; Johns, Margaret A.; Khuri, Fadlo R.; Fu, Haian

    2017-01-01

    Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology. Here we introduce a computational method (MEDICI) to predict PPI essentiality by combining gene knockdown studies with network models of protein interaction pathways in an analytic framework. Our method uses network topology to model how gene silencing can disrupt PPIs, relating the unknown essentialities of individual PPIs to experimentally observed protein essentialities. This model is then deconvolved to recover the unknown essentialities of individual PPIs. We demonstrate the validity of our approach via prediction of sensitivities to compounds based on PPI essentiality and differences in essentiality based on genetic mutations. We further show that lung cancer patients have improved overall survival when specific PPIs are no longer present, suggesting that these PPIs may be potentially new targets for therapeutic development. Software is freely available at https://github.com/cooperlab/MEDICI. Datasets are available at https://ctd2.nci.nih.gov/dataPortal. PMID:28118365

  5. MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development.

    PubMed

    Harati, Sahar; Cooper, Lee A D; Moran, Josue D; Giuste, Felipe O; Du, Yuhong; Ivanov, Andrei A; Johns, Margaret A; Khuri, Fadlo R; Fu, Haian; Moreno, Carlos S

    2017-01-01

    Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology. Here we introduce a computational method (MEDICI) to predict PPI essentiality by combining gene knockdown studies with network models of protein interaction pathways in an analytic framework. Our method uses network topology to model how gene silencing can disrupt PPIs, relating the unknown essentialities of individual PPIs to experimentally observed protein essentialities. This model is then deconvolved to recover the unknown essentialities of individual PPIs. We demonstrate the validity of our approach via prediction of sensitivities to compounds based on PPI essentiality and differences in essentiality based on genetic mutations. We further show that lung cancer patients have improved overall survival when specific PPIs are no longer present, suggesting that these PPIs may be potentially new targets for therapeutic development. Software is freely available at https://github.com/cooperlab/MEDICI. Datasets are available at https://ctd2.nci.nih.gov/dataPortal.

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2017-04-26

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

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

    PubMed Central

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

    2017-01-01

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

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

    PubMed

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

    2017-01-01

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

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

    PubMed

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

    2016-07-01

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

  11. Direct AKAP-mediated protein-protein interactions as potential drug targets.

    PubMed

    Hundsrucker, C; Klussmann, E

    2008-01-01

    A-kinase-anchoring proteins (AKAPs) are a diverse family of about 50 scaffolding proteins. They are defined by the presence of a structurally conserved protein kinase A (PKA)-binding domain. AKAPs tether PKA and other signalling proteins such as further protein kinases, protein phosphatases and phosphodiesterases by direct protein-protein interactions to cellular compartments. Thus, AKAPs form the basis of signalling modules that integrate cellular signalling processes and limit these to defined sites. Disruption of AKAP functions by gene targeting, knockdown approaches and, in particular, pharmacological disruption of defined AKAP-dependent protein-protein interactions has revealed key roles of AKAPs in numerous processes, including the regulation of cardiac myocyte contractility and vasopressin-mediated water reabsorption in the kidney. Dysregulation of such processes causes diseases, including cardiovascular and renal disorders. In this review, we discuss AKAP functions elucidated by gene targeting and knockdown approaches, but mainly focus on studies utilizing peptides for disruption of direct AKAP-mediated protein-protein interactions. The latter studies point to direct AKAP-mediated protein-protein interactions as targets for novel drugs.

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

    PubMed Central

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

    2015-01-01

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

  13. 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.

  14. 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

  15. Properties of Protein Drug Target Classes

    PubMed Central

    Bull, Simon C.; Doig, Andrew J.

    2015-01-01

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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed Central

    Mardinoglu, Adil; Cregg, P. J.

    2015-01-01

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

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

    PubMed Central

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

    2013-01-01

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

  19. 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

  20. Using an in Silico Approach to Teach 3D Pharmacodynamics of the Drug-Target Interaction Process Focusing on Selective COX2 Inhibition by Celecoxib

    ERIC Educational Resources Information Center

    Tavares, Maurício T.; Primi, Marina C.; Silva, Nuno A. T. F.; Carvalho, Camila F.; Cunha, Micael R.; Parise-Filho, Roberto

    2017-01-01

    Teaching the molecular aspects of drug-target interactions and selectivity is not always an easy task. In this context, the use of alternative and engaging approaches could help pharmacy and chemistry students better understand this important topic of medicinal chemistry. Herein a 4 h practical exercise that uses freely available software as a…

  1. GWAS and drug targets

    PubMed Central

    2014-01-01

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

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

    Cancer.gov

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

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

    NASA Astrophysics Data System (ADS)

    Huang, Lu; Jiang, Yuyang; Chen, Yuzong

    2017-01-01

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

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

    PubMed Central

    Huang, Lu; Jiang, Yuyang; Chen, Yuzong

    2017-01-01

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

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

    PubMed

    Huang, Lu; Jiang, Yuyang; Chen, Yuzong

    2017-01-19

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

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

    PubMed

    Li, Ying Hong; Wang, Pan Pan; Li, Xiao Xu; Yu, Chun Yan; Yang, Hong; Zhou, Jin; Xue, Wei Wei; Tan, Jun; Zhu, Feng

    2016-01-01

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

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

    PubMed Central

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

    2016-01-01

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

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

    Cancer.gov

    Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology.

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

    PubMed

    Sugaya, Nobuyoshi

    2013-10-28

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

  10. 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

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

    PubMed

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

    2017-05-31

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

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

    PubMed

    Sugaya, Nobuyoshi

    2014-10-27

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

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

    PubMed

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

    2013-01-01

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

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

    PubMed Central

    2015-01-01

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed

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

    2016-07-21

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

  17. Interaction of 14-3-3 proteins with the Estrogen Receptor Alpha F domain provides a drug target interface

    PubMed Central

    De Vries-van Leeuwen, Ingrid J.; da Costa Pereira, Daniel; Flach, Koen D.; Piersma, Sander R.; Haase, Christian; Bier, David; Yalcin, Zeliha; Michalides, Rob; Feenstra, K. Anton; Jiménez, Connie R.; de Greef, Tom F. A.; Brunsveld, Luc; Ottmann, Christian; Zwart, Wilbert; de Boer, Albertus H.

    2013-01-01

    Estrogen receptor alpha (ERα) is involved in numerous physiological and pathological processes, including breast cancer. Breast cancer therapy is therefore currently directed at inhibiting the transcriptional potency of ERα, either by blocking estrogen production through aromatase inhibitors or antiestrogens that compete for hormone binding. Due to resistance, new treatment modalities are needed and as ERα dimerization is essential for its activity, interference with receptor dimerization offers a new opportunity to exploit in drug design. Here we describe a unique mechanism of how ERα dimerization is negatively controlled by interaction with 14-3-3 proteins at the extreme C terminus of the receptor. Moreover, the small-molecule fusicoccin (FC) stabilizes this ERα/14-3-3 interaction. Cocrystallization of the trimeric ERα/14-3-3/FC complex provides the structural basis for this stabilization and shows the importance of phosphorylation of the penultimate Threonine (ERα-T594) for high-affinity interaction. We confirm that T594 is a distinct ERα phosphorylation site in the breast cancer cell line MCF-7 using a phospho-T594–specific antibody and by mass spectrometry. In line with its ERα/14-3-3 interaction stabilizing effect, fusicoccin reduces the estradiol-stimulated ERα dimerization, inhibits ERα/chromatin interactions and downstream gene expression, resulting in decreased cell proliferation. Herewith, a unique functional phosphosite and an alternative regulation mechanism of ERα are provided, together with a small molecule that selectively targets this ERα/14-3-3 interface. PMID:23676274

  18. Interaction of 14-3-3 proteins with the estrogen receptor alpha F domain provides a drug target interface.

    PubMed

    De Vries-van Leeuwen, Ingrid J; da Costa Pereira, Daniel; Flach, Koen D; Piersma, Sander R; Haase, Christian; Bier, David; Yalcin, Zeliha; Michalides, Rob; Feenstra, K Anton; Jiménez, Connie R; de Greef, Tom F A; Brunsveld, Luc; Ottmann, Christian; Zwart, Wilbert; de Boer, Albertus H

    2013-05-28

    Estrogen receptor alpha (ERα) is involved in numerous physiological and pathological processes, including breast cancer. Breast cancer therapy is therefore currently directed at inhibiting the transcriptional potency of ERα, either by blocking estrogen production through aromatase inhibitors or antiestrogens that compete for hormone binding. Due to resistance, new treatment modalities are needed and as ERα dimerization is essential for its activity, interference with receptor dimerization offers a new opportunity to exploit in drug design. Here we describe a unique mechanism of how ERα dimerization is negatively controlled by interaction with 14-3-3 proteins at the extreme C terminus of the receptor. Moreover, the small-molecule fusicoccin (FC) stabilizes this ERα/14-3-3 interaction. Cocrystallization of the trimeric ERα/14-3-3/FC complex provides the structural basis for this stabilization and shows the importance of phosphorylation of the penultimate Threonine (ERα-T(594)) for high-affinity interaction. We confirm that T(594) is a distinct ERα phosphorylation site in the breast cancer cell line MCF-7 using a phospho-T(594)-specific antibody and by mass spectrometry. In line with its ERα/14-3-3 interaction stabilizing effect, fusicoccin reduces the estradiol-stimulated ERα dimerization, inhibits ERα/chromatin interactions and downstream gene expression, resulting in decreased cell proliferation. Herewith, a unique functional phosphosite and an alternative regulation mechanism of ERα are provided, together with a small molecule that selectively targets this ERα/14-3-3 interface.

  19. 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.

  20. An integrated strategy for the discovery of drug targets by the analysis of protein-protein interactions

    NASA Astrophysics Data System (ADS)

    Peltier, John M.; Askovic, Srdjan; Becklin, Robert R.; Chepanoske, Cindy Lou; Ho, Yew-Seng J.; Kery, Vladimir; Lai, Shuping; Mujtaba, Tahmina; Pyne, Mike; Robbins, Paul B.; Rechenberg, Moritz Von; Richardson, Bonnie; Savage, Justin; Sheffield, Peter; Thompson, Sam; Weir, Lawrence; Widjaja, Kartika; Xu, Nafei; Zhen, Yuejun; Boniface, J. Jay

    2004-11-01

    Proteomics-based technologies have the potential to accelerate the development of drugs, but such technologies must be well integrated in order to have a positive impact. We describe, herein, a multi-step process for the discovery of protein-protein interactions. It is shown that process stages are interdependent and can influence, either positively or negatively, subsequent steps. Optimization of each step, in the context of the full process, is essential for the overall success of the experiment.

  1. Yeast genetic interaction screen of human genes associated with amyotrophic lateral sclerosis: identification of MAP2K5 kinase as a potential drug target.

    PubMed

    Jo, Myungjin; Chung, Ah Young; Yachie, Nozomu; Seo, Minchul; Jeon, Hyejin; Nam, Youngpyo; Seo, Yeojin; Kim, Eunmi; Zhong, Quan; Vidal, Marc; Park, Hae Chul; Roth, Frederick P; Suk, Kyoungho

    2017-09-01

    To understand disease mechanisms, a large-scale analysis of human-yeast genetic interactions was performed. Of 1305 human disease genes assayed, 20 genes exhibited strong toxicity in yeast. Human-yeast genetic interactions were identified by en masse transformation of the human disease genes into a pool of 4653 homozygous diploid yeast deletion mutants with unique barcode sequences, followed by multiplexed barcode sequencing to identify yeast toxicity modifiers. Subsequent network analyses focusing on amyotrophic lateral sclerosis (ALS)-associated genes, such as optineurin (OPTN) and angiogenin (ANG), showed that the human orthologs of the yeast toxicity modifiers of these ALS genes are enriched for several biological processes, such as cell death, lipid metabolism, and molecular transport. When yeast genetic interaction partners held in common between human OPTN and ANG were validated in mammalian cells and zebrafish, MAP2K5 kinase emerged as a potential drug target for ALS therapy. The toxicity modifiers identified in this study may deepen our understanding of the pathogenic mechanisms of ALS and other devastating diseases. © 2017 Jo et al.; Published by Cold Spring Harbor Laboratory Press.

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

    PubMed

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

    2014-01-01

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

  3. Homodimeric enzymes as drug targets.

    PubMed

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

    2010-01-01

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

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

    PubMed Central

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

    2012-01-01

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

  5. 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

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

    PubMed

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

    2010-06-01

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

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

    PubMed Central

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

    2012-01-01

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

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

    PubMed

    Kumari, Priyanka; Nath, Abhigyan; Chaube, Radha

    2015-01-01

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

  9. Drug-Target Kinetics in Drug Discovery.

    PubMed

    Tonge, Peter J

    2017-07-14

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

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

    PubMed Central

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

    2015-01-01

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

  11. 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.

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

    PubMed

    Mehraei, Mani; Bashirov, Rza; Tüzmen, Şükrü

    2016-10-01

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

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

    PubMed

    Kamath, Suman; Skariyachan, Sinosh

    2017-02-20

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

  14. 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.

  15. 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

  16. 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

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

    PubMed

    Broomhead, Neal K; Soliman, Mahmoud E

    2017-03-01

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

  18. Synthetic lethal genetic interactions that decrease somatic cell proliferation in Caenorhabditis elegans identify the alternative RFC CTF18 as a candidate cancer drug target.

    PubMed

    McLellan, Jessica; O'Neil, Nigel; Tarailo, Sanja; Stoepel, Jan; Bryan, Jennifer; Rose, Ann; Hieter, Philip

    2009-12-01

    Somatic mutations causing chromosome instability (CIN) in tumors can be exploited for selective killing of cancer cells by knockdown of second-site genes causing synthetic lethality. We tested and statistically validated synthetic lethal (SL) interactions between mutations in six Saccharomyces cerevisiae CIN genes orthologous to genes mutated in colon tumors and five additional CIN genes. To identify which SL interactions are conserved in higher organisms and represent potential chemotherapeutic targets, we developed an assay system in Caenorhabditis elegans to test genetic interactions causing synthetic proliferation defects in somatic cells. We made use of postembryonic RNA interference and the vulval cell lineage of C. elegans as a readout for somatic cell proliferation defects. We identified SL interactions between members of the cohesin complex and CTF4, RAD27, and components of the alternative RFC(CTF18) complex. The genetic interactions tested are highly conserved between S. cerevisiae and C. elegans and suggest that the alternative RFC components DCC1, CTF8, and CTF18 are ideal therapeutic targets because of their mild phenotype when knocked down singly in C. elegans. Furthermore, the C. elegans assay system will contribute to our knowledge of genetic interactions in a multicellular animal and is a powerful approach to identify new cancer therapeutic targets.

  19. Multidrug transporters as drug targets.

    PubMed

    Liang, X-J; Aszalos, A

    2006-08-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed Central

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

    2012-01-01

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

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

    PubMed

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

    2012-01-01

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

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

    PubMed

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

    2016-12-30

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

  4. Magnetic Drug Targeting in Arterial Flows

    NASA Astrophysics Data System (ADS)

    Williams, Alicia; Puri, Ishwar; Vlachos, Pavlos

    2006-11-01

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

  5. Hsp70 Protein Complexes as Drug Targets

    PubMed Central

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

    2013-01-01

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

  6. Hsp70 protein complexes as drug targets.

    PubMed

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

    2013-01-01

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

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

    PubMed

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

    2017-05-18

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

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

    PubMed

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

    2016-01-01

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

  9. Drug target identification in protozoan parasites

    PubMed Central

    Müller, Joachim; Hemphill, Andrew

    2016-01-01

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

  10. Drug target identification in protozoan parasites.

    PubMed

    Müller, Joachim; Hemphill, Andrew

    2016-08-01

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

  11. 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.

  12. Mouse model phenotypes provide information about human drug targets

    PubMed Central

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

    2014-01-01

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

  13. Biocomputational strategies for microbial drug target identification.

    PubMed

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

    2008-01-01

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

  14. Hyperthermia-induced drug targeting.

    PubMed

    May, Jonathan P; Li, Shyh-Dar

    2013-04-01

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

  15. Automated High Throughput Drug Target Crystallography

    SciTech Connect

    Rupp, B

    2005-02-18

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

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

    PubMed Central

    2011-01-01

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

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

    PubMed

    Takigawa, Ichigaku; Tsuda, Koji; Mamitsuka, Hiroshi

    2011-02-23

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

  18. 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.

  19. Fluid mechanics aspects of magnetic drug targeting.

    PubMed

    Odenbach, Stefan

    2015-10-01

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

  20. Systematic Prediction of Pharmacodynamic Drug-Drug Interactions through Protein-Protein-Interaction Network

    PubMed Central

    Huang, Jialiang; Niu, Chaoqun; Green, Christopher D.; Yang, Lun; Mei, Hongkang; Han, Jing-Dong J.

    2013-01-01

    Identifying drug-drug interactions (DDIs) is a major challenge in drug development. Previous attempts have established formal approaches for pharmacokinetic (PK) DDIs, but there is not a feasible solution for pharmacodynamic (PD) DDIs because the endpoint is often a serious adverse event rather than a measurable change in drug concentration. Here, we developed a metric “S-score” that measures the strength of network connection between drug targets to predict PD DDIs. Utilizing known PD DDIs as golden standard positives (GSPs), we observed a significant correlation between S-score and the likelihood a PD DDI occurs. Our prediction was robust and surpassed existing methods as validated by two independent GSPs. Analysis of clinical side effect data suggested that the drugs having predicted DDIs have similar side effects. We further incorporated this clinical side effects evidence with S-score to increase the prediction specificity and sensitivity through a Bayesian probabilistic model. We have predicted 9,626 potential PD DDIs at the accuracy of 82% and the recall of 62%. Importantly, our algorithm provided opportunities for better understanding the potential molecular mechanisms or physiological effects underlying DDIs, as illustrated by the case studies. PMID:23555229

  1. Amphotericin B formulations and drug targeting.

    PubMed

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

    2008-07-01

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

  2. Prediction of Effective Drug Combinations by Chemical Interaction, Protein Interaction and Target Enrichment of KEGG Pathways

    PubMed Central

    Chen, Lei; Zheng, Ming-Yue; Zhang, Jian; Feng, Kai-Yan; Cai, Yu-Dong

    2013-01-01

    Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1) chemical interaction between drugs, (2) protein interactions between drugs' targets, and (3) target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations. PMID:24083237

  3. 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

  4. "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.

  5. Drug targeting using solid lipid nanoparticles.

    PubMed

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

    2014-07-01

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

  6. Data-driven prediction of drug effects and interactions.

    PubMed

    Tatonetti, Nicholas P; Ye, Patrick P; Daneshjou, Roxana; Altman, Russ B

    2012-03-14

    Adverse drug events remain a leading cause of morbidity and mortality around the world. Many adverse events are not detected during clinical trials before a drug receives approval for use in the clinic. Fortunately, as part of postmarketing surveillance, regulatory agencies and other institutions maintain large collections of adverse event reports, and these databases present an opportunity to study drug effects from patient population data. However, confounding factors such as concomitant medications, patient demographics, patient medical histories, and reasons for prescribing a drug often are uncharacterized in spontaneous reporting systems, and these omissions can limit the use of quantitative signal detection methods used in the analysis of such data. Here, we present an adaptive data-driven approach for correcting these factors in cases for which the covariates are unknown or unmeasured and combine this approach with existing methods to improve analyses of drug effects using three test data sets. We also present a comprehensive database of drug effects (Offsides) and a database of drug-drug interaction side effects (Twosides). To demonstrate the biological use of these new resources, we used them to identify drug targets, predict drug indications, and discover drug class interactions. We then corroborated 47 (P < 0.0001) of the drug class interactions using an independent analysis of electronic medical records. Our analysis suggests that combined treatment with selective serotonin reuptake inhibitors and thiazides is associated with significantly increased incidence of prolonged QT intervals. We conclude that confounding effects from covariates in observational clinical data can be controlled in data analyses and thus improve the detection and prediction of adverse drug effects and interactions.

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

    PubMed

    Park, Junseong; Lee, Jungsul; Choi, Chulhee

    2015-09-04

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

  8. Leveraging human genetics to guide drug target discovery.

    PubMed

    Stitziel, Nathan O; Kathiresan, Sekar

    2017-07-01

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

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

    PubMed

    Gyertyán, István

    2017-02-01

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

  10. P2X Receptors as Drug Targets

    PubMed Central

    Jarvis, Michael F.

    2013-01-01

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

  11. Zika Virus Protease: An Antiviral Drug Target.

    PubMed

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

    2017-10-01

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

  12. Drug targets for rational design against emerging coronaviruses.

    PubMed

    Zhao, Qi; Weber, Erin; Yang, Haitao

    2013-04-01

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

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

    PubMed

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

    2015-01-01

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

  14. The evolution of social interactions changes predictions about interacting phenotypes.

    PubMed

    Kazancıoğlu, Erem; Klug, Hope; Alonzo, Suzanne H

    2012-07-01

    In many traits involved in social interactions, such as courtship and aggression, the phenotype is an outcome of interactions between individuals. Such traits whose expression in an individual is partly determined by the phenotype of its social partner are called "interacting phenotypes." Quantitative genetic models suggested that interacting phenotypes can evolve much faster than nonsocial traits. Current models, however, consider the interaction between phenotypes of social partners as a fixed phenotypic response rule, represented by an interaction coefficient (ψ). Here, we extend existing theoretical models and incorporate the interaction coefficient as a trait that can evolve. We find that the evolution of the interaction coefficient can change qualitatively the predictions about the rate and direction of evolution of interacting phenotypes. We argue that it is crucial to determine whether and how the phenotypic response of an individual to its social partner can evolve to make accurate predictions about the evolution of traits involved in social interactions. © 2012 The Author(s).

  15. ING Proteins as Potential Anticancer Drug Targets

    PubMed Central

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

    2009-01-01

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

  16. Enhancing interacting residue prediction with integrated contact matrix prediction in protein-protein interaction.

    PubMed

    Du, Tianchuan; Liao, Li; Wu, Cathy H

    2016-12-01

    Identifying the residues in a protein that are involved in protein-protein interaction and identifying the contact matrix for a pair of interacting proteins are two computational tasks at different levels of an in-depth analysis of protein-protein interaction. Various methods for solving these two problems have been reported in the literature. However, the interacting residue prediction and contact matrix prediction were handled by and large independently in those existing methods, though intuitively good prediction of interacting residues will help with predicting the contact matrix. In this work, we developed a novel protein interacting residue prediction system, contact matrix-interaction profile hidden Markov model (CM-ipHMM), with the integration of contact matrix prediction and the ipHMM interaction residue prediction. We propose to leverage what is learned from the contact matrix prediction and utilize the predicted contact matrix as "feedback" to enhance the interaction residue prediction. The CM-ipHMM model showed significant improvement over the previous method that uses the ipHMM for predicting interaction residues only. It indicates that the downstream contact matrix prediction could help the interaction site prediction.

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

    PubMed Central

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

    2014-01-01

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

  18. Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data.

    PubMed

    Zhang, Wen; Chen, Yanlin; Liu, Feng; Luo, Fei; Tian, Gang; Li, Xiaohong

    2017-01-05

    Drug-drug interactions (DDIs) are one of the major concerns in drug discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions in the entire lifecycle of drugs, and are important for the drug safety surveillance. Since many DDIs are not detected or observed in clinical trials, this work is aimed to predict unobserved or undetected DDIs. In this paper, we collect a variety of drug data that may influence drug-drug interactions, i.e., drug substructure data, drug target data, drug enzyme data, drug transporter data, drug pathway data, drug indication data, drug side effect data, drug off side effect data and known drug-drug interactions. We adopt three representative methods: the neighbor recommender method, the random walk method and the matrix perturbation method to build prediction models based on different data. Thus, we evaluate the usefulness of different information sources for the DDI prediction. Further, we present flexible frames of integrating different models with suitable ensemble rules, including weighted average ensemble rule and classifier ensemble rule, and develop ensemble models to achieve better performances. The experiments demonstrate that different data sources provide diverse information, and the DDI network based on known DDIs is one of most important information for DDI prediction. The ensemble methods can produce better performances than individual methods, and outperform existing state-of-the-art methods. The datasets and source codes are available at https://github.com/zw9977129/drug-drug-interaction/ .

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

    PubMed

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

    2016-12-01

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

  20. Is hippocampal atrophy a future drug target?

    PubMed

    Dhikav, Vikas; Anand, Kuljeet Singh

    2007-01-01

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

  1. Predicting Deviancy in Family Interaction.

    ERIC Educational Resources Information Center

    Karpowitz, Dennis H.

    Should a stimulus be defined as the single event immediately preceding a response (simple stimulus) or as a constellation of antecedents representing several preceding events (complex stimulus)? Sixty-eight families with a child between four and eight years of age were observed, and family interactions coded in the naturalistic setting of the…

  2. Predictions of spray combustion interactions

    NASA Technical Reports Server (NTRS)

    Shuen, J. S.; Solomon, A. S. P.; Faeth, G. M.

    1984-01-01

    Mean and fluctuating phase velocities; mean particle mass flux; particle size; and mean gas-phase Reynolds stress, composition and temperature were measured in stationary, turbulent, axisymmetric, and flows which conform to the boundary layer approximations while having well-defined initial and boundary conditions in dilute particle-laden jets, nonevaporating sprays, and evaporating sprays injected into a still air environment. Three models of the processes, typical of current practice, were evaluated. The local homogeneous flow and deterministic separated flow models did not provide very satisfactory predictions over the present data base. In contrast, the stochastic separated flow model generally provided good predictions and appears to be an attractive approach for treating nonlinear interphase transport processes in turbulent flows containing particles (drops).

  3. Predicting the fission yeast protein interaction network.

    PubMed

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

    2012-04-01

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

  4. Predicting the Fission Yeast Protein Interaction Network

    PubMed Central

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

    2012-01-01

    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). PMID:22540037

  5. Prediction of Chemical-Protein Interactions Network with Weighted Network-Based Inference Method

    PubMed Central

    Cheng, Feixiong; Zhou, Yadi; Li, Weihua; Liu, Guixia; Tang, Yun

    2012-01-01

    Chemical-protein interaction (CPI) is the central topic of target identification and drug discovery. However, large scale determination of CPI is a big challenge for in vitro or in vivo experiments, while in silico prediction shows great advantages due to low cost and high accuracy. On the basis of our previous drug-target interaction prediction via network-based inference (NBI) method, we further developed node- and edge-weighted NBI methods for CPI prediction here. Two comprehensive CPI bipartite networks extracted from ChEMBL database were used to evaluate the methods, one containing 17,111 CPI pairs between 4,741 compounds and 97 G protein-coupled receptors, the other including 13,648 CPI pairs between 2,827 compounds and 206 kinases. The range of the area under receiver operating characteristic curves was 0.73 to 0.83 for the external validation sets, which confirmed the reliability of the prediction. The weak-interaction hypothesis in CPI network was identified by the edge-weighted NBI method. Moreover, to validate the methods, several candidate targets were predicted for five approved drugs, namely imatinib, dasatinib, sertindole, olanzapine and ziprasidone. The molecular hypotheses and experimental evidence for these predictions were further provided. These results confirmed that our methods have potential values in understanding molecular basis of drug polypharmacology and would be helpful for drug repositioning. PMID:22815915

  6. Predictive Systems for Customer Interactions

    NASA Astrophysics Data System (ADS)

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

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

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

    PubMed Central

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

    2012-01-01

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

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

    PubMed Central

    2012-01-01

    Background Altered networks of gene regulation underlie many complex conditions, including cancer. Inferring gene regulatory networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the gene regulatory network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality. Methods We report a comprehensive comparative evaluation of nine state-of-the art gene regulatory network inference methods encompassing the main algorithmic approaches (mutual information, correlation, partial correlation, random forests, support vector machines) using 38 simulated datasets and empirical serous papillary ovarian adenocarcinoma expression-microarray data. We then apply the best-performing method to infer normal and cancer networks. We assess the druggability of the proteins encoded by our predicted target genes using the CancerResource and PharmGKB webtools and databases. Results We observe large differences in the accuracy with which these methods predict the underlying gene regulatory network depending on features of the data, network size, topology, experiment type, and parameter settings. Applying the best-performing method (the supervised method SIRENE) to the serous papillary ovarian adenocarcinoma dataset, we infer and rank regulatory interactions, some previously reported and others novel. For selected novel interactions we propose testable mechanistic models linking gene regulation to cancer. Using network analysis and visualization, we uncover cross-regulation of angiogenesis-specific genes through three key transcription factors in normal and cancer conditions. Druggabilty analysis

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

    DTIC Science & Technology

    2016-06-01

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

  10. Predicting Pharmacodynamic Drug-Drug Interactions through Signaling Propagation Interference on Protein-Protein Interaction Networks

    PubMed Central

    Park, Kyunghyun; Kim, Docyong; Ha, Suhyun; Lee, Doheon

    2015-01-01

    As pharmacodynamic drug-drug interactions (PD DDIs) could lead to severe adverse effects in patients, it is important to identify potential PD DDIs in drug development. The signaling starting from drug targets is propagated through protein-protein interaction (PPI) networks. PD DDIs could occur by close interference on the same targets or within the same pathways as well as distant interference through cross-talking pathways. However, most of the previous approaches have considered only close interference by measuring distances between drug targets or comparing target neighbors. We have applied a random walk with restart algorithm to simulate signaling propagation from drug targets in order to capture the possibility of their distant interference. Cross validation with DrugBank and Kyoto Encyclopedia of Genes and Genomes DRUG shows that the proposed method outperforms the previous methods significantly. We also provide a web service with which PD DDIs for drug pairs can be analyzed at http://biosoft.kaist.ac.kr/targetrw. PMID:26469276

  11. 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.

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

    PubMed

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

    2001-02-01

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

  13. 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

  14. How to Predict Molecular Interactions between Species?

    PubMed Central

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

    2016-01-01

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

  15. Stapled peptides for intracellular drug targets.

    PubMed

    Verdine, Gregory L; Hilinski, Gerard J

    2012-01-01

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

  16. Predicting and Analyzing Interactions between Mycobacterium tuberculosis and Its Human Host

    PubMed Central

    Rapanoel, Holifidy A.; Mazandu, Gaston K.; Mulder, Nicola J.

    2013-01-01

    The outcome of infection by Mycobacterium tuberculosis (Mtb) depends greatly on how the host responds to the bacteria and how the bacteria manipulates the host, which is facilitated by protein–protein interactions. Thus, to understand this process, there is a need for elucidating protein interactions between human and Mtb, which may enable us to characterize specific molecular mechanisms allowing the bacteria to persist and survive under different environmental conditions. In this work, we used the interologs method based on experimentally verified intra-species and inter-species interactions to predict human-Mtb functional interactions. These interactions were further filtered using known human-Mtb interactions and genes that are differentially expressed during infection, producing 190 interactions. Further analysis of the subcellular location of proteins involved in these human-Mtb interactions confirms feasibility of these interactions. We also conducted functional analysis of human and Mtb proteins involved in these interactions, checking whether these proteins play a role in infection and/or disease, and enriching Mtb proteins in a previously predicted list of drug targets. We found that the biological processes of the human interacting proteins suggested their involvement in apoptosis and production of nitric oxide, whereas those of the Mtb interacting proteins were relevant to the intracellular environment of Mtb in the host. Mapping these proteins onto KEGG pathways highlighted proteins belonging to the tuberculosis pathway and also suggested that Mtb proteins might use the host to acquire nutrients, which is in agreement with the intracellular lifestyle of Mtb. This indicates that these interactions can shed light on the interplay between Mtb and its human host and thus, contribute to the process of designing novel drugs with new biological mechanisms of action. PMID:23844013

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

    PubMed Central

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

    2013-01-01

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

  18. Interactions of Timing and Prediction Error Learning

    PubMed Central

    Kirkpatrick, Kimberly

    2013-01-01

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

  19. Interactions of timing and prediction error learning.

    PubMed

    Kirkpatrick, Kimberly

    2014-01-01

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

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

    PubMed

    Skariyachan, Sinosh; Prakash, Nisha; Bharadwaj, Navya

    2012-12-01

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

  1. [Monogenic hypercholesterolemias: new genes, new drug targets].

    PubMed

    Mandel'shtam, M Iu; Vasil'ev, V B

    2008-10-01

    This review is focused on recent data on structure and functions of PCSK9 proprotein convertase, a newly identified participant in cholesterol metabolism in mammalian organisms, including humans. Proprotein convertase acts as a molecular chaperone for the low density lipoprotein (LDL) receptor, targeting it to the lysosomal degradation pathway. Various mutations increasing the PCSK9 affinity toward the LDL receptor cause autosomal dominant hypercholesterolemia. In contrast, loss-of-function mutations in PCSK9 gene decrease the blood plasma cholesterol level, thus acting as a protection factor against atherosclerosis and coronary heart disease. It is supposed that pharmacological agents inhibiting the interaction between PCSK9 and LDL receptor may substantially amplify the benefits of drugs--statins and cholesterol absorption blockers--in the treatment of all types of hypercholesterolemia, including its widespread multigenic and multifactorial forms.

  2. Histone as future drug target for malaria.

    PubMed

    Rawat, D S; Lumb, V; Sharma, Y D; Pasha, S T; Singh, G

    2007-06-01

    Malaria continues to be a major cause of mortality and morbidity in tropical countries and affecting around 100 countries of the world. As per WHO estimates, 300-500 million are being infected and 1-3 million deaths annually due to malaria. With the emerging knowledge about genome sequence of all the three counterparts involved in the disease of malaria, the parasite Plasmodium, vector Anopheles and host Homo sapien have helped the scientists to understand interactions between them. Simultaneous advancement in technology further improves the prospects to discover new targets for vaccines and drugs. Though the malaria vaccine is still far away in this situation there is need to develop a potent and affordable drug(s). Histones are the key protein of chromatin and play an important role in DNA packaging, replication and gene expression. They also show frequent post-translation modifications. The specific combinations of these posttranslational modifications are thought to alter chromatin structure by forming epigenetic bar codes that specify either transient or heritable patterns of genome function. Chromatin regulators and upstream pathways are therefore seen as promising targets for development of therapeutic drugs.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed Central

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

    2013-01-01

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

  5. Is the Mitochondrion a Good Malaria Drug Target?

    PubMed

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

    2017-03-01

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

  6. PREFACE: Protein protein interactions: principles and predictions

    NASA Astrophysics Data System (ADS)

    Nussinov, Ruth; Tsai, Chung-Jung

    2005-06-01

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

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

    PubMed

    Batool, Nisha; Waqar, Maleeha; Batool, Sidra

    2016-01-15

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

  8. Predicting polymer nanofiber interactions via molecular simulations.

    PubMed

    Buell, Sezen; Rutledge, Gregory C; Vliet, Krystyn J Van

    2010-04-01

    Physical and functional properties of nonwoven textiles and other fiberlike materials depend strongly on the number and type of fiber-fiber interactions. For nanoscale polymeric fibers in particular, these interactions are governed by the surfaces of and contacts between fibers. We employ both molecular dynamics (MD) simulations at a temperature below the glass transition temperature T(g) of the polymer bulk, and molecular statics (MS), or energy minimization, to study the interfiber interactions between prototypical polymeric fibers of 4.6 nm diameter, comprising multiple macromolecular chains each of 100 carbon atoms per chain (C100). Our MD simulations show that fibers aligned parallel and within 9 nm of one another experience a significant force of attraction. These fibers tend toward coalescence on a very short time scale, even below T(g). In contrast, our MS calculations suggest an interfiber interaction that transitions from an attractive to a repulsive force at a separation distance of 6 nm. The results of either approach can be used to obtain a quantitative, closed-form relation describing fiber-fiber interaction energies U(s). However, the predicted form of interaction is quite different for the two approaches, and can be understood in terms of differences in the extent of molecular mobility within and between fibers for these different modeling perspectives. The results of these molecular-scale calculations of U(s) are used to interpret experimental observations for electrospun polymer nanofiber mats. These findings highlight the role of temperature and kinetically accessible molecular configurations in predicting interface-dominated interactions at polymer fiber surfaces, and prompt further experiments and simulations to confirm these effects in the properties of nonwoven mats comprising such nanoscale fibers.

  9. 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

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  11. Sirtuins as potential drug targets for metablic diseases

    USDA-ARS?s Scientific Manuscript database

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

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

    PubMed

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

    2009-11-01

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

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

    PubMed Central

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

    2012-01-01

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

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

    PubMed

    Malipatil, Vijayakumari; Madagi, Shivkumar; Bhattacharjee, Biplab

    2013-01-01

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

  15. Predicting protein-peptide interactions from scratch

    NASA Astrophysics Data System (ADS)

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

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

  16. Predicting protein interactions by Brownian dynamics simulations.

    PubMed

    Meng, Xuan-Yu; Xu, Yu; Zhang, Hong-Xing; Mezei, Mihaly; Cui, Meng

    2012-01-01

    We present a newly adapted Brownian-Dynamics (BD)-based protein docking method for predicting native protein complexes. The approach includes global BD conformational sampling, compact complex selection, and local energy minimization. In order to reduce the computational costs for energy evaluations, a shell-based grid force field was developed to represent the receptor protein and solvation effects. The performance of this BD protein docking approach has been evaluated on a test set of 24 crystal protein complexes. Reproduction of experimental structures in the test set indicates the adequate conformational sampling and accurate scoring of this BD protein docking approach. Furthermore, we have developed an approach to account for the flexibility of proteins, which has been successfully applied to reproduce the experimental complex structure from the structure of two unbounded proteins. These results indicate that this adapted BD protein docking approach can be useful for the prediction of protein-protein interactions.

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

    PubMed

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

    2013-11-05

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

  19. Modular composition predicts kinase/substrate interactions

    PubMed Central

    2010-01-01

    Background Phosphorylation events direct the flow of signals and metabolites along cellular protein networks. Current annotations of kinase-substrate binding events are far from complete. In this study, we scanned the entire human protein sequences using the PROSITE domain annotation tool to identify patterns of domain composition in kinases and their substrates. We identified statistically enriched pairs of strings of domains (signature pairs) in kinase-substrate couples presented in the 2006 version of the PTM database. Results The signature pairs enriched in kinase - substrate binding interactions turned out to be highly specific to kinase subtypes. The resulting list of signature pairs predicted kinase-substrate interactions in validation dataset not used in learning with high statistical accuracy. Conclusions The method presented here produces predictions of protein phosphorylation events with high accuracy and mid-level coverage. Our method can be used in expanding the currently available drafts of cell signaling pathways and thus will be an important tool in the development of combination drug therapies targeting complex diseases. PMID:20579376

  20. 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

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

    PubMed

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

    2017-08-19

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

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

    PubMed

    De Backer, Marianne D; Van Dijck, Patrick

    2003-10-01

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

  3. Prediction of protein-protein interaction sites using an ensemble method

    PubMed Central

    2009-01-01

    Background Prediction of protein-protein interaction sites is one of the most challenging and intriguing problems in the field of computational biology. Although much progress has been achieved by using various machine learning methods and a variety of available features, the problem is still far from being solved. Results In this paper, an ensemble method is proposed, which combines bootstrap resampling technique, SVM-based fusion classifiers and weighted voting strategy, to overcome the imbalanced problem and effectively utilize a wide variety of features. We evaluate the ensemble classifier using a dataset extracted from 99 polypeptide chains with 10-fold cross validation, and get a AUC score of 0.86, with a sensitivity of 0.76 and a specificity of 0.78, which are better than that of the existing methods. To improve the usefulness of the proposed method, two special ensemble classifiers are designed to handle the cases of missing homologues and structural information respectively, and the performance is still encouraging. The robustness of the ensemble method is also evaluated by effectively classifying interaction sites from surface residues as well as from all residues in proteins. Moreover, we demonstrate the applicability of the proposed method to identify interaction sites from the non-structural proteins (NS) of the influenza A virus, which may be utilized as potential drug target sites. Conclusion Our experimental results show that the ensemble classifiers are quite effective in predicting protein interaction sites. The Sub-EnClassifiers with resampling technique can alleviate the imbalanced problem and the combination of Sub-EnClassifiers with a wide variety of feature groups can significantly improve prediction performance. PMID:20015386

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

    PubMed

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

    2013-03-01

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

  5. 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.

  6. 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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

    Stumm, Gabriele; Russ, Andreas; Nehls, Michael

    2002-01-01

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

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

    PubMed

    Chen, Shao-Jun

    2015-10-01

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

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

    PubMed

    Tan, Hepan; Ge, Xiaoxia; Xie, Lei

    2013-08-01

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

  11. DDTRP: Database of Drug Targets for Resistant Pathogens

    PubMed Central

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

    2011-01-01

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

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

    PubMed Central

    Bruce, Fanshawe; Sanz-Moreno, Victoria

    2016-01-01

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

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

    PubMed

    Tigli Aydin, R Seda

    2015-01-01

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

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

    PubMed Central

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

    2011-01-01

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

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

    PubMed

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

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    1998-05-01

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

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

    PubMed

    Yazdani, Saleh; Bansal, Ruchi; Prakash, Jai

    2017-07-16

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

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

    PubMed

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

    2017-01-01

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

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

    PubMed

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

    2014-12-01

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

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

    PubMed Central

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

    2015-01-01

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

  1. 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

  2. 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

  3. 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

  4. Revisiting AMPA receptors as an antiepileptic drug target.

    PubMed

    Rogawski, Michael A

    2011-03-01

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

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

    PubMed

    Maguire, Bruce A

    2009-03-01

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

  6. Candidate Drug Targets for Prevention or Modification of Epilepsy

    PubMed Central

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

    2015-01-01

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

  7. Voltage-gated Potassium Channels as Therapeutic Drug Targets

    PubMed Central

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

    2009-01-01

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

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

    PubMed

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

    2013-01-01

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

  9. Toxoplasma histone acetylation remodelers as novel drug targets

    PubMed Central

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

    2013-01-01

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

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

    PubMed Central

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

    2013-01-01

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

  11. 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

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

    PubMed

    Goodman, C D; McFadden, G I

    2007-01-01

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

  13. Increasing the structural coverage of tuberculosis drug targets

    DOE PAGES

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

    2014-12-19

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

  14. Increasing the structural coverage of tuberculosis drug targets

    SciTech Connect

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

    2014-12-19

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

  15. Predictability of Genetic Interactions from Functional Gene Modules

    PubMed Central

    Young, Jonathan H.; Marcotte, Edward M.

    2016-01-01

    Characterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets, yet experimentally determining whether genes interact is technically nontrivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. By leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal. PMID:28007839

  16. 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.

  17. GRIP: A web-based system for constructing Gold Standard datasets for protein-protein interaction prediction

    PubMed Central

    Browne, Fiona; Wang, Haiying; Zheng, Huiru; Azuaje, Francisco

    2009-01-01

    Background Information about protein interaction networks is fundamental to understanding protein function and cellular processes. Interaction patterns among proteins can suggest new drug targets and aid in the design of new therapeutic interventions. Efforts have been made to map interactions on a proteomic-wide scale using both experimental and computational techniques. Reference datasets that contain known interacting proteins (positive cases) and non-interacting proteins (negative cases) are essential to support computational prediction and validation of protein-protein interactions. Information on known interacting and non interacting proteins are usually stored within databases. Extraction of these data can be both complex and time consuming. Although, the automatic construction of reference datasets for classification is a useful resource for researchers no public resource currently exists to perform this task. Results GRIP (Gold Reference dataset constructor from Information on Protein complexes) is a web-based system that provides researchers with the functionality to create reference datasets for protein-protein interaction prediction in Saccharomyces cerevisiae. Both positive and negative cases for a reference dataset can be extracted, organised and downloaded by the user. GRIP also provides an upload facility whereby users can submit proteins to determine protein complex membership. A search facility is provided where a user can search for protein complex information in Saccharomyces cerevisiae. Conclusion GRIP is developed to retrieve information on protein complex, cellular localisation, and physical and genetic interactions in Saccharomyces cerevisiae. Manual construction of reference datasets can be a time consuming process requiring programming knowledge. GRIP simplifies and speeds up this process by allowing users to automatically construct reference datasets. GRIP is free to access at . PMID:19171033

  18. GRIP: A web-based system for constructing Gold Standard datasets for protein-protein interaction prediction.

    PubMed

    Browne, Fiona; Wang, Haiying; Zheng, Huiru; Azuaje, Francisco

    2009-01-26

    Information about protein interaction networks is fundamental to understanding protein function and cellular processes. Interaction patterns among proteins can suggest new drug targets and aid in the design of new therapeutic interventions. Efforts have been made to map interactions on a proteomic-wide scale using both experimental and computational techniques. Reference datasets that contain known interacting proteins (positive cases) and non-interacting proteins (negative cases) are essential to support computational prediction and validation of protein-protein interactions. Information on known interacting and non interacting proteins are usually stored within databases. Extraction of these data can be both complex and time consuming. Although, the automatic construction of reference datasets for classification is a useful resource for researchers no public resource currently exists to perform this task. GRIP (Gold Reference dataset constructor from Information on Protein complexes) is a web-based system that provides researchers with the functionality to create reference datasets for protein-protein interaction prediction in Saccharomyces cerevisiae. Both positive and negative cases for a reference dataset can be extracted, organised and downloaded by the user. GRIP also provides an upload facility whereby users can submit proteins to determine protein complex membership. A search facility is provided where a user can search for protein complex information in Saccharomyces cerevisiae. GRIP is developed to retrieve information on protein complex, cellular localisation, and physical and genetic interactions in Saccharomyces cerevisiae. Manual construction of reference datasets can be a time consuming process requiring programming knowledge. GRIP simplifies and speeds up this process by allowing users to automatically construct reference datasets. GRIP is free to access at http://rosalind.infj.ulst.ac.uk/GRIP/.

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

    PubMed

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

    2017-07-01

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

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

    PubMed Central

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

    2012-01-01

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

  1. 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.

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

    PubMed

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

    2013-01-01

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

  3. 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

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

    PubMed

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

    2012-03-01

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

  5. 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.

  6. Three-dimensional structure model and predicted ATP interaction rewiring of a deviant RNA ligase 2.

    PubMed

    Moreira, Sandrine; Noutahi, Emmanuel; Lamoureux, Guillaume; Burger, Gertraud

    2015-10-09

    RNA ligases 2 are scarce and scattered across the tree of life. Two members of this family are well studied: the mitochondrial RNA editing ligase from the parasitic trypanosomes (Kinetoplastea), a promising drug target, and bacteriophage T4 RNA ligase 2, a workhorse in molecular biology. Here we report the identification of a divergent RNA ligase 2 (DpRNL) from Diplonema papillatum (Diplonemea), a member of the kinetoplastids' sister group. We identified DpRNL with methods based on sensitive hidden Markov Model. Then, using homology modeling and molecular dynamics simulations, we established a three dimensional structure model of DpRNL complexed with ATP and Mg2+. The 3D model of Diplonema was compared with available crystal structures from Trypanosoma brucei, bacteriophage T4, and two archaeans. Interaction of DpRNL with ATP is predicted to involve double π-stacking, which has not been reported before in RNA ligases. This particular contact would shift the orientation of ATP and have considerable consequences on the interaction network of amino acids in the catalytic pocket. We postulate that certain canonical amino acids assume different functional roles in DpRNL compared to structurally homologous residues in other RNA ligases 2, a reassignment indicative of constructive neutral evolution. Finally, both structure comparison and phylogenetic analysis show that DpRNL is not specifically related to RNA ligases from trypanosomes, suggesting a unique adaptation of the latter for RNA editing, after the split of diplonemids and kinetoplastids. Homology modeling and molecular dynamics simulations strongly suggest that DpRNL is an RNA ligase 2. The predicted innovative reshaping of DpRNL's catalytic pocket is worthwhile to be tested experimentally.

  7. Hyperlipidemia, Disease Associations, and Top 10 Potential Drug Targets: A Network View.

    PubMed

    Rai, Sneha; Bhatnagar, Sonika

    2016-03-01

    The prevalence of acquired hyperlipidemia has increased due to sedentary life style and lipid-rich diet. In this work, a lipid-protein-protein interaction network (LPPIN) for acquired hyperlipidemia was prepared by incorporating differentially expressed genes in obese fatty liver as seed nodes, protein interactions from PathwayLinker, and lipid interactions from STITCH4.0. Cholesterol, diacylglycreol, phosphatidylinositol-bis-phosphate, and inositol triphosphate were identified as core lipids that influence the signaling pathways in the LPPIN. RACα serine/threonine-protein kinase (AKT1) was a highly essential central protein. The gastrin-CREB pathway was greatly enriched; all enriched pathways in the LPPIN showed crosstalk with the phosphatidylinositol-3-kinase-Akt pathway, correlating with the central role of AKT1 in the network. The disease clusters identified in the LPPIN were cardiovascular disease, cancer, Alzheimer's disease, and Type II diabetes. In this context, we note that the commercially approved drug targets for hyperlipidemia in each disease cluster may potentially be repurposed for treatment of the specific disease. We report here top 10 potential drug targets that may mediate progression from hyperlipidemia to the respective disease state. ToppGene Suite was employed to identify candidates followed by a) discarding high closeness centrality nodes, and b) selecting nodes with high bridging centrality. Three potential targets could be mapped to specific disease clusters in the LPPIN. Lipids associated with acquired hyperlipidemia and each disease cluster identified may be useful as prognostic fingerprints. These findings provide an integrative view of lipid-protein interactions leading to acquired hyperlipidemia and the associated diseases, and might prove useful in future translational pharmaceutical research.

  8. Predicting community composition from pairwise interactions

    NASA Astrophysics Data System (ADS)

    Friedman, Jonathan; Higgins, Logan; Gore, Jeff

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

  9. 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.

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

    PubMed

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

    2017-01-20

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

  11. The prospect of FKBP51 as a drug target.

    PubMed

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

    2012-08-01

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

  12. 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

  13. Dominant drug targets suppress the emergence of antiviral resistance

    PubMed Central

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

    2014-01-01

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

  14. 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.

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

    PubMed

    Supuran, Claudiu T; Capasso, Clemente

    2017-07-15

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

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

    PubMed

    Lehmann, Anders

    2009-06-01

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

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

    PubMed

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

    2015-01-01

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

  18. 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.

  19. Protease-inhibitor interaction predictions: Lessons on the complexity of protein-protein interactions.

    PubMed

    Fortelny, Nikolaus; Butler, Georgina S; Overall, Christopher Mark; Pavlidis, Paul

    2017-04-06

    Protein interactions shape proteome function and thus biology. Identification of protein interactions is a major goal in molecular biology, but biochemical methods, although improving, remain limited in coverage and accuracy. Whereas computational predictions can guide biochemical experiments, low validation rates of predictions remain a major limitation. Here, we investigated computational methods in the prediction of a specific type of interaction, the inhibitory interactions between proteases and their inhibitors. Proteases generate thousands of proteoforms that dynamically shape the functional state of proteomes. Despite the important regulatory role of proteases, knowledge of their inhibitors remains largely incomplete with the vast majority of proteases lacking an annotated inhibitor. To link inhibitors to their target proteases on a large scale, we applied computational methods to predict inhibitory interactions between proteases and their inhibitors based on complementary data including coexpression, phylogenetic similarity, structural information, co-annotation, and colocalization, and also surveyed general protein interaction networks for potential inhibitory interactions. In testing nine predicted interactions biochemically, we validated the inhibition of kallikrein 5 by serpin B12. Despite the use of a wide array of complementary data, we found a high false positive rate of computational predictions in biochemical follow-up. Based on a protease-specific definition of true negatives derived from the biochemical classification of proteases and inhibitors, we analyzed prediction accuracy of individual features. Thereby we identified feature-specific limitations, which also affected general protein interaction prediction methods. Interestingly, proteases were often not coexpressed with most of their functional inhibitors, contrary to what is commonly assumed and extrapolated predominantly from cell culture experiments. Predictions of inhibitory interactions

  20. 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

  1. PEG-g-chitosan nanoparticles functionalized with the monoclonal antibody OX26 for brain drug targeting.

    PubMed

    Monsalve, Yuliana; Tosi, Giovanni; Ruozi, Barbara; Belletti, Daniela; Vilella, Antonietta; Zoli, Michele; Vandelli, Maria Angela; Forni, Flavio; López, Betty L; Sierra, Ligia

    2015-01-01

    Drug targeting to the CNS is challenging due to the presence of blood-brain barrier. We investigated chitosan (Cs) nanoparticles (NPs) as drug transporter system across the blood-brain barrier, based on mAb OX26 modified Cs. Cs NPs functionalized with PEG, modified and unmodified with OX26 (Cs-PEG-OX26) were prepared and chemico-physically characterized. These NPs were administered (intraperitoneal) in mice to define their ability to reach the brain. Brain uptake of OX26-conjugated NPs is much higher than of unmodified NPs, because: long-circulating abilities (conferred by PEG), interaction between cationic Cs and brain endothelium negative charges and OX26 TfR receptor affinity. Cs-PEG-OX26 NPs are promising drug delivery system to the CNS.

  2. Important biology events and pathways in Brucella infection and implications for novel antibiotic drug targets.

    PubMed

    Gao, Guangjun; Xu, Jie

    2013-01-01

    Brucellosis caused by Brucella spp. is a common zoonosis in many parts of the world. Humans are infected through contact with infected animals or their dirty products. Many mechanisms are needed for this successful infection, although the mechanisms are still unclear. Host immune response and some signaling molecules play an important role in the infection event. Bacterial pathogens operate by attacking crucial intracellular pathways or some important molecules in each of these pathways for survival in their hosts. The crucial components (molecules) of immunity or pathway play a critical role in the whole process of Brucella infection. Here we summarize the findings of the Brucella-host interactions' immune system and signaling molecular cascades involved in the TLR-initiated immune response to Brucella spp. infection. The paper serves to deepen our understanding of this complex process and to provide some clues regarding the discovery of drug targets for prevention and control.

  3. 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.

  4. Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database.

    PubMed

    Barneh, Farnaz; Jafari, Mohieddin; Mirzaie, Mehdi

    2016-11-01

    Network pharmacology elucidates the relationship between drugs and targets. As the identified targets for each drug increases, the corresponding drug-target network (DTN) evolves from solely reflection of the pharmaceutical industry trend to a portrait of polypharmacology. The aim of this study was to evaluate the potentials of DrugBank database in advancing systems pharmacology. We constructed and analyzed DTN from drugs and targets associations in the DrugBank 4.0 database. Our results showed that in bipartite DTN, increased ratio of identified targets for drugs augmented density and connectivity of drugs and targets and decreased modular structure. To clear up the details in the network structure, the DTNs were projected into two networks namely, drug similarity network (DSN) and target similarity network (TSN). In DSN, various classes of Food and Drug Administration-approved drugs with distinct therapeutic categories were linked together based on shared targets. Projected TSN also showed complexity because of promiscuity of the drugs. By including investigational drugs that are currently being tested in clinical trials, the networks manifested more connectivity and pictured the upcoming pharmacological space in the future years. Diverse biological processes and protein-protein interactions were manipulated by new drugs, which can extend possible target combinations. We conclude that network-based organization of DrugBank 4.0 data not only reveals the potential for repurposing of existing drugs, also allows generating novel predictions about drugs off-targets, drug-drug interactions and their side effects. Our results also encourage further effort for high-throughput identification of targets to build networks that can be integrated into disease networks. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  5. 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

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

    PubMed

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

    2016-12-12

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

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

    PubMed

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

    2008-08-01

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

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

    PubMed Central

    Nazıroğlu, Mustafa

    2015-01-01

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

  9. 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

  10. In Silico Investigations of Chemical Constituents of Clerodendrum colebrookianum in the Anti-Hypertensive Drug Targets: ROCK, ACE, and PDE5.

    PubMed

    Arya, Hemant; Syed, Safiulla Basha; Singh, Sorokhaibam Sureshkumar; Ampasala, Dinakar R; Coumar, Mohane Selvaraj

    2017-06-16

    Understanding the molecular mode of action of natural product is a key step for developing drugs from them. In this regard, this study is aimed to understand the molecular-level interactions of chemical constituents of Clerodendrum colebrookianum Walp., with anti-hypertensive drug targets using computational approaches. The plant has ethno-medicinal importance for the treatment of hypertension and reported to show activity against anti-hypertensive drug targets-Rho-associated coiled-coil protein kinase (ROCK), angiotensin-converting enzyme, and phosphodiesterase 5 (PDE5). Docking studies showed that three chemical constituents (acteoside, martinoside, and osmanthuside β6) out of 21 reported from the plant to interact with the anti-hypertensive drug targets with good glide score. In addition, they formed H-bond interactions with the key residues Met156/Met157 of ROCK I/ROCK II and Gln817 of PDE5. Further, molecular dynamics (MD) simulation of protein-ligand complexes suggest that H-bond interactions between acteoside/osmanthuside β6 and Met156/Met157 (ROCK I/ROCK II), acteoside and Gln817 (PDE5) were stable. The present investigation suggests that the anti-hypertensive activity of the plant is due to the interaction of acteoside and osmanthuside β6 with ROCK and PDE5 drug targets. The identified molecular mode of binding of the plant constituents could help to design new drugs to treat hypertension.

  11. Interactional Personality, Mathematical Simulation, and Prediction of Interpersonal Compatability.

    ERIC Educational Resources Information Center

    Kunce, Joseph T.; And Others

    1981-01-01

    Used a mathematical simulation procedure adaptable to an interactional concept of personality to predict the interpersonal compatibility of couples. Strife scores derived from computer simulation of interactional personality data correlated significantly with partner ratings for the quality and the stability of their relationship. Significance…

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

    ERIC Educational Resources Information Center

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

    2014-01-01

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

  13. RNA-RNA interaction prediction using genetic algorithm.

    PubMed

    Montaseri, Soheila; Zare-Mirakabad, Fatemeh; Moghadam-Charkari, Nasrollah

    2014-01-01

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

    Brucella melitensis 16M is a Gram-negative coccobacillus that infects both animals and humans. It causes a disease known as brucellosis, which is characterized by acute febrile illness in humans and causes abortions in livestock. To prevent and control brucellosis, identification of putative drug targets is crucial. The present study aimed to identify drug targets in B. melitensis 16M by using a subtractive genomic approach. We used available database repositories (Database of Essential Genes, Kyoto Encyclopedia of Genes and Genomes Automatic Annotation Server, and Kyoto Encyclopedia of Genes and Genomes) to identify putative genes that are nonhomologous to humans and essential for pathogen B. melitensis 16M. The results revealed that among 3 Mb genome size of pathogen, 53 putative characterized and 13 uncharacterized hypothetical genes were identified; further, from Basic Local Alignment Search Tool protein analysis, one hypothetical protein showed a close resemblance (50%) to Silicibacter pomeroyi DUF1285 family protein (2RE3). A further homology model of the target was constructed using MODELLER 9.12 and optimized through variable target function method by molecular dynamics optimization with simulating annealing. The stereochemical quality of the restrained model was evaluated by PROCHECK, VERIFY-3D, ERRAT, and WHATIF servers. Furthermore, structure-based virtual screening was carried out against the predicted active site of the respective protein using the glycerol structural analogs from the PubChem database. We identified five best inhibitors with strong affinities, stable interactions, and also with reliable drug-like properties. Hence, these leads might be used as the most effective inhibitors of modeled protein. The outcome of the present work of virtual screening of putative gene targets might facilitate design of potential drugs for better treatment against brucellosis. PMID:25834405

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

    PubMed Central

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

    2010-01-01

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

  17. A method for predicting protein-protein interaction types.

    PubMed

    Silberberg, Yael; Kupiec, Martin; Sharan, Roded

    2014-01-01

    Protein-protein interactions (PPIs) govern basic cellular processes through signal transduction and complex formation. The diversity of those processes gives rise to a remarkable diversity of interactions types, ranging from transient phosphorylation interactions to stable covalent bonding. Despite our increasing knowledge on PPIs in humans and other species, their types remain relatively unexplored and few annotations of types exist in public databases. Here, we propose the first method for systematic prediction of PPI type based solely on the techniques by which the interaction was detected. We show that different detection methods are better suited for detecting specific types. We apply our method to ten interaction types on a large scale human PPI dataset. We evaluate the performance of the method using both internal cross validation and external data sources. In cross validation, we obtain an area under receiver operating characteristic (ROC) curve ranging from 0.65 to 0.97 with an average of 0.84 across the predicted types. Comparing the predicted interaction types to external data sources, we obtained significant agreements for phosphorylation and ubiquitination interactions, with hypergeometric p-value = 2.3e(-54) and 5.6e(-28) respectively. We examine the biological relevance of our predictions using known signaling pathways and chart the abundance of interaction types in cell processes. Finally, we investigate the cross-relations between different interaction types within the network and characterize the discovered patterns, or motifs. We expect the resulting annotated network to facilitate the reconstruction of process-specific subnetworks and assist in predicting protein function or interaction.

  18. 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.

  19. Prediction of Protein–Protein Interactions by Evidence Combining Methods

    PubMed Central

    Chang, Ji-Wei; Zhou, Yan-Qing; Ul Qamar, Muhammad Tahir; Chen, Ling-Ling; Ding, Yu-Duan

    2016-01-01

    Most cellular functions involve proteins’ features based on their physical interactions with other partner proteins. Sketching a map of protein–protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy. PMID:27879651

  20. Computational Prediction of Protein-Protein Interactions of Human Tyrosinase

    PubMed Central

    Wang, Su-Fang; Oh, Sangho; Si, Yue-Xiu; Wang, Zhi-Jiang; Han, Hong-Yan; Lee, Jinhyuk; Qian, Guo-Ying

    2012-01-01

    The various studies on tyrosinase have recently gained the attention of researchers due to their potential application values and the biological functions. In this study, we predicted the 3D structure of human tyrosinase and simulated the protein-protein interactions between tyrosinase and three binding partners, four and half LIM domains 2 (FHL2), cytochrome b-245 alpha polypeptide (CYBA), and RNA-binding motif protein 9 (RBM9). Our interaction simulations showed significant binding energy scores of −595.3 kcal/mol for FHL2, −859.1 kcal/mol for CYBA, and −821.3 kcal/mol for RBM9. We also investigated the residues of each protein facing toward the predicted site of interaction with tyrosinase. Our computational predictions will be useful for elucidating the protein-protein interactions of tyrosinase and studying its binding mechanisms. PMID:22577521

  1. Prediction of cancer drugs by chemical-chemical interactions.

    PubMed

    Lu, Jing; Huang, Guohua; Li, Hai-Peng; Feng, Kai-Yan; Chen, Lei; Zheng, Ming-Yue; Cai, Yu-Dong

    2014-01-01

    Cancer, which is a leading cause of death worldwide, places a big burden on health-care system. In this study, an order-prediction model was built to predict a series of cancer drug indications based on chemical-chemical interactions. According to the confidence scores of their interactions, the order from the most likely cancer to the least one was obtained for each query drug. The 1(st) order prediction accuracy of the training dataset was 55.93%, evaluated by Jackknife test, while it was 55.56% and 59.09% on a validation test dataset and an independent test dataset, respectively. The proposed method outperformed a popular method based on molecular descriptors. Moreover, it was verified that some drugs were effective to the 'wrong' predicted indications, indicating that some 'wrong' drug indications were actually correct indications. Encouraged by the promising results, the method may become a useful tool to the prediction of drugs indications.

  2. Charting the molecular network of the drug target Bcr-Abl

    PubMed Central

    Brehme, Marc; Hantschel, Oliver; Colinge, Jacques; Kaupe, Ines; Planyavsky, Melanie; Köcher, Thomas; Mechtler, Karl; Bennett, Keiryn L.; Superti-Furga, Giulio

    2009-01-01

    The tyrosine kinase Bcr-Abl causes chronic myeloid leukemia and is the cognate target of tyrosine kinase inhibitors like imatinib. We have charted the protein–protein interaction network of Bcr-Abl by a 2-pronged approach. Using a monoclonal antibody we have first purified endogenous Bcr-Abl protein complexes from the CML K562 cell line and characterized the set of most tightly-associated interactors by MS. Nine interactors were subsequently subjected to tandem affinity purifications/MS analysis to obtain a molecular interaction network of some hundred cellular proteins. The resulting network revealed a high degree of interconnection of 7 “core” components around Bcr-Abl (Grb2, Shc1, Crk-I, c-Cbl, p85, Sts-1, and SHIP-2), and their links to different signaling pathways. Quantitative proteomics analysis showed that tyrosine kinase inhibitors lead to a disruption of this network. Certain components still appear to interact with Bcr-Abl in a phosphotyrosine-independent manner. We propose that Bcr-Abl and other drug targets, rather than being considered as single polypeptides, can be considered as complex protein assemblies that remodel upon drug action. PMID:19380743

  3. Host–pathogen protein interactions predicted by comparative modeling

    PubMed Central

    Davis, Fred P.; Barkan, David T.; Eswar, Narayanan; McKerrow, James H.; Sali, Andrej

    2007-01-01

    Pathogens have evolved numerous strategies to infect their hosts, while hosts have evolved immune responses and other defenses to these foreign challenges. The vast majority of host–pathogen interactions involve protein–protein recognition, yet our current understanding of these interactions is limited. Here, we present and apply a computational whole-genome protocol that generates testable predictions of host–pathogen protein interactions. The protocol first scans the host and pathogen genomes for proteins with similarity to known protein complexes, then assesses these putative interactions, using structure if available, and, finally, filters the remaining interactions using biological context, such as the stage-specific expression of pathogen proteins and tissue expression of host proteins. The technique was applied to 10 pathogens, including species of Mycobacterium, apicomplexa, and kinetoplastida, responsible for “neglected” human diseases. The method was assessed by (1) comparison to a set of known host–pathogen interactions, (2) comparison to gene expression and essentiality data describing host and pathogen genes involved in infection, and (3) analysis of the functional properties of the human proteins predicted to interact with pathogen proteins, demonstrating an enrichment for functionally relevant host–pathogen interactions. We present several specific predictions that warrant experimental follow-up, including interactions from previously characterized mechanisms, such as cytoadhesion and protease inhibition, as well as suspected interactions in hypothesized networks, such as apoptotic pathways. Our computational method provides a means to mine whole-genome data and is complementary to experimental efforts in elucidating networks of host–pathogen protein interactions. PMID:17965183

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

    DTIC Science & Technology

    2016-09-01

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

  5. Understanding and predicting synthetic lethal genetic interactions in Saccharomyces cerevisiae using domain genetic interactions

    PubMed Central

    2011-01-01

    Background Synthetic lethal genetic interactions among proteins have been widely used to define functional relationships between proteins and pathways. However, the molecular mechanism of synthetic lethal genetic interactions is still unclear. Results In this study, we demonstrated that yeast synthetic lethal genetic interactions can be explained by the genetic interactions between domains of those proteins. The domain genetic interactions rarely overlap with the domain physical interactions from iPfam database and provide a complementary view about domain relationships. Moreover, we found that domains in multidomain yeast proteins contribute to their genetic interactions differently. The domain genetic interactions help more precisely define the function related to the synthetic lethal genetic interactions, and then help understand how domains contribute to different functionalities of multidomain proteins. Using the probabilities of domain genetic interactions, we were able to predict novel yeast synthetic lethal genetic interactions. Furthermore, we had also identified novel compensatory pathways from the predicted synthetic lethal genetic interactions. Conclusion The identification of domain genetic interactions helps the understanding of originality of functional relationship in SLGIs at domain level. Our study significantly improved the understanding of yeast mulitdomain proteins, the synthetic lethal genetic interactions and the functional relationships between proteins and pathways. PMID:21586150

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

    SciTech Connect

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

    2010-10-02

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

  7. A Perspective on Monoamine Oxidase Enzyme as Drug Target: Challenges and Opportunities.

    PubMed

    Kumar, Bhupinder; Gupta, Vivek Prakash; Kumar, Vinod

    2017-01-01

    The monoamine oxidase (MAO) enzyme is responsible for the deamination of monoamine neurotransmitters and regulates their concentration in the central and peripheral nervous systems. Imbalance in the concentration of neurotransmitters in the brain and central nervous system is linked with the biochemical pathology of various neurogenic disorders. Irreversible MAO inhibitors were the first line drugs developed for the management of severe depression but most of these were withdrawn from the clinical practice due to their fatal side effects including food-drug interactions. New generations of MAO inhibitors were developed which were reversible and selective for one of the enzyme isoform and showed improved pharmacological profile. The discovery of crystal structure of MAO-A & MAO-B isoforms helped in understanding the drug-receptor interactions at the molecular level and designing of ligands with selectivity for either of the isoforms. The current article provides an overview on the MAO enzyme as potential drug target for different disease states. The article describes catalytic mechanism of MAO enzyme, crystal structures of the two MAO isoforms, traditional MAO inhibitors and various problems associated with their use, new developments in the MAO inhibitors and their potential as therapeutic agents especially in neurological disorders.

  8. Trypano-PPI: a web server for prediction of unique targets in trypanosome proteome by using electrostatic parameters of protein-protein interactions.

    PubMed

    Rodriguez-Soca, Yamilet; Munteanu, Cristian R; Dorado, Julián; Pazos, Alejandro; Prado-Prado, Francisco J; González-Díaz, Humberto

    2010-02-05

    Trypanosoma brucei causes African trypanosomiasis in humans (HAT or African sleeping sickness) and Nagana in cattle. The disease threatens over 60 million people and uncounted numbers of cattle in 36 countries of sub-Saharan Africa and has a devastating impact on human health and the economy. On the other hand, Trypanosoma cruzi is responsible in South America for Chagas disease, which can cause acute illness and death, especially in young children. In this context, the discovery of novel drug targets in Trypanosome proteome is a major focus for the scientific community. Recently, many researchers have spent important efforts on the study of protein-protein interactions (PPIs) in pathogen Trypanosome species concluding that the low sequence identities between some parasite proteins and their human host render these PPIs as highly promising drug targets. To the best of our knowledge, there are no general models to predict Unique PPIs in Trypanosome (TPPIs). On the other hand, the 3D structure of an increasing number of Trypanosome proteins is reported in databases. In this regard, the introduction of a new model to predict TPPIs from the 3D structure of proteins involved in PPI is very important. For this purpose, we introduced new protein-protein complex invariants based on the Markov average electrostatic potential xi(k)(R(i)) for amino acids located in different regions (R(i)) of i-th protein and placed at a distance k one from each other. We calculated more than 30 different types of parameters for 7866 pairs of proteins (1023 TPPIs and 6823 non-TPPIs) from more than 20 organisms, including parasites and human or cattle hosts. We found a very simple linear model that predicts above 90% of TPPIs and non-TPPIs both in training and independent test subsets using only two parameters. The parameters were (d)xi(k)(s) = |xi(k)(s(1)) - xi(k)(s(2))|, the absolute difference between the xi(k)(s(i)) values on the surface of the two proteins of the pairs. We also tested

  9. A new probabilistic rule for drug–dug interaction prediction

    PubMed Central

    Zhou, Jihao; Qin, Zhaohui; Quinney, Sara K.; Kim, Seongho; Wang, Zhiping; Yu, Menggang; Chien, Jenny Y.; Lucksiri, Aroonrut; Hall, Stephen D.; Li, L

    2009-01-01

    An innovative probabilistic rule is proposed to predict the clinical significance or clinical insignificance of DDI. This rule is coupled with a hierarchical Bayesian model approach to summarized substrate/inhibitor's PK models from multiple published resources. This approach incorporates between-subject and between-study variances into DDI prediction. Hence, it can predict both population-average and subject-specific AUCR. The clinically significant DDI, weak DDI, and clinically insignificant inhibition are decided by the probabilities of predicted AUCR falling into three intervals, (– ∞, 1.25), (1.25, 2), and (2, ∞). The main advantage of this probabilistic rule to predict clinical significance of DDI over the deterministic rule is that the probabilisticrule considers the sample variability, and the decision is independent of sampling variation; while deterministic rule based decision will vary from sample to sample. The probabilistic rule proposed in this paper is best suited for the situation when in vivo PK studies and models are available for both the inhibitor and substrate. An early decision on clinically significant or clinically insignificant inhibition can avoid additional DDI studies. Ketoconazole and midazolam are used as an interaction pair to illustrate our idea. AUCR predictions incorporating between-subject variability always have greater variances than population-average AUCR predictions. A clinically insignificant AUCR at population-average level is not necessarily true when considering between-subject variability. Additional simulation studies suggest thatpredicted AUCRs highly depend on the interaction constant Ki and dose combinations. PMID:19156505

  10. Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring

    PubMed Central

    Jiang, Xia; Jao, Jeremy; Neapolitan, Richard

    2015-01-01

    Background The problems of correlation and classification are long-standing in the fields of statistics and machine learning, and techniques have been developed to address these problems. We are now in the era of high-dimensional data, which is data that can concern billions of variables. These data present new challenges. In particular, it is difficult to discover predictive variables, when each variable has little marginal effect. An example concerns Genome-wide Association Studies (GWAS) datasets, which involve millions of single nucleotide polymorphism (SNPs), where some of the SNPs interact epistatically to affect disease status. Towards determining these interacting SNPs, researchers developed techniques that addressed this specific problem. However, the problem is more general, and so these techniques are applicable to other problems concerning interactions. A difficulty with many of these techniques is that they do not distinguish whether a learned interaction is actually an interaction or whether it involves several variables with strong marginal effects. Methodology/Findings We address this problem using information gain and Bayesian network scoring. First, we identify candidate interactions by determining whether together variables provide more information than they do separately. Then we use Bayesian network scoring to see if a candidate interaction really is a likely model. Our strategy is called MBS-IGain. Using 100 simulated datasets and a real GWAS Alzheimer’s dataset, we investigated the performance of MBS-IGain. Conclusions/Significance When analyzing the simulated datasets, MBS-IGain substantially out-performed nine previous methods at locating interacting predictors, and at identifying interactions exactly. When analyzing the real Alzheimer’s dataset, we obtained new results and results that substantiated previous findings. We conclude that MBS-IGain is highly effective at finding interactions in high-dimensional datasets. This result is

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

    PubMed

    Mobley, James L

    2006-01-01

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

  12. 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.

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

    PubMed Central

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

    2017-01-01

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

  14. Comparative genomics allowed the identification of drug targets against human fungal pathogens

    PubMed Central

    2011-01-01

    Background The prevalence of invasive fungal infections (IFIs) has increased steadily worldwide in the last few decades. Particularly, there has been a global rise in the number of infections among immunosuppressed people. These patients present severe clinical forms of the infections, which are commonly fatal, and they are more susceptible to opportunistic fungal infections than non-immunocompromised people. IFIs have historically been associated with high morbidity and mortality, partly because of the limitations of available antifungal therapies, including side effects, toxicities, drug interactions and antifungal resistance. Thus, the search for alternative therapies and/or the development of more specific drugs is a challenge that needs to be met. Genomics has created new ways of examining genes, which open new strategies for drug development and control of human diseases. Results In silico analyses and manual mining selected initially 57 potential drug targets, based on 55 genes experimentally confirmed as essential for Candida albicans or Aspergillus fumigatus and other 2 genes (kre2 and erg6) relevant for fungal survival within the host. Orthologs for those 57 potential targets were also identified in eight human fungal pathogens (C. albicans, A. fumigatus, Blastomyces dermatitidis, Paracoccidioides brasiliensis, Paracoccidioides lutzii, Coccidioides immitis, Cryptococcus neoformans and Histoplasma capsulatum). Of those, 10 genes were present in all pathogenic fungi analyzed and absent in the human genome. We focused on four candidates: trr1 that encodes for thioredoxin reductase, rim8 that encodes for a protein involved in the proteolytic activation of a transcriptional factor in response to alkaline pH, kre2 that encodes for α-1,2-mannosyltransferase and erg6 that encodes for Δ(24)-sterol C-methyltransferase. Conclusions Our data show that the comparative genomics analysis of eight fungal pathogens enabled the identification of four new potential drug

  15. Giardia fatty acyl-CoA synthetases as potential drug targets

    PubMed Central

    Guo, Fengguang; Ortega-Pierres, Guadalupe; Argüello-García, Raúl; Zhang, Haili; Zhu, Guan

    2015-01-01

    Giardiasis caused by Giardia intestinalis (syn. G. lamblia, G. duodenalis) is one of the leading causes of diarrheal parasitic diseases worldwide. Although limited drugs to treat giardiasis are available, there are concerns regarding toxicity in some patients and the emerging drug resistance. By data-mining genome sequences, we observed that G. intestinalis is incapable of synthesizing fatty acids (FA) de novo. However, this parasite has five long-chain fatty acyl-CoA synthetases (GiACS1 to GiACS5) to activate FA scavenged from the host. ACS is an essential enzyme because FA need to be activated to form acyl-CoA thioesters before they can enter subsequent metabolism. In the present study, we performed experiments to explore whether some GiACS enzymes could serve as drug targets in Giardia. Based on the high-throughput datasets and protein modeling analyses, we initially studied the GiACS1 and GiACS2, because genes encoding these two enzymes were found to be more consistently expressed in varied parasite life cycle stages and when interacting with host cells based on previously reported transcriptome data. These two proteins were cloned and expressed as recombinant proteins. Biochemical analysis revealed that both had apparent substrate preference toward palmitic acid (C16:0) and myristic acid (C14:0), and allosteric or Michaelis–Menten kinetics on palmitic acid or ATP. The ACS inhibitor triacsin C inhibited the activity of both enzymes (IC50 = 1.56 μM, Ki = 0.18 μM for GiACS1, and IC50 = 2.28 μM, Ki = 0.23 μM for GiACS2, respectively) and the growth of G. intestinalis in vitro (IC50 = 0.8 μM). As expected from giardial evolutionary characteristics, both GiACSs displayed differences in overall folding structure as compared with their human counterparts. These observations support the notion that some of the GiACS enzymes may be explored as drug targets in this parasite. PMID:26257723

  16. The Proteomics Big Challenge for Biomarkers and New Drug-Targets Discovery

    PubMed Central

    Savino, Rocco; Paduano, Sergio; Preianò, Mariaimmacolata; Terracciano, Rosa

    2012-01-01

    In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets. PMID:23203042

  17. 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

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

    DOE PAGES

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

    2016-04-29

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

  19. Predicting PDZ domain mediated protein interactions from structure

    PubMed Central

    2013-01-01

    Background PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors. Results We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training–testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling. Conclusions We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on

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

    PubMed Central

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

    2017-01-01

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

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

    PubMed

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

    2017-05-01

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

  2. Program Predicts Time Courses of Human/Computer Interactions

    NASA Technical Reports Server (NTRS)

    Vera, Alonso; Howes, Andrew

    2005-01-01

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

  3. Exploring Function Prediction in Protein Interaction Networks via Clustering Methods

    PubMed Central

    Trivodaliev, Kire; Bogojeska, Aleksandra; Kocarev, Ljupco

    2014-01-01

    Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach. PMID:24972109

  4. 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.

  5. Plant Interactions Alter the Predictions of Metabolic Scaling Theory

    PubMed Central

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

    2013-01-01

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

  6. Protein-protein interaction predictions using text mining methods.

    PubMed

    Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Iliopoulos, Ioannis

    2015-03-01

    It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools. Copyright © 2014 Elsevier Inc. All rights reserved.

  7. Prediction of Cancer Drugs by Chemical-Chemical Interactions

    PubMed Central

    Li, Hai-Peng; Feng, Kai-Yan; Chen, Lei; Zheng, Ming-Yue; Cai, Yu-Dong

    2014-01-01

    Cancer, which is a leading cause of death worldwide, places a big burden on health-care system. In this study, an order-prediction model was built to predict a series of cancer drug indications based on chemical-chemical interactions. According to the confidence scores of their interactions, the order from the most likely cancer to the least one was obtained for each query drug. The 1st order prediction accuracy of the training dataset was 55.93%, evaluated by Jackknife test, while it was 55.56% and 59.09% on a validation test dataset and an independent test dataset, respectively. The proposed method outperformed a popular method based on molecular descriptors. Moreover, it was verified that some drugs were effective to the ‘wrong’ predicted indications, indicating that some ‘wrong’ drug indications were actually correct indications. Encouraged by the promising results, the method may become a useful tool to the prediction of drugs indications. PMID:24498372

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

    PubMed

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

    2013-10-01

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

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

    PubMed Central

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

    2015-01-01

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

  10. Interaction site prediction by structural similarity to neighboring clusters in protein-protein interaction networks.

    PubMed

    Monji, Hiroyuki; Koizumi, Satoshi; Ozaki, Tomonobu; Ohkawa, Takenao

    2011-02-15

    Recently, revealing the function of proteins with protein-protein interaction (PPI) networks is regarded as one of important issues in bioinformatics. With the development of experimental methods such as the yeast two-hybrid method, the data of protein interaction have been increasing extremely. Many databases dealing with these data comprehensively have been constructed and applied to analyzing PPI networks. However, few research on prediction interaction sites using both PPI networks and the 3D protein structures complementarily has explored. We propose a method of predicting interaction sites in proteins with unknown function by using both of PPI networks and protein structures. For a protein with unknown function as a target, several clusters are extracted from the neighboring proteins based on their structural similarity. Then, interaction sites are predicted by extracting similar sites from the group of a protein cluster and the target protein. Moreover, the proposed method can improve the prediction accuracy by introducing repetitive prediction process. The proposed method has been applied to small scale dataset, then the effectiveness of the method has been confirmed. The challenge will now be to apply the method to large-scale datasets.

  11. Herb-drug interactions: challenges and opportunities for improved predictions.

    PubMed

    Brantley, Scott J; Argikar, Aneesh A; Lin, Yvonne S; Nagar, Swati; Paine, Mary F

    2014-03-01

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

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

    Vollesen, Anne Luise Haulund; Ashina, Messoud

    2017-05-01

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

  14. Focus on flaviviruses: current and future drug targets

    PubMed Central

    Geiss, Brian J; Stahla, Hillary; Hannah, Amanda M; Gari, Harmid H; Keenan, Susan M

    2009-01-01

    Background Infection by mosquito-borne flaviviruses (family Flaviviridae) is increasing in prevalence worldwide. The vast global, social and economic impact due to the morbidity and mortality associated with the diseases caused by these viruses necessitates therapeutic intervention. There is currently no effective clinical treatment for any flaviviral infection. Therefore, there is a great need for the identification of novel inhibitors to target the virus lifecycle. Discussion In this article, we discuss structural and nonstructural viral proteins that are the focus of current target validation and drug discovery efforts. Both inhibition of essential enzymatic activities and disruption of necessary protein–protein interactions are considered. In addition, we address promising new targets for future research. Conclusion As our molecular and biochemical understanding of the flavivirus life cycle increases, the number of targets for antiviral therapeutic discovery grows and the possibility for novel drug discovery continues to strengthen. PMID:20165556

  15. Predicting Cell Cycle Regulated Genes by Causal Interactions

    PubMed Central

    Emmert-Streib, Frank; Dehmer, Matthias

    2009-01-01

    The fundamental difference between classic and modern biology is that technological innovations allow to generate high-throughput data to get insights into molecular interactions on a genomic scale. These high-throughput data can be used to infer gene networks, e.g., the transcriptional regulatory or signaling network, representing a blue print of the current dynamical state of the cellular system. However, gene networks do not provide direct answers to biological questions, instead, they need to be analyzed to reveal functional information of molecular working mechanisms. In this paper we propose a new approach to analyze the transcriptional regulatory network of yeast to predict cell cycle regulated genes. The novelty of our approach is that, in contrast to all other approaches aiming to predict cell cycle regulated genes, we do not use time series data but base our analysis on the prior information of causal interactions among genes. The major purpose of the present paper is to predict cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions between genes, and a list of known periodic genes. No further data are used. Our approach utilizes the causal membership of genes and the hierarchical organization of the transcriptional regulatory network leading to two groups of periodic genes with a well defined direction of information flow. We predict genes as periodic if they appear on unique shortest paths connecting two periodic genes from different hierarchy levels. Our results demonstrate that a classical problem as the prediction of cell cycle regulated genes can be seen in a new light if the concept of a causal membership of a gene is applied consequently. This also shows that there is a wealth of information buried in the transcriptional regulatory network whose unraveling may require more elaborate concepts than it might seem at first. PMID:19688096

  16. Boosting compound-protein interaction prediction by deep learning.

    PubMed

    Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

    2016-11-01

    The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets.

  17. Protein complexes predictions within protein interaction networks using genetic algorithms.

    PubMed

    Ramadan, Emad; Naef, Ahmed; Ahmed, Moataz

    2016-07-25

    Protein-protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein-protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein-protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks. In this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets. Our algorithm can identify protein complexes that are significantly enriched in the gold-standard data sets. Furthermore, our method surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes. The source code and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/GACluster.zip .

  18. On the earthquake predictability of fault interaction models

    PubMed Central

    Marzocchi, W; Melini, D

    2014-01-01

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

  19. On the earthquake predictability of fault interaction models

    NASA Astrophysics Data System (ADS)

    Marzocchi, W.; Melini, D.

    2014-12-01

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

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

    SciTech Connect

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

    2009-04-20

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

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

    PubMed

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

    2016-05-01

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

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

    PubMed

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

    2017-02-01

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

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

    PubMed

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

    2010-12-01

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

  4. Inducible Mouse Models for Cancer Drug Target Validation

    PubMed Central

    Jeong, Joseph H.

    2016-01-01

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

  5. 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

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

    PubMed

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

    2013-02-01

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

  7. Molecular chaperones as rational drug targets for Parkinson's disease therapeutics.

    PubMed

    Kalia, S K; Kalia, L V; McLean, P J

    2010-12-01

    Parkinson's disease is a neurodegenerative movement disorder that is caused, in part, by the loss of dopaminergic neurons within the substantia nigra pars compacta of the basal ganglia. The presence of intracellular protein aggregates, known as Lewy bodies and Lewy neurites, within the surviving nigral neurons is the defining neuropathological feature of the disease. Accordingly, the identification of specific genes mutated in families with Parkinson's disease and of genetic susceptibility variants for idiopathic Parkinson's disease has implicated abnormalities in proteostasis, or the handling and elimination of misfolded proteins, in the pathogenesis of this neurodegenerative disorder. Protein folding and the refolding of misfolded proteins are regulated by a network of interactive molecules, known as the chaperone system, which is composed of molecular chaperones and co-chaperones. The chaperone system is intimately associated with the ubiquitin-proteasome system and the autophagy-lysosomal pathway which are responsible for elimination of misfolded proteins and protein quality control. In addition to their role in proteostasis, some chaperone molecules are involved in the regulation of cell death pathways. Here we review the role of the molecular chaperones Hsp70 and Hsp90, and the cochaperones Hsp40, BAG family members such as BAG5, CHIP and Hip in modulating neuronal death with a focus on dopaminergic neurodegeneration in Parkinson's disease. We also review current progress in preclinical studies aimed at targetting the chaperone system to prevent neurodegeneration. Finally, we discuss potential future chaperone-based therapeutics for the symptomatic treatment and possible disease modification of Parkinson's disease.

  8. Anchoring Junctions As Drug Targets: Role in Contraceptive Development

    PubMed Central

    Mruk, Dolores D.; Silvestrini, Bruno; Cheng, C. Yan

    2010-01-01

    In multicellular organisms, cell-cell interactions are mediated in part by cell junctions, which underlie tissue architecture. Throughout spermatogenesis, for instance, preleptotene leptotene spermatocytes residing in the basal compartment of the seminiferous epithelium must traverse the blood-testis barrier to enter the adluminal compartment for continued development. At the same time, germ cells must also remain attached to Sertoli cells, and numerous studies have reported extensive restructuring at the Sertoli-Sertoli and Sertoli-germ cell interface during germ cell movement across the seminiferous epithelium. Furthermore, the proteins and signaling cascades that regulate adhesion between testicular cells have been largely delineated. These findings have unveiled a number of potential “druggable” targets that can be used to induce premature release of germ cells from the seminiferous epithelium, resulting in transient infertility. Herein, we discuss a novel approach with the aim of developing a nonhormonal male contraceptive for future human use, one that involves perturbing adhesion between Sertoli and germ cells in the testis. PMID:18483144

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

    PubMed Central

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

    2012-01-01

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

  10. Structure of pyrR (Rv1379) from Mycobacterium tuberculosis: A persistence gene and protein drug target

    SciTech Connect

    Kantardjieff, K A; Vasquez, C; Castro, P; Warfel, N M; Rho, B; Lekin, T; Kim, C; Segelke, B W; Terwilliger, T C; Rupp, B

    2004-09-24

    The 1.9 {angstrom} native structure of pyrimidine biosynthesis regulatory protein encoded by the Mycobacterium tuberculosis pyrR gene (Rv1379) is reported. Because pyrimidine biosynthesis is an essential step in the progression of TB, pyrR is an attractive antitubercular drug target. The Mycobacterium tuberculosis pyrR gene (Rv1379) encodes a protein that regulates expression of pyrimidine nucleotide biosynthesis (pyr) genes in a UMP-dependent manner. Because pyrimidine biosynthesis is an essential step in the progression of TB, the gene product pyrR is an attractive antitubercular drug target. We report the 1.9 {angstrom} native structure of Mtb pyrR determined by the TB Structural Genomics Consortium facilities (PDB entry 1W30) in trigonal space group P3{sub 1}21, with cell dimensions at 120K of a = 66.64 {angstrom}, c = 154.72 {angstrom}, and two molecules in the asymmetric unit. The 3D structure and residual uracil phosphoribosyltransferase activity point to a common PRTase ancestor for pyrR. However, while PRPP and UMP binding sites have been retained in Mtb pyrR, a novel dimer interaction among subunits creates a deep, positively charged cleft capable of binding pyr mRNA. In silico screening of pyrimidine nucleoside analogs has revealed a number of potential leads compounds that, if bound to Mtb pyrR, could facilitate transcriptional attenuation, particularly cyclopentenyl nucleosides.

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

    PubMed

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

    2012-01-01

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

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

    PubMed

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

    2015-04-01

    One of the challenging problems in drug discovery is to identify the novel targets for drugs. Most of the traditional methods for drug targets optimization focused on identifying the particular families of "druggable targets", but ignored their topological properties based on the biological pathways. In this study, we characterized the topological properties of human anticancer drug targets (ADTs) in the context of biological pathways. We found that the ADTs tended to present the following seven topological properties: influence the number of the pathways related to cancer, be localized at the start or end of the pathways, interact with cancer related genes, exhibit higher connectivity, vulnerability, betweenness, and closeness than other genes. We first ranked ADTs based on their topological property values respectively, then fused them into one global-rank using the joint cumulative distribution of an N-dimensional order statistic to optimize human ADTs. We applied the optimization method to 13 anticancer drugs, respectively. Results demonstrated that over 70% of known ADTs were ranked in the top 20%. Furthermore, the performance for mercaptopurine was significant: 6 known targets (ADSL, GMPR2, GMPR, HPRT1, AMPD3, AMPD2) were ranked in the top 15 and other four out of the top 15 (MAT2A, CDKN1A, AREG, JUN) have the potentialities to become new targets for cancer therapy. Copyright © 2015 Elsevier Inc. All rights reserved.

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

    PubMed Central

    Mears, Emily Rose; Modabber, Farrokh; Don, Robert; Johnson, George E.

    2015-01-01

    The current in vivo models for the utility and discovery of new potential anti-leishmanial drugs targeting Cutaneous Leishmaniasis (CL) differ vastly in their immunological responses to the disease and clinical presentation of symptoms. Animal models that show similarities to the human form of CL after infection with Leishmania should be more representative as to the effect of the parasite within a human. Thus, these models are used to evaluate the efficacy of new anti-leishmanial compounds before human clinical trials. Current animal models aim to investigate (i) host–parasite interactions, (ii) pathogenesis, (iii) biochemical changes/pathways, (iv) in vivo maintenance of parasites, and (v) clinical evaluation of drug candidates. This review focuses on the trends of infection observed between Leishmania parasites, the predictability of different strains, and the determination of parasite load. These factors were used to investigate the overall effectiveness of the current animal models. The main aim was to assess the efficacy and limitations of the various CL models and their potential for drug discovery and evaluation. In conclusion, we found that the following models are the most suitable for the assessment of anti-leishmanial drugs: L. major–C57BL/6 mice (or–vervet monkey, or–rhesus monkeys), L. tropica–CsS-16 mice, L. amazonensis–CBA mice, L. braziliensis–golden hamster (or–rhesus monkey). We also provide in-depth guidance for which models are not suitable for these investigations. PMID:26334763

  14. Virtual screening of chemical compounds active against breast cancer cell lines based on cell cycle modelling, prediction of cytotoxicity and interaction with targets.

    PubMed

    Konova, V; Lagunin, A; Pogodin, P; Kolotova, E; Shtil, A; Poroikov, V

    2015-01-01

    Bio- and chemoinformatics methods are widely used for the detection of mechanisms of cancer, to search for potential drug targets and their ligands. Regulatory network analysis based on signalling pathways, and cell cycle regulation provides better understanding of diseases with multiple mechanisms of pathogenesis. We developed an approach for in silico prediction of the cytotoxic effect of chemical compounds in non-transformed and breast cancer cell lines. This approach combines the prediction of the interaction between chemical compounds and human proteins, cytotoxicity and regulatory network modelling taking into account gene expression. Application of our approach to virtual screening of libraries of commercially available compounds allowed selection of dozens of promising hits. These molecules are predicted to interact with the identified targets and exhibit cytotoxicity against breast cancer cell lines but not non-tumour human cell lines. Experimental testing of 49 selected compounds against MDA-MB-231 and MCF7 breast cancer cell lines confirmed the activity of eight compounds with IC50 values ranged from 0.8 to 50 μM. Thus, the developed approach may be applied for virtual screening for cytotoxic compounds against tumour cell lines.

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

    NASA Technical Reports Server (NTRS)

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

    2003-01-01

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

  16. 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.

  17. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.

    PubMed

    Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A; Burgueño, Juan; Pérez-Rodríguez, Paulino; de Los Campos, Gustavo

    2017-01-05

    The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects [Formula: see text] that can be assessed by the Kronecker product of variance-covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model [Formula: see text] plus an extra component, F: , that captures random effects between environments that were not captured by the random effects [Formula: see text] We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with [Formula: see text] over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect [Formula: see text]. Copyright © 2017 Cuevas et al.

  18. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

    PubMed Central

    Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A.; Burgueño, Juan; Pérez-Rodríguez, Paulino; de los Campos, Gustavo

    2016-01-01

    The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u. We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u. PMID:27793970

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

    PubMed

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

    2016-02-01

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

  20. Predicting genetic interactions from Boolean models of biological networks.

    PubMed

    Calzone, Laurence; Barillot, Emmanuel; Zinovyev, Andrei

    2015-08-01

    Genetic interaction can be defined as a deviation of the phenotypic quantitative effect of a double gene mutation from the effect predicted from single mutations using a simple (e.g., multiplicative or linear additive) statistical model. Experimentally characterized genetic interaction networks in model organisms provide important insights into relationships between different biological functions. We describe a computational methodology allowing us to systematically and quantitatively characterize a Boolean mathematical model of a biological network in terms of genetic interactions between all loss of function and gain of function mutations with respect to all model phenotypes or outputs. We use the probabilistic framework defined in MaBoSS software, based on continuous time Markov chains and stochastic simulations. In addition, we suggest several computational tools for studying the distribution of double mutants in the space of model phenotype probabilities. We demonstrate this methodology on three published models for each of which we derive the genetic interaction networks and analyze their properties. We classify the obtained interactions according to their class of epistasis, dependence on the chosen initial conditions and the phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed.

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

    PubMed Central

    2010-01-01

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

  2. Interaction prediction optimization in multidisciplinary design optimization problems.

    PubMed

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

    2014-01-01

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

  3. 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

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

    PubMed

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

    2016-06-01

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

  5. The α7 nicotinic acetylcholine receptor complex: one, two or multiple drug targets?

    PubMed

    Thomsen, Morten S; Mikkelsen, Jens D

    2012-05-01

    The α7 nicotinic acetylcholine receptor (nAChR) is a promising drug target for a number of diseases ranging from schizophrenia and Alzheimer's disease to chronic pain and inflammatory diseases. Focusing on the central nervous system, we describe how endogenous and experimental compounds and proteins regulate expression and function of the α7 nAChR. Drug development efforts have hitherto focused on direct manipulation of the α7 nAChR, but it is still not clear, whether agonism/antagonism or allosteric modulation is preferable as a potential drug therapy. In addition, the action of such compounds in vivo is highly dependent on α7 nAChR-interacting proteins, such as RIC-3 and lynx1, which modulate expression and function of the receptor. These regulatory proteins are often not expressed in in vitro models used to study α7 nAChR function, and it is not known to what extent they are involved in diseases such as schizophrenia and Alzheimer's disease. Furthermore, α7 nAChR agonists and allosteric modulators differentially alter expression and functionality of the α7 nAChR with repeated administration, which suggests that there may be fundamentally different outcomes of long-term administration with these different types of compounds. Finally, we describe the special case of Aβ1-42 binding to the α7 nAChR, which may pose a unique challenge to drug development of α7 nAChR-specific ligands for Alzheimer's disease. Hopefully, a greater knowledge of the many factors influencing α7 nAChR function as well as an increasing pipeline of specific drug candidates, enabling a more subtle manipulation of α7 nAChR function, may facilitate α7 nAChR drug development efforts.

  6. 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

  7. Explicit and Implicit Approach Motivation Interact to Predict Interpersonal Arrogance

    PubMed Central

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

    2012-01-01

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

  8. 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

  9. Predicting novel trophic interactions in a non-native world.

    PubMed

    Pearse, Ian S; Altermatt, Florian

    2013-08-01

    Humans are altering the global distributional ranges of plants, while their co-evolved herbivores are frequently left behind. Native herbivores often colonise non-native plants, potentially reducing invasion success or causing economic loss to introduced agricultural crops. We developed a predictive model to forecast novel interactions and verified it with a data set containing hundreds of observed novel plant-insect interactions. Using a food network of 900 native European butterfly and moth species and 1944 native plants, we built an herbivore host-use model. By extrapolating host use from the native herbivore-plant food network, we accurately forecasted the observed novel use of 459 non-native plant species by native herbivores. Patterns that governed herbivore host breadth on co-evolved native plants were equally important in determining non-native hosts. Our results make the forecasting of novel herbivore communities feasible in order to better understand the fate and impact of introduced plants.

  10. Structure-based prediction of host-pathogen protein interactions.

    PubMed

    Mariano, Rachelle; Wuchty, Stefan

    2017-03-16

    The discovery, validation, and characterization of protein-based interactions from different species are crucial for translational research regarding a variety of pathogens, ranging from the malaria parasite Plasmodium falciparum to HIV-1. Here, we review recent advances in the prediction of host-pathogen protein interfaces using structural information. In particular, we observe that current methods chiefly perform machine learning on sequence and domain information to produce large sets of candidate interactions that are further assessed and pruned to generate final, highly probable sets. Structure-based studies have also emphasized the electrostatic properties and evolutionary transformations of pathogenic interfaces, supplying crucial insight into antigenic determinants and the ways pathogens compete for host protein binding. Advancements in spectroscopic and crystallographic methods complement the aforementioned techniques, permitting the rigorous study of true positives at a molecular level. Together, these approaches illustrate how protein structure on a variety of levels functions coordinately and dynamically to achieve host takeover.

  11. Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.

    PubMed

    Cuevas, Jaime; Crossa, José; Soberanis, Víctor; Pérez-Elizalde, Sergio; Pérez-Rodríguez, Paulino; Campos, Gustavo de Los; Montesinos-López, O A; Burgueño, Juan

    2016-11-01

    In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propose using two nonlinear Gaussian kernels: the reproducing kernel Hilbert space with kernel averaging (RKHS KA) and the Gaussian kernel with the bandwidth estimated through an empirical Bayesian method (RKHS EB). We performed single-environment analyses and extended to account for G × E interaction (GBLUP-G × E, RKHS KA-G × E and RKHS EB-G × E) in wheat ( L.) and maize ( L.) data sets. For single-environment analyses of wheat and maize data sets, RKHS EB and RKHS KA had higher prediction accuracy than GBLUP for all environments. For the wheat data, the RKHS KA-G × E and RKHS EB-G × E models did show up to 60 to 68% superiority over the corresponding single environment for pairs of environments with positive correlations. For the wheat data set, the models with Gaussian kernels had accuracies up to 17% higher than that of GBLUP-G × E. For the maize data set, the prediction accuracy of RKHS EB-G × E and RKHS KA-G × E was, on average, 5 to 6% higher than that of GBLUP-G × E. The superiority of the Gaussian kernel models over the linear kernel is due to more flexible kernels that accounts for small, more complex marker main effects and marker-specific interaction effects.

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

    PubMed

    Randhawa, Vinay; Bagler, Ganesh

    2012-10-01

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

  13. Simulation of Parallel Interacting Faults and Earthquake Predictability

    NASA Astrophysics Data System (ADS)

    Mora, P.; Weatherley, D.; Klein, B.

    2003-04-01

    Numerical shear experiments of a granular region using the lattice solid model often exhibit accelerating energy release in the lead-up to large events (Mora et al, 2000) and a growth in correlation lengths in the stress field (Mora and Place, 2002). While these results provide evidence for a Critical Point-like mechanism in elasto-dynamic systems and the possibility of earthquake forecasting but they do not prove such a mechanism occurs in the crust. Cellular automaton models simulations exhibit accelerating energy release prior to large events or unpredictable behaviour in which large events may occur at any time depending on tuning parameters such as dissipation ratio and stress transfer ratio (Weatherley and Mora, 2003). The mean stress plots from the particle simulations are most similar to the CA mean stress plots near the boundary of the predictable and unpredictable regimes suggesting that elasto-dynamic systems may be close to the borderline of predictable and unpredictable. To progress in resolving the question of whether more realistic fault system models exhibit predictable behaviour and to determine whether they also have an unpredictable and predictable regime depending on tuning parameters like that seen in CA simulations, we developed a 2D elasto-dynamic model of parallel interacting faults. The friction is slip weakening until a critical slip distance. Henceforth, the friction is at the dynamic value until the slip rate drops below the value it attained when the critical slip distance was exceeded. As the slip rate continues to drop, the friction increases back to the static value as a function of slip rate. Numerical shear experiments are conducted in a model with 41 parallel interacting faults. Calculations of the inverse metric defined in Klein et al (2000) indicate that the system is non-ergodic. Furthermore, by calculating the correllation between the stress fields at different times we determine that the system exhibits so called ``glassy

  14. Interactive decision support system to predict print quality.

    PubMed

    Leman, Sugani; Lehto, Mark R

    2003-01-15

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

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

    PubMed

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

    2013-06-28

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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2014-07-01

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

  18. Evolutionary analysis and interaction prediction for protein-protein interaction network in geometric space.

    PubMed

    Huang, Lei; Liao, Li; Wu, Cathy H

    2017-01-01

    Prediction of protein-protein interaction (PPI) remains a central task in systems biology. With more PPIs identified, forming PPI networks, it has become feasible and also imperative to study PPIs at the network level, such as evolutionary analysis of the networks, for better understanding of PPI networks and for more accurate prediction of pairwise PPIs by leveraging the information gained at the network level. In this work we developed a novel method that enables us to incorporate evolutionary information into geometric space to improve PPI prediction, which in turn can be used to select and evaluate various evolutionary models. The method is tested with cross-validation using human PPI network and yeast PPI network data. The results show that the accuracy of PPI prediction measured by ROC score is increased by up to 14.6%, as compared to a baseline without using evolutionary information. The results also indicate that our modified evolutionary model DANEOsf-combining a gene duplication/neofunctionalization model and scale-free model-has a better fitness and prediction efficacy for these two PPI networks. The improved PPI prediction performance may suggest that our DANEOsf evolutionary model can uncover the underlying evolutionary mechanism for these two PPI networks better than other tested models. Consequently, of particular importance is that our method offers an effective way to select evolutionary models that best capture the underlying evolutionary mechanisms, evaluating the fitness of evolutionary models from the perspective of PPI prediction on real PPI networks.

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

    PubMed

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

    2014-01-01

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

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

    PubMed

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

    2017-05-01

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

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

    PubMed Central

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

    2014-01-01

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

  2. Predicting global community properties from uncertain estimates of interaction strengths

    PubMed Central

    Barabás, György; Allesina, Stefano

    2015-01-01

    The community matrix measures the direct effect of species on each other in an ecological community. It can be used to determine whether a system is stable (returns to equilibrium after small perturbations of the population abundances), reactive (perturbations are initially amplified before damping out), and to determine the response of any individual species to perturbations of environmental parameters. However, several studies show that small errors in estimating the entries of the community matrix translate into large errors in predicting individual species responses. Here, we ask whether there are properties of complex communities one can still predict using only a crude, order-of-magnitude estimate of the community matrix entries. Using empirical data, randomly generated community matrices, and those generated by the Allometric Trophic Network model, we show that the stability and reactivity properties of systems can be predicted with good accuracy. We also provide theoretical insight into when and why our crude approximations are expected to yield an accurate description of communities. Our results indicate that even rough estimates of interaction strengths can be useful for assessing global properties of large systems. PMID:26246417

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

    PubMed

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

    2015-10-01

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

  4. Predicting Molecular Crowding Effects in Ion-RNA Interactions.

    PubMed

    Yu, Tao; Zhu, Yuhong; He, Zhaojian; Chen, Shi-Jie

    2016-09-01

    We develop a new statistical mechanical model to predict the molecular crowding effects in ion-RNA interactions. By considering discrete distributions of the crowders, the model can treat the main crowder-induced effects, such as the competition with ions for RNA binding, changes of electrostatic interaction due to crowder-induced changes in the dielectric environment, and changes in the nonpolar hydration state of the crowder-RNA system. To enhance the computational efficiency, we sample the crowder distribution using a hybrid approach: For crowders in the close vicinity of RNA surface, we sample their discrete distributions; for crowders in the bulk solvent away from the RNA surface, we use a continuous mean-field distribution for the crowders. Moreover, using the tightly bound ion (TBI) model, we account for ion fluctuation and correlation effects in the calculation for ion-RNA interactions. Applications of the model to a variety of simple RNA structures such as RNA helices show a crowder-induced increase in free energy and decrease in ion binding. Such crowding effects tend to contribute to the destabilization of RNA structure. Further analysis indicates that these effects are associated with the crowder-ion competition in RNA binding and the effective decrease in the dielectric constant. This simple ion effect model may serve as a useful framework for modeling more realistic crowders with larger, more complex RNA structures.

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

    PubMed Central

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

    2015-01-01

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

  6. Predicting drugs side effects based on chemical-chemical interactions and protein-chemical interactions.

    PubMed

    Chen, Lei; Huang, Tao; Zhang, Jian; Zheng, Ming-Yue; Feng, Kai-Yan; Cai, Yu-Dong; Chou, Kuo-Chen

    2013-01-01

    A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our method can rank the potential side effects for any query drug according to their predicted level of risk. A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method. By a jackknife test on the training dataset, the 1st order prediction accuracy was 86.30%, while it was 89.16% on the test dataset. It is expected that the new method may become a useful tool for drug design, and that the findings obtained by hybridizing various interactions in a network system may provide useful insights for conducting in-depth pharmacological research as well, particularly at the level of systems biomedicine.

  7. Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions

    PubMed Central

    Chen, Lei; Huang, Tao; Zhang, Jian; Zheng, Ming-Yue; Feng, Kai-Yan; Cai, Yu-Dong; Chou, Kuo-Chen

    2013-01-01

    A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our method can rank the potential side effects for any query drug according to their predicted level of risk. A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method. By a jackknife test on the training dataset, the 1st order prediction accuracy was 86.30%, while it was 89.16% on the test dataset. It is expected that the new method may become a useful tool for drug design, and that the findings obtained by hybridizing various interactions in a network system may provide useful insights for conducting in-depth pharmacological research as well, particularly at the level of systems biomedicine. PMID:24078917

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

    PubMed

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

    2010-12-01

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

  9. RNA-RNA interaction prediction based on multiple sequence alignments.

    PubMed

    Li, Andrew X; Marz, Manja; Qin, Jing; Reidys, Christian M

    2011-02-15

    Many computerized methods for RNA-RNA interaction structure prediction have been developed. Recently, O(N(6)) time and O(N(4)) space dynamic programming algorithms have become available that compute the partition function of RNA-RNA interaction complexes. However, few of these methods incorporate the knowledge concerning related sequences, thus relevant evolutionary information is often neglected from the structure determination. Therefore, it is of considerable practical interest to introduce a method taking into consideration both: thermodynamic stability as well as sequence/structure covariation. We present the a priori folding algorithm ripalign, whose input consists of two (given) multiple sequence alignments (MSA). ripalign outputs (i) the partition function, (ii) base pairing probabilities, (iii) hybrid probabilities and (iv) a set of Boltzmann-sampled suboptimal structures consisting of canonical joint structures that are compatible to the alignments. Compared to the single sequence-pair folding algorithm rip, ripalign requires negligible additional memory resource but offers much better sensitivity and specificity, once alignments of suitable quality are given. ripalign additionally allows to incorporate structure constraints as input parameters. The algorithm described here is implemented in C as part of the rip package.

  10. Passing Messages between Biological Networks to Refine Predicted Interactions

    PubMed Central

    Glass, Kimberly; Huttenhower, Curtis; Quackenbush, John; Yuan, Guo-Cheng

    2013-01-01

    Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net. PMID:23741402

  11. Passing messages between biological networks to refine predicted interactions.

    PubMed

    Glass, Kimberly; Huttenhower, Curtis; Quackenbush, John; Yuan, Guo-Cheng

    2013-01-01

    Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.

  12. 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.

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

    PubMed

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

    2013-05-01

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

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

    DTIC Science & Technology

    2015-09-01

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

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

    PubMed

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

    2015-06-01

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

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

    PubMed

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

    2017-02-01

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

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

    PubMed

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

    2012-03-01

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

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

    PubMed

    Khan, Sameena

    2016-04-12

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

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

    DTIC Science & Technology

    2014-10-01

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

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

    NASA Astrophysics Data System (ADS)

    Rothlisberger, U.; Carloni, P.

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

  1. 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

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

    PubMed

    Müller, Joachim; Hemphill, Andrew

    2013-01-01

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

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

    PubMed

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

    2010-01-17

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

  4. Predicting drug pharmacokinetic properties using molecular interaction fields and SIMCA

    NASA Astrophysics Data System (ADS)

    Wolohan, Philippa R. N.; Clark, Robert D.

    2003-01-01

    We have developed a method that combines molecular interaction fields with soft independent modeling of class analogy (SIMCA) Wold:1977 to predict pharmacokinetic drug properties. Several additional considerations to those made in traditional QSAR are required in order to develop a successful QSPR strategy that is capable of accommodating the many complex factors that contribute to key pharmacokinetic properties such as ADME (absorption, distribution, metabolism, and excretion) and toxicology. An accurate prediction of oral bioavailability, for example, requires that absorption and first-pass hepatic elimination both be taken into consideration. To accomplish this, general properties of molecules must be related to their solubility and ability to penetrate biological membranes, and specific features must be related to their particular metabolic and toxicological profiles. Here we describe a method, which is applicable to structurally diverse data sets while utilizing as much detailed structural information as possible. We address the issue of the molecular alignment of a structurally diverse set of compounds using idiotropic field orientation (IFO), a generalization of inertial field orientation Clark:1998. We have developed a second flavor of this method, which directly incorporates electrostatics into the molecular alignment. Both variations of IFO produce a characteristic orientation for each structure and the corresponding molecular fields can then be analyzed using SIMCA. Models are presented for human intestinal absorption, blood-brain barrier penetration and bioavailability to demonstrate ways in which this tool can be used early in the drug development process to identify leads likely to exhibit poor pharmacokinetic behavior in pre-clinical studies, and we have explored the influence of conformation and molecular field type on the statistical properties of the models obtained.

  5. The interaction between stress and positive affect in predicting mortality.

    PubMed

    Okely, Judith A; Weiss, Alexander; Gale, Catharine R

    2017-09-01

    Positive affect is associated with longevity; according to the stress-buffering hypothesis, this is because positive affect reduces the health harming effects of psychological stress. If this mechanism plays a role, then the association between positive affect and mortality risk should be most apparent among individuals who report higher stress. Here, we test this hypothesis. The sample consisted of 8542 participants aged 32-86 from the National Health and Nutrition Examination Survey (NHANES I) Epidemiological Follow-up Study (NHEFS). We used Cox's proportional hazards regression to test for the main effects of and the interaction between positive affect and perceived stress in predicting mortality risk over a 10year follow up period. Greater positive affect was associated with lower mortality risk. We found a significant interaction between positive affect and perceived stress such that the association between positive affect and mortality risk was stronger in people reporting higher stress. In the fully adjusted model, a standard deviation increase in positive affect was associated with a 16% (HR=0.84; 95% CI=0.75, 0.95) reduction in mortality risk among participants who reported high levels of stress. The association between positive affect and mortality risk was weaker and not significant among participants who reported low levels of stress (HR=0.98; 95% CI=0.89, 1.08). Our results support the stress-buffering model and illustrate that the association between positive affect and reduced risk may be strongest under challenging circumstances. Copyright © 2017. Published by Elsevier Inc.

  6. Structure-templated predictions of novel protein interactions from sequence information.

    PubMed

    Betel, Doron; Breitkreuz, Kevin E; Isserlin, Ruth; Dewar-Darch, Danielle; Tyers, Mike; Hogue, Christopher W V

    2007-09-01

    The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain-motif interactions from structural topology (D-MIST) for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information.

  7. Structure-Templated Predictions of Novel Protein Interactions from Sequence Information

    PubMed Central

    Betel, Doron; Breitkreuz, Kevin E; Isserlin, Ruth; Dewar-Darch, Danielle; Tyers, Mike; Hogue, Christopher W. V

    2007-01-01

    The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain–motif interactions from structural topology (D-MIST) for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information. PMID:17892321

  8. Predicting protein-protein interactions from sequence using correlation coefficient and high-quality interaction dataset.

    PubMed

    Shi, Ming-Guang; Xia, Jun-Feng; Li, Xue-Ling; Huang, De-Shuang

    2010-03-01

    Identifying protein-protein interactions (PPIs) is critical for understanding the cellular function of the proteins and the machinery of a proteome. Data of PPIs derived from high-throughput technologies are often incomplete and noisy. Therefore, it is important to develop computational methods and high-quality interaction dataset for predicting PPIs. A sequence-based method is proposed by combining correlation coefficient (CC) transformation and support vector machine (SVM). CC transformation not only adequately considers the neighboring effect of protein sequence but describes the level of CC between two protein sequences. A gold standard positives (interacting) dataset MIPS Core and a gold standard negatives (non-interacting) dataset GO-NEG of yeast Saccharomyces cerevisiae were mined to objectively evaluate the above method and attenuate the bias. The SVM model combined with CC transformation yielded the best performance with a high accuracy of 87.94% using gold standard positives and gold standard negatives datasets. The source code of MATLAB and the datasets are available on request under smgsmg@mail.ustc.edu.cn.

  9. Effect of the quality of the interaction data on predicting protein function from protein-protein interactions.

    PubMed

    Ni, Qing-Shan; Wang, Zheng-Zhi; Li, Gang-Guo; Wang, Guang-Yun; Zhao, Ying-Jie

    2009-03-01

    Protein function prediction is an important issue in the post-genomic era. When protein function is deduced from protein interaction data, the traditional methods treat each interaction sample equally, where the qualities of the interaction samples are seldom taken into account. In this paper, we investigate the effect of the quality of protein-protein interaction data on predicting protein function. Moreover, two improved methods, weight neighbour counting method (WNC) and weight chi-square method (WCHI), are proposed by considering the quality of interaction samples with the neighbour counting method (NC) and chi-square method (CHI). Experimental results have shown that the qualities of interaction samples affect the performances of protein function prediction methods seriously. It is also demonstrated that WNC and WCHI methods outperform NC and CHI methods in protein function prediction when example weights are chosen properly.

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

    PubMed

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

    2017-08-28

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

  11. Predicting interactions from mechanistic information: Can omic data validate theories?

    SciTech Connect

    Borgert, Christopher J.

    2007-09-01

    To address the most pressing and relevant issues for improving mixture risk assessment, researchers must first recognize that risk assessment is driven by both regulatory requirements and scientific research, and that regulatory concerns may expand beyond the purely scientific interests of researchers. Concepts of 'mode of action' and 'mechanism of action' are used in particular ways within the regulatory arena, depending on the specific assessment goals. The data requirements for delineating a mode of action and predicting interactive toxicity in mixtures are not well defined from a scientific standpoint due largely to inherent difficulties in testing certain underlying assumptions. Understanding the regulatory perspective on mechanistic concepts will be important for designing experiments that can be interpreted clearly and applied in risk assessments without undue reliance on extrapolation and assumption. In like fashion, regulators and risk assessors can be better equipped to apply mechanistic data if the concepts underlying mechanistic research and the limitations that must be placed on interpretation of mechanistic data are understood. This will be critically important for applying new technologies to risk assessment, such as functional genomics, proteomics, and metabolomics. It will be essential not only for risk assessors to become conversant with the language and concepts of mechanistic research, including new omic technologies, but also, for researchers to become more intimately familiar with the challenges and needs of risk assessment.

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

    PubMed

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

    2013-01-01

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

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

    PubMed Central

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

    2013-01-01

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

  14. Transporter-enzyme interactions: implications for predicting drug-drug interactions from in vitro data.

    PubMed

    Benet, L Z; Cummins, C L; Wu, C Y

    2003-10-01

    As discussed in earlier articles, predictions of in vivo drug-drug interactions from in vitro studies is a subject of high interest with obvious therapeutic as well as economic benefits. Up until now little attention has been given to the potential interplay between metabolic enzymes and transporters that could confound the in vivo-in vitro relationships. Drug efflux by intestinal P-glycoprotein (P-gp) is known to decrease the bioavailability of many CYP3A4 substrates. We have demonstrated that the interplay between P-gp and CYP3A4 at the apical intestinal membrane can increase the opportunity for drug metabolism by determining bidirectional extraction ratios across CYP3A4 transfected Caco-2 cells for two dual P-gp/CYP3A4 substrates, K77 (an experimental cysteine protease inhibitor) and sirolimus, as well as two negative control, CYP3A4 only substrates, midazolam and felodipine. Studies were carried out under control conditions, with a P-gp inhibitor (GG918) and with a dual inhibitor (cyclosporine). Measurement of intracellular concentration changes is an important component in calculating the extraction ratios. We hypothesize that the inverse orientation of P-gp and CYP3A4 in the liver will result in an opposite interactive effect in that organ. In vivo rat intestinal perfusion studies with K77 and rat liver perfusion studies with tacrolimus under control conditions and with inhibitors of CYP3A4 (troleandomycin), P-gp (GG918) and both CYP3A4/P-gp (cyclosporine) lend support to our hypotheses. These results serve as a template for predicting enzyme- transporter (both absorptive and efflux) interactions in the intestine and the liver.

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

    NASA Astrophysics Data System (ADS)

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

    2017-03-01

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

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

    PubMed

    Singh, Vinayak; Donini, Stefano; Pacitto, Angela; Sala, Claudia; Hartkoorn, Ruben C; Dhar, Neeraj; Keri, Gyorgy; Ascher, David B; Mondésert, Guillaume; Vocat, Anthony; Lupien, Andréanne; Sommer, Raphael; Vermet, Hélène; Lagrange, Sophie; Buechler, Joe; Warner, Digby F; McKinney, John D; Pato, Janos; Cole, Stewart T; Blundell, Tom L; Rizzi, Menico; Mizrahi, Valerie

    2017-01-13

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

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

    PubMed Central

    2016-01-01

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

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed Central

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

    2011-01-01

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

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

    PubMed

    Leitsch, David; Müller, Joachim; Müller, Norbert

    2016-12-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed Central

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

    2011-01-01

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

  3. Prediction of transporter-mediated drug-drug interactions using endogenous compounds.

    PubMed

    Fromm, M F

    2012-11-01

    Therapy with two or more drugs is more the rule than the exception, particularly in aging societies. Drug-drug interactions are frequently undesirable and may lead to increased toxicity and mortality. Inhibition of transporters is one major mechanism underlying drug-drug interactions. The myriad of potential drug combinations makes it very challenging to predict drug-drug interactions. This Commentary discusses potential advantages and limitations of endogenous compounds for predicting transporter-mediated drug-drug interactions.

  4. Predicting Trophic Interactions and Habitat Utilization in the California Current Ecosystem

    DTIC Science & Technology

    2014-09-30

    1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Predicting Trophic Interactions and Habitat Utilization...on trophic interactions affecting habitat utilization and foraging patterns of California sea lions (CSL) in the California Current Large Marine...structure and trophic interactions OBJECTIVES The main research objective is to quantify habitat utilization and trophic interactions in the CCLME by

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

    PubMed

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

    2016-04-01

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

  6. 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.

  7. 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.

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

    Utsumi, Ryutaro; Igarashi, Masayuki

    2012-01-01

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

  11. 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.

  12. 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

  13. 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

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

    PubMed Central

    2014-01-01

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

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

    PubMed

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

    2009-12-25

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

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

    PubMed

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

    2014-08-01

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

  17. 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

  18. 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

  19. 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

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

    PubMed

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

    2010-01-01

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

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

    PubMed

    Basha, Syed Hussain; Talluri, Deepthi; Raminni, Nalini Prasad

    2013-04-15

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

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

    PubMed Central

    2013-01-01

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

  3. QTL x Genetic Background Interaction: Application to Predicting Progeny Value

    USDA-ARS?s Scientific Manuscript database

    Failures of the additive infinitesimal model continue to provide incentive to study other modes of gene action, in particular, epistasis. Epistasis can be modeled as a QTL by genetic background interaction. Association mapping models lend themselves to fitting such an interaction because they often ...

  4. 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.

  5. Predicting Children's Interactions with Unfamiliar Peers: Contributions of Parent-Child Interaction Style and Child Individual Behavior.

    ERIC Educational Resources Information Center

    Carrillo, Sonia; And Others

    This study examined children's play interaction styles with unfamiliar peers; used mother-child and father-child dyadic qualities independently to predict children's social behavior; determined the relationship between children's individual behaviors and peer dyadic characteristics; and compared mother-child and father-child interactions on both…

  6. Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering.

    PubMed

    Yang, Lei; Tang, Xianglong

    2014-01-01

    Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

  7. Determining confidence of predicted interactions between HIV-1 and human proteins using conformal method.

    PubMed

    Nouretdinov, Ilia; Gammerman, Alex; Qi, Yanjun; Klein-Seetharaman, Judith

    2012-01-01

    Identifying protein-protein interactions (PPI's) is critical for understanding virtually all cellular molecular mechanisms. Previously, predicting PPI's was treated as a binary classification task and has commonly been solved in a supervised setting which requires a positive labeled set of known PPI's and a negative labeled set of non-interacting protein pairs. In those methods, the learner provides the likelihood of the predicted interaction, but without a confidence level associated with each prediction. Here, we apply a conformal prediction framework to make predictions and estimate confidence of the predictions. The conformal predictor uses a function measuring relative 'strangeness' interacting pairs to check whether prediction of a new example added to the sequence of already known PPI's would conform to the 'exchangeability' assumption: distribution of interacting pairs is invariant with any permutations of the pairs. In fact, this is the only assumption we make about the data. Another advantage is that the user can control a number of errors by providing a desirable confidence level. This feature of CP is very useful for a ranking list of possible interactive pairs. In this paper, the conformal method has been developed to deal with just one class - class interactive proteins - while there is not clearly defined of 'non-interactive'pairs. The confidence level helps the biologist in the interpretation of the results, and better assists the choices of pairs for experimental validation. We apply the proposed conformal framework to improve the identification of interacting pairs between HIV-1 and human proteins.

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

    PubMed

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

    2013-05-01

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

  9. Predicting protein interactions via parsimonious network history inference.

    PubMed

    Patro, Rob; Kingsford, Carl

    2013-07-01

    Reconstruction of the network-level evolutionary history of protein-protein interactions provides a principled way to relate interactions in several present-day networks. Here, we present a general framework for inferring such histories and demonstrate how it can be used to determine what interactions existed in the ancestral networks, which present-day interactions we might expect to exist based on evolutionary evidence and what information extant networks contain about the order of ancestral protein duplications. Our framework characterizes the space of likely parsimonious network histories. It results in a structure that can be used to find probabilities for a number of events associated with the histories. The framework is based on a directed hypergraph formulation of dynamic programming that we extend to enumerate many optimal and near-optimal solutions. The algorithm is applied to reconstructing ancestral interactions among bZIP transcription factors, imputing missing present-day interactions among the bZIPs and among proteins from five herpes viruses, and determining relative protein duplication order in the bZIP family. Our approach more accurately reconstructs ancestral interactions than existing approaches. In cross-validation tests, we find that our approach ranks the majority of the left-out present-day interactions among the top 2 and 17% of possible edges for the bZIP and herpes networks, respectively, making it a competitive approach for edge imputation. It also estimates relative bZIP protein duplication orders, using only interaction data and phylogenetic tree topology, which are significantly correlated with sequence-based estimates. The algorithm is implemented in C++, is open source and is available at http://www.cs.cmu.edu/ckingsf/software/parana2. Supplementary data are available at Bioinformatics online.

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

    NASA Astrophysics Data System (ADS)

    Bonfà, Pietro; De Renzi, Roberto

    2016-09-01

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

  11. Application of CYP3A4 in vitro data to predict clinical drug–drug interactions; predictions of compounds as objects of interaction

    PubMed Central

    Youdim, Kuresh A; Zayed, Aref; Dickins, Maurice; Phipps, Alex; Griffiths, Michelle; Darekar, Amanda; Hyland, Ruth; Fahmi, Odette; Hurst, Susan; Plowchalk, David R; Cook, Jack; Guo, Feng; Obach, R Scott

    2008-01-01

    AIMS The aim of this study was to explore and optimize the in vitro and in silico approaches used for predicting clinical DDIs. A data set containing clinical information on the interaction of 20 Pfizer compounds with ketoconazole was used to assess the success of the techniques. METHODS The study calculated the fraction and the rate of metabolism of 20 Pfizer compounds via each cytochrome P450. Two approaches were used to determine fraction metabolized (fm); 1) by measuring substrate loss in human liver microsomes (HLM) in the presence and absence of specific chemical inhibitors and 2) by measuring substrate loss in individual cDNA expressed P450s (also referred to as recombinant P450s (rhCYP)) The fractions metabolized via each CYP were used to predict the drug–drug interaction due to CYP3A4 inhibition by ketoconazole using the modelling and simulation software SIMCYP®. RESULTS When in vitro data were generated using Gentest supersomes, 85% of predictions were within two-fold of the observed clinical interaction. Using PanVera baculosomes, 70% of predictions were predicted within two-fold. In contrast using chemical inhibitors the accuracy was lower, predicting only 37% of compounds within two-fold of the clinical value. Poorly predicted compounds were found to either be metabolically stable and/or have high microsomal protein binding. The use of equilibrium dialysis to generate accurate protein binding measurements was especially important for highly bound drugs. CONCLUSIONS The current study demonstrated that the use of rhCYPs with SIMCYP® provides a robust in vitro system for predicting the likelihood and magnitude of changes in clinical exposure of compounds as a consequence of CYP3A4 inhibition by a concomitantly administered drug. WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT Numerous retrospective analyses have shown the utility of in vitro systems for predicting potential drug–drug interactions (DDIs). Prediction of DDIs from in vitro data is commonly

  12. Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces.

    PubMed

    Tuncbag, Nurcan; Gursoy, Attila; Keskin, Ozlem

    2011-06-01

    The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.

  13. Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces

    NASA Astrophysics Data System (ADS)

    Tuncbag, Nurcan; Gursoy, Attila; Keskin, Ozlem

    2011-06-01

    The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.

  14. Prediction of protein-protein interactions by combining structure and sequence conservation in protein interfaces.

    PubMed

    Aytuna, A Selim; Gursoy, Attila; Keskin, Ozlem

    2005-06-15

    Elucidation of the full network of protein-protein interactions is crucial for understanding of the principles of biological systems and processes. Thus, there is a need for in silico methods for predicting interactions. We present a novel algorithm for automated prediction of protein-protein interactions that employs a unique bottom-up approach combining structure and sequence conservation in protein interfaces. Running the algorithm on a template dataset of 67 interfaces and a sequentially non-redundant dataset of 6170 protein structures, 62 616 potential interactions are predicted. These interactions are compared with the ones in two publicly available interaction databases (Database of Interacting Proteins and Biomolecular Interaction Network Database) and also the Protein Data Bank. A significant number of predictions are verified in these databases. The unverified ones may correspond to (1) interactions that are not covered in these databases but known in literature, (2) unknown interactions that actually occur in nature and (3) interactions that do not occur naturally but may possibly be realized synthetically in laboratory conditions. Some unverified interactions, supported significantly with studies found in the literature, are discussed. http://gordion.hpc.eng.ku.edu.tr/prism agursoy@ku.edu.tr; okeskin@ku.edu.tr.

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

    PubMed Central

    Di Martino, Emanuela; Rust, Seabourne

    2016-01-01

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

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

    PubMed

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

    2015-10-01

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

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

    PubMed Central

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

    2012-01-01

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

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

    PubMed Central

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

    2008-01-01

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

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

    PubMed

    Jost, P; Peschel, C; Ruland, J

    2006-10-01

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

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

    PubMed

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

    2010-04-01

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

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

    PubMed

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

    2010-01-01

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

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

    PubMed

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

    2012-10-01

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

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

    PubMed Central

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

    2011-01-01

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