Analysis of Huntingtin BioID Datasets 2019/04/09
Bibliographic record
Abstract
Project: Investigation of putative HTT interacting proteins Experiment: Analysis of HTT BioID datasets Date completed: 2019/04/09 Rationale: BioID technology employs a promiscuous biotin ligase (BirA) fused to the terminus of the target protein, huntingtin, allowing proximal proteins to be biotinylated and then subsequently identified through mass spectrometry experiments. This technique has not been applied to assess huntingtin interactors to date in the published literature, so will provide a novel methodology to characterize the huntingtin interactome. As huntingtin is a large protein molecule and the precise location of the N and C-termini remain unresolved due to their flexible nature, both N and C terminally BirA-tagged constructs for full-length huntingtin will be generated for overexpression as well as a truncated construct spanning amino acids 80-3100, the region of the protein resolved in the recent cryo-electron microscopy structure which omits the flexible termini. Huntingtin fusion proteins will be overexpressed in cells subjected to different ROS stresses as well as control conditions. Resultant cell lysates will be analysed through collaboration with Prof. Anne-Claude Gingras (Lunenfeld Tanenbaum Research Institute, University of Toronto). From this work, we hope to obtain a list of putative huntingtin interactors which will be compared to previously published findings and assessed for stable complex formation with huntingtin
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.036 |
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".