First-principles model of optimal translation factors stoichiometry

Abstract
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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.

The following previously published data sets were used

1. Lalanne JB et al
(2018) Data from: Evolutionary Convergence of Pathway-specific Enzyme Expression Stoichiometry
NCBI Gene Expression Omnibus, GSE95211.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95211
1. Li et al
(2014) Data from: Absolute quantification of protein production reveals principles underlying protein synthesis rates
NCBI Gene Expression Omnibus, GSE53767.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE53767
1. Mori et al
(2021) Data from: From coarse to fine: The absolute Escherichia coli proteome under diverse growth conditions
NCBI Gene Expression Omnibus, GSE53767.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139983

Article and author information

Author details

Jean-Benoît Lalanne

Genome Sciences, University of Washington, Seattle, United States

Competing interests
The authors declare that no competing interests exist.
Gene-Wei Li

Department of Biology, Massachusetts Institute of Technology, Cambridge, United States

For correspondence
gwli@mit.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-7036-8511

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

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.