Method for the Affinity Purification of Covalently Linked Peptides Following Cyanogen Bromide Cleavage of Proteins
Bibliographic record
Abstract
The low resolution structure of a protein can sometimes be inferred from information about existing disulfide bridges or experimentally introduced chemical crosslinks. Frequently, this task involves enzymatic digestion of a protein followed by mass spectrometry-based identification of covalently linked peptides. To facilitate this task, we developed a method for the enrichment of covalently linked peptides following the chemical cleavage of a protein. The method capitalizes on the availability of homoserine lactone moieties at the C-termini of cyanogen bromide cleavage products which support selective conjugation of affinity tags. The availability of two C-termini within covalently linked peptides allows for the conjugation of two distinct affinity tags and thereby enables subsequent removal of unmodified peptides by tandem affinity chromatography. Here, we demonstrate the stepwise implementation of this method using a polyhistidine tag and a biotin tag for the selective two-step purification of covalently linked cyanogen bromide fragments from increasingly complex protein samples. The method is independent of the nature of the covalent bond, is adaptable to fully denaturing conditions, and requires only low picomole quantities of starting material.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".