Suppr超能文献

对m7G mRNA帽特异性识别的结构要求。

Structural requirements for the specific recognition of an m7G mRNA cap.

作者信息

Hsu P C, Hodel M R, Thomas J W, Taylor L J, Hagedorn C H, Hodel A E

机构信息

Department of Biochemistry, Emory University School of Medicine, 1510 Clifton Road, Atlanta, Georgia 30322, USA.

出版信息

Biochemistry. 2000 Nov 14;39(45):13730-6. doi: 10.1021/bi000623p.

Abstract

7-Methylguanosine (m(7)G), also known as the mRNA "cap", is used as a molecular tag in eukaryotic cells to mark the 5' end of messenger RNAs. The mRNA cap is required for several key events in gene expression in which the m(7)G moiety is specifically recognized by cellular proteins. The configurations of the m(7)G-binding pockets of a cellular (eIF4E) and a viral (VP39) cap-binding protein have been determined by X-ray crystallography. The binding energy has been hypothesized to result from a pi-pi stacking interaction between aromatic residues sandwiching the m(7)G base in addition to hydrogen bonds between the base and acidic protein side chains. To further understand the structural requirements for the specific recognition of an m(7)G mRNA cap, we determined the effects of amino acid substitutions in eIF4E and VP39 cap-binding sites on their affinity for m(7)GDP. The requirements for residues suggested to pi-pi stack and hydrogen bond with the m(7)G base were examined in each protein by measuring their affinities for m(7)GDP by fluorimetry. The results suggest that both eIF4E and VP39 require a complicated pattern of both orientation and identity of the stacking aromatic residues to permit the selective binding of m(7)GDP.

摘要

7-甲基鸟苷(m(7)G),也被称为mRNA“帽”,在真核细胞中用作分子标签来标记信使RNA的5'端。mRNA帽是基因表达中几个关键事件所必需的,其中m(7)G部分被细胞蛋白特异性识别。通过X射线晶体学已确定了一种细胞(eIF4E)和一种病毒(VP39)帽结合蛋白的m(7)G结合口袋的结构。据推测,结合能除了来自碱基与酸性蛋白侧链之间的氢键外,还源于夹着m(7)G碱基的芳香族残基之间的π-π堆积相互作用。为了进一步了解对m(7)G mRNA帽特异性识别的结构要求,我们确定了eIF4E和VP39帽结合位点中氨基酸取代对它们与m(7)GDP亲和力的影响。通过荧光法测量它们与m(7)GDP的亲和力,研究了每种蛋白质中与m(7)G碱基进行π-π堆积和形成氢键的残基的要求。结果表明,eIF4E和VP39都需要堆积芳香族残基具有复杂的取向和同一性模式,以允许m(7)GDP的选择性结合。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验