Evolution tunes functional sub-state interconversion to boost enzyme function
Bibliographic record
Abstract
Enzymes do not operate as static structures, but continuously fluctuate between different conformations. Enzymes therefore dynamically sample conformations with varying catalytic activity. However, it remains largely unexplored whether evolution can exploit the conformational dynamics between sub-states to improve activity. Here, we dissect the evolutionary trajectory of the β-lactamase OXA-48 toward improved ceftazidime hydrolysis. Evolution relieved conformational bottlenecks by promoting alternate functional sub-states, gradually shifting the rate-limiting step from substrate binding to sub-state interconversion, and finally to the chemical step. Reorganization of the conformational landscape enhanced OXA-48's ability to hydrolyze ceftazidime and introduced a trade-off in its native activity against meropenem. This trade-off stemmed from catalytic incompatibility between the native and the evolved sub-state populations. Our findings highlight the transitions between functional sub-states as a mechanism of natural selection, shaping functional divergence and offering new strategies for enzyme and antibiotic engineering.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".