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蛋白质家族内的异质折叠景观和预定的断裂点。

Heterogeneous folding landscapes and predetermined breaking points within a protein family.

机构信息

Sebastian Pechmann Research Lab, Saarbrücken, Germany.

出版信息

Protein Sci. 2024 Dec;33(12):e5205. doi: 10.1002/pro.5205.

Abstract

The accurate prediction of protein structures with artificial intelligence has been a spectacular success. Yet, how proteins fold into their native structures inside the cell remains incompletely understood. Of particular interest is to rationalize how proteins interact with the protein homeostasis network, an organism specific set of protein folding and quality control enzymes. Failure of protein homeostasis leads to widespread misfolding and aggregation, and thus neurodegeneration. Here, I present a comparative analysis of the folding of 16 single-domain proteins from the same organism across a protein family, the Saccharomyces cerevisiae small GTPases. Using computational modeling to directly probe protein folding dynamics, this work shows how near identical structures from the same folding environment can exhibit heterogeneous folding landscapes. Remarkably, yeast small GTPases are found to unfold along different pathways either via the N- or C-terminus initiated by structure-encoded predetermined breaking points. Degrons as recognition signals for ubiquitin-dependent degradation were systematically absent from the initial unfolding sites, as if to protect from too rapid degradation upon spontaneous unfolding or before completion of the folding. The presented results highlight a direct coordination of folding pathway and protein homeostasis interaction signals across a protein family. A deeper understanding of the interdependence of proteins with their folding environment will help to rationalize and combat diseases linked to protein misfolding and dysregulation. More generally, this work underlines the importance of understanding protein folding in the cellular context, and highlights valuable constraints towards a systems-level understanding of protein homeostasis.

摘要

人工智能准确预测蛋白质结构取得了巨大成功。然而,蛋白质在细胞内如何折叠成其天然结构仍不完全清楚。特别感兴趣的是如何合理地解释蛋白质如何与蛋白质动态平衡网络相互作用,蛋白质动态平衡网络是一种特定于生物体的蛋白质折叠和质量控制酶的集合。蛋白质动态平衡的失败会导致广泛的错误折叠和聚集,从而导致神经退行性变。在这里,我对来自同一生物体的同一个蛋白质家族——酿酒酵母小 GTP 酶中的 16 个单结构域蛋白质的折叠进行了比较分析。使用计算建模直接探测蛋白质折叠动力学,这项工作展示了来自相同折叠环境的几乎相同结构如何表现出异质的折叠景观。值得注意的是,酵母小 GTP 酶被发现通过结构编码的预定断裂点从 N 端或 C 端沿不同的途径展开。泛素依赖性降解的降解信号肽作为识别信号,从初始展开位点系统地缺失,就好像是为了防止自发展开或在折叠完成之前过快降解。所提出的结果强调了蛋白质折叠途径和蛋白质动态平衡相互作用信号在整个蛋白质家族中的直接协调。深入了解蛋白质与其折叠环境的相互依存关系将有助于合理治疗与蛋白质错误折叠和失调相关的疾病。更一般地说,这项工作强调了在细胞环境中理解蛋白质折叠的重要性,并强调了对蛋白质动态平衡的系统级理解的有价值的约束。

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