In Vivo Investigation of Protein–Protein Interactions for Helicases Using Tandem Affinity Purification
Bibliographic record
Abstract
A key component in determining the functional role of any protein is the elucidation of its binding partners using protein-protein interaction (PPI) data. Here we examine the use of tandem affinity purification (TAP) tagging to study RNA/DNA helicase PPIs in Escherichia coli. The tag, which consists of a calmodulin-binding region, a TEV protease recognition sequence, and an IgG-binding domain, is introduced into E. coli using a lambdared recombination system. This method prevents the overproduction of the target protein, which could generate false interactions. The interacting proteins are then affinity purified using double affinity purification steps and are separated by SDS-PAGE followed by mass spectrometry identification. Each protein identified would represent a physical interaction in the cell. These interactions may potentially be mediated by an RNA/DNA template, for which the helicase would likely be needed to disrupt the secondary structures.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".