淀粉样多肽A<b>β</b><sub>42</sub>与Medin共聚集的分子动力学模拟研究

潘文雁; 程传勇; 牛菁菁; 元冰; 杨恺; 董雪薇

doi:10.7498/aps.74.20250616

摘要

Medin淀粉样蛋白的聚集导致动脉壁退化和脑血管功能障碍, 参与多种血管疾病的发生与发展. 在血管性痴呆或阿尔茨海默病患者的脑小动脉中发现Medin聚集体增加, 且Medin与血管β-淀粉样蛋白(Aβ)沉积物共定位. 实验证实Medin能够与Aβ共同形成异源纤维, 并通过交叉接种机制调控Aβ的聚集. 然而, Medin与Aβ共聚集的微观机制仍不清楚. 本文利用大规模的全原子副本交换分子动力学模拟(累计模拟时间72 μs), 对Aβ₄₂与Medin三聚体在不同多肽环境(即自聚集vs.共聚集)的相互作用及构象分布进行了研究. 结果表明, Aβ₄₂与Medin的亲和力更高, Aβ₄₂与Medin在自身或彼此结合时具有相似的分子识别位点或区域, 为共聚集提供基础. Aβ₄₂的N端与Medin的C端在Aβ₄₂-Medin交叉聚集中起到关键作用. 更重要的是, 共聚集显著地改变了Aβ₄₂与Medin的相互作用强度、方式以及结构特征. Aβ₄₂-Medin三聚体中, Aβ₄₂分子间相互作用减弱, 仅保留疏水核心区域(¹⁶KLVFFA²¹)之间的结合而提高其他区域的自由度; Medin形成更多的β结构与更少的helix结构, 但Aβ₄₂却形成更多的helix与更少的β; 而Medin中高β倾向性区域向肽链中部和C端迁移, 表明Medin可能通过C端形成β结构作为核心从而驱动其与Aβ₄₂的协同聚集. 本工作在原子水平上详尽地阐明了共聚集对Aβ₄₂与Medin相互作用与结构特征的影响, 为理解Aβ₄₂-Medin共聚集分子机制以及不同疾病之间交叉关联的病理机制提供了有益见解.

关键词:

Abstract

The aggregation of Medin is closely related to the arterial wall degeneration and cerebrovascular dysfunction. In patients with vascular dementia or Alzheimer’s disease, the concentration of medin in cerebral arterioles increases, and Medin is co-localized with vascular amyloid-β (Aβ) deposits. Previous study demonstrates that Medin interacts directly with Aβ, forming heterologous fibrils with Aβ and promoting Aβ aggregation. However, the basic mechanism of the co-aggregation between Medin and Aβ remains largely elusive. Here, we explore the interactions and conformational ensembles of Aβ₄₂/Medin trimers in different peptide environments (self-aggregation vs. co-aggregation) by performing all-atom replica exchange molecular dynamic simulation on Aβ₄₂/Medin homotrimers and Aβ₄₂-Medin heterotrimer with an accumulated simulation time of 72 μs. Our results reveal that Aβ₄₂ exhibits higher affinity with Medin, and Aβ₄₂ and Medin have similar molecular recognition sites in self-aggregation and co-aggregation. The N-terminus of Aβ₄₂ and the C-terminus of Medin play critical roles in Aβ₄₂-Medin cross-talk. More importantly, co-aggregation significantly changes the interaction strength, binding patterns and structural characteristics of Aβ₄₂ and Medin. Intermolecular interactions of Aβ₄₂ trimers are relatively weak among three trimers, and the binding sites are concentrated between N- and N-termini, between N- and C-termini, and between C- and C-termini of Aβ₄₂. In contrast, intermolecular interactions of Medin trimers are the strongest, and the binding sites are widely and uniformly distributed in Medin peptides. Intermolecular interactions of Aβ₄₂ in Aβ₄₂-Medin heterotrimer decrease compared with those of Aβ₄₂ trimers, only the binding of the hydrophobic core regions (¹⁶KLVFFA²¹) is retained and other regions of Aβ₄₂ gain increase flexibility. Two-dimensional free energy landscapes reveal distinct conformational diversities between the homo- and heterotrimers, with the order of diversity being Medin/Aβ42-Medin trimers > Aβ₄₂ trimers. The R_g of Aβ₄₂ trimers is smaller than those of the other two trimers, implying that Aβ₄₂ trimers possess a more compact structure, whereas Medin/Aβ₄₂-Medin trimers exhibit a relatively loose conformation. The Aβ₄₂ trimers possess the highest β content whereas Medin trimers exhibit the lowest β probability. It is found that Aβ₄₂-Medin co-aggregation induces Medin to form more β-structures with longer lengths and fewer helices, while promoting Aβ₄₂ to form more helices and fewer β-structures. High β-propensity regions of Medin in heterotrimers shift towards the C-terminus of Medin, suggesting that Medin utilizes its C-terminal β region as a core motif to drive its co-aggregation with Aβ₄₂. These results elucidate the detailed influences of co-aggregation on the interactions and conformations of Aβ₄₂ and Medin. This work provides key insights into the molecular mechanism of Aβ₄₂-Medin co-aggregation and the pathological mechanisms of cross-linking between related diseases.

Keywords:

作者及机构信息

1.
苏州大学物理科学与技术学院, 软凝聚态物理及交叉研究中心, 苏州　215006

2.
松山湖材料实验室, 东莞　523808

3.
复旦大学应用表面物理国家重点实验室, 上海　200433

通信作者: 元冰, yuanbing@sslab.org.cn ; 杨恺, yangkai@suda.edu.cn ; 董雪薇, dongxuewei@suda.edu.cn

基金项目: 国家自然科学基金(批准号: 12274307, 32230063, 22303060)、江苏省自然科学基金(批准号: BK20230470)、广东省基础与应用基础研究基金(批准号: 2023A1515011610, 2023B1515120001)和复旦大学应用表面物理国家重点实验室(批准号: KF2023_03)资助的课题.

Authors and contacts

1.
Center for Soft Condensed Matter Physics and Interdisciplinary Research & School of Physical Science and Technology, Soochow University, Suzhou 215006, China

2.
Songshan Lake Materials Laboratory, Dongguan 523808, China

3.
State Key Laboratory of Surface Physics, Fudan University, Shanghai 200433, China

Corresponding author: YUAN Bing, yuanbing@sslab.org.cn ; YANG Kai, yangkai@suda.edu.cn ; DONG Xuewei, dongxuewei@suda.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 12274307, 32230063, 22303060), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20230470), the Guangdong Basic and Applied Basic Research Foundation, China (Grant Nos. 2023A1515011610, 2023B1515120001), and the Open Research Fund of State Key Laboratory of Surface Physics of Fudan University, China (Grant No. KF2023_03).

[1]	Giordano X, Fernandez M C 2023 Alzheimers Dementia. 19 e075955 Google Scholar
[2]	Scheltens P, De Strooper B, Kivipelto M, Holstege H, Chételat G, Teunissen C E, Cummings J, van der Flier W M 2021 Lancet 397 1577 Google Scholar
[3]	Li X, Yang Z, Chen Y, Zhang S, Wei G, Zhang L 2023 J. Phys. Chem. B 127 4050 Google Scholar
[4]	Ball K A, Phillips A H, Wemmer D E, Head-Gordon T 2013 Biophys. J. 104 2714 Google Scholar
[5]	Li X, Zhang Y, Wang Y, Zhang S, Zhang L 2024 J. Phys. Chem. B 128 1843 Google Scholar
[6]	Nunan J, Small D H 2000 FEBS Lett. 483 6 Google Scholar
[7]	Huang Y, Potter R, Sigurdson W, Santacruz A, Shih S, Ju Y E, Kasten T, Morris J C, Mintun M, Duntley S, Bateman R J 2012 Arch. Neurol. 69 51 Google Scholar
[8]	Häggqvist B, Näslund J, Sletten K, Westermark G T, Mucchiano G, Tjernberg L O, Nordstedt C, Engström U, Westermark P 1999 Proc. Natl. Acad. Sci. U. S. A. 96 8669 Google Scholar
[9]	Karamanova N, Truran S, Serrano G E, Beach T G, Madine J, Weissig V, Davies H A, Veldhuizen J, Nikkhah M, Hansen M, Zhang W, D'Souza K, Franco D A, Migrino R Q 2020 J. Am. Heart Assoc. 9 e014810 Google Scholar
[10]	Madine J, Davies H A, Migrino R Q, Ruotsalainen S E, Wagner J, Neher J J 2023 Nat. Aging 3 1039 Google Scholar
[11]	Larsson A, Söderberg L, Westermark G T, Sletten K, Engström U, Tjernberg L O, Näslund J, Westermark P 2007 Biochem. Biophys. Res. Commun. 361 822 Google Scholar
[12]	Eisenberg D, Jucker M 2012 Cell 148 1188 Google Scholar
[13]	Iadanza M G, Jackson M P, Hewitt E W, Ranson N A, Radford S E 2018 Nat. Rev. Mol. Cell Biol. 19 755 Google Scholar
[14]	Ren B, Zhang Y, Zhang M, Liu Y, Zhang D, Gong X, Feng Z, Tang J, Chang Y, Zheng J 2019 J. Mater. Chem. B 7 7267 Google Scholar
[15]	Zhang Y, Tang Y, Zhang D, Liu Y, He J, Chang Y, Zheng J 2021 Chin. J. Chem. Eng. 30 225 Google Scholar
[16]	Migrino R Q, Karamanova N, Truran S, Serrano G E, Davies H A, Madine J, Beach T G 2020 Alzheimers Dementia 12 e12072 Google Scholar
[17]	Tayler H, Miners J S, Güzel Ö, MacLachlan R, Love S 2021 Brain Pathol. 31 e12935 Google Scholar
[18]	Benson M D, Buxbaum J N, Eisenberg D S, Merlini G, Saraiva M J M, Sekijima Y, Sipe J D, Westermark P 2020 Amyloid 27 217 Google Scholar
[19]	Jackson R J, Rudinskiy N, Herrmann A G, Croft S, Kim J M, Petrova V, Ramos-Rodriguez J J, Pitstick R, Wegmann S, Garcia-Alloza M, Carlson G A, Hyman B T, Spires-Jones T L 2016 Eur. J. Neurosci. 44 3056 Google Scholar
[20]	Wagner J, Degenhardt K, Veit M, Louros N, Konstantoulea K, Skodras A, Wild K, Liu P, Obermüller U, Bansal V, Dalmia A, Häsler L M, Lambert M, De Vleeschouwer M, Davies H A, Madine J, Kronenberg-Versteeg D, Feederle R, Del Turco D, Nilsson K P R, Lashley T, Deller T, Gearing M, Walker L C, Heutink P, Rousseau F, Schymkowitz J, Jucker M, Neher J J 2022 Nature 612 123 Google Scholar
[21]	Huang F, Fan X, Wang Y, Zou Y, Lian J, Wang C, Ding F, Sun Y 2024 Brief. Bioinform. 25 bbad526 Google Scholar
[22]	Howitz W J, Wierzbicki M, Cabanela R W, Saliba C, Motavalli A, Tran N, Nowick J S 2020 J. Am. Chem. Soc. 142 15870 Google Scholar
[23]	Davies H A, Madine J, Middleton D A 2015 J. Biol. Chem. 290 7791 Google Scholar
[24]	Davies H A, Rigden D J, Phelan M M, Madine J 2017 Sci. Rep. 7 45224 Google Scholar
[25]	Huang F, Yan J, Zhang X, Xu H, Lian J, Yang X, Wang C, Ding F, Sun Y 2024 Colloids Surf. , B 244 114192 Google Scholar
[26]	Huang F, Fan X, Wang Y, Wang C, Zou Y, Lian J, Ding F, Sun Y 2023 J. Chem. Inf. Model. 63 6376 Google Scholar
[27]	徐成, 林召, 杨恺, 元冰 2020 69 108701 Google Scholar Xu C, Lin Z, Yang K, Yuan B 2020 Acta Phys. Sin. 69 108701 Google Scholar
[28]	王康, 徐成, 吴晋锋, 杨恺, 元冰 2021 70 178701 Google Scholar Wang K, Xu C, Wu J F, Yang K, Yuan B 2021 Acta Phys. Sin. 70 178701 Google Scholar
[29]	谭金鹏, 张婉婷, 徐成, 卢雪梅, 朱文圣, 杨恺, 元冰 2024 73 188702 Google Scholar Tan J P, Zhang W T, Xu C, Lu X M, Zhu W S, Yang K, Yuan B 2024 Acta Phys. Sin. 73 188702 Google Scholar
[30]	Tu W, Dong X, Ou L, Zhang X, Yuan B, Yang K 2023 Chem. Res. Chin. Univ. 39 829 Google Scholar
[31]	Lao Z, Tang Y, Dong X, Tan Y, Li X, Liu X, Li L, Guo C, Wei G 2024 Nanoscale 16 4025 Google Scholar
[32]	Liu X, Lao Z, Li X, Dong X, Wei G 2022 Phys. Chem. Chem. Phys. 24 16263 Google Scholar
[33]	Qi R, Wei G, Ma B, Nussinov R 2018 Methods Mol. Biol. 1777 101 Google Scholar
[34]	Sugita Y, Okamoto Y 1999 Chem. Phys. Lett. 314 141 Google Scholar
[35]	Miron R A, Fichthorn K A 2003 J. Chem. Phys. 119 6210 Google Scholar
[36]	Kästner J 2011 Wiley Interdiscip. Rev. Comput. Mol. Sci. 1 932 Google Scholar
[37]	Qi R, Luo Y, Wei G, Nussinov R, Ma B 2015 J. Phys. Chem. Lett. 6 3276 Google Scholar
[38]	Dong X, Bera S, Qiao Q, Tang Y, Lao Z, Luo Y, Gazit E, Wei G 2021 J. Phys. Chem. Lett. 12 2576 Google Scholar
[39]	Li X, Chen Y, Yang Z, Zhang S, Wei G, Zhang L 2024 Int. J. Biol. Macromol. 254 127841 Google Scholar
[40]	Tan Y, Chen Y, Liu X, Tang Y, Lao Z, Wei G 2023 Int. J. Biol. Macromol. 241 124659 Google Scholar
[41]	Lao Z, Dong X, Liu X, Li F, Chen Y, Tang Y, Wei G 2022 J. Chem. Inf. Model. 62 3227 Google Scholar
[42]	Dong X, Qi R, Qiao Q, Li X, Li F, Wan J, Zhang Q, Wei G 2021 Phys. Chem. Chem. Phys. 23 20406 Google Scholar
[43]	Mo Y, Brahmachari S, Lei J, Gilead S, Tang Y, Gazit E, Wei G 2018 ACS Chem. Neurosci. 9 2741 Google Scholar
[44]	Guo C, Côté S, Mousseau N, Wei G 2015 J. Phys. Chem. B 119 3366 Google Scholar
[45]	Gremer L, Schölzel D, Schenk C, Reinartz E, Labahn J, Ravelli R B G, Tusche M, Lopez-Iglesias C, Hoyer W, Heise H, Willbold D, Schröder G F 2017 Science 358 116 Google Scholar
[46]	Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A, Meyer C, Kohl S A A, Ballard A J, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior A W, Kavukcuoglu K, Kohli P, Hassabis D 2021 Nature 596 583 Google Scholar
[47]	Humphrey W, Dalke A, Schulten K 1996 J. Mol. Graphics 14 33 Google Scholar
[48]	Abraham M J, Murtola T, Schulz R, Páll S, Smith J C, Hess B, Lindahl E 2015 SoftwareX 1–2 19 Google Scholar
[49]	Lindorff-Larsen K, Piana S, Palmo K, Maragakis P, Klepeis J L, Dror R O, Shaw D E 2010 Proteins 78 1950 Google Scholar
[50]	Chen Y, Li X, Zhan C, Lao Z, Li F, Dong X, Wei G 2021 ACS Chem. Neurosci. 12 4007 Google Scholar
[51]	Lopes P E, Guvench O, MacKerell Jr. A D 2015 Methods Mol. Biol. 1215 47 Google Scholar
[52]	Tan Y, Chen Y, Pan T, Tang Y, Liu X, Yu Y, Wei G 2025 J. Chem. Inf. Model. 65 4643 Google Scholar
[53]	Wang W 2021 Phys. Chem. Chem. Phys. 23 777 Google Scholar
[54]	Best R B, Zheng W, Mittal J 2014 J. Chem. Theory Comput. 10 5113 Google Scholar
[55]	Huang J, Rauscher S, Nawrocki G, Ran T, Feig M, de Groot B L, Grubmüller H, MacKerell A D, Jr. 2017 Nat. Methods 14 71 Google Scholar
[56]	Piana S, Donchev A G, Robustelli P, Shaw D E 2015 J. Phys. Chem. B 119 5113 Google Scholar
[57]	Zerze G H, Zheng W, Best R B, Mittal J 2019 J. Phys. Chem. Lett. 10 2227 Google Scholar
[58]	Parrinello M, Rahman A 1981 J. Appl. Phys. 52 7182 Google Scholar
[59]	Bussi G, Donadio D, Parrinello M 2007 J. Chem. Phys. 126 014101 Google Scholar
[60]	Li M, Johnson W L, Goddard W A 1992 MRS Online Proc. Lib. 291 285 Google Scholar
[61]	Miyamoto S, Kollman P A 1992 J. Comput. Chem. 13 952 Google Scholar
[62]	Hess B 2008 J. Chem. Theory Comput. 4 116 Google Scholar
[63]	Kabsch W, Sander C 1983 Biopolymers 22 2577 Google Scholar
[64]	Daura X, Gademann K, Jaun B, Seebach D, van Gunsteren W F, Mark A E 1999 Angew. Chem. Int. Ed. 38 236 Google Scholar
[65]	Zhang Y, Liu Y, Zhao W, Sun Y 2021 Int. J. Biol. Macromol. 193 1 Google Scholar
[66]	Li X, Lao Z, Zou Y, Dong X, Li L, Wei G 2021 J. Phys. Chem. B 125 2050 Google Scholar
[67]	Delano W L http://pymol.org [2025-5-10]
[68]	Rigsby R E, Parker A B 2016 Biochem. Mol. Biol. Educ. 44 433 Google Scholar
[69]	Okumura H, Itoh S G 2022 Molecules 27 2483 Google Scholar
[70]	Reches M, Gazit E 2004 Amyloid 11 81 Google Scholar
[71]	Madine J, Copland A, Serpell L C, Middleton D A 2009 Biochemistry 48 3089 Google Scholar

施引文献

图 1 Aβ₄₂/Medin同源三聚体与Aβ₄₂-Medin异源三聚体的相互作用分析　(a), (b)三个体系中多肽的(a)接触数与(b)氢键数的概率密度分布(probability density function, PDF); (c), (d) Aβ₄₂与Aβ₄₂-Medin三聚体体系中, Aβ₄₂-Aβ₄₂、Aβ₄₂-Medin之间的(c)接触数与(d)氢键数的PDF; (e)—(h)多肽分子间的残基-残基接触作用数图, 即(e) Aβ₄₂三聚体中Aβ₄₂-Aβ₄₂的残基-残基接触数图、(f) Aβ₄₂-Medin三聚体中Aβ₄₂-Aβ₄₂的残基-残基接触数图、(g) Medin三聚体中Medin-Medin的残基-残基接触数图、(h) Aβ₄₂-Medin三聚体中Aβ₄₂-Medin的残基-残基接触数图; (i)—(k)展示Aβ₄₂与Medin结合区域的代表性结构快照. 多肽以cartoon方式呈现. Aβ₄₂与Medin结合区域以蓝色和橘色突出显示, 并将区域两端对应的氨基酸标记在结构上

Fig. 1. Analysis of interactions in Aβ₄₂/Medin homotrimers and Aβ₄₂-Medin heterotrimer: (a), (b) Probability density function (PDF) of (a) contact number and (b) hydrogen-bond (H-bond) number of peptides in three systems; (c), (d) PDF of (c) contact number and (d) H-bond number between Aβ₄₂ and Aβ₄₂ as well as between Aβ₄₂ and Medin in A-A^trimer and A-M^trimer systems; (e)–(h) 2D residue-residue contact maps of intermolecular interactions for (e) Aβ₄₂-Aβ₄₂ in A-A^trimer, (f) Aβ₄₂-Aβ₄₂ in A-M^trimer, (g) Medin-Medin in M-M^trimer and (h) Aβ₄₂-Medin in A-M^trimer; (i)–(k) representative snapshots illustrate the binding regions between Aβ₄₂ and Medin in A-M^trimer system.

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图 2 Aβ₄₂与Medin在同源与异源三聚体体系中的构象特征分析　(a)以链间接触数和回转半径为反应坐标的二维构象FELs: A-A^trimer体系(左)、M-M^trimer体系(中)、A-M^trimer体系(右); (b)—(d)聚类分析中概率最高的六个聚类构象簇(C1—C6)的中心结构及每个聚类对应的概率(标记在对应中心结构下方): (b) Aβ₄₂三聚体、(c) Medin三聚体以及(d) Aβ₄₂-Medin三聚体. 这些聚类中心结构的位置标记在二维自由能图景(a)中. 多肽以cartoon方式呈现, 每条多肽链N端残基的Cα原子以小球标记. Aβ₄₂和Medin分别用橙色系和蓝色系颜色显示

Fig. 2. Analysis of conformational characteristics of Aβ₄₂/Medin homotrimers and Aβ₄₂-Medin heterotrimer: (a) FELs as a function of intermolecular contact number and trimeric R_g in A-A^trimer (left), M-M^trimer (middle) and A-M^trimer (right) systems; (b)–(d) representative conformations for the six most-populated clusters (C1–C6) along with their corresponding populations (marked below the corresponding representative conformations) of (b) Aβ₄₂ trimer, (c) Medin trimer and (d) Aβ₄₂-Medin trimer. The locations of those representative conformations are labeled on the FEL plots. Aβ₄₂ and Medin are shown in cartoon, with the N-terminal Cα atom of each chain represented by a sphere. Aβ₄₂ and Medin peptides are colored in blue and orange, respectively.

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图 3 Aβ₄₂与Medin在同源与异源三聚体体系中的二级结构分析　(a)三个体系中的各种二级结构的概率统计; (b) Aβ₄₂与Medin在不同体系中形成helix结构的概率: A-A^trimer和A-M^trimer体系中的Aβ₄₂ (浅蓝vs.深蓝)以及M-M^trimer和A-M^trimer体系中的Medin (浅橙色vs.深橙色), 其中异源聚集体体系用*号表示; (c) Aβ₄₂与Medin在不同体系中形成β结构的概率; (d) β结构以链间和链内方式进行排列的概率; (e)三个体系的三聚体中β-sheet长度的概率分布; (f) A-A^trimer和A-M^trimer (*) 体系中Aβ₄₂的β-sheet长度的概率分布; (g) M-M^trimer和A-M^trimer (*) 体系中Medin的β-sheet长度的概率分布; (h)三个体系的三聚体中β-sheet尺寸的概率分布

Fig. 3. Analysis of secondary structures of Aβ₄₂/Medin homotrimers and Aβ₄₂-Medin heterotrimer: (a) Each secondary structure probability of Aβ₄₂ homotrimer, Medin homotrimer and Aβ₄₂-Medin heterotrimer; (b) helix probability of Aβ₄₂ and Medin in different systems: Aβ₄₂ in A-A^trimer system (light blue) vs. Aβ₄₂ in A-M^trimer system (*, blue) and Medin in M-M^trimer system (light orange) vs. Medin in A-M^trimer system (*, orange); (c) β probability of Aβ₄₂ and Medin in different systems; (d) probability of β arrangement with interchain and intrachain manners; (e) probability of β-sheet length in three systems; (f) probability of β-sheet length of Aβ₄₂ in A-A^trimer and A-M^trimer systems; (g) probability of β-sheet length of Medin in M-M^trimer and A-M^trimer systems; (h) probability of β-sheet size in three systems.

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图 4 β结构概率和β结构形成区域的分析　(a), (c) A-A^trimer和A-M^trimer体系中Aβ₄₂的每个氨基酸形成β结构的概率; (b), (d) M-M^trimer和A-M^trimer体系中Medin的每个氨基酸形成β结构的概率. 富含β-sheet结构的代表性Aβ₄₂, Medin和Aβ₄₂-Medin三聚体构象展示在对应的残基-β结构概率图中. 多肽采用cartoon表示形式, Aβ₄₂与Medin中的β结构区域以蓝色和橙色突出显示

Fig. 4. β-sheet probability and β-sheet formation regions of Aβ₄₂ and Medin in homotrimer and heterotrimer. Residue-based β probability distribution of Aβ₄₂ and Medin peptides respectively in (a) Aβ₄₂ homotrimer/(b) Medin homotrimer and (c), (d) Aβ₄₂-Medin heterotrimer. Representative β-sheet-rich conformations of Aβ₄₂, Medin and Aβ₄₂-Medin trimers are illustrated as insets in (a)–(d). Aβ₄₂ and Medin peptides are shown in cartoon with the β-sheet-rich regions highlighted in blue and orange, respectively.

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map

[1]	Giordano X, Fernandez M C 2023 Alzheimers Dementia. 19 e075955 Google Scholar
[2]	Scheltens P, De Strooper B, Kivipelto M, Holstege H, Chételat G, Teunissen C E, Cummings J, van der Flier W M 2021 Lancet 397 1577 Google Scholar
[3]	Li X, Yang Z, Chen Y, Zhang S, Wei G, Zhang L 2023 J. Phys. Chem. B 127 4050 Google Scholar
[4]	Ball K A, Phillips A H, Wemmer D E, Head-Gordon T 2013 Biophys. J. 104 2714 Google Scholar
[5]	Li X, Zhang Y, Wang Y, Zhang S, Zhang L 2024 J. Phys. Chem. B 128 1843 Google Scholar
[6]	Nunan J, Small D H 2000 FEBS Lett. 483 6 Google Scholar
[7]	Huang Y, Potter R, Sigurdson W, Santacruz A, Shih S, Ju Y E, Kasten T, Morris J C, Mintun M, Duntley S, Bateman R J 2012 Arch. Neurol. 69 51 Google Scholar
[8]	Häggqvist B, Näslund J, Sletten K, Westermark G T, Mucchiano G, Tjernberg L O, Nordstedt C, Engström U, Westermark P 1999 Proc. Natl. Acad. Sci. U. S. A. 96 8669 Google Scholar
[9]	Karamanova N, Truran S, Serrano G E, Beach T G, Madine J, Weissig V, Davies H A, Veldhuizen J, Nikkhah M, Hansen M, Zhang W, D'Souza K, Franco D A, Migrino R Q 2020 J. Am. Heart Assoc. 9 e014810 Google Scholar
[10]	Madine J, Davies H A, Migrino R Q, Ruotsalainen S E, Wagner J, Neher J J 2023 Nat. Aging 3 1039 Google Scholar
[11]	Larsson A, Söderberg L, Westermark G T, Sletten K, Engström U, Tjernberg L O, Näslund J, Westermark P 2007 Biochem. Biophys. Res. Commun. 361 822 Google Scholar
[12]	Eisenberg D, Jucker M 2012 Cell 148 1188 Google Scholar
[13]	Iadanza M G, Jackson M P, Hewitt E W, Ranson N A, Radford S E 2018 Nat. Rev. Mol. Cell Biol. 19 755 Google Scholar
[14]	Ren B, Zhang Y, Zhang M, Liu Y, Zhang D, Gong X, Feng Z, Tang J, Chang Y, Zheng J 2019 J. Mater. Chem. B 7 7267 Google Scholar
[15]	Zhang Y, Tang Y, Zhang D, Liu Y, He J, Chang Y, Zheng J 2021 Chin. J. Chem. Eng. 30 225 Google Scholar
[16]	Migrino R Q, Karamanova N, Truran S, Serrano G E, Davies H A, Madine J, Beach T G 2020 Alzheimers Dementia 12 e12072 Google Scholar
[17]	Tayler H, Miners J S, Güzel Ö, MacLachlan R, Love S 2021 Brain Pathol. 31 e12935 Google Scholar
[18]	Benson M D, Buxbaum J N, Eisenberg D S, Merlini G, Saraiva M J M, Sekijima Y, Sipe J D, Westermark P 2020 Amyloid 27 217 Google Scholar
[19]	Jackson R J, Rudinskiy N, Herrmann A G, Croft S, Kim J M, Petrova V, Ramos-Rodriguez J J, Pitstick R, Wegmann S, Garcia-Alloza M, Carlson G A, Hyman B T, Spires-Jones T L 2016 Eur. J. Neurosci. 44 3056 Google Scholar
[20]	Wagner J, Degenhardt K, Veit M, Louros N, Konstantoulea K, Skodras A, Wild K, Liu P, Obermüller U, Bansal V, Dalmia A, Häsler L M, Lambert M, De Vleeschouwer M, Davies H A, Madine J, Kronenberg-Versteeg D, Feederle R, Del Turco D, Nilsson K P R, Lashley T, Deller T, Gearing M, Walker L C, Heutink P, Rousseau F, Schymkowitz J, Jucker M, Neher J J 2022 Nature 612 123 Google Scholar
[21]	Huang F, Fan X, Wang Y, Zou Y, Lian J, Wang C, Ding F, Sun Y 2024 Brief. Bioinform. 25 bbad526 Google Scholar
[22]	Howitz W J, Wierzbicki M, Cabanela R W, Saliba C, Motavalli A, Tran N, Nowick J S 2020 J. Am. Chem. Soc. 142 15870 Google Scholar
[23]	Davies H A, Madine J, Middleton D A 2015 J. Biol. Chem. 290 7791 Google Scholar
[24]	Davies H A, Rigden D J, Phelan M M, Madine J 2017 Sci. Rep. 7 45224 Google Scholar
[25]	Huang F, Yan J, Zhang X, Xu H, Lian J, Yang X, Wang C, Ding F, Sun Y 2024 Colloids Surf. , B 244 114192 Google Scholar
[26]	Huang F, Fan X, Wang Y, Wang C, Zou Y, Lian J, Ding F, Sun Y 2023 J. Chem. Inf. Model. 63 6376 Google Scholar
[27]	徐成, 林召, 杨恺, 元冰 2020 69 108701 Google Scholar Xu C, Lin Z, Yang K, Yuan B 2020 Acta Phys. Sin. 69 108701 Google Scholar
[28]	王康, 徐成, 吴晋锋, 杨恺, 元冰 2021 70 178701 Google Scholar Wang K, Xu C, Wu J F, Yang K, Yuan B 2021 Acta Phys. Sin. 70 178701 Google Scholar
[29]	谭金鹏, 张婉婷, 徐成, 卢雪梅, 朱文圣, 杨恺, 元冰 2024 73 188702 Google Scholar Tan J P, Zhang W T, Xu C, Lu X M, Zhu W S, Yang K, Yuan B 2024 Acta Phys. Sin. 73 188702 Google Scholar
[30]	Tu W, Dong X, Ou L, Zhang X, Yuan B, Yang K 2023 Chem. Res. Chin. Univ. 39 829 Google Scholar
[31]	Lao Z, Tang Y, Dong X, Tan Y, Li X, Liu X, Li L, Guo C, Wei G 2024 Nanoscale 16 4025 Google Scholar
[32]	Liu X, Lao Z, Li X, Dong X, Wei G 2022 Phys. Chem. Chem. Phys. 24 16263 Google Scholar
[33]	Qi R, Wei G, Ma B, Nussinov R 2018 Methods Mol. Biol. 1777 101 Google Scholar
[34]	Sugita Y, Okamoto Y 1999 Chem. Phys. Lett. 314 141 Google Scholar
[35]	Miron R A, Fichthorn K A 2003 J. Chem. Phys. 119 6210 Google Scholar
[36]	Kästner J 2011 Wiley Interdiscip. Rev. Comput. Mol. Sci. 1 932 Google Scholar
[37]	Qi R, Luo Y, Wei G, Nussinov R, Ma B 2015 J. Phys. Chem. Lett. 6 3276 Google Scholar
[38]	Dong X, Bera S, Qiao Q, Tang Y, Lao Z, Luo Y, Gazit E, Wei G 2021 J. Phys. Chem. Lett. 12 2576 Google Scholar
[39]	Li X, Chen Y, Yang Z, Zhang S, Wei G, Zhang L 2024 Int. J. Biol. Macromol. 254 127841 Google Scholar
[40]	Tan Y, Chen Y, Liu X, Tang Y, Lao Z, Wei G 2023 Int. J. Biol. Macromol. 241 124659 Google Scholar
[41]	Lao Z, Dong X, Liu X, Li F, Chen Y, Tang Y, Wei G 2022 J. Chem. Inf. Model. 62 3227 Google Scholar
[42]	Dong X, Qi R, Qiao Q, Li X, Li F, Wan J, Zhang Q, Wei G 2021 Phys. Chem. Chem. Phys. 23 20406 Google Scholar
[43]	Mo Y, Brahmachari S, Lei J, Gilead S, Tang Y, Gazit E, Wei G 2018 ACS Chem. Neurosci. 9 2741 Google Scholar
[44]	Guo C, Côté S, Mousseau N, Wei G 2015 J. Phys. Chem. B 119 3366 Google Scholar
[45]	Gremer L, Schölzel D, Schenk C, Reinartz E, Labahn J, Ravelli R B G, Tusche M, Lopez-Iglesias C, Hoyer W, Heise H, Willbold D, Schröder G F 2017 Science 358 116 Google Scholar
[46]	Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A, Meyer C, Kohl S A A, Ballard A J, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior A W, Kavukcuoglu K, Kohli P, Hassabis D 2021 Nature 596 583 Google Scholar
[47]	Humphrey W, Dalke A, Schulten K 1996 J. Mol. Graphics 14 33 Google Scholar
[48]	Abraham M J, Murtola T, Schulz R, Páll S, Smith J C, Hess B, Lindahl E 2015 SoftwareX 1–2 19 Google Scholar
[49]	Lindorff-Larsen K, Piana S, Palmo K, Maragakis P, Klepeis J L, Dror R O, Shaw D E 2010 Proteins 78 1950 Google Scholar
[50]	Chen Y, Li X, Zhan C, Lao Z, Li F, Dong X, Wei G 2021 ACS Chem. Neurosci. 12 4007 Google Scholar
[51]	Lopes P E, Guvench O, MacKerell Jr. A D 2015 Methods Mol. Biol. 1215 47 Google Scholar
[52]	Tan Y, Chen Y, Pan T, Tang Y, Liu X, Yu Y, Wei G 2025 J. Chem. Inf. Model. 65 4643 Google Scholar
[53]	Wang W 2021 Phys. Chem. Chem. Phys. 23 777 Google Scholar
[54]	Best R B, Zheng W, Mittal J 2014 J. Chem. Theory Comput. 10 5113 Google Scholar
[55]	Huang J, Rauscher S, Nawrocki G, Ran T, Feig M, de Groot B L, Grubmüller H, MacKerell A D, Jr. 2017 Nat. Methods 14 71 Google Scholar
[56]	Piana S, Donchev A G, Robustelli P, Shaw D E 2015 J. Phys. Chem. B 119 5113 Google Scholar
[57]	Zerze G H, Zheng W, Best R B, Mittal J 2019 J. Phys. Chem. Lett. 10 2227 Google Scholar
[58]	Parrinello M, Rahman A 1981 J. Appl. Phys. 52 7182 Google Scholar
[59]	Bussi G, Donadio D, Parrinello M 2007 J. Chem. Phys. 126 014101 Google Scholar
[60]	Li M, Johnson W L, Goddard W A 1992 MRS Online Proc. Lib. 291 285 Google Scholar
[61]	Miyamoto S, Kollman P A 1992 J. Comput. Chem. 13 952 Google Scholar
[62]	Hess B 2008 J. Chem. Theory Comput. 4 116 Google Scholar
[63]	Kabsch W, Sander C 1983 Biopolymers 22 2577 Google Scholar
[64]	Daura X, Gademann K, Jaun B, Seebach D, van Gunsteren W F, Mark A E 1999 Angew. Chem. Int. Ed. 38 236 Google Scholar
[65]	Zhang Y, Liu Y, Zhao W, Sun Y 2021 Int. J. Biol. Macromol. 193 1 Google Scholar
[66]	Li X, Lao Z, Zou Y, Dong X, Li L, Wei G 2021 J. Phys. Chem. B 125 2050 Google Scholar
[67]	Delano W L http://pymol.org [2025-5-10]
[68]	Rigsby R E, Parker A B 2016 Biochem. Mol. Biol. Educ. 44 433 Google Scholar
[69]	Okumura H, Itoh S G 2022 Molecules 27 2483 Google Scholar
[70]	Reches M, Gazit E 2004 Amyloid 11 81 Google Scholar
[71]	Madine J, Copland A, Serpell L C, Middleton D A 2009 Biochemistry 48 3089 Google Scholar

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淀粉样多肽Aβ₄₂与Medin共聚集的分子动力学模拟研究

Molecular dynamic simulation study on co-aggregation between amyloid-β and Medin

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1.
苏州大学物理科学与技术学院, 软凝聚态物理及交叉研究中心, 苏州　215006

2.
松山湖材料实验室, 东莞　523808

3.
复旦大学应用表面物理国家重点实验室, 上海　200433

通信作者: 元冰, yuanbing@sslab.org.cn ; 杨恺, yangkai@suda.edu.cn ; 董雪薇, dongxuewei@suda.edu.cn

Authors and contacts

Corresponding author: YUAN Bing, yuanbing@sslab.org.cn ; YANG Kai, yangkai@suda.edu.cn ; DONG Xuewei, dongxuewei@suda.edu.cn

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淀粉样多肽Aβ42与Medin共聚集的分子动力学模拟研究

Molecular dynamic simulation study on co-aggregation between amyloid-β and Medin

1. 苏州大学物理科学与技术学院, 软凝聚态物理及交叉研究中心, 苏州 215006 2. 松山湖材料实验室, 东莞 523808 3. 复旦大学应用表面物理国家重点实验室, 上海 200433

通信作者: 元冰, yuanbing@sslab.org.cn ; 杨恺, yangkai@suda.edu.cn ; 董雪薇, dongxuewei@suda.edu.cn

Corresponding author: YUAN Bing, yuanbing@sslab.org.cn ; YANG Kai, yangkai@suda.edu.cn ; DONG Xuewei, dongxuewei@suda.edu.cn

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淀粉样多肽Aβ₄₂与Medin共聚集的分子动力学模拟研究

1.
苏州大学物理科学与技术学院, 软凝聚态物理及交叉研究中心, 苏州　215006

2.
松山湖材料实验室, 东莞　523808

3.
复旦大学应用表面物理国家重点实验室, 上海　200433