Protein-Templated Click Ligation Reaction Triggered by Protein-Split Aptamer Interactions
Bibliographic record
Abstract
DNA-templated reactions have found wide applications in sensing and drug discovery. However, this strategy has been limited to the use of nucleic acids as templating elements to direct the proximity effect. Herein, we describe a versatile p rotein- t emplated sp lit a ptamer c lick l igation r eaction (PT-SpA-CLR) in which the protein template-induced covalent proximity ligation of split aptamer elements enables translating protein/aptamer binding events into the output of ligated DNA products. A ligation yield of >80% is observed for three model protein templates, including VEGF 165, PDGF-BB, and SARS-CoV-2 S1. The ligation reaction compensates for the weakness of reduced binding affinity resulting from splitting the aptamer, as evidenced by an approximately 2-fold lower dissociation constant than the non-ligated system. This newly developed PT-SpA-CLR strategy is further integrated with colorimetric or fluorescent reporting mechanisms to achieve easy-to-use and low-cost biosensors utilizing ligation to produce a fully active G-quadruplex or an RNA-cleaving DNAzyme to report protein binding. Both assays can achieve specific detection of an intended protein target with a limit of detection at the picomolar level even when challenged in biological samples. The combined PT-SpA-CLR and versatile sensing strategies offer attractive universal platforms for efficient detection of protein biomarkers.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".