Design of <i>Deinococcus radiodurans</i> thioredoxin reductase with altered thioredoxin specificity using computational alanine mutagenesis
Bibliographic record
Abstract
In this study, the X-ray crystal structure of the complex between Escherichia coli thioredoxin reductase (EC TrxR) and its substrate thioredoxin (Trx) was used as a guide to design a Deinococcus radiodurans TrxR (DR TrxR) mutant with altered Trx specificity. Previous studies have shown that TrxRs have higher affinity for cognate Trxs (same species) than that for Trxs from different species. Computational alanine scanning mutagenesis and visual inspection of the EC TrxR-Trx interface suggested that only four residues (F81, R130, F141, and F142) account for the majority of the EC TrxR-Trx interface stability. Individual replacement of equivalent residues in DR TrxR (M84, K137, F148, and F149) with alanine resulted in drastic changes in binding affinity, confirming that the four residues account for most of TrxR-Trx interface stability. When M84 and K137 were changed to match equivalent EC TrxR residues (K137R and M84F), the DR TrxR substrate specificity was altered from its own Trx to that of EC Trx. The results suggest that a small subset of the TrxR-Trx interface residues is responsible for the majority of Trx binding affinity and species-specific recognition.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".