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Record W1593377767 · doi:10.1111/mmi.13079

Nonoptimal codon usage influences protein structure in intrinsically disordered regions

2015· article· en· W1593377767 on OpenAlexfundno aff
Mian Zhou, Tao Wang, Jingjing Fu, Guanghua Xiao, Yi Liu

Bibliographic record

VenueMolecular Microbiology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Cancer InstituteFonds de Recherche du Québec - SantéNational Institutes of HealthWelch Foundation
KeywordsCodon usage biasBiologyTranslation (biology)GeneticsGenomeFolding (DSP implementation)Protein foldingComputational biologyNeurosporaGeneMessenger RNACell biologyNeurospora crassa

Abstract

fetched live from OpenAlex

Synonymous codons are not used with equal frequencies in most genomes. Codon usage has been proposed to play a role in regulating translation kinetics and co-translational protein folding. The relationship between codon usage and protein structures and the in vivo role of codon usage in eukaryotic protein folding is not clear. Here, we show that there is a strong codon usage bias in the filamentous fungus Neurospora. Importantly, we found genome-wide correlations between codon choices and predicted protein secondary structures: Nonoptimal codons are preferentially used in intrinsically disordered regions, and more optimal codons are used in structured domains. The functional importance of such correlations in vivo was confirmed by structure-based codon manipulation of codons in the Neurospora circadian clock gene frequency (frq). The codon optimization of the predicted disordered, but not well-structured regions of FRQ impairs clock function and altered FRQ structures. Furthermore, the correlations between codon usage and protein disorder tendency are conserved in other eukaryotes. Together, these results suggest that codon choices and protein structures co-evolve to ensure proper protein folding in eukaryotic organisms.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.940

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations125
Published2015
Admission routes1
Has abstractyes

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