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Computational Models for Predicting Interactions with Membrane Transporters

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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.

Keywords: Computational methods; QSAR models; homology modeling; membrane transporter; phamacophore models; supervised-unsupervised learning algorithms

Document Type: Research Article

Publication date: May 1, 2013

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  • Current Medicinal Chemistry covers all the latest and outstanding developments in medicinal chemistry and rational drug design. Each issue contains a series of timely in-depth reviews written by leaders in the field covering a range of the current topics in medicinal chemistry. Current Medicinal Chemistry is an essential journal for every medicinal chemist who wishes to be kept informed and up-to-date with the latest and most important developments.

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