Revealing the amino acid composition of proteins within an expanded genetic code
Bibliographic record
Abstract
The genetic code can be manipulated to reassign codons for the incorporation of non-standard amino acids (NSAA). Deletion of release factor 1 in Escherichia coli enhances translation of UAG (Stop) codons, yet may also extended protein synthesis at natural UAG terminated messenger RNAs. The fidelity of protein synthesis at reassigned UAG codons and the purity of the NSAA containing proteins produced require careful examination. Proteomics would be an ideal tool for these tasks, but conventional proteomic analyses cannot readily identify the extended proteins and accurately discover multiple amino acid (AA) insertions at a single UAG. To address these challenges, we created a new proteomic workflow that enabled the detection of UAG readthrough in native proteins in E. coli strains in which UAG was reassigned to encode phosphoserine. The method also enabled quantitation of NSAA and natural AA incorporation at UAG in a recombinant reporter protein. As a proof-of-principle, we measured the fidelity and purity of the phosphoserine orthogonal translation system (OTS) and used this information to improve its performance. Our results show a surprising diversity of natural AAs at reassigned stop codons. Our method can be used to improve OTSs and to quantify amino acid purity at reassigned codons in organisms with expanded genetic codes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".