A modified tandem affinity purification strategy identifies cofactors of the <b><i>Drosophila</i></b> nuclear receptor dHNF4
Bibliographic record
Abstract
With the completion of numerous genome projects, new high-throughput methods are required to ascribe gene function and interactions. A method proven successful in yeast for protein interaction studies is tandem affinity purification (TAP) of native protein complexes followed by MS. Here, we show that TAP, using Protein A and CBP tags, is not generally suitable for the purification and identification of proteins from tissues. A head-to-head comparison of tags shows that two others, FLAG and His, provide protein yields from Drosophila tissues that are an order of magnitude higher than Protein A and CBP. FLAG-His purification worked sufficiently well so that two cofactors of the Drosophila nuclear receptor protein dHNF4 could be purified from whole animals. These proteins, Hsc70 and Hsp83, are important chaperones and cofactors of other nuclear receptor proteins. However, this is the first time that they have been shown to interact with a non-steroid binding nuclear receptor. We show that the two proteins increase the ability of dHNF4 to bind DNA in vitro and to function in vivo. The tags and approaches developed here will help facilitate the routine purification of proteins from complex cells, tissues and whole organisms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".