Design and characterisation of mutant and wild-type huntingtin proteins produced from a toolkit of scalable eukaryotic expression systems
Bibliographic record
Abstract
ABSTRACT: The pathogenic Huntington’s disease (HD) mutation causes polyglutamine (polyQ) tract expansion of the 348 kDa HTT protein above a critical threshold of ~35 glutamines. HD mutation effect on HTT is poorly understood, partly due to difficulties in performing biochemical studies with this large protein. To facilitate such studies, we generated resources for HTT production in multiple eukaryotic expression systems, comprising constructs with polyQ lengths representing general population, HD patients, juvenile HD patients and the more extreme expansions used in some tissue and animal models. These reagents yield milligram quantities of pure HTT protein. We biophysically characterised HTT samples produced using this HD resource, gleaning insight into the nature of full-length HTT in its apo form and when bound to its binding partner HAP40. Work outlined in this manuscript and the tools generated, lay a foundation for further biochemical study of the HTT protein and its functional interactions with other biomolecules. NB: HTT_Manuscript_20190319.zip should contain all of the raw data used for this manuscript including raw images for the Western blots, missing in the previous upload.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".