Distal mutations in a designed retro-aldolase alter loop dynamics to shift and accelerate the rate-limiting step
Bibliographic record
Abstract
Abstract Amino-acid residues distant from an enzyme’s active site are known to influence catalysis, but their mechanistic contributions to the catalytic cycle remain poorly understood. Here, we investigate the structural, functional, and mechanistic impacts of distal and active-site mutations discovered through directed evolution of the computationally designed retro-aldolase RA95. Active-site mutations improve catalytic efficiency by 3,600-fold, while distal mutations alone offer no improvement. When combined with active-site mutations, distal mutations further increase efficiency by 6-fold, demonstrating an epistatic effect. X-ray crystallography and molecular dynamics simulations reveal that distal mutations promote active site opening by altering loop dynamics. Kinetic solvent viscosity effects and electrostatic analysis show that distal mutations accelerate the chemical transformation by 100-fold, shifting the rate-limiting step to product release, which is further accelerated by the increased opening of the active site. These findings highlight the critical role of distal residues in shaping the active-site environment and facilitating the structural dynamics essential for progression through the catalytic cycle.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".