Literature DB >> 26716464

Physical Origins of Codon Positions That Strongly Influence Cotranslational Folding: A Framework for Controlling Nascent-Protein Folding.

Ajeet K Sharma1, Bernd Bukau2, Edward P O'Brien1.   

Abstract

An emerging paradigm in the field of in vivo protein biophysics is that nascent-protein behavior is a type of nonequilibrium phenomenon, where translation-elongation kinetics can be more important in determining nascent-protein behavior than the thermodynamic properties of the protein. Synonymous codon substitutions, which change the translation rate at select codon positions along a transcript, have been shown to alter cotranslational protein folding, suggesting that evolution may have shaped synonymous codon usage in the genomes of organisms in part to increase the amount of folded and functional nascent protein. Here, we develop a Monte Carlo-master-equation method that allows for the control of nascent-chain folding during translation through the rational design of mRNA sequences to guide the cotranslational folding process. We test this framework using coarse-grained molecular dynamics simulations and find it provides optimal mRNA sequences to control the simulated, cotranslational folding of a protein in a user-prescribed manner. With this approach we discover that some codon positions in a transcript can have a much greater impact on nascent-protein folding than others because they tend to be positions where the nascent chain populates states that are far from equilibrium, as well as being dependent on a complex ratio of time scales. As a consequence, different cotranslational profiles of the same protein can have different critical codon positions and different numbers of synonymous mRNA sequences that encode for them. These findings explain that there is a fundamental connection between the nonequilibrium nature of cotranslational processes, nascent-protein behavior, and synonymous codon usage.

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Year:  2016        PMID: 26716464     DOI: 10.1021/jacs.5b08145

Source DB:  PubMed          Journal:  J Am Chem Soc        ISSN: 0002-7863            Impact factor:   15.419


  11 in total

1.  Mechanochemistry in Translation.

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2.  Precise quantification of translation inhibition by mRNA structures that overlap with the ribosomal footprint in N-terminal coding sequences.

Authors:  Amin Espah Borujeni; Daniel Cetnar; Iman Farasat; Ashlee Smith; Natasha Lundgren; Howard M Salis
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3.  Quantitative determination of ribosome nascent chain stability.

Authors:  Avi J Samelson; Madeleine K Jensen; Randy A Soto; Jamie H D Cate; Susan Marqusee
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4.  Fast Protein Translation Can Promote Co- and Posttranslational Folding of Misfolding-Prone Proteins.

Authors:  Fabio Trovato; Edward P O'Brien
Journal:  Biophys J       Date:  2017-05-09       Impact factor: 4.033

5.  Within-Gene Shine-Dalgarno Sequences Are Not Selected for Function.

Authors:  Adam J Hockenberry; Michael C Jewett; Luís A N Amaral; Claus O Wilke
Journal:  Mol Biol Evol       Date:  2018-10-01       Impact factor: 16.240

6.  Effect of Nascent Peptide Steric Bulk on Elongation Kinetics in the Ribosome Exit Tunnel.

Authors:  Pengse Po; Erin Delaney; Howard Gamper; D Miklos Szantai-Kis; Lee Speight; LiWei Tu; Andrey Kosolapov; E James Petersson; Ya-Ming Hou; Carol Deutsch
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Review 7.  The ribosome in action: Tuning of translational efficiency and protein folding.

Authors:  Marina V Rodnina
Journal:  Protein Sci       Date:  2016-06-08       Impact factor: 6.725

8.  Hierarchical Markov State Model Building to Describe Molecular Processes.

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Journal:  J Chem Theory Comput       Date:  2020-02-17       Impact factor: 6.006

9.  Evidence of evolutionary selection for cotranslational folding.

Authors:  William M Jacobs; Eugene I Shakhnovich
Journal:  Proc Natl Acad Sci U S A       Date:  2017-10-10       Impact factor: 11.205

10.  A ribosome-associated chaperone enables substrate triage in a cotranslational protein targeting complex.

Authors:  Hao-Hsuan Hsieh; Jae Ho Lee; Sowmya Chandrasekar; Shu-Ou Shan
Journal:  Nat Commun       Date:  2020-11-17       Impact factor: 14.919

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