A Click Chemistry-Based Biorthogonal Approach for the Detection and Identification of Protein Lysine Malonylation for Osteoarthritis Research
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Lysine malonylation is a post-translational modification in which a malonyl group, characterized by a negatively charged carboxylate, is covalently attached to the ε-amino side chain of lysine, influencing protein structure and function. Our laboratory identified Mak upregulation in cartilage under aging and obesity, contributing to osteoarthritis (OA). Current antibody-based detection methods face limitations in identifying Mak targets. Here, we introduce an alkyne-functionalized probe, MA-diyne, which metabolically incorporates into proteins, enabling copper(I) ion-catalyzed click reactions to conjugate labeled proteins with azide-based fluorescent dyes or affinity purification tags. In-gel fluorescence confirms MA-diyne incorporation into proteins across various cell types and species, including mouse chondrocytes, adipocytes, HEK293T cells, and Caenorhabditis elegans . Pull-down experiments identified known Mak proteins, such as GAPDH and Aldolase. The extent of MA-diyne modification was higher in Sirtuin 5-deficient cells, suggesting these modified proteins are Sirtuin 5 substrates. Pulse-chase experiments confirmed the dynamic nature of the protein malonylation. Quantitative proteomics identified 1136 proteins corresponding to 8903 peptides, with 429 proteins showing a 1-fold increase in the labeled group. Sirtuin 5 regulated 374 of these proteins. Pull down of newly identified proteins, such as β-actin and Stat3, was also done. This study highlights MA-diyne as a powerful chemical tool to investigate the molecular targets and functions of lysine malonylation under OA conditions.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".