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Record W2767093006 · doi:10.1101/208462

Hsp90 dictates viral sequence space by balancing the evolutionary tradeoffs between protein stability, aggregation and translation rate

2017· preprint· en· W2767093006 on OpenAlexaff
Ron Geller, Sebastian Pechmann, Ashley Acevedo, Raul Andino, Judith Frydman

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2017
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsUniversité de Montréal
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthMinisterio de Economía y Competitividad
KeywordsEvolvabilityProtein foldingBiologyChaperone (clinical)Protein aggregationMutation rateGeneticsComputational biologyCell biologyGene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.264
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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