PDF(Pubmed)
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.
摘要：
甘醇酰辅酶A羧化酶（GCC）是一种新的自然酶，可催化tartronyl-CoA（TaCo）途径中的关键反应，一种合成光呼吸旁路，最近设计用于改善光合CO2固定。GCC由丙酰辅酶A羧化酶(PCC)通过5个突变产生。然而,尽管达到了自然进化的生物素依赖性羧化酶的活性，与其模板PCC相比，五重取代变体GCCM5的催化效率仍然落后4倍，并且在CO2固定过程中遭受徒劳的ATP水解。为了进一步改进GCCM5，我们开发了一个机器学习支持的工作流程，减少了鉴定改进酶的筛选工作。使用此工作流，我们提出了两个新的GCC变体，其羧化率增加了2倍，能量需求减少了60％，分别,它们能够解决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中的筛选工作的潜力。
