Protein Trans-Splicing of Multiple Atypical Split Inteins Engineered from Natural Inteins
Bibliographic record
Abstract
Protein trans-splicing by split inteins has many uses in protein production and research. Splicing proteins with synthetic peptides, which employs atypical split inteins, is particularly useful for site-specific protein modifications and labeling, because the synthetic peptide can be made to contain a variety of unnatural amino acids and chemical modifications. For this purpose, atypical split inteins need to be engineered to have a small N-intein or C-intein fragment that can be more easily included in a synthetic peptide that also contains a small extein to be trans-spliced onto target proteins. Here we have successfully engineered multiple atypical split inteins capable of protein trans-splicing, by modifying and testing more than a dozen natural inteins. These included both S1 split inteins having a very small (11-12 aa) N-intein fragment and S11 split inteins having a very small (6 aa) C-intein fragment. Four of the new S1 and S11 split inteins showed high efficiencies (85-100%) of protein trans-splicing both in E. coli cells and in vitro. Under in vitro conditions, they exhibited reaction rate constants ranging from ~1.7 × 10(-4) s(-1) to ~3.8 × 10(-4) s(-1), which are comparable to or higher than those of previously reported atypical split inteins. These findings should facilitate a more general use of trans-splicing between proteins and synthetic peptides, by expanding the availability of different atypical split inteins. They also have implications on understanding the structure-function relationship of atypical split inteins, particularly in terms of intein fragment complementation.
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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".