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Deciphering the mechanism of protein aggregation and effects of inhibitors using single-molecule mass photometry

Lyons, A.; Paul, S. S.; MacArthur, J. E.; Hoffmann, G. N.; Yarahmady, A.; Mok, S.-A.; Woodside, M. T.

2025-08-13 biophysics
10.1101/2025.08.11.668757 bioRxiv
Show abstract

Aggregation of misfolded proteins is a prominent feature of many diseases and hence an attractive drug target. However, the small oligomers that are critical early species in the aggregation cascade are difficult to monitor directly owing to their heterogeneity and transience, complicating efforts to define aggregation mechanisms and target oligomers therapeutically. Here, we observe changes in oligomer populations directly using single-molecule mass photometry (SMMP). Studying the pathogenic P301L mutant of tau protein linked to frontotemporal dementia, we globally fit the growth/decay kinetics for every oligomer population observed by SMMP to microscopic models of aggregation. A simple extension to the best-fit model also accounts for amyloid fibril kinetics, as monitored by Thioflavin T fluorescence, providing the first quantitative model of aggregation kinetics across all stages of the cascade based on direct observation. Crucially, we find that models fitting amyloid kinetics alone fail to capture oligomer behavior, implying that--contrary to standard practice--amyloid kinetics cannot be relied on to deduce aggregation mechanisms. Furthermore, there is no single rate-limiting nucleation step preceding rapid growth, as generally assumed, suggesting that standard models of aggregation are overly simplistic. Repeating the analysis in the presence of aggregation inhibitors allows identification of the discrete steps in the cascade affected by the inhibitors. This work presents a powerful new approach for defining protein aggregation mechanisms and the mechanism of action of inhibitors, with applications to understanding many diseases and developing novel therapeutics.

Published in Proceedings of the National Academy of Sciences (predicted rank #6) · training set

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