Sensitivity of secondary structure propensities to sequence differences between α‐ and γ‐synuclein: Implications for fibrillation
Bibliographic record
Abstract
The synucleins are a family of intrinsically disordered proteins involved in various human diseases. alpha-Synuclein has been extensively characterized due to its role in Parkinson's disease where it forms intracellular aggregates, while gamma-synuclein is overexpressed in a majority of late-stage breast cancers. Despite fairly strong sequence similarity between the amyloid-forming regions of alpha- and gamma-synuclein, gamma-synuclein has only a weak propensity to form amyloid fibrils. We hypothesize that the different fibrillation tendencies of alpha- and gamma-synuclein may be related to differences in structural propensities. Here we have measured chemical shifts for gamma-synuclein and compared them to previously published shifts for alpha-synuclein. In order to facilitate direct comparison, we have implemented a simple new technique for re-referencing chemical shifts that we have found to be highly effective for both disordered and folded proteins. In addition, we have developed a new method that combines different chemical shifts into a single residue-specific secondary structure propensity (SSP) score. We observe significant differences between alpha- and gamma-synuclein secondary structure propensities. Most interestingly, gamma-synuclein has an increased alpha-helical propensity in the amyloid-forming region that is critical for alpha-synuclein fibrillation, suggesting that increased structural stability in this region may protect against gamma-synuclein aggregation. This comparison of residue-specific secondary structure propensities between intrinsically disordered homologs highlights the sensitivity of transient structure to sequence changes, which we suggest may have been exploited as an evolutionary mechanism for fast modulation of protein structure and, hence, function.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".