Chemically Informed Coarse-Graining of Electrostatic Forces in Charge-Rich Biomolecular Condensates.

IF 10.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY ACS Central Science Pub Date : 2025-02-11 eCollection Date: 2025-02-26 DOI:10.1021/acscentsci.4c01617
Andrés R Tejedor, Anne Aguirre Gonzalez, M Julia Maristany, Pin Yu Chew, Kieran Russell, Jorge Ramirez, Jorge R Espinosa, Rosana Collepardo-Guevara
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Abstract

Biomolecular condensates composed of highly charged biomolecules, such as DNA, RNA, chromatin, and nucleic-acid binding proteins, are ubiquitous in the cell nucleus. The biophysical properties of these charge-rich condensates are largely regulated by electrostatic interactions. Residue-resolution coarse-grained models that describe solvent and ions implicitly are widely used to gain mechanistic insights into the biophysical properties of condensates, offering transferability, computational efficiency, and accurate predictions for multiple systems. However, their predictive accuracy diminishes for charge-rich condensates due to the implicit treatment of solvent and ions. Here, we present Mpipi-Recharged, a residue-resolution coarse-grained model that improves the description of charge effects in biomolecular condensates containing disordered proteins, multidomain proteins, and/or disordered single-stranded RNAs. Mpipi-Recharged introduces a pair-specific asymmetric Yukawa electrostatic potential, informed by atomistic simulations. We show that this asymmetric coarse-graining of electrostatic forces captures intricate effects, such as charge blockiness, stoichiometry variations in complex coacervates, and modulation of salt concentration, without requiring explicit solvation. Mpipi-Recharged provides excellent agreement with experiments in predicting the phase behavior of highly charged condensates. Overall, Mpipi-Recharged improves the computational tools available to investigate the physicochemical mechanisms regulating biomolecular condensates, enhancing the scope of computer simulations in this field.

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富电荷生物分子凝聚物中静电力的化学信息粗粒化。
由高电荷的生物分子组成的生物分子凝聚物,如DNA、RNA、染色质和核酸结合蛋白,在细胞核中无处不在。这些富电荷凝析物的生物物理性质在很大程度上受静电相互作用的调节。残差分辨率粗粒度模型隐含地描述溶剂和离子,广泛用于获得凝聚物生物物理特性的机理见解,提供可转移性、计算效率和对多个系统的准确预测。然而,由于溶剂和离子的隐式处理,它们对富电荷凝析油的预测精度降低。在这里,我们提出了mpipi - recharge,这是一种残基分辨率的粗粒度模型,可以改善对含有无序蛋白、多结构域蛋白和/或无序单链rna的生物分子凝聚物中的电荷效应的描述。mpipi -充电引入了一个对特定的不对称汤川静电势,由原子模拟告知。我们表明,这种不对称的静电力粗粒化捕获了复杂的效应,如电荷块性、复杂凝聚中的化学计量变化和盐浓度的调制,而不需要显式的溶剂化。mpipi - recharge在预测高荷电凝析油的相行为方面与实验结果非常吻合。总的来说,mpipi - recharge改进了研究生物分子凝聚物的物理化学机制的计算工具,扩大了该领域计算机模拟的范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Central Science
ACS Central Science Chemical Engineering-General Chemical Engineering
CiteScore
25.50
自引率
0.50%
发文量
194
审稿时长
10 weeks
期刊介绍: ACS Central Science publishes significant primary reports on research in chemistry and allied fields where chemical approaches are pivotal. As the first fully open-access journal by the American Chemical Society, it covers compelling and important contributions to the broad chemistry and scientific community. "Central science," a term popularized nearly 40 years ago, emphasizes chemistry's central role in connecting physical and life sciences, and fundamental sciences with applied disciplines like medicine and engineering. The journal focuses on exceptional quality articles, addressing advances in fundamental chemistry and interdisciplinary research.
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