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蛋白质的混沌博弈表示法。

Chaos game representation of proteins.

作者信息

Basu S, Pan A, Dutta C, Das J

机构信息

Biophysics Division, Indian Institute of Chemical Biology, Calcutta, India.

出版信息

J Mol Graph Model. 1997 Oct;15(5):279-89. doi: 10.1016/s1093-3263(97)00106-x.

Abstract

The present report proposes a new method for the chaos game representation (CGR) of different families of proteins. Using concatenated amino acid sequences of proteins belonging to a particular family and a 12-sided regular polygon, each vertex of which represents a group of amino acid residues leading to conservative substitutions, the method can generate the CGR of the family and allows pictorial representation of the pattern characterizing the family. An estimation of the percentages of points plotted in different segments of the CGR (grid points) allows quantification of the nonrandomness of the CGR patterns generated. The CGRs of different protein families exhibited distinct visually identifiable patterns. This implies that different functional classes of proteins follow specific statistical biases in the distribution of different mono-, di-, tri-, or higher order peptides along their primary sequences. The potential of grid counts as the discriminative and diagnostic signature of a family of proteins is discussed.

摘要

本报告提出了一种针对不同蛋白质家族的混沌博弈表示(CGR)的新方法。利用属于特定家族的蛋白质的串联氨基酸序列和一个十二边形正多边形,该多边形的每个顶点代表一组导致保守替换的氨基酸残基,此方法可以生成该家族的CGR,并允许以图形方式表示表征该家族的模式。对CGR不同片段(网格点)中绘制的点的百分比进行估计,可以量化所生成的CGR模式的非随机性。不同蛋白质家族的CGR呈现出明显的、视觉上可识别的模式。这意味着不同功能类别的蛋白质在其一级序列上不同的单肽、二肽、三肽或更高阶肽的分布遵循特定的统计偏差。文中讨论了网格计数作为蛋白质家族鉴别和诊断特征的潜力。

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