@inbook{f1848c2b8dc449719cca21c598cbcf99,
title = "In silico identification of novel G protein-coupled receptors",
abstract = "The G protein-coupled receptors (GPCRs) form the largest and most multi-functional protein-superfamilies known. From a drug discovery and pharmaceutical industry perspective, the GPCRs are among the most commercially and economically important groups of proteins yet identified, since they have so many vital metabolic functions and interact with such a diversity of ligands. Many distinct methodologies have been proposed to classify the GPCRs: motif-based techniques, machine learning, and several alignment-free techniques have all been used successful in this regard. This chapter reviews the available methodologies for classifying GPCRs. In particular, we allude to several innate problems in developing such approaches, such as the lack of sequence similarity between the six GPCR classes and the low sequence similarity of many newly identified family members to other GPCRs.",
keywords = "Former Faculty of Pharmaceutical Sciences",
author = "Davies, {Matthew N} and Gloriam, {David E} and Flower, {Darren R}",
note = "Part 1: The G protein-coupled receptor in the genome Keywords: receptor classification; genomics; machine learning; in silico",
year = "2011",
doi = "10.1007/978-1-61779-179-6_1",
language = "English",
isbn = "978-1-61779-178-9",
volume = "60",
series = "Neuromethods",
publisher = "Humana Press",
pages = "3--18",
editor = "Stevens, {Craig W}",
booktitle = "Methods for the discovery and characterization of G protein-coupled receptors",
address = "United States",
}