A Novel Proteomics Approach for the Discovery of Chromatin-associated Protein Networks
Bibliographic record
Abstract
Protein-protein interaction mapping has progressed rapidly in recent years, enabling the completion of several high throughput studies. However, knowledge of physical interactions is limited for numerous classes of proteins, such as chromatin-bound proteins, because of their poor solubility when bound to DNA. To address this problem, we have developed a novel method, termed modified chromatin immunopurification (mChIP), that allows for the efficient purification of protein-DNA macromolecules, enabling subsequent protein identification by mass spectrometry. mChIP consists of a single affinity purification step whereby chromatin-bound protein networks are isolated from mildly sonicated and gently clarified cellular extracts using magnetic beads coated with antibodies. We applied the mChIP method in Saccharomyces cerevisiae cells expressing endogenously tandem affinity purification (TAP)-tagged histone H2A or the histone variant Htz1p and successfully co-purified numerous chromatin-bound protein networks as well as DNA. We further challenged the mChIP procedure by purifying three chromatin-bound bait proteins that have proven difficult to purify by traditional methods: Lge1p, Mcm5p, and Yta7p. The protein interaction networks of these three baits dramatically expanded our knowledge of their chromatin environments and illustrate that the innovative mChIP procedure enables an improved characterization of chromatin-associated 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".