Department of Chemistry and Biochemistry, University of Arizona, 1306 E University Blvd, Tucson, Arizona 85721, United States.
J Chem Inf Model. 2024 Mar 25;64(6):2101-2111. doi: 10.1021/acs.jcim.4c00045. Epub 2024 Mar 7.
It is hoped that artificial enzymes designed in laboratories can be efficient alternatives to chemical catalysts that have been used to synthesize organic molecules. However, the design of artificial enzymes is challenging and requires a detailed molecular-level analysis to understand the mechanism they promote in order to design efficient variants. In this study, we computationally investigate the mechanism of proficient Morita-Baylis-Hillman enzymes developed using a combination of computational design and directed evolution. The powerful transition path sampling method coupled with in-depth post-processing analysis has been successfully used to elucidate the different chemical pathways, transition states, protein dynamics, and free energy barriers of reactions catalyzed by such laboratory-optimized enzymes. This research provides an explanation for how different chemical modifications in an enzyme affect its catalytic activity in ways that are not predictable by static design algorithms.
人们希望在实验室中设计的人工酶可以成为化学催化剂的有效替代品,这些催化剂已经被用于合成有机分子。然而,人工酶的设计具有挑战性,需要进行详细的分子水平分析,以了解它们促进的机制,从而设计出高效的变体。在这项研究中,我们通过计算方法研究了使用计算设计和定向进化相结合开发的高效 Morita-Baylis-Hillman 酶的机制。强大的过渡态抽样方法结合深入的后处理分析已成功用于阐明此类实验室优化酶催化的不同化学反应途径、过渡态、蛋白质动力学和自由能垒。这项研究解释了酶中的不同化学修饰如何以静态设计算法无法预测的方式影响其催化活性。