Abstract
Enzyme–substrate interactions are essential to both biological processes and industrial applications. Advanced machine learning techniques have significantly accelerated biocatalysis research, revolutionizing the prediction of biocatalytic activities and facilitating the discovery of novel biocatalysts. However, the limited availability of data for specific enzyme functions, such as conversion efficiency and stereoselectivity, presents challenges for prediction accuracy. In this study, we developed BioStructNet, a structure-based deep learning network that integrates both protein and ligand structural data to capture the complexity of enzyme–substrate interactions. Benchmarking studies with different algorithms showed the enhanced predictive accuracy of BioStructNet. To further optimize the prediction accuracy for the small data set, we implemented transfer learning in the framework, training a source model on a large data set and fine-tuning it on a small, function-specific data set, using the CalB data set as a case study. The model performance was validated by comparing the attention heat maps generated by the BioStructNet interaction module with the enzyme–substrate interactions revealed from molecular dynamics simulations of enzyme–substrate complexes. BioStructNet would accelerate the discovery of functional enzymes for industrial use, particularly in cases where the training data sets for machine learning are small.
Original language | English |
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Pages (from-to) | 474–490 |
Number of pages | 17 |
Journal | Journal of Chemical Theory and Computation |
Volume | 21 |
Issue number | 1 |
Early online date | 20 Dec 2024 |
DOIs | |
Publication status | Published - 14 Jan 2025 |
Bibliographical note
Publisher Copyright:© 2024 The Authors. Published by American Chemical Society.
Keywords
- BioStructNet
- structure-based network
- transfer learning
- biocatalyst functions
ASJC Scopus subject areas
- Computer Science Applications
- Physical and Theoretical Chemistry
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BioStructNet: Structure-Based Network with Transfer Learning for Predicting Biocatalyst Functions
Huang, M. (Owner), Queen's University Belfast, Oct 2024
https://github.com/Xiangwen-Wang/BioStructNet
Dataset