原生细胞网络中 GTPase 开关的完整异构图。

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Cell Systems Pub Date : 2023-03-15 Epub Date: 2023-02-17 DOI:10.1016/j.cels.2023.01.003
Christopher J P Mathy, Parul Mishra, Julia M Flynn, Tina Perica, David Mavor, Daniel N A Bolon, Tanja Kortemme
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引用次数: 0

摘要

异构调节是细胞网络中蛋白质功能的核心。一个基本的悬而未决的问题是,细胞对异构蛋白的调控是只发生在几个确定的位置上,还是发生在分布在整个结构中的许多位置上。在这里,我们通过在原生生物网络中进行深度诱变,在残基级分辨率上探究了 GTP 酶(通过调节构象循环控制信号传递的蛋白质开关)的调控。对于 GTP 酶 Gsp1/Ran,我们发现 4315 个检测突变中有 28% 显示出明显的功能增益反应。功能增益突变富集的 60 个位置中有 20 个位于典型 GTPase 活性位点开关区域之外。动力学分析表明,这些远端位点与活性位点存在异构耦合。我们的结论是,GTPase 开关机制对细胞异构调控具有广泛的敏感性。我们对新调控位点的系统性发现提供了一个功能图谱,可以对控制许多重要生物过程的 GTPase 进行询问和定位。
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A complete allosteric map of a GTPase switch in its native cellular network.

Allosteric regulation is central to protein function in cellular networks. A fundamental open question is whether cellular regulation of allosteric proteins occurs only at a few defined positions or at many sites distributed throughout the structure. Here, we probe the regulation of GTPases-protein switches that control signaling through regulated conformational cycling-at residue-level resolution by deep mutagenesis in the native biological network. For the GTPase Gsp1/Ran, we find that 28% of the 4,315 assayed mutations show pronounced gain-of-function responses. Twenty of the sixty positions enriched for gain-of-function mutations are outside the canonical GTPase active site switch regions. Kinetic analysis shows that these distal sites are allosterically coupled to the active site. We conclude that the GTPase switch mechanism is broadly sensitive to cellular allosteric regulation. Our systematic discovery of new regulatory sites provides a functional map to interrogate and target GTPases controlling many essential biological processes.

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来源期刊
Cell Systems
Cell Systems Medicine-Pathology and Forensic Medicine
CiteScore
16.50
自引率
1.10%
发文量
84
审稿时长
42 days
期刊介绍: In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.
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