The Impact of Native State Switching on Protein Sequence Evolution
Bibliographic record
Abstract
For proteins with a single well-defined native state, protein 3Dstructure is a major determinant of sequence evolution. On the other hand, many proteins adopt multiple, distinct native structures under different conditions ("conformational switches"), yet the impact of such native state switching on protein evolution is not fully understood. Here, we performed a proteome-wide analysis of how protein structure impacts sequence evolution for protein conformational switches in Saccharomyces cerevisiae using pooled analysis of sites with similar packing or burial. We observed a strong linear relationship between residue evolutionary rate and residue burial for conformational switches. In addition, we found that conformational switches evolve significantly and consistently more slowly than proteins with a single native state, even after controlling for degree of residue burial or packing. Next, we focused on proteins that switch conformations upon molecular binding. We found that interfacial residues in these conformational switches evolve more slowly than interfacial residues in proteins with a single native state, and that the bound conformation is a better predictor for residue evolutionary rate than the unbound conformation. Our findings suggest that for conformational switches, the necessity to encode multiple distinct native structures under different conditions imposes strong evolutionary constraints on the entire protein, rather than just a few key residues. Our results provide new insights into the structure-evolution relationship of protein conformational switches.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".