Proximal Methionine Amino Acid Residue Affects the Properties of Redox-Active Tryptophan in an Artificial Model Protein
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Redox-active amino acid residues are at the heart of biological electron-transfer reactions. They play important roles in natural protein functions and are implicated in disease states (e.g., oxidative-stress-associated disorders). Tryptophan (Trp) is one such redox-active amino acid residue, and it has long been known to serve a functional role in proteins. Broadly speaking, there is still much to learn about the local features that make some Trp redox active and others inactive. Herein, we describe a new protein model system where we investigate how a methionine (Met) residue proximal to a redox-active Trp affects its reactivity and spectroscopy. We use an artificial variant of azurin from Pseudomonas aeruginosa to produce these models. We employ a series of UV–visible spectroscopy, electrochemistry, electron paramagnetic resonance, and density functional theory experiments to demonstrate the effect that placing Met near Trp radicals has in the context of redox proteins. The introduction of Met proximal to Trp lowers its reduction potential by ca. 30 mV and causes clear shifts in the optical spectra of the corresponding radicals. While the effect may be small, it is significant enough to be a way for natural systems to tune Trp reactivity.
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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 | 0.000 |
| 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".