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

文章全文 : translate this paragraph

参考文献

[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 物理学报 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 物理学报 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
[27]	Gates Z P, Baxa M C, Yu W, Riback J A, Li H, Roux B, Kent S B, Sosnick T R 2017 Proc. Natl. Acad. Sci. U.S.A. 114 2241 Google Scholar
[28]	Riback J A, Katanski C D, Kear-Scott J L, Pilipenko E V, Rojek A E, Sosnick T R, Drummond D A 2017 Cell 168 1028 Google Scholar
[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
[34]	Murthy A C, Dignon G L, Kan Y, Zerze G H, Parekh S H, Mittal J, Fawzi N L 2019 Nat. Struct. Mol. Biol. 26 637 Google Scholar
[35]	Ruff K M, Pappu R V, Holehouse A S 2019 Curr. Opin. Struct. Biol. 56 1 Google Scholar
[36]	Monahan Z, Ryan V H, Janke A M, Burke K A, Rhoads S N, Zerze G H, O'Meally R, Dignon G L, Conicella A E, Zheng W W, Best R B, Cole R N, Mittal J, Shewmaker F, Fawzi N L 2017 EMBO J. 36 2951 Google Scholar
[37]	Best R B 2017 Curr. Opin. Struct. Biol. 42 147 Google Scholar
[38]	Robustelli P, Piana S, Shaw D E 2018 Proc. Natl. Acad. Sci. U.S.A. 115 E4758 Google Scholar
[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
[40]	Mittag T, Forman-Kay J D 2007 Curr. Opin. Struct. Biol. 17 3 Google Scholar
[41]	Flory P J 1942 J. Chem. Phys. 10 51 Google Scholar
[42]	Huggins M L 1942 J. Chem. Phys. 46 151 Google Scholar
[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
[78]	Ryan V H, Dignon G L, Zerze G H, Chabata C V, Silva R, Conicella A E, Amaya J, Burke K A, Mittal J, Fawzi N L 2018 Mol. Cell 69 465 Google Scholar
[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

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
[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 物理学报 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 物理学报 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
[27]	Gates Z P, Baxa M C, Yu W, Riback J A, Li H, Roux B, Kent S B, Sosnick T R 2017 Proc. Natl. Acad. Sci. U.S.A. 114 2241 Google Scholar
[28]	Riback J A, Katanski C D, Kear-Scott J L, Pilipenko E V, Rojek A E, Sosnick T R, Drummond D A 2017 Cell 168 1028 Google Scholar
[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
[34]	Murthy A C, Dignon G L, Kan Y, Zerze G H, Parekh S H, Mittal J, Fawzi N L 2019 Nat. Struct. Mol. Biol. 26 637 Google Scholar
[35]	Ruff K M, Pappu R V, Holehouse A S 2019 Curr. Opin. Struct. Biol. 56 1 Google Scholar
[36]	Monahan Z, Ryan V H, Janke A M, Burke K A, Rhoads S N, Zerze G H, O'Meally R, Dignon G L, Conicella A E, Zheng W W, Best R B, Cole R N, Mittal J, Shewmaker F, Fawzi N L 2017 EMBO J. 36 2951 Google Scholar
[37]	Best R B 2017 Curr. Opin. Struct. Biol. 42 147 Google Scholar
[38]	Robustelli P, Piana S, Shaw D E 2018 Proc. Natl. Acad. Sci. U.S.A. 115 E4758 Google Scholar
[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
[40]	Mittag T, Forman-Kay J D 2007 Curr. Opin. Struct. Biol. 17 3 Google Scholar
[41]	Flory P J 1942 J. Chem. Phys. 10 51 Google Scholar
[42]	Huggins M L 1942 J. Chem. Phys. 46 151 Google Scholar
[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
[78]	Ryan V H, Dignon G L, Zerze G H, Chabata C V, Silva R, Conicella A E, Amaya J, Burke K A, Mittal J, Fawzi N L 2018 Mol. Cell 69 465 Google Scholar
[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

[1]	贺华丹, 钟琦超, 解文军. 声悬浮条件下双水相液滴的蒸发与相分离. 物理学报, 2024, 73(3): 034304. doi: 10.7498/aps.73.20230963
[2]	王浩, 徐进良. 油面上相邻Leidenfrost液滴的相互作用及运动机制. 物理学报, 2023, 72(5): 054401. doi: 10.7498/aps.72.20221822
[3]	罗雨晨, 李晓鹏. 相互作用费米子的量子模拟. 物理学报, 2022, 71(22): 226701. doi: 10.7498/aps.71.20221756
[4]	梁燚然, 梁清. 带电纳米颗粒与相分离的带电生物膜之间相互作用的分子模拟. 物理学报, 2019, 68(2): 028701. doi: 10.7498/aps.68.20181891
[5]	叶学民, 李明兰, 张湘珊, 李春曦. 表面弹性和分离压耦合作用下的垂直液膜排液过程. 物理学报, 2018, 67(16): 164701. doi: 10.7498/aps.67.20180349
[6]	郑监, 张舵, 蒋邦海, 卢芳云. 气泡与自由液面相互作用形成水射流的机理研究. 物理学报, 2017, 66(4): 044702. doi: 10.7498/aps.66.044702
[7]	叶学民, 杨少东, 李春曦. 分离压和表面黏度的协同作用对液膜排液过程的影响. 物理学报, 2017, 66(19): 194701. doi: 10.7498/aps.66.194701
[8]	邓海游, 贾亚, 张阳. 蛋白质结构预测. 物理学报, 2016, 65(17): 178701. doi: 10.7498/aps.65.178701
[9]	毛斌斌, 程晨, 陈富州, 罗洪刚. 一维扩展t-J模型中密度-自旋相互作用诱导的相分离. 物理学报, 2015, 64(18): 187105. doi: 10.7498/aps.64.187105
[10]	张阿漫, 王超, 王诗平, 程晓达. 气泡与自由液面相互作用的实验研究. 物理学报, 2012, 61(8): 084701. doi: 10.7498/aps.61.084701
[11]	万茜, 周进, 刘曾荣. 蛋白质相互作用网络特征的理论再现. 物理学报, 2012, 61(1): 010203. doi: 10.7498/aps.61.010203
[12]	周丰茂, 孙东科, 朱鸣芳. 偏晶合金液-液相分离的格子玻尔兹曼方法模拟. 物理学报, 2010, 59(5): 3394-3401. doi: 10.7498/aps.59.3394
[13]	邵宇飞, 王绍青. 基于准连续介质方法模拟纳米多晶体Ni中裂纹的扩展. 物理学报, 2010, 59(10): 7258-7265. doi: 10.7498/aps.59.7258
[14]	贺新福, 杨文, 樊胜. 论FeCr合金辐照损伤的多尺度模拟. 物理学报, 2009, 58(12): 8657-8669. doi: 10.7498/aps.58.8657
[15]	陈军, 徐云, 陈栋泉, 孙锦山. 冲击作用下纳米孔洞动力学行为的多尺度方法模拟研究. 物理学报, 2008, 57(10): 6437-6443. doi: 10.7498/aps.57.6437
[16]	刘峰, 刘式达, 刘刚, 刘式适. Lorenz方程中两种尺度的相互作用. 物理学报, 2007, 56(10): 5629-5634. doi: 10.7498/aps.56.5629
[17]	谭霞, 张成强, 夏云杰. 双模场与原子相互作用中的量子纠缠和内禀退相干. 物理学报, 2006, 55(5): 2263-2268. doi: 10.7498/aps.55.2263
[18]	杨涛, 何冬慧, 张磬兰, 马红孺. 电解液中带电平板与带电胶体球之间的有效相互作用. 物理学报, 2005, 54(12): 5937-5942. doi: 10.7498/aps.54.5937
[19]	赵晓鹏, 郜丹军. 考虑多颗粒近程相互作用的电流变液泊肃叶流动行为. 物理学报, 2001, 50(6): 1115-1120. doi: 10.7498/aps.50.1115
[20]	王藩侯, 杨传路, 李西军, 经福谦. 液氩多体作用势研究及其Hugoniot曲线的分子动力学模拟　　. 物理学报, 2000, 49(1): 114-118. doi: 10.7498/aps.49.114

计量

文章访问数: 16845
PDF下载量: 462
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

搜索

留言板

蛋白质“液-液相分离”的理论和计算方法进展