Modeling of cytochrome P450 (Cyt P450, CYP) channels
Bibliographic record
Abstract
The precise location of a substrate in cytochrome P450 (CYP) governs the orientation of the oxidation position. Such information is generally obtained from biochemical data, but modeling approaches have also been used to explain these locations. We used X-ray data and modeling techniques to distinguish between the series of putative linear or curved channels which lead the substrate from the outer side of the protein to the inner, and then into the heme pocket; these techniques were also used to identify the largest such channels. Two new methods for precisely determining the 3-D structure of proteins using X-ray crystallography were proposed in order to identify these channels: first, the use of both straight and curved channels, and second, the sphere method. These data are compared with Poulos channels, and with Caver (or Mol on line) modeling methodologies. Our methods were developed from studies of the interaction between cytochrome P450CAM(CYP101) fromPseudomonas putida(as expressed inEscherichia coli) and the indolic base β-carboline. Apart from the identification of potential access channels leading to the heme-containing active site, a new explanation was advanced for the substrate's hydroxylation position. The sphere method seems to have potential to become a general and direct method for prediction of substrate access channels from reduced- or low-resolution crystallographic data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".