Title:Computational Models for Predicting Interactions with Membrane Transporters
Volume: 20
Issue: 16
Author(s): Y. Xu, Q. Shen, X. Liu, J. Lu, S. Li, C. Luo, L. Gong, X. Luo, M. Zheng and H. Jiang
Affiliation:
Keywords:
Computational methods, homology modeling, membrane transporter, phamacophore models, QSAR models, supervised⁄
unsupervised learning algorithms
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.
