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タイトル | Machine Learning-Supported Enzyme Engineering toward Improved CO-Fixation of Glycolyl-CoA Carboxylase. |
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ジャーナル・号・ページ | ACS Synth Biol, Vol. 12, Issue 12, Page 3521-3530, Year 2023 |
掲載日 | 2023年12月15日 |
![]() | Daniel G Marchal / Luca Schulz / Ingmar Schuster / Jelena Ivanovska / Nicole Paczia / Simone Prinz / Jan Zarzycki / Tobias J Erb / ![]() |
PubMed 要旨 | 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 ...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 CO 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 CO 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. |
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手法 | EM (単粒子) |
解像度 | 2.03 - 2.31 Å |
構造データ | EMDB-17777, PDB-8pn7: EMDB-17778: Engineered glycolyl-CoA carboxylase (G20R variant) with bound CoA |
化合物 | ![]() ChemComp-COA: ![]() ChemComp-BTI: ![]() ChemComp-HOH: |
由来 |
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![]() | LIGASE / glycolyl-CoA carboxylase |