Comparison of Steroid Hormone Hydroxylations by and Docking to Human Cytochromes P450 3A4 and 3A5
Bibliographic record
Abstract
PURPOSE: Hydroxylation activity at the 6β-position of steroid hormones (testosterone, progesterone, and cortisol) by human cytochromes P450 (P450 or CYP) 3A4 and CYP3A5 and their molecular docking energy values were compared to understand the catalytic properties of the major forms of human CYP3A, namely, CYP3A4 and CYP3A5. METHODS: Testosterone, progesterone, and cortisol 6β-hydroxylation activities of recombinant CYP3A4 and CYP3A5 were determined by liquid chromatography. Docking simulations of these substrates to the heme moiety of reported crystal structures of CYP3A4 (Protein Data Bank code ITQN) and CYP3A5 (6MJM) were conducted. RESULTS: Michaelis constants (Km) for CYP3A5- mediated 6β-hydroxylation of testosterone and progesterone were approximately twice those for CYP3A4, whereas the value for cortisol 6β-hydroxylation mediated by CYP3A5 was similar to the value for that by CYP3A4. Maximal velocities (Vmax) of the three steroid hormones 6β-hydroxylation catalyzed by CYP3A5 were 30%-63% of those by CYP3A4. Thus, Vmax/ Km values of these hormones for CYP3A5 resulted in 22%- 31% of those for CYP3A4. The differences in the docking energies between CYP3A4 and CYP3A5 for steroid hormones were slightly correlated to the logarithm of CYP3A5/CYP3A4 ratios for Km values (substrate affinity). CONCLUSIONS: The Vmax, rather than Km values, for CYP3A5-mediated 6β-hydroxylation of three steroid hormones were different from those for CYP3A4. Molecular docking simulations could partially explain the differences in the accessibility of substrates to the heme moiety of human CYP3A molecules, resulting in the enzymatic affinity of CYP3A4 and CYP3A5.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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 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".