Nanobodies as Probes for Protein Dynamics in Vitro and in Cells
Bibliographic record
Abstract
Nanobodies are the recombinant antigen-recognizing domains of the minimalistic heavy chain-only antibodies produced by camels and llamas. Nanobodies can be easily generated, effectively optimized, and variously derivatized with standard molecular biology protocols. These properties have triggered the recent explosion in the nanobody use in basic and clinical research. This review focuses on the emerging use of nanobodies for understanding and monitoring protein dynamics on the scales ranging from isolated protein domains to live cells, from nanoseconds to hours. The small size and high solubility make nanobodies uniquely suited for studying protein dynamics by NMR. The ability to produce conformation-sensitive nanobodies in cells enables studies that link structural dynamics of a target protein to its cellular behavior. The link between in vitro and in-cell dynamics, afforded by nanobodies, brings the analysis of such important events as receptor signaling, membrane protein trafficking, and protein interactions to the next level of resolution.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".