Hsp90 dictates viral sequence space by balancing the evolutionary tradeoffs between protein stability, aggregation and translation rate
Bibliographic record
Abstract
Abstract Acquisition of mutations is central to evolution but the detrimental effects of most mutations on protein folding and stability limit protein evolvability. Molecular chaperones, which suppress aggregation and facilitate polypeptide folding, are proposed to promote sequence diversification by buffering destabilizing mutations. However, whether and how chaperones directly control protein evolution remains poorly understood. Here, we examine the effect of reducing the activity of the key eukaryotic chaperone Hsp90 on poliovirus evolution. Contrary to predictions of a buffering model, inhibiting Hsp90 increases population sequence diversity and promotes accumulation of mutations reducing protein stability. Explaining this counterintuitive observation, we find that Hsp90 offsets the evolutionary tradeoff between protein stability and aggregation. Lower chaperone levels favor sequence variants of reduced hydrophobicity, thus decreasing protein aggregation propensity but at a cost to protein stability. Notably, reducing Hsp90 activity also promotes clusters of codon-deoptimized synonymous mutations at inter-domain boundaries, likely to promote local ribosomal slowdown to facilitate cotranslational domain folding. Our results reveal how a chaperone can shape the sequence landscape at both the protein and RNA levels to harmonize the competing constraints posed by protein stability, aggregation propensity and translation rate on successful protein biogenesis.
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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 | 0.000 |
| 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".