Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints

Feiran Li, Yu Chen, Qi Qi, Yanyan Wang, Le Yuan, Mingtao Huang, Ibrahim E. Elsemman, Amir Feizi (), Eduard J. Kerkhoven and Jens Nielsen ()
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Feiran Li: Chalmers University of Technology
Yu Chen: Chalmers University of Technology
Qi Qi: Chalmers University of Technology
Yanyan Wang: Chalmers University of Technology
Le Yuan: Chalmers University of Technology
Mingtao Huang: Chalmers University of Technology
Ibrahim E. Elsemman: Chalmers University of Technology
Amir Feizi: Chalmers University of Technology
Eduard J. Kerkhoven: Chalmers University of Technology
Jens Nielsen: Chalmers University of Technology

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract Eukaryotic cells are used as cell factories to produce and secrete multitudes of recombinant pharmaceutical proteins, including several of the current top-selling drugs. Due to the essential role and complexity of the secretory pathway, improvement for recombinant protein production through metabolic engineering has traditionally been relatively ad-hoc; and a more systematic approach is required to generate novel design principles. Here, we present the proteome-constrained genome-scale protein secretory model of yeast Saccharomyces cerevisiae (pcSecYeast), which enables us to simulate and explain phenotypes caused by limited secretory capacity. We further apply the pcSecYeast model to predict overexpression targets for the production of several recombinant proteins. We experimentally validate many of the predicted targets for α-amylase production to demonstrate pcSecYeast application as a computational tool in guiding yeast engineering and improving recombinant protein production.

Date: 2022
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DOI: 10.1038/s41467-022-30689-7

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