人类基因组中无内含子紫红质样g蛋白偶联受体(GPCR)的计算机研究:特征和分类。

K Alem, A Louhichi, A Ladjama, A Rebaï
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引用次数: 0

摘要

g蛋白偶联受体(gpcr)是人类基因组中一个庞大的蛋白质家族,已被广泛研究并划分为类和系统发育亚家族。然而,仍然存在未在任何已知亚家族中分类的孤儿gpcr,并且仍然需要新的生物信息学方法来解决这一问题。gpcr的一个有趣的特征是,这些蛋白质的很大一部分是由无内含子基因编码的。在这项工作中,我们对这类基因编码的rhodpsin样gpcr蛋白的研究很感兴趣。在对它们的基因结构进行人工验证后,我们研究了它们的一些特性,包括外显子数量、染色体位置和蛋白质长度。与总gpcr相比,无内含子的gpcr也有同样的趋势,特别是染色体分布不均匀,1号和11号染色体上有大量的gpcr(三分之一)。在所有rhdopsin样gpcr中,无内含子gpcr的比例估计约为26%,明显低于之前的报道。亚家族之间的蛋白质长度存在显著差异。然后,我们利用DNA和蛋白质序列的组成特性对无内含子的视紫红质样gpcr进行分类。采用主成分分析确定关键变量,然后采用判别分析计算出区分系统发育亚家族的判别函数。我们发现区分类群的最重要特征是蛋白质序列中芳香氨基酸的比例以及编码序列中(A+T)与(G+C)的对比。这些功能最终用于对14个推测的或未分类的gpcr进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In silico investigation of intronless Rhodopsin-like G-protein coupled receptors (GPCR) in the human genome: features and classification.

The G-protein coupled receptors (GPCRs) form a large protein family in the human genome that have been widely studied and classified into classes and phylogenetic subfamilies. However, there still exist orphan GPCRs that are not classified in any of the known subfamilies and new bioinformatics approaches are still needed to address this issue. One of the interesting features of GPCRs is that a large proportion of these proteins are encoded by intronless genes. In this work, we are interested in the study of Rhodpsin-like GPCRs proteins encoded by this kind of genes. After a manual validation of their gene structure, we studied some of their properties including the number of exons, chromosomal location and protein length. The same trend was found for intronless GPCRs as compared to total GPCRs, particularly the uneven chromosomal distribution with a large number (one third) of GPCRs on chromosomes 1 and 11. The proportion of intronless GPCRs among all Rhdopsin-like GPCRs was estimated to about 26% which is significantly less than previously reported. Significant differences in protein length were found between subfamilies. We then used composition properties of DNA and protein sequences to classify intronless Rhodopsin-like GPCRs. Principal component analysis was used to identify key variable and then a discriminant analysis was used to compute discriminant functions that best separates the phylogenetic subfamilies. We found that the most important features to separates the groups is the proportion of aromatic amino acids in protein sequence and the contrast between (A+T) versus (G+C) in coding sequence. These functions are finally used to classify fourteen putative or unclassified GPCRs.

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