Huang Enoch S
Pfizer Discovery Technology Center, 620 Memorial Drive, Cambridge, MA 02139, USA.
Protein Sci. 2003 Jul;12(7):1360-7. doi: 10.1110/ps.0305603.
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.
本文描述了一种发现配体类别特征性序列模式的方法,并将其应用于胺能G蛋白偶联受体(GPCR)。通过考虑三条证据线索来推断假定的配体结合残基位置:在超家族中缺失或代表性不足的亚家族中的保守性、任何可用的突变数据以及配体的物理化学性质。对于胺能GPCR,该基序由第三个跨膜(TM)结构域中保守的天冬氨酸(视紫红质位置117)和第七个TM结构域中保守的色氨酸(视紫红质位置293)组成;通过配体-受体相互作用的分子建模,很容易证明每个残基的作用。这个定义最简化的基序是一个合适的计算工具,可用于从一组实验上未表征的“孤儿”GPCR中识别其他潜在的新型胺能GPCR,对现有的序列匹配、聚类和机器学习技术起到补充作用。基序敏感性源于逐步添加整个配体类别特有的残基(而非针对任何特定生物胺量身定制)。通过仔细考虑残基(从突变数据得出的证据、配体性质与残基性质的相关性以及相对于细胞外表面的位置)来平衡这种敏感性,从而保持对胺能类别的特异性。后来发现,通过该基序归类为胺能类别的许多孤儿GPCR是痕量胺GPCR的一个新亚家族,组胺H4受体的成功分类也是如此。