Protein aging dynamics: A perspective from non-equilibrium coarse-grained models
Yue Shan(单月)1, Chun-Lai Ren(任春来)1,2,†, and Yu-Qiang Ma(马余强)1,2
1 National Laboratory of Solid State Microstructures and Department of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China; 2 Hefei National Laboratory, Hefei 230088, China
Abstract The aging of biomolecular condensates has been implicated in the pathogenesis of various neurodegenerative diseases, characterized by a transition from a physiologically liquid-like state to a pathologically ordered structure. However, the mechanisms governing the formation of these pathological aggregates remain poorly understood. To address this, the present study utilizes coarse-grained molecular dynamics simulations based on Langevin dynamics to explore the structural, dynamical, and material property changes of protein condensates during the aging process. Here, we further develop a non-equilibrium simulation algorithm that not only captures the characteristics of time-dependent amount of aging beads but also reflects the structural information of chain-like connections between aging beads. Our findings reveal that aging induces compaction of the condensates, accompanied by a decrease in diffusion rates and an increase in viscosity. Further analysis suggests that the heterogeneous diffusivity within the condensates may drive the aging process to initiate preferentially at the condensate surface. Our simulation results align with the experimental phenomena and provide a clear physical picture of the aging dynamics.
Fund: We are grateful to the High-Performance Computing Center (HPCC) of Nanjing University for the numerical calculations in this paper on its blade cluster system. This work is supported by the National Key Research and Development Program of China (Grant No. 2022YFA1405000), the National Natural Science Foundation of China (Grant Nos. 12274212, 12347102, and 12174184), and Innovation Program for Quantum Science and Technology (Grant No. 2024ZD0300101).
Yue Shan(单月), Chun-Lai Ren(任春来), and Yu-Qiang Ma(马余强) Protein aging dynamics: A perspective from non-equilibrium coarse-grained models 2025 Chin. Phys. B 34 058301
[1] Protter D S and Parker R 2016 Trends Cell Biol. 26 668 [2] Gall J G 2000 Annu. Rev. Cell Dev. Biol. 16 273 [3] Daniel S W L, Strom A R and Brangwynne C P 2022 APL Bioeng. 6 021503 [4] Li P, Banjade S, Cheng H C, Soyeon K, Chen B, Guo L, Liaguno M, Hollingsworth J V, King D S, Banani S F, Russo P S, Jiang Q X, Nixon B T and Rosen M K 2012 Nature 483 336 [5] Zeng M, Shang Y, Araki Y, Guo T, Huganir R L and Zhang M 2016 Cell 166 1163 [6] Brangwynne C P, Mitchison T J and Hyman A A 2011 Proc. Natl. Acad. Sci. USA 108 4334 [7] Yang P, Mathieu C, Kolaitis R M, Zhang P, Messing J, Yurtsever U, Yang Z, Wu J, Li Y, Pan Q, Yu J, Martin E W, Mittag T, Kim H J and Taylor J P 2020 Cell 181 325 [8] Lindström M S 2009 Biochem. Biophys. Res. Commun. 379 167 [9] Iborra F J, Jackson D A and Cook P R 2001 Science 293 1139 [10] Brangwynne C P, Eckmann C R, Courson D S, Rybarska A, Hoege C, Gharakhani J, Julicher F and Hyman A A 2009 Science 324 1729 [11] Hyman A A, Christoph A W and Frank J 2014 Annu. Rev. Cell Dev. Biol. 30 39 [12] Zhang H, Ji X, Li P, Liu C, Lou J, Wang Z, Wen W, Xiao Y, Zhang M and Zhu X 2020 Sci. China Life Sci. 63 953 [13] Alberti S, Gladfelter A and Mittag T 2019 Cell 176 419 [14] Wang J, Choi J M, Holehouse A S, Lee H, Zhang X, Jahnel M, Maharana S, Lemaitre R, Pozniakovsky A, Drechsel D, Poser I, Pappu R V, Alberti S and Hyman A A 2018 Cell 174 688 [15] Bah A, Vernon R M, Siddiqui Z, Krzeminski M, Muhandiram R, Zhao C, Sonenberg N, Kay L E and Forman-Kay J D 2015 Nature 519 106 [16] Gupta V, Nath S and Chand S 2002 Indian J. Biotechnol 1 87 [17] Conicella A E, Zerze G H, Mittal J and Fawzi N L 2016 Structure 24 1537 [18] Vernon R M, Chong P A, Tsang B, Kim T H, Bah A, Farber P, Lin H and Forman-Kay J D 2018 eLife 7 e31486 [19] Harmon T S, Holehouse A S, Rosen M K and Pappu R V 2017 eLife 6 e30294 [20] Alberti S and Hyman A A 2021 Nat. Rev. Mol. Cell Biol. 22 196 [21] Eisenberg D S and Michael R S 2017 Annu. Rev. Biochem. 86 69 [22] Tan R, Xia K, Xun D, ZongWand Yu Y 2023 Chin. Phys. B 32 128703 [23] Patel A, Lee H O, Jawerth L, Maharana S, Jahnel M, Hein M Y, Stoynov S, Mahamid J, Saha S, Franzmann T M, Pozniakovski A, Poser I, Maghelli N, Royer L A,Weigert M, Myers EW, Grill S, Drechsel D, Hyman A A and Alberti S 2015 Cell 162 1066 [24] Portz B, Lee B L and Shorter J 2021 Trends Biochem. Sci. 46 550 [25] Murakami T, Qamar S, Lin J Q, Schierle G S K, Rees E, Miyashita A, Costa A R, Dodd R B, Chan F T S, Michel C H, Kronenberg-Versteeg D, Li Y, Yang S,Wakutani Y, MeadowsW, Ferry R R, Dong L, Tartaglia G G, Favrin G, Lin W, Dickson D W, Zhen M, Ron D, Schmitt-Ulms G, Fraser P E, Shneider N A, Holt C, Vendruscolo M, Kaminski C F and George-Hyslop P S 2015 Neuron 88 678 [26] Zhang H 2020 Science 370 1271 [27] Ray S, Singh N, Kumar R, Patel K, Pandey S, Datta D, Mahato J, Panigrahi R, Navalkar A, Mehra S, Gadhe L, Chatterjee D, Sawner A S, Maiti S, Bhatia S, Gerez J A, Chowdhury A, Kumar A, Padinhateeri R, Riek R, Krishnamoorthy G and Maji S K 2020 Nat. Chem. 12 705 [28] Pantoja-Uceda D, Stuani C, Laurents D V, McDermott A E, Buratti E and Mompean M 2021 PLoS Biol. 19 e3001198 [29] Emmanouilidis L, Bartalucci E, Kan Y, Ijavi M, Perez M E, Afanasyev P, Boehringer D, Zehnder J, Parekh S H, Bonn M, Michaels T C T, Wiegand T and Allain F H T 2024 Nat. Chem. Biol. 20 1044 [30] Wu M, Ma H, Fang H, Yang L and Lei X 2023 Chin. Phys. B 32 018701 [31] Fatafta H, Khaled M, OwenMC, Sayyed-Ahmad A and Strodel B 2021 Proc. Natl. Acad. Sci. USA 118 e2106210118 [32] Tejedor A R, Sanchez-Burgos I, Estevez-Espinosa M, Garaizar A, Collepardo-Guevara R, Ramirez J and Espinosa J R 2022 Nat. Commun. 13 5717 [33] Ranganathan S and Shakhnovich E 2022 Biophys. J. 121 2751 [34] Espinosa J R, Garaizar A, Vega C, Frenkel D and Collepardo-Guevara R 2019 J. Chem. Phys. 150 224510 [35] Ruff K M, Dar F and Pappu R V 2021 Proc. Natl. Acad. Sci. USA 118 e2017184118 [36] Farag M, Borcherds W M, Bremer A, Mittag T and Pappu R V 2023 Nat. Commun. 14 5527 [37] Biswas S and Potoyan D A 2024 PRX Life 2 023011 [38] Garaizar A, Espinosa J R, Joseph J A, Krainer G, Shen Y, Knowles T P J and Collepardo-Guevara R 2022 Proc. Natl. Acad. Sci. USA 119 e2119800119 [39] Blazquez S, Sanchez-Burgos I, Ramirez J, Higginbotham T, Conde M M, Collepardo-Guevara R, Tejedor A R and Espinosa J R 2023 Adv. Sci. 10 2207742 [40] Tejedor A R, Collepardo-Guevara R, Ramírez J and Espinosa J R 2023 J. Phys. Chem. B 127 4441 [41] Shen Y, Chen A, Wang W, Shen Y, Rugger F S, Aime S, Wang Z, Qamar S, Espinosa J R, Garaizar A, Georgr-Hyslop P S, Collepardo- Guevara R, Weitz D A, Vigolo D and Knowles T P J 2023 Proc. Natl. Acad. Sci. USA 120 e2301366120 [42] Luo F, Gui X, Zhou H, Gu J, Li Y, Liu X, Zhao M, Li D, Li X and Liu C 2018 Nat. Struct. Mol. Biol. 25 341 [43] Hughes M P, Michael R S, Boyer D R, Goldschmidt L, Rodriguez J A, Cascio D, Chong L, Gonen T and Eisenberg D S 2018 Science 359 698 [44] Ren C L, Shan Y, Zhang P, Ding H M and Ma Y Q 2022 Sci. Adv. 8 eabo7885 [45] Murray D, Kato M, Lin Y, Thurber K R, Hung I, Mcknight S L and Tycko R 2017 Cell 171 615 [46] Zhang Y, Xu B, Weiner B G, Meir Y and Wingreen N S 2021 eLife 10 e62403 [47] Espinosa J R, Garaizar A, Vega C, Frenkel D and Collepardo-Guevara R 2019 J. Chem. Phys. 150 224510 [48] Plimpton S 1995 J. Comput. Phys. 117 1 [49] Lee K H, Zhang P, Kim H J, Mitrea D M, Sarkar M, Freibaum B D, Cika J, Coughlin M, Messing J, Molliex A, Maxwell B A, Kim N C, Temirov J, Moore J, Kolaitis R H, Shaw T L, Bai B, Peng J, Kriwacki R W and Taylor J P 2016 Cell 167 774 [50] Hu G, Song H, Chen X and Li J Y 2024 J. Phys. Chem. Lett. 15 8315 [51] Takaki R and Thirumalai D 2024 Proc. Natl. Acad. Sci. USA 121 e2409973121 [52] Zhang Y, Pyo A G, Kliegman R, Jiang Y, Brangwynne C P, Stone H A and Wingreen N S 2024 eLife 12 RP91680 [53] Dar F, Cohen S R, Mitrea D M, Phillips A H, Nagy G, LeiteWC, Stanley C B, Choi J M, Kriwacki R W and Pappu R V 2024 Nat. Commun. 15 3413 [54] Mathur A, Ghosh R and Nunes-Alves A 2024 J. Chem. Inf. Model. 64 1549 [55] Rubinstein M and Colby R H 2003 Polymer Physics (New York: Oxford University Press) p. 23 [56] Cao X Z, Merlitz H and Forest M G 2019 J. Phys. Chem. Lett. 10 4968 [57] Ramírez J, Sukumaran S K, Vorselaars B and Likhtman A E 2010 J. Chem. Phys. 133 154103 [58] Likhtman A E 2012 Polymer Science: A Comprehensive Reference 1 133 [59] Chandler D 1987 Mechanics (Oxford: Oxford University Press) 5 11 [60] Likhtman A E 2005 Macromolecules 38 6128 [61] Tan Y, Chen Y, Liu X, Tang Y, Lao Z and Wei G 2023 Int. J. Biol. Macromol. 241 124659 [62] Soustelle L, Aimond F, Lpez-Andrs C, Brugioti V, Raoul C and Layalle S 2023 J. Neurosci. 43 8058
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