Studying Protein Function and the Role of Altered Protein Expression by Antibody Interference and Three-dimensional Reconstructions
Bibliographic record
Abstract
A strict management of protein expression is not only essential to every organism alive, but also an important strategy to investigate protein functions in cellular models. Therefore, recent research invented different tools to target protein expression in mammalian cell lines or even animal models, including RNA and antibody interference. While the first strategy has gathered much attention during the past two decades, peptides mediating a translocation of antibody cargos across cellular membranes and into cells, obtained much less interest. In this publication, we provide a detailed protocol how to utilize a peptide carrier named Chariot in human embryonic kidney cells as well as in primary hippocampal neurons to perform antibody interference experiments and further illustrate the application of three-dimensional reconstructions in analyzing protein function. Our findings suggest that Chariot is, probably due to its nuclear localization signal, particularly well-suited to target proteins residing in the soma and the nucleus. Remarkably, when applying Chariot to primary hippocampal cultures, the reagent turned out to be surprisingly well accepted by dissociated neurons.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".