Orthogonal translation with 5‐cyanotryptophan as an infrared probe for local structural information, electrostatics, and hydrogen bonding
Bibliographic record
Abstract
Orthogonal translation is an efficient tool that provides many valuable spectral probes capable of covering different parts of the electromagnetic spectrum and thus enabling parameterization of various structural and dynamic phenomena in proteins. In this context, nitrile-containing tryptophan analogs are very useful probes to study local electrostatics and hydrogen bonding in both rigid and dynamic environments. Here, we report a semi-rational approach to engineer a tyrosyl-tRNA synthetase (TyrRS) variant of Methanocaldococcus jannaschii capable of incorporating 5-cyanotryptophan (5CNW) via orthogonal translation. We combined one round of the well-established positive selection system with saturation mutagenesis at preselected TyrRS positions, resulting in a novel 5CNW-specific enzyme that also exhibits high substrate tolerance to other aromatic noncanonical amino acids. We demonstrated the utility of our orthogonal pair by inserting 5CNW into the cyanobacteriochrome Slr1393g3, a bilin-binding photosensor of the phytochrome superfamily. The nitrile (CN) group of the inserted 5CNW provides non-invasive labeling in the local structural context while yielding information on local electrostatics and hydrogen bonding by IR spectroscopy. 5CNW is a versatile probe that can be used for both static and dynamic measurements.
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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) | 0.000 | 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".