Stochastic detection of Pim protein kinases reveals electrostatically enhanced association of a peptide substrate
Bibliographic record
Abstract
In stochastic sensing, the association and dissociation of analyte molecules is observed as the modulation of an ionic current flowing through a single engineered protein pore, enabling the label-free determination of rate and equilibrium constants with respect to a specific binding site. We engineered sensors based on the staphylococcal α-hemolysin pore to allow the single-molecule detection and characterization of protein kinase-peptide interactions. We enhanced this approach by using site-specific proteolysis to generate pores bearing a single peptide sensor element attached by an N-terminal peptide bond to the trans mouth of the pore. Kinetics and affinities for the Pim protein kinases (Pim-1, Pim-2, and Pim-3) and cAMP-dependent protein kinase were measured and found to be independent of membrane potential and in good agreement with previously reported data. Kinase binding exhibited a distinct current noise behavior that forms a basis for analyte discrimination. Finally, we observed unusually high association rate constants for the interaction of Pim kinases with their consensus substrate Pimtide (~10(7) to 10(8) M(-1) · s(-1)), the result of electrostatic enhancement, and propose a cellular role for this phenomenon.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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".