Simultaneous Inhibition Assay for Human and Microbial Kinases via MALDI‐MS/MS
Bibliographic record
Abstract
Selective inhibition of one kinase over another is a critical issue in drug development. For antimicrobial development, it is particularly important to selectively inhibit bacterial kinases, which can phosphorylate antimicrobial compounds such as aminoglycosides, without affecting human kinases. Previous work from our group showed the development of a MALDI-MS/MS assay for the detection of small molecule modulators of the bacterial aminoglycoside kinase APH3'IIIa. Herein, we demonstrate the development of an enhanced kinase MALDI-MS/MS assay involving simultaneous assaying of two kinase reactions, one for APH3'IIIa, and the other for human protein kinase A (PKA), which leads to an output that provides direct information on selectivity and mechanism of action. Specificity of the respective enzyme substrates were verified, and the assay was validated through generation of Z'-factors of 0.55 for APH3'IIIa with kanamycin and 0.60 for PKA with kemptide. The assay was used to simultaneously screen a kinase-directed library of mixtures of ten compounds each against both enzymes, leading to the identification of selective inhibitors for each enzyme as well as one non-selective inhibitor following mixture deconvolution.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".