Mosaic translation hypothesis: chimeric polypeptides produced via multiple ribosomal frameshifting as a basis for adaptability
Bibliographic record
Abstract
How many different proteins can be produced from a single spliced transcript? Genome annotation projects overlook the coding potential of reading frames other than that of the reference open reading frames (refORFs). Recently, alternative open reading frames (altORFs) and their translational products, alternative proteins, have been shown to carry out important functions in various organisms. AltORFs overlapping refORFs or other altORFs in a different reading frame may be involved in one fundamental mechanism so far overlooked. A few years ago, it was proposed that altORFs may act as building blocks for chimeric (mosaic) polypeptides, which are produced via multiple ribosomal frameshifting events from a single mature transcript. We adopt terminology from that earlier discussion and call this mechanism mosaic translation. This way of extracting and combining genetic information may significantly increase proteome diversity. Thus, we hypothesize that this mechanism may have contributed to the flexibility and adaptability of organisms to a variety of environmental conditions. Specialized ribosomes acting as sensors probably played a central role in this process. Importantly, mosaic translation may be the main source of protein diversity in genomes that lack alternative splicing. The idea of mosaic translation is a testable hypothesis, although its direct demonstration is challenging. Should mosaic translation occur, we would currently highly underestimate the complexity of translation mechanisms and thus the proteome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".