27. Designing γ-Carbonic Anhydrase as a Broad-Scope Metalloreductase with Ultrathermostability and Organic-Solvent Tolerance
- Yixian Bao
- Yunhao Li
- Zhekai Xie
- Pu Song
- Jun Huang
- Xuan Zhang*
- Pengfei Ji*
Enzymes are essential for industrial catalysis but are often limited by poor thermostability and low activity in organic solvents. In this study, we utilized ProteinMPNN, a deep learning-based protein sequence design algorithm combined with evolutionary information, to design γ-carbonic anhydrase (γ-CA) as a broad-scope metalloreductase. Compared with the wild-type enzyme, the designed variants exhibited significantly enhanced catalytic activity. Subsequent optimization through directed evolution further improved the catalytic activity and superior enantioselectivity toward the asymmetric reduction of ketones and alkenes. Remarkably, the engineered γ-CA demonstrated ultrathermostability with a Tm of 96 °C, and resistance to organic solvents, even including 50% polar organic solvents, addressing key challenges in industrial biocatalysis. Mechanistic study through protein crystallography, MD simulations, and QM/MM calculations revealed the advantage of the identified L83A mutant, along with the reaction profile through the terminal zinc hydride intermediate. This study showcases the potential of combining deep learning-based protein design with traditional engineering methods to create robust and efficient biocatalysts. The results not only establish a framework for optimizing γ-CA but also provide a generalizable strategy for engineering enzymes with enhanced stability and activity under extreme conditions, paving the way for potential industrial applications.
酶是工业催化的关键，但其热稳定性差、在有机溶剂中的活性低等问题常常制约其应用。本研究利用基于深度学习的蛋白质序列设计算法ProteinMPNN，结合进化信息，设计了一种广谱金属还原酶γ-碳酸酐酶（γ-CA）。与野生型酶相比，设计的变体表现出显著增强的催化活性。后续通过定向进化进行优化，进一步提高了催化活性，并使其在酮和烯烃的不对称还原中表现出优异的对映选择性。值得注意的是，改造后的γ-CA表现出极高的热稳定性，Tm高达96 °C，并且耐有机溶剂，甚至包括50%的极性有机溶剂，从而解决了工业生物催化中的关键挑战。通过蛋白质晶体学、分子动力学模拟和量子力学/几何模型计算进行的机理研究揭示了已鉴定的L83A突变体的优势，以及通过末端锌氢化物中间体的反应曲线。这项研究展示了将基于深度学习的蛋白质设计与传统工程方法相结合，以创造稳健高效的生物催化剂的潜力。研究结果不仅建立了优化γ-CA的框架，还为在极端条件下增强稳定性和活性的酶工程设计提供了一种可推广的策略。