Evidence map›Paper›PMID 31175370›Full record

ArticleCellular and molecular life sciences : CMLS2020

Codon selection reduces GC content bias in nucleic acids encoding for intrinsically disordered proteins.

Christopher J Oldfield, Zhenling Peng, Vladimir N Uversky, Lukasz Kurgan

Abstract read
In one paragraph

Article in Cellular and molecular life sciences : CMLS, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Comprehensive analysis of codon bias in 13Frontiers in microbiology · 2023
    Article
  4. Article
  5. Deciphering the co-adaptation of codon usage between respiratory coronaviruses and their human host uncovers candidate therapeutics for COVID-19.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2020
    Article
4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Christopher J OldfieldDepartment of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA. cjoldfield@vcu.edu.
Zhenling PengCenter for Applied Mathematics, Tianjin University, Tianjin, 300072, China.
Vladimir N UverskyDepartment of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, 33612, USA.
Lukasz KurganDepartment of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA. lkurgan@vcu.edu.

Funding

National Science Foundation 1617369
6 · The paper itself

Abstract

Protein-coding nucleic acids exhibit composition and codon biases between sequences coding for intrinsically disordered regions (IDRs) and those coding for structured regions. IDRs are regions of proteins that are folding self-insufficient and which function without the prerequisite of folded structure. Several authors have investigated composition bias or codon selection in regions encoding for IDRs, primarily in Eukaryota, and concluded that elevated GC content is the result of the biased amino acid composition of IDRs. We substantively extend previous work by examining GC content in regions encoding IDRs, from 44 species in Eukaryota, Archaea, and Bacteria, spanning a wide range of GC content. We confirm that regions coding for IDRs show a significantly elevated GC content, even across all domains of life. Although this is largely attributable to the amino acid composition bias of IDRs, we show that this bias is independent of the overall GC content and, most importantly, we are the first to observe that GC content bias in IDRs is significantly different than expected from IDR amino acid composition alone. We empirically find compensatory codon selection that reduces the observed GC content bias in IDRs. This selection is dependent on the overall GC content of the organism. The codon selection bias manifests as use of infrequent, AT-rich codons in encoding IDRs. Further, we find these relationships to be independent of the intrinsic disorder prediction method used, and independent of estimated translation efficiency. These observations are consistent with the previous work, and we speculate on whether the observed biases are causal or symptomatic of other driving forces.

Indexed as

AnimalsBase CompositionCodonCodon UsageHumansIntrinsically Disordered ProteinsProtein BiosynthesisProtein ConformationCodonIntrinsically Disordered ProteinsAmino acid compositionCodon selectionGC contentIntrinsically disordered proteins

Identifiers

PMID31175370
PMCPMC11104855

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.