BioStructNet: structure-based network with transfer learning for predicting biocatalyst functions

Xiangwen Wang, Jiahui Zhou, Jane Mueller, Derek Quinn, Alexandra Carvalho, Thomas S. Moody, Meilan Huang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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 languageEnglish
Pages (from-to)474–490
Number of pages17
JournalJournal of Chemical Theory and Computation
Volume21
Issue number1
Early online date20 Dec 2024
DOIs
Publication statusPublished - 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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