Amplification of Screening Sensitivity through Selective Destruction: Theory and Screening of a Library of Carbonic Anhydrase Inhibitors
Bibliographic record
Abstract
A new method for identifying enzyme inhibitors is to conduct their synthesis in the presence of the targeted enzyme. Good inhibitors form in larger amounts than poorer ones because the binding either speeds up synthesis (target-accelerated synthesis) or shifts the synthesis equilibrium (dynamic combinatorial libraries). Several groups have successfully demonstrated this approach with simple systems, but application to larger libraries is challenging because of the need to accurately measure the amount of each inhibitor. In this report, we dramatically simplify this analysis by adding a reaction that destroys the unbound inhibitors. This works similar to a kinetic resolution, with the best inhibitor being the last one remaining. We demonstrate this method for a static library of several sulfonamide inhibitors of carbonic anhydrase. Four sulfonamide-containing dipeptides, EtOC-Phe(sa)-Phe (4a), EtOC-Phe(sa)-Gly (4b), EtOC-Phe(sa)-Leu (4c) and EtOC-Phe(sa)-Pro (4d), were prepared and their inhibition constants measured. These inhibitors migrated to the carbonic anhydrase compartment of a two-compartment vessel. Although higher concentrations of the better inhibitors were observed in the carbonic anhydrase compartment, the concentration differences were small (1.83:1.71:1.54:1.46:1 for 4a:4b:4c:4d:5, where 5 is a noninhibiting dipeptide EtOC-Phe-Phe). Addition of a protease rapidly cleaved the weaker inhibitors (4d and 5). Intermediate inhibitor 4c was cleaved at a slower rate, and at the end of the reaction, only 4a and 4b remained. In a separate experiment, the ratio of 4a to 4b was found to increase over time to a final ratio of nearly 4:1. This is greater than the ratio of their inhibition constants (approximately 2:1). The theoretical model predicts that these ratios would increase even further as the destruction proceeds. This removal of poorer inhibitors simplifies identification of the best inhibitor in a complex 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".