Convergence, stability, and thermal adaptation in the rubisco enzyme in plants
Bibliographic record
Abstract
Enzymes are thought to be tuned to perform similarly in different thermal regimes. Whether the photosynthetic enzyme ribulose-1,5-bisphosphate carboxylase/oxygenase (rubisco) follows similar rules, especially when considering evolutionary history, is uncertain. The molecular, structural, and ecological factors of the rubisco large subunit (RbcL) were examined in four plant clades: wood ferns, pines, sea lavenders, and viburnums. Using rbcL gene sequences, codon evolutionary models were used to test for positive and divergent selection and convergent evolution. Protein structure modeling was performed to predict side chain changes and protein stability. Phylogenetic comparative methods were used to examine the relationship between protein stability to growing season temperature. All four clades showed significant evidence of positive selection, with multiple convergent substitutions predicted to alter side chain polarity and interactions with the solvent. In viburnums, biome transition rates were dependent on amino acid substitution, with positive selection was concentrated in cold temperate and cloud forest clades. Rubiscos with higher stability occurred in species from warmer environments. However, this correlation was weaker after correcting for phylogeny. These analyses support a hypothesis that RbcL evolution is influenced by both environmental tuning and evolutionary history.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".