Mechanistic Characterization of Covalent Enzyme Inhibition by Isothermal Titration Calorimetry Kinetic Competition (ITC-KC)
Bibliographic record
Abstract
Covalent enzyme inhibitors can offer high potency and specificity and are increasingly sought after in drug discovery. They typically inhibit in two steps: noncovalent binding followed by covalent bond formation. Rational optimization requires quantitative information on both steps. Current methods for measuring these steps are technically demanding, time-consuming, and are not well suited for routine insertion into drug discovery pipelines. We have developed a new approach, using isothermal titration calorimetry kinetic competition (ITC-KC), that overcomes many of these challenges. ITC-KC measures enzyme activity directly, via the heat flow generated during catalysis, making it a sensitive and nearly universal approach. We performed extensive numerical simulations in which ITC-KC outperformed current methods with 3- to 10-fold greater accuracy. We applied ITC-KC to a library of 19 inhibitors of the protease 3CL pro from SARS-CoV-2 and found that the reactive warheads and noncovalent binding portions of these molecules influenced the two-step inhibition mechanism in complex and unpredictable ways. This highlights the need for detailed mechanistic information in the development of covalent inhibitors, information that ITC-KC can provide rapidly, accurately, and essentially universally.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".