Purification of the HTT N-HEAT (81-1643) with various buffer conditions
Bibliographic record
Abstract
Previously, I purified the HTT N-HEAT_81-1643 domain and showed that the HTT N-HEAT_81-1643 domain elutes early from a Superose 6 column 10/300 (https://zenodo.org/record/3462496#.XjsVXSNOk2w). Initial biophysical characterization by DLS showed samples of the HTT N-HEAT_81-1643 construct are made up of large particles ( https://zenodo.org/record/3562523#.Xjw57iN, https://zenodo.org/record/3561096#.Xjw6ByNOk2w). Thus, our initial results all pointed out at having high oligomeric states of the HTT N-HEAT_81-1643 construct in solution. To rule out the possibility of a weak interaction with nucleic acid material which could cause the large particle size in solution, we tested whether we could remove the nucleic acid material by adding an additional purification step with heparin resin. However, purification using the additional heparin step showed no improvement in the purity of the sample from other protein impurities or nucleic acid material (https://zenodo.org/record/3562523#.XjsVyyNOk2w). Determination of ideal buffer conditions for the HTT N-HEAT_81-1643 construct using DSLS and DSF (https://zenodo.org/record/3562523#.Xjw57iN https://zenodo.org/record/3519364#.Xjw6ICNOk2w, https://zenodo.org/record/3561087#.Xjw6LiNOk2w) was not possible as no significant changes in the Taag or Tm were observed for the conditions tested. Further, the data for both DSF and DSLS could have presented challenges to fit (e.g. The DSF data showed very high initial fluorescence while the DSLS data showed the Tagg for this construct was at the end of the detection limit ~ 90 °C). Thus, to further assess if we could find an ideal buffer condition where the HTT N-HEAT_81-1643 construct is the most stable and monomeric, we tested different purification conditions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".