Mining versatile feruloyl esterases: phylogenetic classification, structural features, and deep learning model
Abstract 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 in...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Bioresources and Bioprocessing |
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| Online Access: | https://doi.org/10.1186/s40643-024-00835-8 |
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| author | Liang Guo Yuxin Dong Deyong Zhang Xinrong Pan Xinjie Jin Xinyu Yan Yin Lu |
| author_facet | Liang Guo Yuxin Dong Deyong Zhang Xinrong Pan Xinjie Jin Xinyu Yan Yin Lu |
| author_sort | Liang Guo |
| collection | DOAJ |
| description | Abstract 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. Graphical Abstract |
| format | Article |
| id | doaj-art-3b9ef6789e264a81bfee0988e5b9ea8f |
| institution | Kabale University |
| issn | 2197-4365 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Bioresources and Bioprocessing |
| spelling | doaj-art-3b9ef6789e264a81bfee0988e5b9ea8f2025-02-02T12:06:20ZengSpringerOpenBioresources and Bioprocessing2197-43652025-01-0112111210.1186/s40643-024-00835-8Mining versatile feruloyl esterases: phylogenetic classification, structural features, and deep learning modelLiang Guo0Yuxin DongDeyong Zhang1Xinrong Pan2Xinjie Jin3Xinyu Yan4Yin Lu5Key Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, College of Biological and Environment Engineering, Zhejiang Shuren UniversityKey Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, College of Biological and Environment Engineering, Zhejiang Shuren UniversityKey Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, College of Biological and Environment Engineering, Zhejiang Shuren UniversityCollege of Life and Environmental Science, Wenzhou UniversityCollege of Agriculture, Yangtze UniversityKey Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, College of Biological and Environment Engineering, Zhejiang Shuren UniversityAbstract 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. Graphical Abstracthttps://doi.org/10.1186/s40643-024-00835-8Feruloyl esterasesEnzyme similarity networkPhylogenetic analysisMolecular dynamics simulationsDeep learning |
| spellingShingle | Liang Guo Yuxin Dong Deyong Zhang Xinrong Pan Xinjie Jin Xinyu Yan Yin Lu Mining versatile feruloyl esterases: phylogenetic classification, structural features, and deep learning model Bioresources and Bioprocessing Feruloyl esterases Enzyme similarity network Phylogenetic analysis Molecular dynamics simulations Deep learning |
| title | Mining versatile feruloyl esterases: phylogenetic classification, structural features, and deep learning model |
| title_full | Mining versatile feruloyl esterases: phylogenetic classification, structural features, and deep learning model |
| title_fullStr | Mining versatile feruloyl esterases: phylogenetic classification, structural features, and deep learning model |
| title_full_unstemmed | Mining versatile feruloyl esterases: phylogenetic classification, structural features, and deep learning model |
| title_short | Mining versatile feruloyl esterases: phylogenetic classification, structural features, and deep learning model |
| title_sort | mining versatile feruloyl esterases phylogenetic classification structural features and deep learning model |
| topic | Feruloyl esterases Enzyme similarity network Phylogenetic analysis Molecular dynamics simulations Deep learning |
| url | https://doi.org/10.1186/s40643-024-00835-8 |
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