Guo Liang, Dong Yuxin, Zhang Deyong, Pan Xinrong, Jin Xinjie, Yan Xinyu, Lu Yin
Key Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, College of Biological and Environment Engineering, Zhejiang Shuren University, Hangzhou, 310015, China.
, Jinan No.1 High School, Jinan, 250014, China.
Bioresour Bioprocess. 2025 Jan 29;12(1):7. doi: 10.1186/s40643-024-00835-8.
Feruloyl esterases (FEs, EC 3.1.1.73) play a crucial role in biological synthesis and metabolism. However, the identification of versatile FEs, capable of catalyzing a wide range of substrates, remains a challenge. In this study, we obtained 2085 FE sequences from the BRENDA database and initiated with an enzyme similarity network analysis, revealing three main clusters (1-3). Notably, both cluster 1 and cluster 3 included the characterized FEs, which exhibited significant differences in sequence length. Subsequent phylogenetic analysis of these clusters unveiled a correlation between phylogenetic classification and substrate promiscuity, and enzymes with broad substrate scope tended to locate within specific branches of the phylogenetic tree. Further, molecular dynamics simulations and dynamical cross-correlation matrix analysis were employed to explore structural dynamics differences between promiscuous and substrate-specific FEs. Finally, to expand the repertoire of versatile FEs, we employed deep learning models to predict potentially promiscuous enzymes and identified 38 and 75 potential versatile FEs from cluster 1 and cluster 3 with a probability score exceeding 90%. Our findings underscore the utility of integrating phylogenetic and structural features with deep learning approaches for mining versatile FEs, shedding light on unexplored enzymatic diversity and expanding the repertoire of biocatalysts for synthetic applications.
阿魏酸酯酶（FEs，EC 3.1.1.73）在生物合成和代谢中起着至关重要的作用。然而，鉴定能够催化多种底物的多功能阿魏酸酯酶仍然是一项挑战。在本研究中，我们从BRENDA数据库中获得了2085条阿魏酸酯酶序列，并首先进行了酶相似性网络分析，揭示了三个主要簇（1-3）。值得注意的是，簇1和簇3都包含已表征的阿魏酸酯酶，它们在序列长度上表现出显著差异。随后对这些簇进行的系统发育分析揭示了系统发育分类与底物混杂性之间的相关性，并且具有广泛底物范围的酶倾向于位于系统发育树的特定分支内。此外，采用分子动力学模拟和动态交叉相关矩阵分析来探索混杂型和底物特异性阿魏酸酯酶之间的结构动力学差异。最后，为了扩大多功能阿魏酸酯酶的种类，我们使用深度学习模型来预测潜在的混杂酶，并从簇1和簇3中鉴定出38个和75个概率得分超过90%的潜在多功能阿魏酸酯酶。我们的研究结果强调了将系统发育和结构特征与深度学习方法相结合用于挖掘多功能阿魏酸酯酶的实用性，为未探索的酶多样性提供了线索，并扩大了用于合成应用的生物催化剂的种类。
