Affinity Purification of Methyllysine Proteome by Site-Specific Covalent Conjugation
Bibliographic record
Abstract
A basic but critical step in targeted proteomics by mass spectrometry is the separation of the targeted proteins from the complex mixture of the whole proteome by affinity purification. The bait protein is usually immobilized on the surface of a solid support to enable affinity-based purification of the targeted proteome. Here, we developed a site-specific covalent immobilization of the bait protein through affinity-guided covalent coupling (AGCC) of a single cysteine residue of an SH2 domain (utilized as an affinity tag for the protein target) with an engineered ligand peptide. Site-specific covalent immobilization of a methyllysine-binding protein HP1β chromodomain on the agarose resin was used to purify the methyllysine proteome from the whole-protein mixture. This new bait immobilization led to a notably low background in the affinity purification step, markedly outperforming the conventional (His) 6 tag–nickel nitrilotriacetic acid (Ni-NTA) immobilization method. Subsequent analysis of the purified proteome identified 275 lysine methylated sites and 184 methylated proteins from 332 HP1β CD-binding proteins, including 30 novel methylated proteins. This work demonstrates that a robust site-specific covalent protein immobilization method is well-suited for proteomic analysis of low-abundance proteins. This method also enables the identification of new methylated proteins and methylation sites in the methyllysine proteome.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".