Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints
Artikel i vetenskaplig tidskrift, 2022

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 alpha-amylase production to demonstrate pcSecYeast application as a computational tool in guiding yeast engineering and improving recombinant protein production. Due to the complexity of the protein secretory pathway, strategy suitable for the production of a certain recombination protein cannot be generalized. Here, the authors construct a proteome-constrained genome-scale protein secretory model for yeast and show its application in the production of different misfolded or recombinant proteins.

Författare

Feiran Li

Chalmers, Biologi och bioteknik, Systembiologi

Yu Chen

Chalmers, Biologi och bioteknik, Systembiologi

Qi Qi

Chalmers, Biologi och bioteknik, Systembiologi

Yanyan Wang

Chalmers, Biologi och bioteknik, Systembiologi

Le Yuan

Chalmers, Biologi och bioteknik, Systembiologi

Mingtao Huang

Chalmers, Biologi och bioteknik, Systembiologi

South China University of Technology

Ibrahim El-Semman

Chalmers, Biologi och bioteknik, Systembiologi

Assiut University

Amir Feizi

Chalmers, Biologi och bioteknik, Systembiologi

Eduard Kerkhoven

Chalmers, Biologi och bioteknik, Systembiologi

Jens B Nielsen

Chalmers, Biologi och bioteknik, Systembiologi

BioInnovation Institute

Nature Communications

2041-1723 (ISSN) 20411723 (eISSN)

Vol. 13 1 2969

Bioinformatics Services for Data-Driven Design of Cell Factories and Communities (DD-DeCaF)

Europeiska kommissionen (EU) (EC/H2020/686070), 2016-03-01 -- 2020-02-28.

Ämneskategorier

Biokemi och molekylärbiologi

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

DOI

10.1038/s41467-022-30689-7

PubMed

35624178

Relaterade dataset

Results for Genome scale modeling of the protein secretory pathway reveals novel targets for improved recombinant protein production in yeast [dataset]

DOI: 10.5281/zenodo.5593653

Mer information

Senast uppdaterat

2023-09-21