Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, P. R. China.
Haihe Laboratory of Synthetic Biology, Tianjin 300308, P. R. China.
Phys Chem Chem Phys. 2024 Oct 30;26(42):26677-26692. doi: 10.1039/d4cp03049d.
Enzymatic capture and conversion of carbon dioxide (CO) into value-added chemicals are of great interest in the field of biocatalysis and have a positive impact on climate change. The quantum chemical methods, recognized as valuable tools for studying reaction mechanisms, have been widely employed in investigating the reaction mechanisms of the enzymes involved in CO utilization. In this perspective, we review the mechanistic studies of representative enzymes that are either currently used or have the potential for converting CO, utilizing the quantum chemical cluster approach and the quantum mechanical/molecular mechanical (QM/MM) method. We begin by summarizing current trends in enzymatic CO conversion, followed by a brief description of the computational details of quantum chemical methods. Then, a series of representative examples of the computational modeling of biocatalytic CO conversion are presented, including the reduction of CO to C1 species (carbon monoxide and formate), and the fixation of CO to form aliphatic and aromatic carboxylic acids. The microscopic views of reaction mechanisms obtained from these studies are helpful in guiding the rational design of current enzymes and the discovery of novel enzymes with enhanced performance in converting CO. Additionally, they provide key information for the design of new-to-nature enzymes. To conclude, we present a perspective on the potential combination of machine learning with quantum description in the study of enzymatic conversion of CO.
酶法捕获和转化二氧化碳(CO)为增值化学品在生物催化领域具有重要意义,对气候变化也有积极影响。量子化学方法被认为是研究反应机制的有价值的工具,已广泛应用于研究参与 CO 利用的酶的反应机制。在这篇综述中,我们利用量子化学团簇方法和量子力学/分子力学(QM/MM)方法,综述了具有代表性的酶的催化 CO 转化的机制研究。我们首先总结了酶法 CO 转化的当前趋势,然后简要描述了量子化学方法的计算细节。然后,介绍了一系列生物催化 CO 转化的计算模拟的代表性实例,包括 CO 还原为 C1 物质(一氧化碳和甲酸盐)和 CO 的固定生成脂肪族和芳香族羧酸。这些研究中获得的反应机制的微观观点有助于指导当前酶的合理设计和发现具有增强 CO 转化性能的新型酶。此外,它们还为设计新型天然酶提供了关键信息。最后,我们对机器学习与量子描述在 CO 酶法转化研究中的潜在结合进行了展望。