Semertzidis M T, Hazout S, Etchebest C, Mornon J P
Département des Macromolécules Biologiques, Université Pierre et Marie Curie, Paris 6, France.
Comput Methods Programs Biomed. 1994 Dec;45(4):265-82. doi: 10.1016/0169-2607(94)01589-8.
Structural biology needs sensitive tools to detect homology between proteins of low sequence identity, but with closely related 3-D structures. Using a conventional dotplot method, we therefore introduced 2 concepts to improve the search for similarities between secondary structures of analyzed proteins: 'hydrophobic neighboring homology' (HNH) and 'amino acid degeneracy classes'. The amino acids are grouped into 3 subsets: hydrophobic, hydrophilic and mimetic. A 'Neighboring Similarity Index' (NSI) is calculated for every residue pair and quantifies its neighbor homology. By thresholding the homology matrix and filtering the dotplot, the homologous patterns are extracted. We have evaluated the efficiency and limits of the method using 21 protein pairs extracted from the Protein Data Bank (PDB), or selected from the recent literature. Globally, we again find the homologous structures (alpha-helices and beta-strands) of these pair proteins. The introduction of neighbor residue hydrophobicity in the conventional dotplot improves the alignment of proteins with low sequence identity (< 25%). HNH, written in standard ANSI C with the graphic library X11, under UNIX, is available on request.
结构生物学需要灵敏的工具来检测低序列同一性但三维结构密切相关的蛋白质之间的同源性。因此,我们使用传统的点阵图方法引入了两个概念,以改进对分析蛋白质二级结构之间相似性的搜索:“疏水邻域同源性”(HNH)和“氨基酸简并类”。氨基酸被分为三个子集:疏水、亲水和模拟。为每对残基计算一个“邻域相似性指数”(NSI),并量化其邻域同源性。通过对同源性矩阵进行阈值处理和过滤点阵图,提取同源模式。我们使用从蛋白质数据库(PDB)中提取或从近期文献中选择的21对蛋白质评估了该方法的效率和局限性。总体而言,我们再次找到了这些成对蛋白质的同源结构(α螺旋和β链)。在传统点阵图中引入相邻残基疏水性可改善低序列同一性(<25%)蛋白质的比对。HNH用标准ANSI C编写,带有图形库X11,在UNIX系统下,可根据要求提供。