Amyloid Aggregation under the Lens of Liquid–Liquid Phase Separation

Yanting Xing(Clemson University), Aparna Nandakumar(Monash University), Aleksandr Käkinen(The University of Queensland), Yunxiang Sun(The University of Queensland), Thomas P. Davis(The University of Queensland), Pu Chun Ke(Monash University), Feng Ding(Ningbo University)
The Journal of Physical Chemistry Letters
December 24, 2020
Cited by 76Open Access
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Abstract

Increasing experiments suggest that amyloid peptides can undergo liquid–liquid phase separation (LLPS) before the formation of amyloid fibrils. However, the exact role of LLPS in amyloid aggregation at the molecular level remains elusive. Here, we investigated the LLPS and amyloid fibrillization of a coarse-grained peptide, capable of capturing fundamental properties of amyloid aggregation over a wide range of concentrations in molecular dynamics simulations. On the basis of the Flory–Huggins theory of polymer solutions, we determined the binodal and spinodal concentrations of LLPS in the low-concentration regime, ϕBL and ϕSL, respectively. Only at concentrations above ϕBL, peptides formed metastable or stable oligomers corresponding to the high-density liquid phase (HDLP) in LLPS, out of which the nucleated conformational conversion to fibril seeds occurred. Below ϕSL, the HDLP was metastable and transient, and the subsequent fibrillization process followed the traditional nucleation and elongation mechanisms. Only above ϕSL, the HDLP became stable, and the initial fibril nucleation and growth were governed by the high local peptide concentrations. The predicted saturation of amyloid aggregation half-times with increasing peptide concentration to a constant, instead of the traditional power-law scaling to zero, was confirmed by simulations and by a thioflavin-T kinetic assay and the transmission electron microscopy of islet amyloid polypeptide (IAPP) aggregation. Our study provides a unified picture of amyloid aggregation for a wide range of concentrations within the framework of LLPS, which may help us better understand the etiology of amyloid diseases, where the amyloid protein concentration can vary by ∼9 orders of magnitude depending on the organ location and facilitate the engineering of novel amyloid-based functional materials.


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