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顺式脯氨酸接触转折:一种在功能位点偏好的罕见基序。

The cis-Pro touch-turn: a rare motif preferred at functional sites.

作者信息

Videau Lizbeth L, Arendall W Bryan, Richardson Jane S

机构信息

Department of Biochemistry, Duke University, Durham, North Carolina 27710-3711, USA.

出版信息

Proteins. 2004 Aug 1;56(2):298-309. doi: 10.1002/prot.20101.

Abstract

A new motif of three-dimensional (3D) protein structure is described, called the cis-Pro touch-turn. In this four-residue, three-peptide motif, the central peptide is cis. Residue 2, which precedes the proline, has phi, psi values either in the "prePro region" of the Ramachandran plot near -130 degrees, 75 degrees or in the Lalpha region near +60 degrees, +60 degrees. The Calpha(1)-Calpha(4) distance is 4-5 A and the two flanking peptides lie parallel to one another, making van der Waals contact rather than a hydrogen bond. Apparently, this arrangement is locally unfavorable and therefore rare, usually occurring only if needed for biological function. Of the 12 examples in a 500-protein database, cis-Pro touch-turns are found at the catalytic sites of pectate lyase, Ni-Fe hydrogenase, glucoamylase, xylanase, and opine dehydrogenase and at the primary binding sites of ribonuclease H, type I DNA polymerase, ribotoxin, and phage gene 3 protein. In each of these protein families, the touch-turns serve different roles; their functional importance is supported by conservation and mutagenesis data. In analyzing the conservation patterns of these 3D motifs, new methods for in-depth quality evaluation of the structural bioinformatic data are employed to distinguish between significant exceptions and errors

摘要

描述了一种新的三维(3D)蛋白质结构基序,称为顺式脯氨酸接触转角。在这个由四个残基组成的三肽基序中,中间的肽是顺式的。位于脯氨酸之前的第2位残基,其φ、ψ值要么在拉氏图的“前脯氨酸区域”接近-130°、75°,要么在Lα区域接近+60°、+60°。Cα(1)-Cα(4)距离为4-5埃,两侧的两个肽段彼此平行,形成范德华接触而非氢键。显然,这种排列在局部是不利的,因此很少见,通常仅在生物学功能需要时才会出现。在一个包含500种蛋白质的数据库中的12个例子中,顺式脯氨酸接触转角出现在果胶酸裂解酶、镍铁氢化酶、葡糖淀粉酶、木聚糖酶和章鱼碱脱氢酶的催化位点,以及核糖核酸酶H、I型DNA聚合酶、核糖毒素和噬菌体基因3蛋白的主要结合位点。在这些蛋白质家族中的每一个中,接触转角都发挥着不同的作用;它们的功能重要性得到了保守性和诱变数据的支持。在分析这些3D基序的保守模式时,采用了新的方法对结构生物信息学数据进行深入的质量评估,以区分显著的例外情况和错误。

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