High‐precision mapping of the protein interaction network for the human transcription machinery reveals a novel class of cellular regulatory factors
Bibliographic record
Abstract
We performed a survey of soluble human protein complexes containing components of the transcription and RNA processing machineries using protein affinity purification coupled to mass spectrometry. Many identified interaction partners were targeted in reciprocal tagging experiments in order to confirm some interactions and to enrich the dataset. High‐confidence interactions were selected computationally using an algorithm that we developed and trained using machine learning to minimize the rate of both false‐positives and false‐negatives. The data produced with 100 affinity tagged proteins was used to (1) build a high‐definition map of interactions that connect components of the transcription and RNA processing machineries in human cells; (2) show that transcription and RNA processing factors from the soluble cellular fraction are associated with proteins that specifically regulate the formation (e.g. assembly, localization and/or stability) of protein complexes; and, (3) assign a putative function to a number of previously‐uncharacterized proteins on a ‘‘guilt by association’’ basis. A number of previously‐uncharacterized proteins that we further characterized functionally and biochemically define a novel class of regulatory factors that target RNA polymerase II and other transcription factors prior and/or after the transcription reaction on chromatin DNA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".