Protein C-Terminal Labeling and Biotinylation Using Synthetic Peptide and Split-Intein
Bibliographic record
Abstract
BACKGROUND: Site-specific protein labeling or modification can facilitate the characterization of proteins with respect to their structure, folding, and interaction with other proteins. However, current methods of site-specific protein labeling are few and with limitations, therefore new methods are needed to satisfy the increasing need and sophistications of protein labeling. METHODOLOGY: A method of protein C-terminal labeling was developed using a non-canonical split-intein, through an intein-catalyzed trans-splicing reaction between a protein and a small synthetic peptide carrying the desired labeling groups. As demonstrations of this method, three different proteins were efficiently labeled at their C-termini with two different labels (fluorescein and biotin) either in solution or on a solid surface, and a transferrin receptor protein was labeled on the membrane surface of live mammalian cells. Protein biotinylation and immobilization on a streptavidin-coated surface were also achieved in a cell lysate without prior purification of the target protein. CONCLUSIONS: We have produced a method of site-specific labeling or modification at the C-termini of recombinant proteins. This method compares favorably with previous protein labeling methods and has several unique advantages. It is expected to have many potential applications in protein engineering and research, which include fluorescent labeling for monitoring protein folding, location, and trafficking in cells, and biotinylation for protein immobilization on streptavidin-coated surfaces including protein microchips. The types of chemical labeling may be limited only by the ability of chemical synthesis to produce the small C-intein peptide containing the desired chemical groups.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".