Understanding the effectiveness of enzyme pre-reaction state by a quantum-based machine learning model
Understanding the effectiveness of enzyme pre-reaction state by a quantum-based machine learning model
复制标题
DOI：
10.1016/j.xcrp.2022.101128
复制
发表时间：
2022
影响因子：
8.9
通讯作者：
Zhao Yi-Lei
中科院分区：
文献类型：
--
作者：
Luo Shenggan;Liu Lanxuan;Lyu Chu-Jun;Sim Byuri;Liu Yihan;Gong Haifan;Nie Yao;Zhao Yi-Lei
Summary Prediction of enzymatic stereochemistry is a significant challenge in computational chemistry because of targeting very small energy gaps in highly complicated macromolecular systems. Here, we report a scenario of four substrates (2-pentanone, 2-hexanone, 2-heptanone, and 2-octanone) within four enzyme variants (wild type, W116A, F285A, and W286A) of a medium-chain dehydrogenase from Candida parapsilopsis. The relative stabilities of pro-R and pro-S pre-reaction states are calculated by umbrella sampling, approximately consistent with the observed stereoselectivity in experiment. Besides, a LASSO-SVM machine-learning model is constructed with structural information of 704 pairs of quantum-mechanistic/molecular-mechanistic transition states (TSs) and pre-reaction states (PRSs), achieving the explanatory power of 99.6\% for the calculated barriers. Intriguingly, the explanatory power with the PRS-alone structural information reaches 90.7\%, but it decreases to 55.4\% with the TS-alone structural information. Thus, the outcomes support that the enzymatic stereoselectivity is substantially determined by the frontier-molecular-orbital-related pre-organization of the enzyme-substrate reacting complexes.
