Optimization of enzyme parameters for fermentative production of biorenewable fuels and chemicals
Microbial biocatalysts such as Escherichia coli and Saccharomyces cerevisiae have been extensively subjected to Metabolic Engineering for the fermentative production of biorenewable fuels and chemicals. This often entails the introduction of new enzymes, deletion of unwanted enzymes and efforts to f...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2012-10-01
|
| Series: | Computational and Structural Biotechnology Journal |
| Online Access: | http://journals.sfu.ca/rncsb/index.php/csbj/article/view/csbj.201210005 |
| _version_ | 1811253850330365952 |
|---|---|
| author | Ping Liu Laura R Jarboe Lonnie O. Ingram Kumar Babu Kautharapu |
| author_facet | Ping Liu Laura R Jarboe Lonnie O. Ingram Kumar Babu Kautharapu |
| author_sort | Ping Liu |
| collection | DOAJ |
| description | Microbial biocatalysts such as Escherichia coli and Saccharomyces cerevisiae have been extensively subjected to Metabolic Engineering for the fermentative production of biorenewable fuels and chemicals. This often entails the introduction of new enzymes, deletion of unwanted enzymes and efforts to fine-tune enzyme abundance in order to attain the desired strain performance. Enzyme performance can be quantitatively described in terms of the Michaelis-Menten type parameters Km, turnover number kcat and Ki, which roughly describe the affinity of an enzyme for its substrate, the speed of a reaction and the enzyme sensitivity to inhibition by regulatory molecules. Here we describe examples of where knowledge of these parameters have been used to select, evolve or engineer enzymes for the desired performance and enabled increased production of biorenewable fuels and chemicals. Examples include production of ethanol, isobutanol, 1-butanol and tyrosine and furfural tolerance. The Michaelis-Menten parameters can also be used to judge the cofactor dependence of enzymes and quantify their preference for NADH or NADPH. Similarly, enzymes can be selected, evolved or engineered for the preferred cofactor preference. Examples of exporter engineering and selection are also discussed in the context of production of malate, valine and limonene. |
| first_indexed | 2024-04-12T16:57:17Z |
| format | Article |
| id | doaj.art-1330f14bc49e4f968a529983b46a2652 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-04-12T16:57:17Z |
| publishDate | 2012-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-1330f14bc49e4f968a529983b46a26522022-12-22T03:24:10ZengElsevierComputational and Structural Biotechnology Journal2001-03702012-10-0134e201210005Optimization of enzyme parameters for fermentative production of biorenewable fuels and chemicalsPing LiuLaura R JarboeLonnie O. IngramKumar Babu KautharapuMicrobial biocatalysts such as Escherichia coli and Saccharomyces cerevisiae have been extensively subjected to Metabolic Engineering for the fermentative production of biorenewable fuels and chemicals. This often entails the introduction of new enzymes, deletion of unwanted enzymes and efforts to fine-tune enzyme abundance in order to attain the desired strain performance. Enzyme performance can be quantitatively described in terms of the Michaelis-Menten type parameters Km, turnover number kcat and Ki, which roughly describe the affinity of an enzyme for its substrate, the speed of a reaction and the enzyme sensitivity to inhibition by regulatory molecules. Here we describe examples of where knowledge of these parameters have been used to select, evolve or engineer enzymes for the desired performance and enabled increased production of biorenewable fuels and chemicals. Examples include production of ethanol, isobutanol, 1-butanol and tyrosine and furfural tolerance. The Michaelis-Menten parameters can also be used to judge the cofactor dependence of enzymes and quantify their preference for NADH or NADPH. Similarly, enzymes can be selected, evolved or engineered for the preferred cofactor preference. Examples of exporter engineering and selection are also discussed in the context of production of malate, valine and limonene.http://journals.sfu.ca/rncsb/index.php/csbj/article/view/csbj.201210005 |
| spellingShingle | Ping Liu Laura R Jarboe Lonnie O. Ingram Kumar Babu Kautharapu Optimization of enzyme parameters for fermentative production of biorenewable fuels and chemicals Computational and Structural Biotechnology Journal |
| title | Optimization of enzyme parameters for fermentative production of biorenewable fuels and chemicals |
| title_full | Optimization of enzyme parameters for fermentative production of biorenewable fuels and chemicals |
| title_fullStr | Optimization of enzyme parameters for fermentative production of biorenewable fuels and chemicals |
| title_full_unstemmed | Optimization of enzyme parameters for fermentative production of biorenewable fuels and chemicals |
| title_short | Optimization of enzyme parameters for fermentative production of biorenewable fuels and chemicals |
| title_sort | optimization of enzyme parameters for fermentative production of biorenewable fuels and chemicals |
| url | http://journals.sfu.ca/rncsb/index.php/csbj/article/view/csbj.201210005 |
| work_keys_str_mv | AT pingliu optimizationofenzymeparametersforfermentativeproductionofbiorenewablefuelsandchemicals AT laurarjarboe optimizationofenzymeparametersforfermentativeproductionofbiorenewablefuelsandchemicals AT lonnieoingram optimizationofenzymeparametersforfermentativeproductionofbiorenewablefuelsandchemicals AT kumarbabukautharapu optimizationofenzymeparametersforfermentativeproductionofbiorenewablefuelsandchemicals |