Enhancing a De Novo Enzyme Activity by Computationally-Focused, Ultra-Low-Throughput Sequence Screening
Bibliographic record
Abstract
Directed evolution has revolutionized protein engineering. Still, enzyme optimization by random library screening remains a sluggish process, in large part due to futile probing of mutations that are catalytically neutral and/or impair stability and folding. FuncLib (funclib-weizmann.ac.il) is a novel automated computational procedure which uses phylogenetic analysis and Rosetta design to rank enzyme variants with multiple mutations, on the basis of a stability metric. Here, we use it to target the active site region of a minimalist-designed, de novo Kemp eliminase. The similarity between the Michaelis complex and transition state for the enzymatic reaction makes this a particularly challenging system to optimize. Yet, experimental screening of a very small number of active-site, multi-point variants at the top of the predicted stability ranking leads to catalytic efficiencies and turnover numbers (~2·104 M-1 s-1 and ~102 s-1) that compare well with modern natural enzymes, and that approach the catalysis levels for the best Kemp eliminases derived from extensive screening. This result illustrates the promise of FuncLib as a powerful tool with which to speed up directed evolution, by guiding screening to regions of the sequence space that encode stable and catalytically diverse enzymes. Empirical valence bond calculations reproduce the experimental activation energies for the optimized eliminases to within ~2 kcal·mol-1 and indicate that the improvements in activity are linked to better geometric preorganization of the active site. This raises the possibility of further enhancing the stability-guidance of FuncLib by EVB-based computational predictions of catalytic activity, as a generalized approach for computational enzyme design.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".