Optical chemosensors for the detection of proximally phosphorylated peptides and proteins
Bibliographic record
Abstract
Proximal multi-site phosphorylation is a critical post-translational modification in protein biology. The additive effects of multiple phosphosite clusters in close spatial proximity triggers integrative and cooperative effects on protein conformation and activity. Proximal phosphorylation has been shown to modulate signal transduction pathways and gene expression, and as a result, is implicated in a broad range of disease states through altered protein function and/or localization including enzyme overactivation or protein aggregation. The role of proximal multi-phosphorylation events is becoming increasingly recognized as mechanistically important, although breakthroughs are limited due to a lack of detection technologies. To date, there is a limited selection of facile and robust sensing tools for proximal phosphorylation. Nonetheless, there have been considerable efforts in developing optical chemosensors for the detection of proximal phosphorylation motifs on peptides and proteins in recent years. This review provides a comprehensive overview of optical chemosensors for proximal phosphorylation, with the majority of work being reported in the past two decades. Optical sensors, in the form of fluorescent and luminescent chemosensors, hybrid biosensors, and inorganic nanoparticles, are described. Emphasis is placed on the rationale behind sensor scaffolds, relevant protein motifs, and applications in protein biology.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".