Modeling Concentration-dependent Phase Separation Processes Involving Peptides and RNA via Residue-Based Coarse-Graining
Modeling Concentration-dependent Phase Separation Processes Involving Peptides and RNA via Residue-Based Coarse-Graining
复制标题
通过基于残基的粗粒度对涉及肽和 RNA 的浓度依赖性相分离过程进行建模
DOI：
10.1021/acs.jctc.2c00856
复制
发表时间：
2023
影响因子：
5.5
通讯作者：
Feig, Michael
中科院分区：
文献类型：
--
作者：
Valdes-Garcia, Gilberto;Heo, Lim;Lapidus, Lisa J.;Feig, Michael
生物分子凝聚，特别是液-液相分离，是一个重要的物理过程，与生物功能的许多不同方面有关。驱动这种凝结的关键问题，特别是在分子组成方面，可以通过计算机模拟来解决，但是开发计算效率高但物理上真实的模型一直具有挑战性。本文介绍了粗粒度模型COCOMO，该模型平衡了多肽和RNA链的聚合物行为及其相分离倾向（作为组成和浓度的函数）。COCOMO是一种基于残留物的模型，它将键合条件与短期和长期条件相结合，包括debye - h<s:1> ckel溶剂化条件。该模型对相分离模型系统的实验数据具有较好的预测能力。它的计算效率也很高，可以在适度的计算资源下达到观察生物分子凝聚的空间和时间尺度。
Biomolecular condensation, especially liquid–liquid phase separation, is an important physical process with relevance for a number of different aspects of biological functions. Key questions of what drives such condensation, especially in terms of molecular composition, can be addressed via computer simulations, but the development of computationally efficient yet physically realistic models has been challenging. Here, the coarse-grained model COCOMO is introduced that balances the polymer behavior of peptides and RNA chains with their propensity to phase separate as a function of composition and concentration. COCOMO is a residue-based model that combines bonded terms with short- and long-range terms, including a Debye–Hückel solvation term. The model is highly predictive of experimental data on phase-separating model systems. It is also computationally efficient and can reach the spatial and temporal scales on which biomolecular condensation is observed with moderate computational resources.