Co-translational assembly promotes functional diversification of paralogous proteins
Bibliographic record
Abstract
ABSTRACT Homomeric proteins are ubiquitous and mediate myriads of cellular functions. When a gene encoding a homomer duplicates, the resulting paralogs can either form distinct homomers, or evolve into a heteromer containing both paralogs. While such events have extensively shaped proteomes, the molecular mechanisms driving these fates and their associated functional consequences remain largely unknown. Here, we conducted a comprehensive phylogenomic analysis tracing gene duplication histories of 7,377 human paralogs across the eukaryotic lineage and identified their fates using protein interaction data. Simulations and data analyses show that cellular constraints must act as barriers to disfavor heteromerization and promote homomerization. We found that multiple cellular and molecular constraints can serve as barriers, including the lack of co-expression and co-localization. The main barrier, however, is co-translational assembly, which naturally promotes the self-assembly of each paralog from its corresponding mRNA, thus hindering heteromerization. We further established that heteromerization constrains functional divergence, with homomeric paralogs exhibiting stronger signatures of adaptive evolution and functional divergence compared to heteromeric paralogs. Together, these findings identify key biochemical and cellular properties that explain protein function diversification following gene duplication. One Sentence Summary Co-translational assembly drives the selective homo-oligomerization of paralogs, which in turn promotes their functional divergence. Graphical Abstract
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".