Site‐specific conjugation of the quencher on peptide's N‐terminal for the synthesis of a targeted non‐spreading activatable optical probe
Bibliographic record
Abstract
Optical imaging offers high sensitivity and portability at low cost. The design of 'smart' or 'activatable' probes can decrease the background noise and increase the specificity of the signal. By conjugating a fluorescent dye and a compatible quencher on each side of an enzyme's substrate, the signal remains in its 'off ' state until it reaches the area where a specific enzyme is expressed. However, the signal can leak from that area unless the dye is attached to a molecule able to bind to a specific target also presented in that area. The aim of this study was to (i) specifically conjugate the quencher on the α-amino group of the peptide's N-terminus, (ii) conjugate the dye on the ε-amino group of a lysine in C-terminus, and (iii) conjugate the carboxyl group of the peptide's C-terminus to an amino group present on an antibody, using carbodiimide chemistry. The use of protecting groups, such as Boc or Fmoc, to allow site-specific conjugation, presents several drawbacks including 'on beads labeling', additional steps required for deprotection and removal from the resin, decreased yield, and dye degradation. A method of preferential labeling of α-amino N-terminal group in slightly acidic solution, proposed by Selo et al. (1996) has partially solved the problem. The present study reports improvements of the method allowing to (i) avoid the homo-bilabeling, (ii) increase the yield of the N-terminal labeling by two folds, and (iii) decrease the cost by 44-fold. Copyright © 2016 European Peptide Society and John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".