Nature of Long-Range Evolutionary Constraint in Enzymes: Insights from Comparison to Pseudoenzymes with Similar Structures
Bibliographic record
Abstract
Enzymes are known to fine-tune their sequences to optimize catalytic function, yet quantitative evolutionary design principles of enzymes remain elusive on the proteomic scale. Recently, it was found that the catalytic site in enzymes induces long-range evolutionary constraint, where even sites distant to the catalytic site are more conserved than expected. Given that protein-fold usage is generally different between enzymes and nonenzymes, it remains an open question to what extent this long-range evolutionary constraint in enzymes is dictated, either directly or indirectly, by the special three-dimensional structure of the enzyme. To investigate this question, we have compared evolutionary properties of enzymes with those of counterpart pseudoenzymes that share the same protein fold but are catalytically inactive. We found that the long-range evolutionary constraint observed in enzymes is significantly reduced in pseudoenzyme counterparts, despite very high structural similarity (∼1.5 Å RMSD on average). Furthermore, this significant reduction in long-range evolutionary constraint is observed even in pseudoenzyme counterparts which retain the ligand-binding ability of enzymes. Finally, the distance between the site that induces the highest gradient of sequence conservation and the pseudocatalytic site in pseudoenzymes is significantly larger than the corresponding distance in enzymes. Taken together, our results suggest that the long-range evolutionary constraint in enzymes is induced mainly by the presence of the catalytic site rather than by the special three-dimensional structure of the enzyme, and that such long-range evolutionary constraint in enzymes depends mainly on the catalytic function of the active site rather than on the ligand-binding ability of the enzyme.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".