Secondary environmental variation creates a shifting evolutionary watershed for the methyl-parathion hydrolase enzyme
Bibliographic record
Abstract
Abstract Enzymes can evolve new catalytic activity when their environments change to present them with novel substrates. Despite this seemingly straightforward relationship, factors other than the direct catalytic target can also impact enzyme adaptation. Here, we characterize the adaptive landscape separating an ancestral dihydrocoumarin hydrolase from a methyl parathion hydrolase descendant under eight different environments supplemented with alternative divalent metals. This variation shifts an evolutionary watershed, causing the outcome of adaptation to depend on the environment in which it occurs. The resultant landscapes also vary in terms both the number and the genotype(s) of “fitness peaks” as a result of genotype-by-environment ( G×E ) interactions and environment-dependent epistasis ( G×G×E ). This suggests that adaptive landscapes may be fluid and that molecular adaptation is highly contingent not only on obvious factors (such as catalytic targets) but also on less obvious secondary environmental factors that can direct it toward distinct outcomes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".