Evidence map›Paper›PMID 31911473›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2020

Cotranslational folding allows misfolding-prone proteins to circumvent deep kinetic traps.

Amir Bitran, William M Jacobs, Xiadi Zhai, Eugene Shakhnovich

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

38 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Translation Rates and Protein Folding.Journal of molecular biology · 2024
    Review
  14. Review
  15. How the ribosome shapes cotranslational protein folding.Current opinion in structural biology · 2024
    Review
  16. Article
  17. Mechanisms and pathology of protein misfolding and aggregation.Nature reviews. Molecular cell biology · 2023
    Review
  18. Review
  19. Article
  20. Learning to learn by using nonequilibrium training protocols for adaptable materials.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Amir BitranDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138.
William M JacobsDepartment of Chemistry, Princeton University, Princeton, NJ 08544.
Xiadi ZhaiDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138.
Eugene ShakhnovichDepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138; shakhnovich@chemistry.harvard.edu.

Funding

MOLECULAR BIOPHYSICS TRAINING GRANTT32GM008313 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI MURTHY, VENKATESH N · 1989 to 2023
$14.6M
Study of Biological Evolution of Structure and Function in ProteinsR01GM068670 · NIGMS · HARVARD UNIVERSITY · PI SHAKHNOVICH, EUGENE I · 2004 to 2020
$7.0M
Biophysical mechanisms of proteomic and fitness effects of synonymous substitutionsR01GM124044 · NIGMS · HARVARD UNIVERSITY · PI SHAKHNOVICH, EUGENE I · 2017 to 2020
$1.3M
Kinetics of macromolecular complex assembly and regulationF32GM116231 · NIGMS · HARVARD UNIVERSITY · PI JACOBS, WILLIAM MONROE · 2016 to 2017
$112k
NIGMS NIH HHS F32 GM116231NIGMS NIH HHS R01 GM068670NIGMS NIH HHS R01 GM124044NIGMS NIH HHS T32 GM008313
6 · The paper itself

Abstract

Many large proteins suffer from slow or inefficient folding in vitro. It has long been known that this problem can be alleviated in vivo if proteins start folding cotranslationally. However, the molecular mechanisms underlying this improvement have not been well established. To address this question, we use an all-atom simulation-based algorithm to compute the folding properties of various large protein domains as a function of nascent chain length. We find that for certain proteins, there exists a narrow window of lengths that confers both thermodynamic stability and fast folding kinetics. Beyond these lengths, folding is drastically slowed by nonnative interactions involving C-terminal residues. Thus, cotranslational folding is predicted to be beneficial because it allows proteins to take advantage of this optimal window of lengths and thus avoid kinetic traps. Interestingly, many of these proteins' sequences contain conserved rare codons that may slow down synthesis at this optimal window, suggesting that synthesis rates may be evolutionarily tuned to optimize folding. Using kinetic modeling, we show that under certain conditions, such a slowdown indeed improves cotranslational folding efficiency by giving these nascent chains more time to fold. In contrast, other proteins are predicted not to benefit from cotranslational folding due to a lack of significant nonnative interactions, and indeed these proteins' sequences lack conserved C-terminal rare codons. Together, these results shed light on the factors that promote proper protein folding in the cell and how biomolecular self-assembly may be optimized evolutionarily.

Indexed as

Protein FoldingAlcohol OxidoreductasesEscherichia coli ProteinsIntrinsically Disordered ProteinsKineticsMolecular Dynamics SimulationPhosphotransferasesProtein BiosynthesisProtein MethyltransferasesRepressor ProteinsTetrahydrofolate Dehydrogenaseacetoacetyl-CoA reductaseAlcohol Oxidoreductasescmk protein, E coliEscherichia coli ProteinsIntrinsically Disordered ProteinsMarR protein, E coliPhosphotransferasesprmC protein, E coliProtein MethyltransferasesRepressor ProteinsTetrahydrofolate Dehydrogenasecodon usagecotranslational foldingevolutionprotein foldingself-assembly

Identifiers

PMID31911473
PMCPMC6983386

What OpenQuestion holds

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Registered trials

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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.