Suppr超能文献

揭示结构域网络的方法。

Methods to reveal domain networks.

作者信息

Santonico Elena, Castagnoli Luisa, Cesareni Gianni

机构信息

Department of Biology, University of Rome Tor Vergata, Rome, Italy.

出版信息

Drug Discov Today. 2005 Aug 15;10(16):1111-7. doi: 10.1016/S1359-6446(05)03513-0.

Abstract

The development and application of high-throughput technology to study protein interactions has led to the construction of complex interaction maps, the correct interpretation of which is crucial to the identification of targets for drug development. Here we propose that a more informative description of protein interaction networks can be achieved by considering explicitly the modular nature of proteins. In this representation, proteins are drawn as covalently linked modular domains binding to their target sites in partner proteins. Families of conserved modules that bind to relatively short peptides mediate a large fraction of the non-covalent interactions linking different proteins in the network. As these interactions are often involved in the propagation of signal transduction, determining the recognition specificity of each domain family member is an essential step toward a functional description of the global interactome.

摘要

高通量技术在蛋白质相互作用研究中的发展与应用已促成了复杂相互作用图谱的构建,而对其进行正确解读对于确定药物研发靶点至关重要。在此,我们提出,通过明确考虑蛋白质的模块化性质,可以实现对蛋白质相互作用网络更具信息性的描述。在这种表示法中,蛋白质被描绘为由共价连接的模块化结构域与其在伴侣蛋白中的靶位点结合组成。与相对短肽结合的保守模块家族介导了网络中连接不同蛋白质的大部分非共价相互作用。由于这些相互作用常常参与信号转导的传播,确定每个结构域家族成员的识别特异性是迈向对全局相互作用组进行功能描述的关键一步。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验