ArticlePLoS computational biology2014
Deducing the kinetics of protein synthesis in vivo from the transition rates measured in vitro.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed.
- The Shape of Things to Come: α-Helical Membrane Protein Folding on the Ribosome.Chemical reviews · 2026Review
- Near-cognate tRNAs dominate codon decoding times in simulated ribosomes.Nucleic acids research · 2025Article
- Ribosomal L12 stalks recruit elongation factors to speed protein synthesis in Escherichia coli.Communications biology · 2025Article
- D-stem mutation in an essential tRNA increases translation speed at the cost of fidelity.PLoS genetics · 2025Article
- On the response of elongating ribosomes to forces opposing translocation.Biophysical journal · 2024Article
- Taming the ribosome.Biophysical journal · 2024Article
- SynonymousNAR genomics and bioinformatics · 2024Article
- Decomposing bulk signals to reveal hidden information in processive enzyme reactions: A case study in mRNA translation.PLoS computational biology · 2024Article
- Article
- Modelling ribosome kinetics and translational control on dynamic mRNA.PLoS computational biology · 2023Article
- Diffusion control in biochemical specificity.Biophysical journal · 2022Article
- Tailoring Codon Usage to the Underlying Biology for Protein Expression Optimization.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Kinetic control of nascent protein biogenesis by peptide deformylase.Scientific reports · 2021Article
- Constraints on error rate revealed by computational study of G•U tautomerization in translation.Nucleic acids research · 2021Article
- An optimal growth law for RNA composition and its partial implementation through ribosomal and tRNA gene locations in bacterial genomes.PLoS genetics · 2021Article
- Codon optimality in cancer.Oncogene · 2021Review
- The protein translation machinery is expressed for maximal efficiency in Escherichia coli.Nature communications · 2020Article
- Long-term effects of the proline-rich antimicrobial peptide Oncocin112 on theThe Journal of biological chemistry · 2020Article
- Co-translational insertion and topogenesis of bacterial membrane proteins monitored in real time.The EMBO journal · 2020Article
- A stochastic model for simulating ribosome kinetics in vivo.PLoS computational biology · 2020Article
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4 authors.
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Abstract
The molecular machinery of life relies on complex multistep processes that involve numerous individual transitions, such as molecular association and dissociation steps, chemical reactions, and mechanical movements. The corresponding transition rates can be typically measured in vitro but not in vivo. Here, we develop a general method to deduce the in-vivo rates from their in-vitro values. The method has two basic components. First, we introduce the kinetic distance, a new concept by which we can quantitatively compare the kinetics of a multistep process in different environments. The kinetic distance depends logarithmically on the transition rates and can be interpreted in terms of the underlying free energy barriers. Second, we minimize the kinetic distance between the in-vitro and the in-vivo process, imposing the constraint that the deduced rates reproduce a known global property such as the overall in-vivo speed. In order to demonstrate the predictive power of our method, we apply it to protein synthesis by ribosomes, a key process of gene expression. We describe the latter process by a codon-specific Markov model with three reaction pathways, corresponding to the initial binding of cognate, near-cognate, and non-cognate tRNA, for which we determine all individual transition rates in vitro. We then predict the in-vivo rates by the constrained minimization procedure and validate these rates by three independent sets of in-vivo data, obtained for codon-dependent translation speeds, codon-specific translation dynamics, and missense error frequencies. In all cases, we find good agreement between theory and experiment without adjusting any fit parameter. The deduced in-vivo rates lead to smaller error frequencies than the known in-vitro rates, primarily by an improved initial selection of tRNA. The method introduced here is relatively simple from a computational point of view and can be applied to any biomolecular process, for which we have detailed information about the in-vitro kinetics.
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