Recent Advances in Predicting G-Protein Coupled Receptor Classification

Xuan      Xiao; Wei-Zhong      Lin; Kuo-Chen      Chou

doi:10.2174/157489312800604426

Abstract

G protein-coupled receptors (GPCRs) are integral membrane proteins with seven trans-membrane helices. Belonging to the largest family of cell surface receptors, GPCRs are among the most frequent targets of therapeutic drugs. Unfortunately, since they are difficult to crystallize and most of them will not dissolve in normal solvents, so far the number of GPCRs with three-dimensional structure determined is very limited.

This situation has challenged us to develop automated methods by which one can predict the family and sub-family classes of GPCRs based on the information of their primary sequences alone, so as to facilitate classifying drugs, a technique called “evolutionary pharmacology” often used in pharmaceutical industries for drug development. In the past eight years, various computational methods were proposed. This review is devoted to summarize their development. Meanwhile, the future challenge in this area has also been briefly addressed.

Keywords: Cellular automata, G-protein coupled receptor, pseudo amino acid composition, protein sequence image expression, sequence-derived features, evolutionary pharmacology, transmembrane, enzyme-substrate, homology bias, redundancy