Receptor Databases and Computational Websites for Ligand Binding
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Ligand binding to receptors is a key step in the regulation of cellular function by neurotransmitters, hormones, and many drugs. Accordingly, this initial event in ligand action is important for understanding disease and designing new drugs. A large body of experimental data describing receptor-ligand interactions exists and is derived from studies of native and transfected cell systems, including a growing number of studies with artificial or naturally occurring receptor mutants. Taken together, genes encoding various receptors appear to form the largest classes of functional genes in mammalian genomes. This large number of genes and gene products, together with the expanding pool of ligands, provides, and will generate in the future, a huge amount of data. Such compilations of data create a need for comprehensive, web-based resources that compile and integrate information on receptor protein and nucleotide sequences, classification, experimental results, and computational tools for modeling interactions. A number of websites in the public domain provide useful data-mining tools and contain information on specific families of receptors or receptor subfamilies, such as the G protein-coupled receptors (GPCRs) (1, 2), nuclear receptors, ion channel receptors, and others. A number of websites provide tools by which potential functions and molecular interactions can be derived to guide experimentalists in studies of receptor-ligand interaction and thus aid in defining the function of the receptor of interest. The goal of this chapter is to identify websites containing information that can facilitate both computational and experimental studies of receptor-ligand interactions. We will identify and briefly review websites and certain software that are available for several different classes of receptors and their ligands.
KeywordsAndrogen Receptor Nuclear Receptor Familial Hypercholesterolemia Familial Hypercholesterolemia GABAC Receptor
- 22.Crasto, C., Marenco, L., Miller, P. L., and Shepherd, G. S. (2002) Olfactory Receptor Database: a metadata-driven automated population from sources of gene and protein sequences. Nucl Acids Res. 354–360Google Scholar
- 26.VanDurme, J.J., Bettler, E., Folkertsma, S., Horn, F., Vriend, G. NRMD: Nuclear Receptor Mutation Database. Nucl. Acids Res. 31, 331–333Google Scholar
- 27.Nakata, K., Takai-Igarashi, T., Nakano, T., Kaminuma, T. (2002) An integrated receptor database (IRDB). Data Science Journ. 1, 140–145.Google Scholar