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

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.

Improving Therapeutic Protein Secretion in the Probiotic YeastSaccharomyces boulardiiusing a Multifactorial Engineering Approach

“…Taken together, by harnessing combinatorial and ‘omics-driven engineering approaches, we generated an Sb strain with a unique genotype (a quadruple protease knockout) demonstrating significantly improved secretory performance. In future, harmonizing omics-driven and computer-guided secretory pathway models can be utilized to further identify novel combinatorial targets to improve production of proteins in Sb ( Huang et al, 2017; F. Li et al, 2022 ).…”

Metabolic Engineering: Methodologies and Applications

“…A recently published consensus-based GEM of S. cerevisiae , named Yeast8, offers the incorporation of enzyme constraints to obtain accurate flux solutions in a better-constrained flux space . This feature was employed to improve the phenotype of protein secretion experimentally in S. cerevisiae …”

“…255 This feature was employed to improve the phenotype of protein secretion experimentally in S. cerevisiae. 256 While GEMs offer insight into the metabolic behavior around a desired phenotype, they do not directly suggest genetic interventions required to achieve such a phenotype. Several algorithms have been implemented that impose an optimization condition along with the FBA framework to achieve a metabolic engineering target.…”

Deep learning-driven automatic reconstruction of genome-scale metabolic networks

“…Genome-scale metabolic models (GEMs) have emerged as a powerful tool for the systematic analysis of cellular metabolic functions [5][6][7][8][9]. With extensive use in the study of model organisms, these models are commonly evaluated through simulation techniques such as flux balance analysis (FBA) [10], which assumes a balanced flux of metabolites in the metabolic network via linear optimization [7,11].…”