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用于研究无序蛋白质序列相关相分离的改进的粗粒度模型。

Improved coarse-grained model for studying sequence dependent phase separation of disordered proteins.

机构信息

Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, Pennsylvania, USA.

Center for Materials Physics and Technology, Naval Research Laboratory, Washington, District of Columbia, USA.

出版信息

Protein Sci. 2021 Jul;30(7):1371-1379. doi: 10.1002/pro.4094. Epub 2021 May 24.

DOI:10.1002/pro.4094
PMID:33934416
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8197430/
Abstract

We present improvements to the hydropathy scale (HPS) coarse-grained (CG) model for simulating sequence-specific behavior of intrinsically disordered proteins (IDPs), including their liquid-liquid phase separation (LLPS). The previous model based on an atomistic hydropathy scale by Kapcha and Rossky (KR scale) is not able to capture some well-known LLPS trends such as reduced phase separation propensity upon mutations (R-to-K and Y-to-F). Here, we propose to use the Urry hydropathy scale instead, which was derived from the inverse temperature transitions in a model polypeptide with guest residues X. We introduce two free parameters to shift (Δ) and scale (µ) the overall interaction strengths for the new model (HPS-Urry) and use the experimental radius of gyration for a diverse group of IDPs to find their optimal values. Interestingly, many possible (Δ, µ) combinations can be used for typical IDPs, but the phase behavior of a low-complexity (LC) sequence FUS is only well described by one of these models, which highlights the need for a careful validation strategy based on multiple proteins. The CG HPS-Urry model should enable accurate simulations of protein LLPS and provide a microscopically detailed view of molecular interactions.

摘要

我们对用于模拟无规卷曲蛋白质(IDP)序列特异性行为的亲水性尺度(HPS)粗粒化(CG)模型进行了改进,包括它们的液-液相分离(LLPS)。之前基于 Kapcha 和 Rossky(KR 尺度)的原子亲水性尺度的模型无法捕捉到一些众所周知的 LLPS 趋势,例如突变(R 到 K 和 Y 到 F)会降低相分离倾向。在这里,我们建议使用 Urry 亲水性尺度,它是从带有侧基 X 的模型多肽的逆温度转变中推导出来的。我们引入了两个自由参数Δ和µ,用于调整新模型(HPS-Urry)的整体相互作用强度,并使用不同 IDP 组的实验回转半径来找到它们的最佳值。有趣的是,许多可能的(Δ,µ)组合可用于典型的 IDP,但低复杂度(LC)序列 FUS 的相行为仅可通过这些模型中的一个来很好地描述,这突出了需要基于多种蛋白质的仔细验证策略。CG HPS-Urry 模型应该能够准确模拟蛋白质的 LLPS,并提供分子相互作用的微观详细视图。

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本文引用的文献

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Reentrant liquid condensate phase of proteins is stabilized by hydrophobic and non-ionic interactions.蛋白质的折返液态凝聚相通过疏水和非离子相互作用得以稳定。
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A predictive coarse-grained model for position-specific effects of post-translational modifications.一种用于预测翻译后修饰位置特异性效应的粗粒度模型。
Biophys J. 2021 Apr 6;120(7):1187-1197. doi: 10.1016/j.bpj.2021.01.034. Epub 2021 Feb 12.
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Sequence-encoded and composition-dependent protein-RNA interactions control multiphasic condensate morphologies.序列编码和组成依赖性的蛋白质-RNA 相互作用控制多相凝聚物形态。
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Methods Enzymol. 2021;646:1-17. doi: 10.1016/bs.mie.2020.07.009. Epub 2020 Aug 17.
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