First-principles model of optimal translation factors stoichiometry
Abstract
Enzymatic pathways have evolved uniquely preferred protein expression stoichiometry in living cells, but our ability to predict the optimal abundances from basic properties remains underdeveloped. Here we report a biophysical, first-principles model of growth optimization for core mRNA translation, a multi-enzyme system that involves proteins with a broadly conserved stoichiometry spanning two orders of magnitude. We show that predictions from maximization of ribosome usage in a parsimonious flux model constrained by proteome allocation agree with the conserved ratios of translation factors. The analytical solutions, without free parameters, provide an interpretable framework for the observed hierarchy of expression levels based on simple biophysical properties, such as diffusion constants and protein sizes. Our results provide an intuitive and quantitative understanding for the construction of a central process of life, as well as a path toward rational design of pathway-specific enzyme expression stoichiometry.
Data availability
Already publicly available ribosome profiling datasets were used (GEO accessions GSE95211, GSE53767, and GSE139983).Computer scripts (Matlab) used for this study were submitted with the present work as computer_code_20210721.zip.Supplementary Files 1-4 contain the numerical data to reproduce figures.
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Data from: Evolutionary Convergence of Pathway-specific Enzyme Expression StoichiometryNCBI Gene Expression Omnibus, GSE95211.
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Data from: From coarse to fine: The absolute Escherichia coli proteome under diverse growth conditionsNCBI Gene Expression Omnibus, GSE53767.
Article and author information
Author details
Funding
National Institutes of Health (R35GM124732)
- Gene-Wei Li
National Science Foundation (MCB 1844668)
- Gene-Wei Li
Richard and Susan Smith Family Foundation (Smith Odyssey Award and Smith Family Award)
- Gene-Wei Li
Pew Charitable Trusts (Pew Scholar)
- Gene-Wei Li
Alfred P. Sloan Foundation (Sloan Research Fellowship)
- Gene-Wei Li
Kinship Foundation (Searle Scholar)
- Gene-Wei Li
National Research Council Canada (Doctoral fellowship)
- Jean-Benoît Lalanne
Howard Hughes Medical Institute (International Student Fellowship)
- Jean-Benoît Lalanne
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Lalanne & Li
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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