A general approach for chemical labeling and rapid, spatially controlled protein inactivation
Bibliographic record
Abstract
Chemical labeling of proteins inside of living cells can enable studies of the location, movement, and function of proteins in vivo. Here we demonstrate an approach for chemical labeling of proteins that uses the high-affinity interaction between an FKBP12 mutant (F36V) and a synthetic, engineered ligand (SLF'). A fluorescein conjugate to the engineered ligand (FL-SLF') retained binding to FKBP12(F36V) and possessed similar fluorescence properties as parental fluorescein. FL-SLF' labeled FKBP12(F36V) fusion proteins in live mammalian cells, and was used to monitor the subcellular localization of a membrane targeted FKBP12(F36V) construct. Chemical labeling of FKBP12(F36V) fusion proteins with FL-SLF' was readily detectable at low expression levels of the FKBP12(F36V) fusion, and the level of fluorescent staining with FL-SLF' was proportional to the FKBP12(F36V) expression level. This FL-SLF'-FKBP12(F36V) labeling technique was tested in fluorophore assisted laser inactivation (FALI), a light-mediated technique to rapidly inactivate fluorophore-labeled target proteins. FL-SLF' mediated FALI of a beta-galactosidase-FKBP12(F36V) fusion protein, causing rapid inactivation of >90% of enzyme activity upon irradiation in vitro. FL-SLF' also mediated FALI of a beta-galactosidase fusion expressed in living NIH 3T3 cells, where beta-galactosidase activity was reduced in 15 s. Thus, FL-SLF' can be used to monitor proteins in vivo and to target rapid, spatially and temporally defined inactivation of target proteins in living cells in a process that we call FK-FALI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".