Site-Specific Fluorogenic Protein Labelling Agent for Bioconjugation
Bibliographic record
Abstract
Many clinically relevant therapeutic agents are formed from the conjugation of small molecules to biomolecules through conjugating linkers. In this study, two novel conjugating linkers were prepared, comprising a central coumarin core, functionalized with a dimaleimide moiety at one end and a terminal alkyne at the other. In our first design, we developed a protein labelling method that site-specifically introduces an alkyne functional group to a dicysteine target peptide tag that was genetically fused to a protein of interest. This method allows for the subsequent attachment of azide-functionalized cargo in the facile synthesis of novel protein-cargo conjugates. However, the fluorogenic aspect of the reaction between the linker and the target peptide was less than we desired. To address this shortcoming, a second linker reagent was prepared. This new design also allowed for the site-specific introduction of an alkyne functional group onto the target peptide, but in a highly fluorogenic and rapid manner. The site-specific addition of an alkyne group to a protein of interest was thus monitored in situ by fluorescence increase, prior to the attachment of azide-functionalized cargo. Finally, we also demonstrated that the cargo can also be attached first, in an azide/alkyne cycloaddition reaction, prior to fluorogenic conjugation with the target peptide-fused protein.
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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) | 0.000 | 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 | 0.001 | 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".