Development of a multifunctional toolkit of intrabody-based biosensors recognizing the V5 peptide tag: highlighting applications with G protein-coupled receptors
Bibliographic record
Abstract
ABSTRACT/SUMMARY Protein-protein interactions (PPIs) form the underpinnings of any cellular signaling network. PPIs are highly dynamic processes and often, cell-based assays can be essential for their study as they closely mimic the biological intricacies of cellular environments. Since no sole platform can perform all needed experiments to gain a thoroughly comprehensive understanding into these processes, developing a versatile toolkit is much needed to address this longstanding gap. The use of small peptide tags, such as the V5-tag, has been extensively used in biological and biomedical research, including labeling the C-termini of one of the largest human genome-wide open-reading frame collections. However, these small peptide tags have been primarily used in vitro and lack the in vivo traceability and functionality of larger specialized tags. In this study, we combined structural studies and computer-aided maturation to generate an intracellular nanobody, interacting with the V5-tag. Suitable for assays commonly used to study protein-protein interactions, our nanobody has been applied herein to interrogate G protein-coupled receptor signalling. This novel serviceable intrabody is the cornerstone of a multipurpose intracellular nanobody-based biosensors toolkit, named iBodyV5, which will be available for the scientific community at large.
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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 | 0.001 | 0.001 |
| 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".