“Inject-Mix-React-Separate-and-Quantitate” (IMReSQ) Method for Screening Enzyme Inhibitors
Bibliographic record
Abstract
Many regulatory enzymes are considered attractive therapeutic targets, and their inhibitors are potential drug candidates. Screening combinatorial libraries for enzyme inhibitors is pivotal to identifying hit compounds for the development of drugs targeting regulatory enzymes. Here, we introduce the first inhibitor screening method that consumes only nanoliters of the reactant solutions and is applicable to regulatory enzymes. The method is termed inject-mix-react-separate-and-quantitate (IMReSQ) and includes five steps. First, nanoliter volumes of substrate, candidate inhibitor, and enzyme solutions are injected by pressure into a capillary as separate plugs. Second, the plugs are mixed inside this capillary microreactor by transverse diffusion of laminar flow profiles. Third, the reaction mixture is incubated to form the enzymatic product. Fourth, the product is separated from the substrate inside the capillary by electrophoresis. Fifth, the amounts of the product and substrate are quantitated. In this proof-of-principle work, we applied IMReSQ to study inhibition of recently cloned protein farnesyltransferase from parasite Entamoeba histolytica. This enzyme is a potential therapeutic target for antiparasitic drugs. We identified three previously unknown inhibitors of this enzyme and proved that IMReSQ could be used for quantitatively ranking the potencies of inhibitors.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".