High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering

doi:10.1016/j.synbio.2021.12.006

. 2022 Jan 5;7(1):541-543.

doi: 10.1016/j.synbio.2021.12.006. eCollection 2022 Mar.

High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering

Zhenghong Li^{1

2}, Chengyu Zhang^{2

3}, Zhengduo Wang^{2

3}, Chuan Li², Zhiheng Yang², Zilong Li³, Lixin Zhang², Weishan Wang^{3

4}

Affiliations

¹ Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China.
² State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
³ State Key Laboratory of Bioreactor Engineering, And School of Biotechnology, East China University of Science and Technology (ECUST), Shanghai, 200237, China.
⁴ University of Chinese Academy of Sciences, Beijing 100049, China.

PMID: 35059513
PMCID: PMC8749077
DOI: 10.1016/j.synbio.2021.12.006

High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering

Zhenghong Li et al. Synth Syst Biotechnol. 2022.

. 2022 Jan 5;7(1):541-543.

doi: 10.1016/j.synbio.2021.12.006. eCollection 2022 Mar.

Authors

Zhenghong Li^{1

2}, Chengyu Zhang^{2

3}, Zhengduo Wang^{2

3}, Chuan Li², Zhiheng Yang², Zilong Li³, Lixin Zhang², Weishan Wang^{3

4}

Affiliations

¹ Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China.
² State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
³ State Key Laboratory of Bioreactor Engineering, And School of Biotechnology, East China University of Science and Technology (ECUST), Shanghai, 200237, China.
⁴ University of Chinese Academy of Sciences, Beijing 100049, China.

PMID: 35059513
PMCID: PMC8749077
DOI: 10.1016/j.synbio.2021.12.006

Abstract

As synthetic biology enters the era of quantitative biology, mathematical information such as kinetic parameters of enzymes can offer us an accurate knowledge of metabolism and growth of cells, and further guidance on precision metabolic engineering. k _cat , termed the turnover number, is a basic parameter of enzymes that describes the maximum number of substrates converted to products each active site per unit time. It reflects enzyme activity and is essential for quantitative understanding of biosystems. Usually, the k _cat values are measured in vitro, thus may not be able to reflect the enzyme activity in vivo. In this case, Davidi et al. defined a surrogate $k_{m a x}^{v i v o}$ (k _app ) for k _cat and developed a high throughput method to acquire $k_{m a x}^{v i v o}$ from omics data. Heckmann et al. and Chen et al. proved that the surrogate parameter can be a good embodiment of the physiological state of enzymes and exhibit superior performance for enzyme-constrained metabolic model to the default one. These breakthroughs will fuel the development of system and synthetic biology.

Keywords: Genome scale models; High throughput; Machine learning; Metabolic reconstitution; Turnover number.

PubMed Disclaimer

Conflict of interest statement

The authors indicate that they have no conflict of interest.

Figures

**Fig. 1**
Stages of the development of k_cat extraction methods for higher throughput and better coverage.

See this image and copyright information in PMC

Cited by

Enzyme-constrained metabolic model and in silico metabolic engineering of Clostridium ljungdahlii for the development of sustainable production processes.
Caivano A, van Winden W, Dragone G, Mussatto SI. Caivano A, et al. Comput Struct Biotechnol J. 2023 Sep 15;21:4634-4646. doi: 10.1016/j.csbj.2023.09.015. eCollection 2023. Comput Struct Biotechnol J. 2023. PMID: 37790242 Free PMC article.
Parameter inference for enzyme and temperature constrained genome-scale models.
Pettersen JP, Almaas E. Pettersen JP, et al. Sci Rep. 2023 Apr 13;13(1):6079. doi: 10.1038/s41598-023-32982-x. Sci Rep. 2023. PMID: 37055413 Free PMC article.

References

1. Bekiaris P.S., Klamt S. Automatic construction of metabolic models with enzyme constraints. Bmc Bioinformatics. 2020;21 doi: 10.1186/s12859-019-3329-9. - DOI - PMC - PubMed
1. Sanchez B.J., Salazar M.J., Weng L.L., et al. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Mol Syst Biol. 2017;13(935) doi: 10.15252/msb.20167411. - DOI - PMC - PubMed
1. NilssonNielsen A.J., Palsson B.O. Metabolic Models of Protein Allocation Call for the Kinetome. Cell Syst. 2017;5:538–541. doi: 10.1016/j.cels.2017.11.013. - DOI - PubMed
1. Davidi D., Milo R. Lessons on enzyme kinetics from quantitative proteomics. Curr Opin Biotechnol. 2017;46:81–89. doi: 10.1016/j.copbio.2017.02.007. - DOI - PubMed
1. van Eunen K., Bouwman J., Daran-Lapujade P., et al. Measuring enzyme activities under standardized in vivo-like conditions for systems biology. FEBS J. 2010;277:749–760. doi: 10.1111/j.1742-4658.2009.07524.x. - DOI - PubMed

LinkOut - more resources

Full Text Sources
Research Materials
- NCI CPTC Antibody Characterization Program
Miscellaneous
- NCI CPTAC Assay Portal

[1] Bekiaris P.S., Klamt S. Automatic construction of metabolic models with enzyme constraints. Bmc Bioinformatics. 2020;21 doi: 10.1186/s12859-019-3329-9. - DOI - PMC - PubMed

[2] Bekiaris P.S., Klamt S. Automatic construction of metabolic models with enzyme constraints. Bmc Bioinformatics. 2020;21 doi: 10.1186/s12859-019-3329-9. - DOI - PMC - PubMed

[3] Sanchez B.J., Salazar M.J., Weng L.L., et al. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Mol Syst Biol. 2017;13(935) doi: 10.15252/msb.20167411. - DOI - PMC - PubMed

[4] Sanchez B.J., Salazar M.J., Weng L.L., et al. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Mol Syst Biol. 2017;13(935) doi: 10.15252/msb.20167411. - DOI - PMC - PubMed

[5] NilssonNielsen A.J., Palsson B.O. Metabolic Models of Protein Allocation Call for the Kinetome. Cell Syst. 2017;5:538–541. doi: 10.1016/j.cels.2017.11.013. - DOI - PubMed

[6] NilssonNielsen A.J., Palsson B.O. Metabolic Models of Protein Allocation Call for the Kinetome. Cell Syst. 2017;5:538–541. doi: 10.1016/j.cels.2017.11.013. - DOI - PubMed

[7] Davidi D., Milo R. Lessons on enzyme kinetics from quantitative proteomics. Curr Opin Biotechnol. 2017;46:81–89. doi: 10.1016/j.copbio.2017.02.007. - DOI - PubMed

[8] Davidi D., Milo R. Lessons on enzyme kinetics from quantitative proteomics. Curr Opin Biotechnol. 2017;46:81–89. doi: 10.1016/j.copbio.2017.02.007. - DOI - PubMed

[9] van Eunen K., Bouwman J., Daran-Lapujade P., et al. Measuring enzyme activities under standardized in vivo-like conditions for systems biology. FEBS J. 2010;277:749–760. doi: 10.1111/j.1742-4658.2009.07524.x. - DOI - PubMed

[10] van Eunen K., Bouwman J., Daran-Lapujade P., et al. Measuring enzyme activities under standardized in vivo-like conditions for systems biology. FEBS J. 2010;277:749–760. doi: 10.1111/j.1742-4658.2009.07524.x. - DOI - PubMed

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering

Affiliations

High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Similar articles

Cited by

References

LinkOut - more resources

Full Text Sources

Research Materials

Miscellaneous