Design of Efficient Artificial Enzymes Using Crystallographically Enhanced Conformational Sampling
Bibliographic record
Abstract
The ability to create efficient artificial enzymes for any chemical reaction is of great interest. Here, we describe a computational design method for increasing the catalytic efficiency of de novo enzymes by several orders of magnitude taking advantage of X- ray crystallography data and ensemble refinement. Our approach circumvents the need for labor-intensive directed evolution and high-throughput screening methods typically used to improve the activity of de novo enzymes. We used Phenix ensemble refinement (Burnley et al. 2012) to generate ensemble models from dynamics-based refinement against room temperature X-ray diffraction data collected from crystals of Kemp eliminases HG3 (kcat/KM 125 M−1 s−1) and KE70 (kcat/KM 57 M−1 s−1). Using backbone templates from these ensemble models, we designed, for each of the two enzymes, ≤10 sequences predicted to catalyze this reaction more efficiently. The most active designs display kcat/KM values improved by 100−250-fold, comparable to mutants obtained after screening thousands of variants in multiple rounds of directed evolution. Crystal structures show excellent agreement with computational models, with catalytic contacts present as designed and transition-state root-mean-square deviations of ≤0.65 Å. Our work shows how a more precise sampling of backbone dynamics and conformational sub-states through ensemble refinement can improve de novo enzyme design algorithms for producing more efficient artificial enzymes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".