Machine Learning-Supported Enzyme Engineering toward Improved CO2-Fixation of Glycolyl-CoA Carboxylase,ACS Synthetic Biology

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Machine Learning-Supported Enzyme Engineering toward Improved CO2-Fixation of Glycolyl-CoA Carboxylase
ACS Synthetic Biology ( IF 3.7 ) Pub Date : 2023-11-20 , DOI: 10.1021/acssynbio.3c00403
Daniel G Marchal ₁ , Luca Schulz ₁ , Ingmar Schuster ₂ , Jelena Ivanovska ₂ , Nicole Paczia ₃ , Simone Prinz ₄ , Jan Zarzycki ₁ , Tobias J Erb _{1,

5}

Affiliation

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.

中文翻译：

机器学习支持的酶工程可改善乙醇酰辅酶A羧化酶的二氧化碳固定作用

甘醇酰辅酶 A 羧化酶 (GCC) 是一种新型酶，可催化醇酰辅酶 A (TaCo) 途径中的关键反应，这是一种合成光呼吸旁路，最近被设计用于改善光合作用 CO ₂固定。 GCC 是由丙酰辅酶 A 羧化酶 (PCC) 通过五次突变产生的。然而，尽管达到了自然进化的生物素依赖性羧化酶的活性，但五重取代变体GCC M5与其模板PCC相比，其催化效率仍然落后4倍，并且在CO ₂固定过程中遭受无效的ATP水解。为了进一步改进 GCC M5，我们开发了一种机器学习支持的工作流程，可以减少识别改进酶的筛选工作。使用此工作流程，我们提出了两种新颖的 GCC 变体，其羧化率分别提高了 2 倍，能量需求降低了 60%，能够解决 TaCo 途径的动力学和热力学限制。我们的工作强调了将机器学习和定向进化策略相结合以减少酶工程筛选工作的潜力。

更新日期：2023-11-20

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