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ABSTRACT: Membrane transporters, including two members: ATP-binding cassette (ABC) transporters and solute carrier (SLC) transporters are proteins that play important roles to facilitate molecules into and out of cells. Consequently, these transporters can be major determinants of the therapeutic efficacy, toxicity and pharmacokinetics of a variety of drugs. Considering the time and expense of bio-experiments taking, research should be driven by evaluation of efficacy and safety. Computational methods arise to be a complementary choice. In this article, we provide an overview of the contribution that computational methods made in transporters field in the past decades. At the beginning, we present a brief introduction about the structure and function of major members of two families in transporters. In the second part, we focus on widely used computational methods in different aspects of transporters research. In the absence of a high-resolution structure of most of transporters, homology modeling is a useful tool to interpret experimental data and potentially guide experimental studies. We summarize reported homology modeling in this review. Researches in computational methods cover major members of transporters and a variety of topics including the classification of substrates and/or inhibitors, prediction of protein-ligand interactions, constitution of binding pocket, phenotype of non-synonymous single-nucleotide polymorphisms, and the conformation analysis that try to explain the mechanism of action. As an example, one of the most important transporters P-gp is elaborated to explain the differences and advantages of various computational models. In the third part, the challenges of developing computational methods to get reliable prediction, as well as the potential future directions in transporter related modeling are discussed.
Current Medicinal Chemistry 01/2013; · 4.86 Impact Factor
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ABSTRACT: Phase I metabolism is an important consideration in drug discovery because it profoundly affects the toxicity and activity profile of a drug candidate. In these metabolic processes, CYP450 family is responsible for the majority of biotransformation events. However, it is still an important challenge to predict sites of metabolism (SOM) of a new chemical entity due to the complex reaction mechanism and variety in CYP450 enzymes. SOMEViz is an online service designed for predicting and visualizing human cytochromes P450 (CYP450)-mediated sites of metabolism (SOM) of a molecule, on the basis of a previously reported model. The service provides an access for predicting sites of metabolism of molecules with reasonable accuracy, and predicted results are shown in a user-friendly as well as interactive way, which may help chemists explore metabolism properties of chemicals in the early stage of drug discovery. The web-based GUI of SOMEViz offers user a straightforward way to manage and visualize the sites of metabolism (SOM) prediction results. The service and examples are available free of charge at http://www.dddc.ac.cn/some.
Protein and Peptide Letters 09/2012; 19(9):905-9. · 1.94 Impact Factor
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ABSTRACT: Three new zinc(ii) complexes: [Zn(2)()(2)Cl(2)](ClO(4))(2)·C(2)H(5)OH () and [Zn(2)X(4)]·2CH(3)CN (X = Br for , Cl for ), utilizing two new and interrelated di-nucleating polypyridyl ligands (, ), have been synthesized and characterized by using various physico-chemical techniques. The interactions of three complexes with CT-DNA have been explored by using absorption, emission and CD spectral methods, which reveal that three complexes bind to CT-DNA by partial intercalation binding modes. Notably, in the presence of H(2)O(2) as a revulsant or an activator, the cleavage abilities of all complexes are obviously enhanced. The hydrolytic mechanism was demonstrated by adding standard radical scavengers and anaerobic reaction. Further, the protein binding ability has been monitored by quenching of tryptophan emission in the presence of complexes using BSA as a model protein. The quenching mechanisms of BSA by the complexes are static procedures. In addition, the in vitro cytotoxicity of the complexes on three human tumor cells lines (HeLa, MCF-7 and RL952) and the apoptosis-inducing activity of were assessed by MTT, Clonogenic assay, Hoechst 33342 staining, Cell cycle and Annexin V binding experiments.
Dalton Transactions 08/2012; 41(39):12220-32. · 3.84 Impact Factor
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ABSTRACT: Carcinogenicity is an important toxicological endpoint that poses high concern to drug discovery. In this study, we developed a method to extract structural alerts (SAs) and modulating factors of carcinogens on the basis of statistical analyses. First, the Gaston algorithm, a frequent subgraph mining method, was used to detect substructures that occurred at least six times. Then, a molecular fragments tree was built and pruned to select high-quality SAs. The p-value of the parent node in the tree and that of its children nodes were compared, and the nodes that had a higher statistical significance in binomial tests were retained. Finally, modulating factors that suppressed the toxic effects of SAs were extracted by three self-defining rules. The accuracy of the 77 SAs plus four SA/modulating factor pairs model for the training set, and the test set was 0.70 and 0.65, respectively. Our model has higher predictive ability than Benigni's model, especially in the test set. The results highlight that this method is preferable in terms of prediction accuracy, and the selected SAs are useful for prediction as well as interpretation. Moreover, our method is convenient to users in that it can extract SAs from a database using an automated and unbiased manner that does not rely on a priori knowledge of mechanism of action.
Journal of Chemical Information and Modeling 07/2012; 52(8):1994-2003. · 4.68 Impact Factor
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ABSTRACT: Skin sensitization is an important toxic endpoint in the risk assessment of chemicals. In this paper, structure-activity relationships analysis was performed on the skin sensitization potential of 357 compounds with local lymph node assay data. Structural fragments were extracted by GASTON (GrAph/Sequence/Tree extractiON) from the training set. Eight fragments with accuracy significantly higher than 0.73 (p<0.1) were retained to make up an indicator descriptor fragment. The fragment descriptor and eight other physicochemical descriptors closely related to the endpoint were calculated to construct the recursive partitioning tree (RP tree) for classification. The balanced accuracy of the training set, test set I, and test set II in the leave-one-out model were 0.846, 0.800, and 0.809, respectively. The results highlight that fragment-based RP tree is a preferable method for identifying skin sensitizers. Moreover, the selected fragments provide useful structural information for exploring sensitization mechanisms, and RP tree creates a graphic tree to identify the most important properties associated with skin sensitization. They can provide some guidance for designing of drugs with lower sensitization level.
Journal of Computer-Aided Molecular Design 09/2011; 25(9):885-93. · 3.39 Impact Factor
