Back

Probing the energy landscape of α-Synuclein amyloid fibril formation by systematic K-to-Q mutagenesis

Kunka, A.; Farzadfard, A.; Larsen, J. A.; Saraceno, F.; Norrild, R. K.; Fricke, C.; Mohammad-Beigi, H.; Sadek, A.; Folke, J.; Aznar, S.; Buell, A. K.

2025-11-03 biophysics
10.1101/2025.11.01.685997 bioRxiv
Show abstract

The aggregation of natively disordered -Synuclein (Syn) into amyloid fibrils is a hallmark of Parkinsons and other neurodegenerative diseases. Understanding Syns pathological role remains a major challenge due to its complex, context-dependent energy landscape characterized by conformational plasticity and fibril polymorphism. Here, we present a systematic mutational analysis as a quantitative probe of the Syn energy landscape, focusing on electrostatic contributions to key aggregation pathways. We engineered Syn variants with one to eight lysine-to-glutamine substitutions and analyzed their aggregation under controlled conditions to delineate their effects on nucleation, elongation, seed amplification, fibril stability, and fibril polymorphism. We find that Syn aggregation from a homogenous solution can be modelled well using global properties, including protein concentration, charge, and ionic strength. Microscopic pathways and the resulting fibril polymorphs are instead modulated by sequence-specific effects. We identify mutations of residues found in fibril cores as perturbations that significantly modify the Syn free energy landscape, creating pathways and energy minima not accessible to the WT under the same experimental conditions. In contrast, mutations outside of the fibril core affect the magnitude of the relevant energy barriers whilst overall maintaining a WT-like free energy landscape. Our work outlines a scalable, quantitative framework that increases the informational output of the mutational studies of Syn using conventional assays. The approach can be extended by incorporating additional mutational and functional data to deepen our understanding of Syns energy landscape and its role in health and disease.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Chemical Science
71 papers in training set
Top 0.1%
18.4%
2
Journal of Chemical Theory and Computation
126 papers in training set
Top 0.1%
10.0%
3
The Journal of Physical Chemistry B
158 papers in training set
Top 0.1%
10.0%
4
Biophysical Journal
545 papers in training set
Top 0.6%
8.3%
5
Journal of Chemical Information and Modeling
207 papers in training set
Top 0.7%
7.1%
50% of probability mass above
6
PLOS Computational Biology
1633 papers in training set
Top 7%
4.8%
7
The Journal of Physical Chemistry Letters
58 papers in training set
Top 0.3%
4.1%
8
Nature Communications
4913 papers in training set
Top 40%
3.5%
9
Scientific Reports
3102 papers in training set
Top 40%
3.2%
10
eLife
5422 papers in training set
Top 32%
2.6%
11
Journal of the American Chemical Society
199 papers in training set
Top 3%
2.1%
12
Frontiers in Molecular Biosciences
100 papers in training set
Top 2%
1.7%
13
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 33%
1.7%
14
Nucleic Acids Research
1128 papers in training set
Top 11%
1.7%
15
Computational and Structural Biotechnology Journal
216 papers in training set
Top 5%
1.5%
16
Journal of The Royal Society Interface
189 papers in training set
Top 3%
1.5%
17
Journal of Molecular Biology
217 papers in training set
Top 2%
1.3%
18
ACS Chemical Neuroscience
60 papers in training set
Top 2%
0.9%
19
PLOS ONE
4510 papers in training set
Top 66%
0.8%
20
Physical Biology
43 papers in training set
Top 2%
0.8%
21
iScience
1063 papers in training set
Top 30%
0.8%
22
Structure
175 papers in training set
Top 3%
0.7%
23
Protein Science
221 papers in training set
Top 2%
0.6%
24
The Journal of Chemical Physics
49 papers in training set
Top 0.5%
0.6%