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本文引用的文献

1
Microscopic and stochastic simulations of chemically active droplets.化学活性液滴的微观和随机模拟
Phys Rev E. 2025 Feb;111(2):L023403. doi: 10.1103/PhysRevE.111.L023403.
2
Electrostatics of salt-dependent reentrant phase behaviors highlights diverse roles of ATP in biomolecular condensates.盐依赖性折返相行为的静电学突出了ATP在生物分子凝聚物中的多种作用。
Elife. 2025 Mar 3;13:RP100284. doi: 10.7554/eLife.100284.
3
How Droplets Can Accelerate Reactions─Coacervate Protocells as Catalytic Microcompartments.液滴如何加速反应─凝聚层状囊泡作为催化微区室。
Acc Chem Res. 2024 Jul 16;57(14):1885-1895. doi: 10.1021/acs.accounts.4c00114. Epub 2024 Jul 5.
4
Active droplets through enzyme-free, dynamic phosphorylation.无酶动态磷酸化作用下的活性液滴。
Nat Commun. 2024 May 17;15(1):4204. doi: 10.1038/s41467-024-48571-z.
5
Tear down this wall: phosphorylation regulates the internal interfaces of postsynaptic condensates.拆毁这堵墙:磷酸化调节突触后滴内部界面。
Trends Cell Biol. 2024 Apr;34(4):274-276. doi: 10.1016/j.tcb.2024.02.007. Epub 2024 Feb 29.
6
Evidence for widespread cytoplasmic structuring into mesoscale condensates.有证据表明细胞质广泛形成介观凝聚物。
Nat Cell Biol. 2024 Mar;26(3):346-352. doi: 10.1038/s41556-024-01363-5. Epub 2024 Feb 29.
7
Phosphorylation-dependent membraneless organelle fusion and fission illustrated by postsynaptic density assemblies.通过突触后密度组装体说明依赖磷酸化的无膜细胞器融合和裂变。
Mol Cell. 2024 Jan 18;84(2):309-326.e7. doi: 10.1016/j.molcel.2023.11.011. Epub 2023 Dec 13.
8
Isotropic active colloids: explicit implicit descriptions of propulsion mechanisms.各向同性活性胶体:推进机制的显式和隐式描述
Soft Matter. 2023 Nov 29;19(46):8997-9005. doi: 10.1039/d3sm00763d.
9
Nucleation of Chemically Active Droplets.化学活性液滴的成核。
Phys Rev Lett. 2023 Jun 16;130(24):248201. doi: 10.1103/PhysRevLett.130.248201.
10
Aberrant phase separation and nucleolar dysfunction in rare genetic diseases.罕见遗传疾病中的异常相分离和核仁功能障碍。
Nature. 2023 Feb;614(7948):564-571. doi: 10.1038/s41586-022-05682-1. Epub 2023 Feb 8.

由酶促反应控制的活性液滴。

Active droplets controlled by enzymatic reactions.

作者信息

Fries Jacques, Diaz Javier, Jardat Marie, Pagonabarraga Ignacio, Illien Pierre, Dahirel Vincent

机构信息

Sorbonne Université, CNRS, Physico-Chimie des Electrolytes et Nanosystèmes Interfaciaux(PHENIX), 4 Place Jussieu, Paris 75005, France.

Departament de Física de la Matèria Condensada, Universitat de Barcelona, Carrer de Martí i Franqués 1, Barcelona 08028, Spain.

出版信息

J R Soc Interface. 2025 May;22(226):20240803. doi: 10.1098/rsif.2024.0803. Epub 2025 May 7.

DOI:10.1098/rsif.2024.0803
PMID:40329837
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12056557/
Abstract

The formation of condensates is now considered a major organizing principle of eukaryotic cells. Several studies have recently shown that the properties of these condensates are affected by enzymatic reactions. We propose here a simple generic model to study the interplay between two enzyme populations and a two-state protein. In one state, the protein forms condensed droplets through attractive interactions, while in the other state, the proteins remain dispersed. Each enzyme catalyses the production of one of these two protein states only when reactants are in its vicinity. A key feature of our model is the explicit representation of enzyme trajectories, capturing the fluctuations in their local concentrations. The spatially dependent growth rate of droplets naturally arises from the stochastic motion of these explicitly modelled enzymes. Using two complementary numerical methods-(i) Brownian dynamics simulations and (ii) a hybrid method combining Cahn-Hilliard-Cook diffusion equations with Brownian dynamics for the enzymes-we investigate how enzyme concentration and dynamics influence the evolution with time and the steady-state number and size of droplets. Our results show that the concentration and diffusion coefficient of enzymes govern the formation and size-selection of biocondensates.

摘要

凝聚物的形成现在被认为是真核细胞的一个主要组织原则。最近的几项研究表明,这些凝聚物的性质会受到酶促反应的影响。我们在此提出一个简单的通用模型,以研究两种酶群体与一种双态蛋白质之间的相互作用。在一种状态下,蛋白质通过吸引相互作用形成凝聚液滴,而在另一种状态下,蛋白质保持分散状态。每种酶仅在反应物在其附近时才催化这两种蛋白质状态之一的产生。我们模型的一个关键特征是酶轨迹的明确表示,捕捉其局部浓度的波动。液滴的空间依赖性生长速率自然源于这些明确建模的酶的随机运动。使用两种互补的数值方法——(i)布朗动力学模拟和(ii)一种将Cahn-Hilliard-Cook扩散方程与酶的布朗动力学相结合的混合方法——我们研究了酶浓度和动力学如何随时间影响演化以及液滴的稳态数量和大小。我们的结果表明,酶的浓度和扩散系数控制着生物凝聚物的形成和尺寸选择。