Customizing the Structure of Minimal TIM Barrels to Craft Efficient <i>De Novo</i> Enzymes
Bibliographic record
Abstract
Abstract The TIM barrel is the most prevalent fold in natural enzymes, supporting efficient catalysis of diverse chemical reactions. While de novo TIM barrels have been successfully designed, their minimalistic architecture lacks structural elements essential for substrate binding and catalysis. Here, we present CANVAS, a computational workflow that introduces a structural lid into a minimal de novo TIM barrel to anchor catalytic residues and form an active-site pocket for enzymatic function. Starting from two de novo TIM barrels, we designed nine variants with distinct lids to form active sites for the Kemp elimination. Four designs showed measurable activity, with the most active reaching a catalytic efficiency of 21,000 M⁻¹ s⁻¹ at its optimal pH. A co-crystal structure of this variant bound to a transition-state analogue confirmed the accuracy of the designed lid and active site. Using the X-ray structure of a lower-activity variant (19 M⁻¹ s⁻¹), we applied ensemble-based design to optimize its active site, increasing catalytic efficiency by >1,600-fold to 32,000 M⁻¹ s⁻¹. These results demonstrate that de novo TIM barrels can be endowed with substrate binding pockets supporting efficient catalytic function, establishing a platform for building enzymes on demand from minimal protein scaffolds.
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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".