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Construction of a sequence motif characteristic of aminergic G protein–coupled receptors

Pfizer Discovery Technology Center, Cambridge, Massachusetts 02139, USA

Reprint requests to: Enoch S. Huang, Pfizer Discovery Technology Center, 620 Memorial Drive, Cambridge, MA 02139, USA; e-mail: enoch_huang{at}cambridge.pfizer.com; fax: (617) 551-3117.

An approach to discover sequence patterns characteristic of ligand classes is described and applied to aminergic G protein–coupled receptors (GPCRs). Putative ligand-binding residue positions were inferred from considering three lines of evidence: conservation in the subfamily absent or underrepresented in the superfamily, any available mutation data, and the physicochemical properties of the ligand. For aminergic GPCRs, the motif is composed of a conserved aspartic acid in the third transmembrane (TM) domain (rhodopsin position 117) and a conserved tryptophan in the seventh TM domain (rhodopsin position 293); the roles of each are readily justified by molecular modeling of ligand-receptor interactions. This minimally defined motif is an appropriate computational tool for identifying additional, potentially novel aminergic GPCRs from a set of experimentally uncharacterized "orphan" GPCRs, complementing existing sequence matching, clustering, and machine-learning techniques. Motif sensitivity stems from the stepwise addition of residues characteristic of an entire class of ligand (and not tailored for any particular biogenic amine). This sensitivity is balanced by careful consideration of residues (evidence drawn from mutation data, correlation of ligand properties to residue properties, and location with respect to the extracellular face), thereby maintaining specificity for the aminergic class. A number of orphan GPCRs assigned to the aminergic class by this motif were later discovered to be a novel subfamily of trace amine GPCRs, as well as the successful classification of the histamine H4 receptor.

Keywords: G protein; coupled receptor; multiple sequence alignment; bioinformatics; motif discovery; protein-ligand interactions; biogenic amines

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