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系统蛋白质周转图谱解析蛋白质降解。

A Systematic Protein Turnover Map for Decoding Protein Degradation.

机构信息

Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA.

Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Boston, MA 02115, USA.

出版信息

Cell Rep. 2020 Nov 10;33(6):108378. doi: 10.1016/j.celrep.2020.108378.

DOI:10.1016/j.celrep.2020.108378
PMID:33176155
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7745259/
Abstract

Protein degradation is mediated by an expansive and complex network of protein modification and degradation enzymes. Matching degradation enzymes with their targets and determining globally which proteins are degraded by the proteasome or lysosome/vacuole have been a major challenge. Furthermore, an integrated view of protein degradation for cellular pathways has been lacking. Here, we present an analytical platform that combines systematic gene deletions with quantitative measures of protein turnover to deconvolve protein degradation pathways for Saccharomyces cerevisiae. The resulting turnover map (T-MAP) reveals target candidates of nearly all E2 and E3 ubiquitin ligases and identifies the primary degradation routes for most proteins. We further mined this T-MAP to identify new substrates of ER-associated degradation (ERAD) involved in sterol biosynthesis and to uncover regulatory nodes for sphingolipid biosynthesis. The T-MAP approach should be broadly applicable to the study of other cellular processes, including mammalian systems.

摘要

蛋白质降解是由一个广泛而复杂的蛋白质修饰和降解酶网络介导的。将降解酶与其靶标匹配,并确定全局范围内哪些蛋白质被蛋白酶体或溶酶体/液泡降解,一直是一个主要的挑战。此外,细胞途径的蛋白质降解的综合视图一直缺乏。在这里,我们提出了一个分析平台,该平台将系统的基因缺失与蛋白质周转率的定量测量相结合,以对酿酒酵母的蛋白质降解途径进行去卷积。所得的周转率图谱(T-MAP)揭示了几乎所有 E2 和 E3 泛素连接酶的靶标候选物,并确定了大多数蛋白质的主要降解途径。我们进一步挖掘这个 T-MAP,以鉴定参与固醇生物合成的 ER 相关降解(ERAD)的新底物,并揭示鞘脂生物合成的调节节点。T-MAP 方法应该广泛适用于其他细胞过程的研究,包括哺乳动物系统。

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