Controllable protein cleavages through intein fragment complementation
Bibliographic record
Abstract
Intein-based protein cleavages, if carried out in a controllable way, can be useful tools of recombinant protein purification, ligation, and cyclization. However, existing methods using contiguous inteins were often complicated by spontaneous cleavages, which could severely reduce the yield of the desired protein product. Here we demonstrate a new method of controllable cleavages without any spontaneous cleavage, using an artificial S1 split-intein consisting of an 11-aa N-intein (I(N)) and a 144-aa C-intein (I(C)). In a C-cleavage design, the I(C) sequence was embedded in a recombinant precursor protein, and the small I(N) was used as a synthetic peptide to trigger a cleavage at the C-terminus of I(C). In an N-cleavage design, the short I(N) sequence was embedded in a recombinant precursor protein, and the separately produced I(C) protein was used to catalyze a cleavage at the N-terminus of I(N). These N- and C-cleavages showed >95% efficiency, and both successfully avoided any spontaneous cleavage during expression and purification of the precursor proteins. The N-cleavage design also revealed an unexpected and interesting structural flexibility of the I(C) protein. These findings significantly expand the effectiveness of intein-based protein cleavages, and they also reveal important insights of intein structural flexibility and 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".