English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Journal Article

Machine learning-supported enzyme engineering toward improved CO2-fixation of Glycolyl-CoA carboxylase

MPS-Authors
/persons/resource/persons267037

Marchal,  Daniel G.
Understanding and Building Metabolism, Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;

/persons/resource/persons254700

Schulz,  Luca
Understanding and Building Metabolism, Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;

/persons/resource/persons261240

Paczia,  Nicole       
Core Facility Metabolomics and small Molecules Mass Spectrometry, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;

/persons/resource/persons254859

Zarzycki,  Jan
Understanding and Building Metabolism, Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;

/persons/resource/persons254247

Erb,  Tobias J.       
Core Facility Metabolomics and small Molecules Mass Spectrometry, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;
Center for Synthetic Microbiology (SYNMIKRO), Philipps University of Marburg, Marburg;

External Resource

https://doi.org/10.1021/acssynbio.3c00403
(Publisher version)

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Marchal, D. G., Schulz, L., Schuster, I., Ivanovska, J., Paczia, N., Prinz, S., et al. (2023). Machine learning-supported enzyme engineering toward improved CO2-fixation of Glycolyl-CoA carboxylase. ACS Synthetic Biology, 12(12), 3521-3530. doi:10.1021/acssynbio.3c00403.


Cite as: https://hdl.handle.net/21.11116/0000-000D-FD76-2
Abstract
Glycolyl-CoA carboxylase (GCC) is a new-to-nature enzyme that catalyzes the key reaction in the tartronyl-CoA (TaCo) pathway, a synthetic photorespiration bypass that was recently designed to improve photosynthetic CO2 fixation. GCC was created from propionyl-CoA carboxylase (PCC) through five mutations. However, despite reaching activities of naturally evolved biotin-dependent carboxylases, the quintuple substitution variant GCC M5 still lags behind 4-fold in catalytic efficiency compared to its template PCC and suffers from futile ATP hydrolysis during CO2 fixation. To further improve upon GCC M5, we developed a machine learning-supported workflow that reduces screening efforts for identifying improved enzymes. Using this workflow, we present two novel GCC variants with 2-fold increased carboxylation rate and 60% reduced energy demand, respectively, which are able to address kinetic and thermodynamic limitations of the TaCo pathway. Our work highlights the potential of combining machine learning and directed evolution strategies to reduce screening efforts in enzyme engineering.