Newfound coding potential of transcripts unveils missing members of human protein communities
Bibliographic record
Abstract
Abstract Recent proteogenomic approaches have led to the discovery that regions of the transcriptome previously annotated as non-coding regions (i.e. UTRs, open reading frames overlapping annotated coding sequences in a different reading frame, and non-coding RNAs) frequently encode proteins (termed alternative proteins). This suggests that previously identified protein-protein interaction networks are partially incomplete since alternative proteins are not present in conventional protein databases. Here we used the proteogenomic resource OpenProt and a combined spectrum- and peptide-centric analysis for the re-analysis of a high throughput human network proteomics dataset thereby revealing the presence of 280 alternative proteins in the network. We found 19 genes encoding both an annotated (reference) and an alternative protein interacting with each other. Of the 136 alternative proteins encoded by pseudogenes, 38 are direct interactors of reference proteins encoded by their respective parental gene. Finally, we experimentally validate several interactions involving alternative proteins. These data improve the blueprints of the human protein-protein interaction network and suggest functional roles for hundreds of alternative 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".