Molecular Dynamics-Derived Descriptor Informs the Impact of Mutation on the Catalytic Turnover Number in Lactonase Across Substrates

Molecular dynamics simulations have been extensively employed to reveal the roles of protein dynamics in mediating enzyme catalysis. However, simulation-derived predictive descriptors that inform the impacts of mutations on catalytic turnover numbers remain largely unexplored. In this work, we report the identification of molecular modeling-derived descriptors to predict mutation effect on the turnover number of lactonase SsoPox with both native and non-native substrates. The study consists of 10 enzyme–substrate complexes resulting from a combination of five enzyme variants with two substrates. For each complex, we derived 15 descriptors from molecular dynamics simulations and applied principal component analysis to rank the predictive capability of the descriptors. A top-ranked descriptor was identified, which is the solvent-accessible surface area (SASA) ratio of the substrate to the active site pocket. A uniform volcano-shaped plot was observed in the distribution of experimental activation free energy against the SASA ratio. To achieve efficient lactonase hydrolysis, a non-native substrate-bound enzyme variant needs to involve a similar range of the SASA ratio to the native substrate-bound wild-type enzyme. The descriptor reflects how well the enzyme active site pocket accommodates a substrate for reaction, which has the potential of guiding optimization of enzyme reaction turnover for non-native chemical transformations.

分子动力学模拟已被广泛应用于揭示蛋白质动力学在介导酶催化过程中的作用机制。然而，源自模拟、能够阐明突变对催化周转数影响的预测性描述符，仍在很大程度上未被充分探索。本研究中，我们报道了一类源自分子建模的描述符的筛选结果，该类描述符可用于预测突变对天然与非天然底物作用下内酯酶SsoPox（lactonase SsoPox）周转数的影响。本研究包含10种酶-底物复合物，由5种酶变体与2种底物组合得到。针对每种复合物，我们通过分子动力学模拟提取了15种描述符，并采用主成分分析对各描述符的预测能力进行排序。最终筛选得到一个排名靠前的描述符：底物与活性位点口袋的溶剂可及表面积（solvent-accessible surface area, SASA）比值。实验测得的活化自由能与该SASA比值的分布呈现均匀的火山图形态。为实现高效的内酯酶水解反应，结合非天然底物的酶变体所需的SASA比值范围，需与结合天然底物的野生型酶相近。该描述符反映了酶活性位点口袋对底物的适配能力，具备指导非天然化学转化中酶反应周转优化的应用潜力。