Computational biology predicts metabolic engineering targets for increased production of 103 valuable chemicals in yeast

• 成果类型: Article
• 署名作者: Domenzain, Ivan; Lu, Yao; Wang, Haoyu; Shi, Junling; Lu, Hongzhong; Nielsen, Jens
• 署名单位: Chalmers University of Technology; Chalmers University of Technology; Northwest A&F University - China; Shanghai Jiao Tong University; Chinese Academy of Sciences; Dalian Institute of Chemical Physics, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Northwestern Polytechnical University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-14802
• DOI: 10.1073/pnas.2417322122
• 发表日期: 2025-03-04
• 关键词: saccharomyces-cerevisiae; escherichia-coli; biosynthesis; platform; ACID

摘要

Development of efficient cell factories that can compete with traditional chemical production processes is complex and generally driven by case- specific strategies, based on the product and microbial host of interest. Despite major advancements in the field of metabolic modeling in recent years, prediction of genetic modifications for increased production remains challenging. Here, we present a computational pipeline that leverages the concept of protein limitations in metabolism for prediction of optimal combinations of gene engineering targets for enhanced chemical bioproduction. We used our pipeline for prediction of engineering targets for 103 different chemicals using Saccharomyces cerevisiae as a host. Furthermore, we identified sets of gene targets predicted for groups of multiple chemicals, suggesting the possibility of rational model- driven design of platform strains for diversified chemical production.