Minghui Wang, Haiyang Cui, Chenlei Gu, Anni Li, Jie Qiao, Ulrich Schwaneberg, Lihui Zhang, Junnan Wei*, Xiujuan Li* and He Huang,
{"title":"Engineering All-Round Cellulase for Bioethanol Production","authors":"Minghui Wang, Haiyang Cui, Chenlei Gu, Anni Li, Jie Qiao, Ulrich Schwaneberg, Lihui Zhang, Junnan Wei*, Xiujuan Li* and He Huang, ","doi":"10.1021/acssynbio.3c00289","DOIUrl":null,"url":null,"abstract":"<p >One strategy to decrease both the consumption of crude oil and environmental damage is through the production of bioethanol from biomass. Cellulolytic enzyme stability and enzymatic hydrolysis play important roles in the bioethanol process. However, the gradually increased ethanol concentration often reduces enzyme activity and leads to inactivation, thereby limiting the final ethanol yield. Herein, we employed an optimized Two-Gene Recombination Process (2GenReP) approach to evolve the exemplary cellulase CBHI for practical bioethanol fermentation. Two all-round CBHI variants (named as R2 and R4) were obtained with simultaneously improved ethanol resistance, organic solvent inhibitor tolerance, and enzymolysis stability in simultaneous saccharification and fermentation (SSF). Notably, CBHI R4 had a 7.0- to 34.5-fold enhanced catalytic efficiency (<i>k</i><sub>cat</sub>/<i>K</i><sub>M</sub>) in the presence/absence of ethanol. Employing the evolved CBHI R2 and R4 in the 1G bioethanol process resulted in up to 10.27% (6.7 g/L) improved ethanol yield (ethanol concentration) than non-cellulase, which was far more beyond than other optimization strategies. Besides bioenergy fields, this transferable protein engineering routine holds the potential to generate all-round enzymes that meet the requirement in biotransformation and bioenergy fields.</p>","PeriodicalId":26,"journal":{"name":"ACS Synthetic Biology","volume":"12 7","pages":"2187–2197"},"PeriodicalIF":3.7000,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Synthetic Biology","FirstCategoryId":"99","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acssynbio.3c00289","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 2
Abstract
One strategy to decrease both the consumption of crude oil and environmental damage is through the production of bioethanol from biomass. Cellulolytic enzyme stability and enzymatic hydrolysis play important roles in the bioethanol process. However, the gradually increased ethanol concentration often reduces enzyme activity and leads to inactivation, thereby limiting the final ethanol yield. Herein, we employed an optimized Two-Gene Recombination Process (2GenReP) approach to evolve the exemplary cellulase CBHI for practical bioethanol fermentation. Two all-round CBHI variants (named as R2 and R4) were obtained with simultaneously improved ethanol resistance, organic solvent inhibitor tolerance, and enzymolysis stability in simultaneous saccharification and fermentation (SSF). Notably, CBHI R4 had a 7.0- to 34.5-fold enhanced catalytic efficiency (kcat/KM) in the presence/absence of ethanol. Employing the evolved CBHI R2 and R4 in the 1G bioethanol process resulted in up to 10.27% (6.7 g/L) improved ethanol yield (ethanol concentration) than non-cellulase, which was far more beyond than other optimization strategies. Besides bioenergy fields, this transferable protein engineering routine holds the potential to generate all-round enzymes that meet the requirement in biotransformation and bioenergy fields.
期刊介绍:
The journal is particularly interested in studies on the design and synthesis of new genetic circuits and gene products; computational methods in the design of systems; and integrative applied approaches to understanding disease and metabolism.
Topics may include, but are not limited to:
Design and optimization of genetic systems
Genetic circuit design and their principles for their organization into programs
Computational methods to aid the design of genetic systems
Experimental methods to quantify genetic parts, circuits, and metabolic fluxes
Genetic parts libraries: their creation, analysis, and ontological representation
Protein engineering including computational design
Metabolic engineering and cellular manufacturing, including biomass conversion
Natural product access, engineering, and production
Creative and innovative applications of cellular programming
Medical applications, tissue engineering, and the programming of therapeutic cells
Minimal cell design and construction
Genomics and genome replacement strategies
Viral engineering
Automated and robotic assembly platforms for synthetic biology
DNA synthesis methodologies
Metagenomics and synthetic metagenomic analysis
Bioinformatics applied to gene discovery, chemoinformatics, and pathway construction
Gene optimization
Methods for genome-scale measurements of transcription and metabolomics
Systems biology and methods to integrate multiple data sources
in vitro and cell-free synthetic biology and molecular programming
Nucleic acid engineering.