Combinatorial exploration of the catalytic site of a drug-resistant dihydrofolate reductase: creating alternative functional configurations
Bibliographic record
Abstract
We have applied a global approach to enzyme active site exploration, where multiple mutations were introduced combinatorially at the active site of Type II R67 dihydrofolate reductase (R67 DHFR), creating numerous new active site environments within a constant framework. By this approach, we combinatorially modified all 16 principal amino acids that constitute the active site of this enzyme. This approach is fundamentally different from active site point mutation in that the native active site context is no longer accounted for. Among the 1536 combinatorially mutated active site variants of R67 DHFR we created, we selected and kinetically characterized three variants with highly altered active site compositions. We determined that they are of high fitness, as defined by a complex function consisting jointly of catalytic activity and resistance to trimethoprim. The k(cat) and K(M) values were similar to those for the native enzyme. The favourable Delta(DeltaG) values obtained (ranging from -0.72 to -1.08 kcal/mol) suggest that, despite their complex mutational pattern, no fundamental change in the catalytic mechanism has occurred. We illustrate that combinatorial active site mutagenesis can allow for the creation of compensatory mutations that could not be predicted and thus provides a route for more extensive exploration of functional sequence space than is allowed by point mutation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".