The bacterial enzyme class of β-lactamases are involved in benzylpenicillin acylation reactions, which are currently being revisited using hybrid quantum mechanical molecular mechanical (QM/MM) chain-of-states pathway optimizations. Minimum energy pathways are sampled by reoptimizing pathway geometry under different representative protein environments obtained through constrained molecular dynamics simulations. Predictive potential energy surface models in the reaction space are trained with machine-learning regression techniques. Herein, using TEM-1/benzylpenicillin acylation reaction as the model system, we introduce two model-independent criteria for delineating the energetic contributions and correlations in the predicted reaction space. Both methods are demonstrated to effectively quantify the energetic contribution of each chemical process and identify the rate limiting step of enzymatic reaction with high degrees of freedom. The consistency of the current workflow is tested under seven levels of quantum chemistry theory and three non-linear machine-learning regression models. The proposed approaches are validated to provide qualitative compliance with experimental mutagenesis studies.

β-内酰胺酶的细菌酶类参与青霉素酰化反应，目前正在使用混合量子力学分子力学 (QM/MM) 状态链途径优化重新审视这一反应。通过在通过约束分子动力学模拟获得的不同代表性蛋白质环境下重新优化路径几何形状，对最小能量路径进行采样。反应空间中的预测势能表面模型通过机器学习回归技术进行训练。在此，使用 TEM-1/苄青霉素酰化反应作为模型系统，我们引入了两个独立于模型的标准来描述预测反应空间中的能量贡献和相关性。两种方法都被证明可以有效地量化每个化学过程的能量贡献，并以高自由度识别酶促反应的限速步骤。当前工作流程的一致性在七个级别的量子化学理论和三个非线性机器学习回归模型下进行了测试。所提出的方法经过验证，可以提供与实验诱变研究的定性一致性。