Development of pan-specific antibody against trimethyllysine for protein research
Bibliographic record
Abstract
BACKGROUND: Trimethylation of the Nepsilon-lysine residues in a protein is one of the most important events of posttranslational modifications. Simple methods for rapid detection and isolation of the Nepsilon-trimethylated protein species are needed. This report introduces a novel method to prepare the affinity purified antibody specific for the Nepsilon-trimethylated lysine (tMeK). The applications of the purified antibody are also reported in this paper. METHODS: We generated the methylated keyhole limpet heomocyanin (KLH) under controlled chemical methylation reaction using CH3I and used it as an immunogen to raise anti-methylated lysine antibodies. The tMeK specific antibody was selectively isolated using a two-step affinity chromatography in which the mMeK/dMeK specific antibodies were removed and the tMeK specific antibody was captured. Finally, the eluted anti-tMeK antibody was characterized. RESULTS: The ELISA results indicated that the antibody reacted only to tMeK but not to mono- and dimethyllysine. Western-blot results showed that the Nepsilon-trimethylated proteins were detected in both animal tissue and cultured cells and that the antibody signal could be competitively inhibited with free tMeK. CONCLUSION: The specific tMeK antibody we developed is useful for one-step isolation of proteins with Nepsilon-trimethyllysine residues and also for the detection, identification and localization of proteins with trimethyllysine residues in the cells.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".