Researchers at the Max Planck Institute for Terrestrial Microbiology are developing two improved variants of Glycolyl-CoA carboxylase (GCC), a key player in the tartronyl-CoA (TaCo) pathway. This synthetic pathway enhances photosynthetic CO2 fixation by transforming photorespiration from a CO2-releasing to a CO2-fixing process. The open-access study, published in ACS Synthetic Biology, combined machine learning with traditional directed evolution techniques, allowing the team to reduce screening efforts while identifying promising enzyme mutations more efficiently.
By applying machine learning, the team developed two standout variants of GCC. One variant demonstrated a twofold increase in catalytic activity, meaning it processes CO2 significantly faster than its predecessor. The second variant showed a 60% reduction in ATP consumption, addressing the enzyme’s previous inefficiencies in energy usage. “Using machine learning allowed us to reduce screening efforts while still identifying promising mutations,” said Tobias J. Erb, the study’s senior author. “This efficiency improvement is crucial because the traditional process of identifying better enzyme variants can be labor-intensive and slow.”
On the path to natural parity
However, the researchers acknowledge that GCC still lags behind its natural counterparts in catalytic efficiency. Despite a more than 1000-fold improvement in activity, the new variants still perform at only 25% of the efficiency of the enzyme from which they were originally derived. The team is optimistic that further refinements will close this gap.
Our work demonstrates the potential of machine learning in enzyme engineering, but additional optimization is essential to fully reach the efficiency of natural enzymes.
Implications for more sustainable agriculture
This research, funded in part by GAIN4CROPS, is highly relevant to agricultural innovation. By engineering synthetic pathways like TaCo, this research could pave the way for more efficient carbon fixation, leading to higher crop yields in the face of growing global food demands and climate change challenges. Our EU-funded project plans to apply these results to improve the efficiency of sunflowers and other plants with C3 metabolism.
