Disrupting Protein Complexes Using Tat-Tagged Peptide Mimics
Bibliographic record
Abstract
Protein-protein interaction is a widely existing phenomenon and is essential for almost all biological processes, extending from the formation of cellular macromolecular structures and enzymatic complexes to the regulation of signal transduction pathways. Proteins interact with each other through the dynamic associations between modular protein domains within different cellular compartments and with distinct temporal dynamics. Disrupting protein interactions has emerged as an effective way to specifically modulate certain signaling pathways. Tat-tagged peptide mimics are a recently developed experimental tool that is used to disrupt specific interactions between protein complexes. TAT, an 11-amino acid protein transduction domain from HIV Tat protein, is tagged to peptides that mimic the functional fragment of protein interaction domains, and facilitates the delivery of peptides into cells to disrupt the associated protein both competitively and selectively. Here we provide a technical description on the utilization of Tat-tagged peptide mimics as a tool to disrupt protein interaction in cultured neurons and in the rat brain.
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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) | 0.001 | 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 | 0.001 | 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".