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高通量解析控制mRNA衰变的无序区域

Deciphering disordered regions controlling mRNA decay in high-throughput.

作者信息

Lobel Joseph H, Ingolia Nicholas T

机构信息

Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA.

Center for Computational Biology and California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA, USA.

出版信息

Nature. 2025 Apr 23. doi: 10.1038/s41586-025-08919-x.

DOI:10.1038/s41586-025-08919-x
PMID:40269159
Abstract

Intrinsically disordered regions within proteins drive specific molecular functions despite lacking a defined structure. Although disordered regions are integral to controlling mRNA stability and translation, the mechanisms underlying these regulatory effects remain unclear. Here we reveal the molecular determinants of this activity using high-throughput functional profiling. Systematic mutagenesis across hundreds of regulatory disordered elements, combined with machine learning, reveals a complex pattern of molecular features important for their activity. The presence and arrangement of aromatic residues strongly predicts the ability of seemingly diverse protein sequences to influence mRNA stability and translation. We further show how many of these regulatory elements exert their effects by engaging core mRNA decay machinery. Our results define molecular features and biochemical pathways that explain how disordered regions control mRNA expression and shed light on broader principles within functional, unstructured proteins.

摘要

蛋白质内的内在无序区域尽管缺乏明确的结构,但仍驱动特定的分子功能。尽管无序区域对于控制mRNA稳定性和翻译至关重要,但其调控作用背后的机制仍不清楚。在这里,我们使用高通量功能分析揭示了这种活性的分子决定因素。对数百个调控无序元件进行系统诱变,并结合机器学习,揭示了对其活性重要的复杂分子特征模式。芳香族残基的存在和排列强烈预测了看似不同的蛋白质序列影响mRNA稳定性和翻译的能力。我们进一步展示了这些调控元件中有许多是如何通过与核心mRNA降解机制相互作用来发挥其作用的。我们的结果定义了分子特征和生化途径,解释了无序区域如何控制mRNA表达,并揭示了功能性非结构化蛋白质的更广泛原理。

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本文引用的文献

1
The molecular basis for cellular function of intrinsically disordered protein regions.无定形蛋白质区域的细胞功能的分子基础。
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Surveying the global landscape of post-transcriptional regulators.调查转录后调控因子的全球格局。
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Light attention predicts protein location from the language of life.轻注意力从生命语言中预测蛋白质位置。
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