Sabanci Universitesi, MDBF, Orhanli, Tuzla 34956, Istanbul, Turkey.
IEEE/ACM Trans Comput Biol Bioinform. 2011 Nov-Dec;8(6):1495-508. doi: 10.1109/TCBB.2010.101.
The classification of G-Protein Coupled Receptor (GPCR) sequences is an important problem that arises from the need to close the gap between the large number of orphan receptors and the relatively small number of annotated receptors. Equally important is the characterization of GPCR Class A subfamilies and gaining insight into the ligand interaction since GPCR Class A encompasses a very large number of drug-targeted receptors. In this work, we propose a method for Class A subfamily classification using sequence-derived motifs which characterizes the subfamilies by discovering receptor-ligand interaction sites. The motifs that best characterize a subfamily are selected by the Distinguishing Power Evaluation (DPE) technique we propose. The experiments performed on GPCR sequence databases show that our method outperforms state-of-the-art classification techniques for GPCR Class A subfamily prediction. An important contribution of our work is to discover key receptor-ligand interaction sites which is very important for drug design.
G 蛋白偶联受体（GPCR）序列的分类是一个重要的问题，它源于需要缩小大量孤儿受体和相对较少注释受体之间的差距。同样重要的是对 GPCR A 类亚家族的特征描述和深入了解配体相互作用，因为 GPCR A 类包含大量药物靶向受体。在这项工作中，我们提出了一种使用基于序列的模体对 A 类亚家族进行分类的方法，该方法通过发现受体-配体相互作用位点来描述亚家族。通过我们提出的区分能力评估（DPE）技术选择最能描述亚家族的模体。在 GPCR 序列数据库上进行的实验表明，我们的方法在 GPCR A 类亚家族预测方面优于最先进的分类技术。我们工作的一个重要贡献是发现关键的受体-配体相互作用位点，这对于药物设计非常重要。
