Combining small-molecule bioconjugation and hydrogen-deuterium exchange mass spectrometry (HDX-MS) to expose allostery: the case of human cytochrome P450 3A4
Bibliographic record
Abstract
Abstract We report herein a novel approach to study allostery which combines the use of carefully selected bioconjugates and hydrogen-deuterium exchange mass spectrometry (HDX-MS). The utility of our method is demonstrated using human cytochrome P450 3A4 (CYP3A4). CYP3A4 is arguably the most important drug-metabolizing enzyme, and as such, is involved in numerous drug interactions. Diverse allosteric ligand effects have been reported for this enzyme, yet the structural mechanism of these phenomena remain poorly understood. We have described different CYP3A4-effector bioconjugates, some of which mimic the allosteric effect of positive effectors on CYP3A4, while others show activity enhancement even though the label does not occupy the allosteric pocket (agonistic), or do not show activation while still blocking the allosteric site (antagonistic). These bioonjugates were studied here by HDX-MS, which enabled us to better define the position of the allosteric site, and to identify important regions involved in CYP3A4 activation.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".