General Chemoselective and Redox-Responsive Ligation and Release Strategy
Bibliographic record
Abstract
We report a switchable redox click and cleave reaction strategy for conjugating and releasing a range of molecules on demand. This chemoselective redox-responsive ligation (CRRL) and release strategy is based on a redox switchable oxime linkage that is controlled by mild chemical or electrochemical redox signals and can be performed at physiological conditions without the use of a catalyst. Both conjugation and release reactions are kinetically well behaved and quantitative. The CRRL strategy is synthetically modular and easily monitored and characterized by routine analytical techniques. We demonstrate how the CRRL strategy can be used for the dynamic generation of cyclic peptides and the ligation of two different peptides that are stable but can be selectively cleaved upon changes in the redox environment. We also demonstrate a new redox based delivery of cargoes to live cells strategy via the CRRL methodology by synthesizing a FRET redox-responsive probe that is selectively activated within a cellular environment. We believe the ease of the CRRL strategy should find wide use in a range of applications in biology, tissue engineering, nanoscience, synthetic chemistry, and material science and will expand the suite of current conjugation and release strategies.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".