In Vivo and In Vitro Protein Ligation by Naturally Occurring and Engineered Split DnaE Inteins
Bibliographic record
Abstract
BACKGROUND: Protein trans-splicing by naturally occurring split DnaE inteins is used for protein ligation of foreign peptide fragments. In order to widen biotechnological applications of protein trans-splicing, it is highly desirable to have split inteins with shorter C-terminal fragments, which can be chemically synthesized. PRINCIPAL FINDINGS: We report the identification of new functional split sites in DnaE inteins from Synechocystis sp. PCC6803 and from Nostoc punctiforme. One of the newly engineered split intein bearing C-terminal 15 residues showed more robust protein trans-splicing activity than naturally occurring split DnaE inteins in a foreign context. During the course of our experiments, we found that protein ligation by protein trans-splicing depended not only on the splicing junction sequences, but also on the foreign extein sequences. Furthermore, we could classify the protein trans-splicing reactions in foreign contexts with a simple kinetic model into three groups according to their kinetic parameters in the presence of various reducing agents. CONCLUSION: The shorter C-intein of the newly engineered split intein could be a useful tool for biotechnological applications including protein modification, incorporation of chemical probes, and segmental isotopic labelling. Based on kinetic analysis of the protein splicing reactions, we propose a general strategy to improve ligation yields by protein trans-splicing, which could significantly enhance the applications of protein ligation by protein trans-splicing.
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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 | 0.001 | 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".