Deciphering the mechanism of protein aggregation and effects of inhibitors using single-molecule mass photometry
Bibliographic record
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 (MP). 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 MP 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 a 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 approach for defining protein aggregation mechanisms and the mechanism of action of inhibitors, with applications to understanding many diseases and developing novel therapeutics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".