SpectraSage unveils specific proteolytic patterns of 20S on mono-ubiquitylated Tau proteoforms involved in neurodegeneration
Bibliographic record
Abstract
, age-related macular degeneration, glaucoma). A common feature of neurodegeneration is the progressive accumulation of amyloidogenic proteins such as beta-amyloid and tau protein (MAPT gene). There is compelling evidence that the aggregation propensity of tau protein is regulated by post-synthetic modifications including phosphorylation and ubiquitylation. These alterations are gaining increasing pathological relevance not only for brain tauopathies but also for the retinal/optic nerve degenerative diseases. In this regard, site-specific mono-ubiquitylated (Ub) tau proteoforms, have been recently identified in neurodegenerative brains. In this work, the cleavage patterns of the uncapped 20S proteasome acting on mono-Ub regio-isomers of tauK18, which covers the 4RD domain, have been unveiled by using SpectraSage, a novel proteomics software conceived for the MS1 identification of complex branched peptide and here introduced for the first time. Ub position was found to affect regio-isomers susceptibility to proteolysis and unexpectedly long Ub-tauK18 branched peptides have been identified, proving distinct catalytic preferences. These findings show that the 20S digests mono-Ub proteins through specific enzymatic mechanisms and the implications of the latter on neurodegeneration are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".