Theoretical and computational methods of protein liquid-liquid phase separation

Zhang Peng-Cheng; Fang Wen-Yu; Bao Lei; Kang Wen-Bin

doi:10.7498/aps.69.20200438

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:

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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References

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Cited By

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]

Figure 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].

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[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
[3]	Lin Y H, Song J H, Forman-Kay J D, Chan H S 2019 J. Mol. Liq. 273 676 Google Scholar
[4]	Dignon G L, Zheng W W, Kim Y C, Mittal J, Best R B 2018 Biophys. J. 114 431a Google Scholar
[5]	Dignon G L, Zheng W W, Best R B, Mittal J 2017 Biophys. J. 112 200a
[6]	Choi J M, Mitrea D M, Stanley C B, Ruff K M, Holehouse A S, Kriwacki R W, Pappu R V 2019 Biophys. J. 116 349a Google Scholar
[7]	Chin A F, Hilser V J, Zheng Y X 2019 Biophys. J. 116 179a Google Scholar
[8]	周丰茂, 孙东科, 朱鸣芳 2010 59 3394 Google Scholar Zhou F M, Sun D K, Zhu M F 2010 Acta Phys. Sin. 59 3394 Google Scholar
[9]	张长胜, 来鲁华 2010 物理化学学报 36 1907053 Google Scholar Zhang C S, Lai L H 2010 Acta Phys. Chim. Sin. 36 1907053 Google Scholar
[10]	Burke K A, Janke A M, Rhine C L, Fawzi N L 2015 Mol. Cell 60 231 Google Scholar
[11]	Brangwynne C P, Tompa P, Pappu R V 2015 Nat. Phys. 11 899 Google Scholar
[12]	Uversky V N 2017 Curr. Opin. Struct. Biol. 44 18 Google Scholar
[13]	Feric M, Vaidya N, Harmon T S, Mitrea D M, Zhu L, Richardson T M, Kriwacki R W, Pappu R V, Brangwynne C P 2016 Cell 165 1686 Google Scholar
[14]	Falahati H, Wieschaus E 2017 Proc. Natl. Acad. Sci. U.S.A. 114 1335 Google Scholar
[15]	Sabari B R, Dall'Agnese A, Boija A, Klein I A, Coffey E L, Shrinivas K, Abraham B J, Hannett N M, Zamudio A V, Manteiga J C, Li C H, Guo Y E, Day D S, Schuijers J, Vasile E, Malik S, Hnisz D, Lee T I, Cisse, I I, Roeder R G, Sharp P A, Chakraborty A K, Young R A 2018 Science 361 eaar3958 Google Scholar
[16]	Das R K, Pappu R V 2013 Proc. Natl. Acad. Sci. U.S.A. 110 13392 Google Scholar
[17]	Dignon G L, Zheng W W, Best R B, Kim Y C, Mittal J 2018 Proc. Natl. Acad. Sci. U.S.A. 115 9929 Google Scholar
[18]	Harmon T S, Holehouse A S, Rosen M K, Pappu R V 2017 ELIFE 6 e30294 Google Scholar
[19]	Kang W B, He C, Liu Z X, Wang J, Wang W 2019 J. Biomol. Struct. Dyn. 37 1956 Google Scholar
[20]	Lin Y, Currie S L, Rosen M K 2017 J. Biol. Chem. 292 19110 Google Scholar
[21]	Lin Y H, Brady J P, Forman-Kay J D, Chan H S 2017 New J. Phys. 19 115003 Google Scholar
[22]	Schuster B S, Dignon G, Jahnke C, Good M C, Hammer D A, Mittal J 2019 Biophys. J. 116 453a Google Scholar
[23]	Smith J, Calidas D, Schmidt H, Lu T, Seydoux G 2016 ELIFE 5 e21337 Google Scholar
[24]	Uebel C J, Anderson D C, Mandarino L M, Manage K I, Aynaszyan S, Phillips C M 2018 Plos Genet. 14 e1007542 Google Scholar
[25]	康文斌, 王骏, 王炜 2018 67 058701 Google Scholar Kang W B, Wang J, Wang W 2018 Acta Phys. Sin. 67 058701 Google Scholar
[26]	Pak C W, Kosno M, Holehouse A S, Padrick S B, Mittal A, A R, Yunus A A, Liu D R, Pappu R V, Rosen M K 2016 Mol. Cell 63 72 Google Scholar
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[29]	Das R K, Ruff K M, Pappu R V 2015 Curr. Opin. Struct. Biol. 32 102 Google Scholar
[30]	Vitalis A, Wang X, Pappu R V 2007 Biophys. J. 93 1923 Google Scholar
[31]	Papoian G A 2008 Proc. Natl. Acad. Sci. U. S. A. 105 14237 Google Scholar
[32]	Levine Z A, Shea J E 2017 Curr. Opin. Struct. Biol. 43 95 Google Scholar
[33]	Dignon G L, Zheng W, Kim Y C, Mittal J 2019 ACS Central Sci. 5 821 Google Scholar
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[35]	Ruff K M, Pappu R V, Holehouse A S 2019 Curr. Opin. Struct. Biol. 56 1 Google Scholar
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[39]	van L R, Buljan M, Lang B, Weatheritt R J, Daughdrill G W, Dunker A K, Fuxreiter M, Gough J, Gsponer J, Jones D T, Kim P M, Kriwacki R W, Oldfield C J, Pappu R V, Tompa P, Uversky V N, Wright P E, Babu M M 2014 Chem. Rev. 114 6589 Google Scholar
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[43]	Samanta H S, Zhuravlev P I, Hinczewski M, Hori N, Chakrabarti S, Thirumalai D 2017 Soft Matter 13 3622 Google Scholar
[44]	Zhou H X, Nguemaha V, Mazarakos K, Qin S B 2018 Trends Biochem. Sci. 43 499 Google Scholar
[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
[50]	Zuccato C, Valenza M, Cattaneo E 2010 Physiol. Rev. 90 905 Google Scholar
[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
[54]	Kapcha L H, Rossky P J 2014 J. Mol. Boil. 426 484 Google Scholar
[55]	Ashbaugh H S, Hatch H W 2008 J. Am. Chem. Soc. 130 9536 Google Scholar
[56]	Ghavami A, Veenhoff L M, van der Giessen E, Onck P R 2014 Biophys. J. 107 1393 Google Scholar
[57]	Borgia A, Borgia M B, Bugge K, Kissling V M, Heidarsson P O, Fernandes C B, Sottini A, Soranno A, Buholzer K J, Nettels D, Kragelund B B, Best R B, Schuler B 2018 Nature 555 61 Google Scholar
[58]	Wang J, Choi J M, Holehouse A S, Lee H O, Zhang X, Jahnel M, Maharana S, Lemaitre R, Pozniakovsky A, Drechsel D, Poser I, Pappu R V, Alberti S, Hyman A A 2018 Cell 174 688 Google Scholar
[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
[72]	Monticelli L, Kandasamy S K, Periole X, Larson R G, Tieleman D P, Marrink S J 2008 J. Chem. Theory Comput. 4 819 Google Scholar
[73]	Marrink S J, Risselada H J, Yefimov S, Tieleman D P, Vries A H 2007 J. Phys. Chem. B 111 7812 Google Scholar
[74]	Lu L Y, Dama J F, Voth G A 2013 J. Chem. Phys. 139 121906 Google Scholar
[75]	Hills R D, Lu L, Voth G A 2010 PLOS Comput. Biol. 6 e1000827 Google Scholar
[76]	Ando D, Zandi R, Kim Y W, Colvin M, Rexach M, Gopinathan A J 2014 Biophys. J. 106 1997 Google Scholar
[77]	Kim Y C, Hummer G 2008 J. Mol. Biol. 375 1416 Google Scholar
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[79]	Choi J-M, Dar F, Pappu R V 2019 PLOS Comput. Biol. 15 e1007028 Google Scholar
[80]	Tran H T, Mao A, Pappu R V 2008 J. Am. Chem. Soc. 130 7380 Google Scholar
[81]	Holehouse A S, Garai K, Lyle N, Vitalis A, Pappu R V 2015 J. Am. Chem. Soc. 137 2984 Google Scholar
[82]	Asthagiri D, Karandur D, Tomar D S, Pettitt B M 2017 J. Phys. Chem. B 121 8078 Google Scholar
[83]	Karandur D, Wong K Y, Pettitt B M 2014 J. Phys. Chem. B 118 9565 Google Scholar
[84]	Vitalis A, Wang X, Pappu R V 2008 J. Mol. Biol. 384 279 Google Scholar
[85]	Vitalis A, Lyle N, Pappu R V 2009 Biophys. J. 97 303 Google Scholar
[86]	Kang H, Vazquez F X, Zhang L, Das P, Toledo-Sherman L, Luan B, Levitt M, Zhou R 2017 J. Am. Chem. Soc. 139 8820 Google Scholar
[87]	Daggett V, Kollman P A, Kuntz I D 1991 Biopolymers 31 1115 Google Scholar

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Vol. 69, Issue 13 - 2020-07-05

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