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Mining versatile feruloyl esterases: phylogenetic classification, structural features, and deep learning model.

Liang Guo¹, Yuxin Dong², Deyong Zhang¹

¹Key Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, College of Biological and Environment Engineering, Zhejiang Shuren University, Hangzhou, 310015, China.

Bioresources and Bioprocessing

|January 29, 2025

Summary

This summary is machine-generated.

Researchers identified versatile feruloyl esterases (FEs) using enzyme similarity networks, phylogenetic analysis, and deep learning. This expands the toolkit of biocatalysts for various synthetic applications.

Keywords:

Deep learning Enzyme similarity network Feruloyl esterases Molecular dynamics simulations Phylogenetic analysis

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Area of Science:

Biocatalysis
Enzymology
Bioinformatics

Background:

Feruloyl esterases (FEs) are vital enzymes in biological processes.
Identifying versatile FEs with broad substrate specificity is challenging but crucial for applications.

Purpose of the Study:

To identify novel versatile feruloyl esterases (FEs) using integrated computational approaches.
To expand the repertoire of biocatalysts for synthetic applications.

Main Methods:

Enzyme similarity network analysis of 2085 FE sequences from BRENDA.
Phylogenetic analysis to correlate enzyme classification with substrate promiscuity.
Molecular dynamics simulations to study structural dynamics.
Deep learning models for predicting versatile FEs.

Main Results:

Three main clusters of FEs were identified, with clusters 1 and 3 containing characterized enzymes.
Phylogenetic analysis revealed a link between enzyme classification and substrate scope.
Deep learning models predicted 38 and 75 potential versatile FEs from clusters 1 and 3, respectively, with high confidence.

Conclusions:

Integrating phylogenetic and structural data with deep learning is effective for discovering versatile FEs.
This study expands the known diversity of FEs and provides new biocatalysts for synthesis.