Optimized aqueous Kinugasa reactions for bioorthogonal chemistry applications
Bibliographic record
Abstract
Kinugasa reactions hold potential for bioorthogonal chemistry in that the reagents can be biocompatible. Unlike other bioorthogonal reaction products, β-lactams are potentially reactive, which can be useful for synthesizing new biomaterials. A limiting factor for applications consists of slow reaction rates. Herein, we report an optimized aqueous copper(i)-catalyzed alkyne-nitrone cycloaddition involving rearrangement (CuANCR) with rate accelerations made possible by the use of surfactant micelles. We have investigated the factors that accelerate the aqueous CuANCR reaction and demonstrate enhanced modification of a model membrane-associated peptide. We discovered that lipids/surfactants and alkyne structure have a significant impact on the reaction rate, with biological lipids and electron-poor alkynes showing greater reactivity. These new findings have implications for the use of CuANCR for modifying integral membrane proteins as well as live cell labelling and other bioorthogonal applications.
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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) | 0.001 | 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) | 0.002 | 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".