神经退行性疾病相关蛋白病理性聚集和液液相分离研究进展

唐一鸣; 姚逸飞; 杨中元; 周运; 王子超; 韦广红

doi:10.12211/2096-8280.2023-005

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神经退行性疾病相关蛋白病理性聚集和液液相分离研究进展

特约评述 | 更新时间：2025-03-20

- 神经退行性疾病相关蛋白病理性聚集和液液相分离研究进展
- Pathological aggregation and liquid-liquid phase separation of proteins associated with neurodegenerative diseases
- 合成生物学 2023年4卷第3期页码：590-610
- 作者机构：
  
  复旦大学物理学系，表面物理国家重点实验室，计算物质科学教育部重点实验室，上海 200438
- 作者简介：
  
  [ "唐一鸣（1994—），男，博士后。研究方向为神经退行性疾病相关蛋白液液相分离的分子动力学模拟。 E-mail：ymtang@fudan.edu.cn" ]
  [ "韦广红（1969—），女，教授，博士生导师。研究方向为短肽纳米结构自组装，神经退行性疾病相关蛋白的构象分布、聚集和相分离微观机理的计算模拟。 E-mail：ghwei@fudan.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(12074079)
- DOI：10.12211/2096-8280.2023-005
  中图分类号： Q816
- 收稿：2023-01-12，
  
  修回：2023-03-28，
  
  纸质出版：2023-06-30
- 稿件说明：
移动端阅览
唐一鸣，姚逸飞，杨中元，周运，王子超，韦广红. 神经退行性疾病相关蛋白病理性聚集和液液相分离研究进展［J］. 合成生物学， 2023， 4（3）： 590-610

TANG Yiming， YAO Yifei， YANG Zhongyuan， ZHOU Yun， WANG Zichao， WEI Guanghong. Pathological aggregation and liquid-liquid phase separation of proteins associated with neurodegenerative diseases［J］. Synthetic Biology Journal， 2023， 4（3）： 590-610
唐一鸣，姚逸飞，杨中元，周运，王子超，韦广红. 神经退行性疾病相关蛋白病理性聚集和液液相分离研究进展［J］. 合成生物学， 2023， 4（3）： 590-610 DOI： 10.12211/2096-8280.2023-005.

TANG Yiming， YAO Yifei， YANG Zhongyuan， ZHOU Yun， WANG Zichao， WEI Guanghong. Pathological aggregation and liquid-liquid phase separation of proteins associated with neurodegenerative diseases［J］. Synthetic Biology Journal， 2023， 4（3）： 590-610 DOI： 10.12211/2096-8280.2023-005.

摘要

蛋白质的错误折叠和聚集与一系列神经退行性疾病密切相关，比如阿尔茨海默病、帕金森病等，其主要病理特征是以蛋白质异常聚集形成的淀粉样纤维为主要成分的包涵体。近期研究表明疾病相关蛋白大多能够发生液液相分离，形成动态可逆的液态凝聚物（亦称无膜细胞器），并参与细胞生理过程，而突变、翻译后修饰以及微环境等因素则能促进其发生不可逆液固相变形成病理性纤维。本文以几种神经退行性疾病相关蛋白为例，重点介绍蛋白质病理性聚集和液液相分离的实验研究方法和前沿进展，蛋白质相互作用、聚集和相分离微观机理的理论和计算研究，以及预测蛋白相分离能力的机器学习方法。这些研究对深入理解蛋白质病理性聚集、相变和相分离的微观机制，以及相关疾病致病机理具有重要的科学意义，并对治疗药物的设计和开发具有潜在应用价值。

Abstract

Protein misfolding and aggregation are closely related to the development of neurodegenerative diseases. Their main pathological hallmark is protein inclusion bodies

whose major components are amyloid fibrils formed by abnormal protein aggregation. For example

Alzheimer's disease is related to the amyloid plaques formed by β-amyloid proteins and the neurofibrillary tangles formed by tubulin-associated unit (tau) proteins. The pathological feature of Parkinson's disease is Lewy bodies formed by aggregation of α-synuclein. In addition

recent studies have shown that a majority of neurodegenerative disease-related proteins including Tau

α-synuclein

and TDP-43 can undergo liquid-liquid phase separation to form liquid condensates or membrane-free organelles. These condensates are involved in a number of cellular physiological processes

such as regulating signal transduction. Pathological fibrosis and liquid-liquid phase separation are two forms of protein aggregation

and protein liquid-liquid phase separation may be a driving force for misaggregation and fibrosis. Disease-related mutations

post-translational modifications including truncations

acetylations

and phosphorylations

and microenvironments such as pH

ion strength

and temperature can promote or inhibit liquid-solid phase transitions and the formation of pathological fibrils. Uncovering molecular mechanism underlying pathological protein aggregation and liquid-liquid phase separation is crucial to understanding the pathogenic process and developing effective therapeutic drugs as well. This review focuses on recent progress in experimental and computational studies on the pathological aggregation and liquid-liquid phase separation of neurodegenerative disease-related proteins

including β-amyloid

α-synuclein

TDP-43

tau

and FUS proteins. We briefly introduce the application of experimental methods (nuclear magnetic resonance

X-ray diffraction

and cryo-electron microscopy) for studying protein aggregation and determining fibril structure with cutting-edge techniques (differential interference contrast and fluorescence recovering after photobleaching) to explore protein phase separation. Advances in the conformational ensemble of proteins using enhanced sampling methods such as replica-exchange molecular dynamics simulations

and studies of the phase behavior of proteins using field-theoretic simulation and multiscale simulations are summarized. Machine learning in predicting protein phase separation ability is also addressed.

关键词

Keywords

references

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