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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".