Methylated-antibody affinity purification to improve proteomic identification of plant RNA polymerase Pol V complex and the interacting proteins
Bibliographic record
Abstract
Abstract Affinity purification followed by enzymatic digestion and mass spectrometry has been widely utilized for the sensitive detection of interacting proteins and protein complexes in various organisms. In plants, the method is technically challenging due to the low abundance proteins, non-specific binding and difficulties of eluting interacting proteins from antibody beads. In this report, we describe a strategy to modify antibodies by reductive methylation of lysines without affecting their binding properties, followed by on-bead digestion of bound proteins with endoproteinase Lys-C. By this method, the antibody remains intact and does not interfere with the downstream identification of interacting proteins. Non-specific binding proteins were excluded using 14N/15N-metabolic labeling of wild-type and the transgenic plant counterparts. The method was employed to identify 12 co-immunoprecipitated protein subunits in Pol V complex and to discover 17 potential interacting protein targets in Arabidopsis. Our results demonstrated that the modification of antibodies by reductive dimethylation can improve the reliability and sensitivity of identifying low-abundance proteins through on-bead digestion and mass spectrometry. We also show that coupling this technique with chemical crosslinking enables in-depth characterization of endogenous protein complexes and the protein-protein interaction networks including mapping the surface topology and post-translational modifications of interacting proteins.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".