Characterization and application studies of ProxyPhos, a chemosensor for the detection of proximally phosphorylated peptides and proteins in aqueous solutions
Bibliographic record
Abstract
Proximal phosphorylation on proteins appears to have functional significance and has been associated with several diseases, including Alzheimer's and cancer. While much remains to be learned about the role of proximal phosphorylation in biological systems, no simple and/or affordable technique is available for its detection. To this end, we have previously developed a ProxyPhos chemosensor, which detects proximally phosphorylated peptides and proteins over mono- and non-phosphorylated motifs in aqueous solutions. In this follow-up work, we performed extensive characterization of peptide and protein ProxyPhos assay conditions to achieve enhanced detection, and further explored the selectivity of ProxyPhos, and its potential off-targets. As a result of characterization studies, selective sensing of proximally phosphorylated over mono-phosphorylated peptides and proteins was achieved. Moreover, studies demonstrated that ProxyPhos was compatible with the detection of all commonly phosphorylated residues (i.e. tyrosine, serine and threonine residues). Under optimized conditions, ProxyPhos efficiently discriminated between peptides derived from the activated (proximally phosphorylated, disease-relevant) and inactive (mono-phosphorylated) forms of JAK2, SYK and MAPK1 kinases. In addition, ProxyPhos can be used to probe phosphatase activity on peptides and proteins via detecting changes in proximal phosphorylation, demonstrating immediate utility of this chemosensing system.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".