Inhibitor Screening Using Immobilized Enzyme Reactor Chromatography/Mass Spectrometry
Bibliographic record
Abstract
We describe the coupling of capillary-scale monolithic enzyme reactor columns directly to a tandem mass spectrometer for screening of enzyme inhibitors. A two-channel nanoLC system is used to continuously infuse substrate or substrate/inhibitor mixtures through the column, allowing continuous variation of inhibitor concentration by simply altering the ratio of flow from the two pumps. In the absence of inhibitor, infusion of substrate leads to formation of product, and both substrate and product ions can be simultaneously monitored in a quantitative manner by MS/MS. The presence of inhibitor leads to a decrease in product and an increase in substrate concentration in the column eluent. Knowing the product/substrate ratio and the total analyte concentration (P + S), the concentration of product eluting, and hence the relative enzyme activity, can be determined. Both IC50 and KI values can then be obtained by direct MS detection of the effect of inhibitors on relative activity. Inhibitor screening is demonstrated using reusable, sol-gel derived, monolithic capillary columns containing adenosine deaminase, directly interfaced to ESI-MS/MS. On-column enzyme activity was assessed by monitoring inosine and adenosine elution. It is shown that the method can be used for automated screening of the effects of compound mixtures on ADA activity and to determine the KI value of the known inhibitor, erythro-9-(2-hydroxy-3-nonyl)adenine, even when the compound is present within a mixture.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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".