Isoform‐specific variation in the intrinsic disorder of troponin I
Bibliographic record
Abstract
Various intrinsic disorder (ID) prediction algorithms were applied to the three tissue isoforms of troponin I (TnI). The results were interpreted in terms of the known structure and dynamics of troponin. In line with previous results, all isoforms of TnI were predicted to have large stretches of ID. The predictions show that the C-termini of all isoforms are extensively disordered as is the N-terminal extension of the cardiac isoform. Cardiac TnI likely belongs to the group of intrinsically disordered signalling hub proteins. For a given portion of the protein sequence, most ID prediction approaches indicate isoform-dependent variations in the probability of disorder. Comparison of machine learning and physically based approaches suggests the ID variations are only partially attributable to local variations in the ratio of charged to hydrophobic residues. The VSL2B algorithm predicts the largest variations in ID across the isoforms, with the cardiac isoform having the highest probability of structured regions, and the fast-skeletal isoform having no intrinsic structure. The region corresponding to residues 57-95 of the fast-skeletal isoform, known to form a coiled coil substructure with troponin T, was highly variable between isoforms. The isoform-specific ID variations may have mechanistic significance, modulating the extent to which conformational fluctuations in tropomyosin are communicated to the troponin complex. We discuss structural mechanisms for this communication. Overall, the results motivate the development of predictors designed to address relative levels of disorder between highly similar proteins.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".