蛋白质“液-液相分离”的理论和计算方法进展

张鹏程; 方文玉; 鲍磊; 康文斌

doi:10.7498/aps.69.20200438

摘要

蛋白质分子的“液-液相分离”是近几年生物物理学领域迅速发展起来的研究热点. 蛋白质相分离在一系列生物学过程中发挥着重要的作用. 蛋白质分子序列和构象的多样性和复杂性, 给蛋白质分子的理论研究、计算机模拟和实验研究都带来了巨大的挑战. 当前, 多尺度理论模型和多分辨率计算方法被广泛地用于蛋白质分子的“液-液相分离”的研究中. 本文将对蛋白质分子“液-液相分离”的理论基础和计算机模拟方法进行简要的综述, 并对这些理论和方法未来的发展趋势进行了初步的探讨和展望. 期望为进一步研究蛋白质“液-液相分离”的物理化学机制和过程提供理论基础和方法借鉴.

关键词:

Abstract

Liquid-liquid phase separation (LLPS) of proteins is an emerging field in the research of biophysics. Many intrinsically disordered proteins (IDPs) are known to have the ability to assemble via LLPS and to organize into protein-rich and dilute phases both in vivo and in vitro. Such a kind of phase separation of proteins plays an important role in a wide range of cellular processes, such as the formation of membraneless organelles (MLOs), signaling transduction, intracellular organization, chromatin organization, etc. In recent years, there appeared a great number of theoretical analysis, computational simulation and experimental research focusing on the physical principles of LLPS. In this article, the theoretical and computational simulation methods for the LLPS are briefly reviewed. To elucidate the physical principle of LLPS and to understand the phase behaviors of the proteins, biophysicists have introduced the concepts and theories from statistical mechanics and polymer sciences. Flory-Huggins theory and its extensions, such as mean-field model, random phase approximation (RPA) and field theory simulations, can conduce to understanding the phase diagram of the LLPS. To reveal the hidden principles in the sequence-dependent phase behaviors of different biomolecular condensates, different simulation methods including lattice models, off-lattice coarse-grained models, and all-atom simulations are introduced to perform computer simulations. By reducing the conformational space of the proteins, lattice models can capture the key points in LLPS and simplify the computations. In the off-lattice models, a polypeptide can be coarse-grained as connected particles representing repeated short peptide fragments. All-atom simulations can describe the structure of proteins at a higher resolution but consume higher computation-power. Multi-scale simulation may provide the key to understanding LLPS at both high computational efficiency and high accuracy. With these methods, we can elucidate the sequence-dependent phase behaviors of proteins at different resolutions. To sum up, it is necessary to choose the appropriate method to model LLPS processes according to the interactions within the molecules and the specific phase behaviors of the system. The simulations of LLPS can facilitate the comprehensive understanding of the key features which regulate the membraneless compartmentalization in cell biology and shed light on the design of artificial cells and the control of neurodegeneration.

Keywords:

作者及机构信息

1.
湖北医药学院, 公共卫生与管理学院, 十堰　442000

2.
湖北省南水北调水源区生物医药研发检测共享平台, 十堰　442000

通信作者: 康文斌, wbkang@hbmu.edu.cn

基金项目: 国家级-国家自然科学基金(11947006)

Authors and contacts

1.
School of Public Health and Management, Hubei University of Medicine, Shiyan 442000, China

2.
Hubei Biomedical Detection Sharing Platform in Water Source Area of South to North Water Diversion Project, Shiyan 442000, China

Corresponding author: Kang Wen-Bin, wbkang@hbmu.edu.cn

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施引文献

图 1 研究蛋白质“液-液相分离”的计算方法: 从左至右分别是解析模型、粗粒化模型(包括多个残基单个粒子、单个残基单个粒子、单个残基多个粒子的模型)和全原子模型, 分辨率的提升对计算资源的耗费程度急剧增高, 而模型假设数的减少会减弱研究涌现行为的能力^[35]

Fig. 1. Computational approaches utilized to study protein liquid-liquid phase separation (LLPS). From left to right are the analytical model, the coarse-grained model (multiple-residues per bead, single-bead per residue and multiple-beads per residue coarse-grained models), and all-atom model. High-resolution descriptions increase the computational cost of the simulations. All-atom model is often impractical for the study of emergent behavior of LLPS^[35].

下载: 全尺寸图片幻灯片

[1]	McCarty J, Delaney K T, Danielsen S P, Fredrickson G H, Shea J E 2019 J. Phys. Chem. Lett. 10 1644 Google Scholar
[2]	Lin Y H, Song J H, Forman-Kay J D, Chan H S 2017 J. Mol. Liq. 228 176 Google Scholar
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[45]	Lin Y H, Forman-Kay J D, Chan H S 2016 Phys. Rev. Lett. 117 178101 Google Scholar
[46]	Liu Y X, Zhang H D, Tong C H, Yang Y L 2011 Macromolecules 44 8261 Google Scholar
[47]	Speck T, Menzel A M, Bialke J, Lowen H 2015 J. Chem. Phys. 142 224109 Google Scholar
[48]	Burke M G, Woscholski R, Yaliraki S N 2003 Proc. Natl. Acad. Sci. U.S.A. 100 13928 Google Scholar
[49]	Ruff K M, Khan S J, Pappu R V 2014 Biophys. J. 107 1226 Google Scholar
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[51]	Condon J E, Martin T B, Jayaraman A 2017 Soft Matter 13 2907 Google Scholar
[52]	Dignon G L, Zheng W, Kim Y C, Best R B, Mittal J 2018 PLOS Comput. Biol. 14 e1005941 Google Scholar
[53]	Das S, Eisen A, Lin Y H, Chan H S 2018 J. Phys. Chem. B 122 5418 Google Scholar
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[59]	Song J, Gomes G N, Shi T, Gradinaru C C, Chan H S 2017 Biophys. J. 113 1012 Google Scholar
[60]	Noid W G 2013 J. Chem. Phys. 139 090901 Google Scholar
[61]	Voegler Smith A, Hall C K 2001 Proteins 44 344 Google Scholar
[62]	Marchut A J, Hall C K 2006 Biophys. J. 90 4574 Google Scholar
[63]	Marchut A J, Hall C K 2007 Proteins 66 96 Google Scholar
[64]	Nguyen H D, Hall C K 2006 J. Am. Chem. Soc. 128 1890 Google Scholar
[65]	Cheon M, Chang I, Hall C K 2010 Proteins 78 2950 Google Scholar
[66]	Yeo J J, Huang W W, Tarakanova A, Zhang Y W, Kaplan D L, Buehler M J 2018 J. Mater. Chem. B 6 3727 Google Scholar
[67]	Bereau T, Deserno M 2009 J. Chem. Phys. 130 235106 Google Scholar
[68]	Rutter G O, Brown A H, Quigley D, Walsh T R, Allen M P 2015 Phys. Chem. Chem. Phys. 17 31741 Google Scholar
[69]	Chen M C, Wolynes P G 2017 Proc. Natl. Acad. Sci. U.S.A. 114 4406 Google Scholar
[70]	Seo M, Rauscher S, Pomes R, Tieleman D P 2012 J. Chem. Theory Comput. 8 1774 Google Scholar
[71]	Miao L, Schulten K 2009 Structure 17 449 Google Scholar
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[74]	Lu L Y, Dama J F, Voth G A 2013 J. Chem. Phys. 139 121906 Google Scholar
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蛋白质“液-液相分离”的理论和计算方法进展