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A Three-Module Machine Learning Framework for Protein Sequence- and Temperature-Dependent kcat/Km Prediction in
Mehmet Emre Erkanli1, Yunseok Jang2, Ali Malli1
1Department of Chemical and Biomolecular Engineering, New York University, 6 MetroTech Center, Brooklyn, New York 11201, United States.
ACS Synthetic Biology
|October 2, 2025
Summary
We developed a three-module machine learning framework to predict enzyme activity (kcat/Km) using protein sequence and temperature. This approach accurately models temperature-dependent enzyme function, outperforming traditional methods.
Area of Science:
- Enzyme kinetics and biocatalysis
- Computational biology and bioinformatics
- Machine learning in biochemistry
Background:
- Enzyme activity is governed by amino acid sequence and assay conditions like temperature.
- Predicting comprehensive enzyme activity (kcat/Km) from sequence alone is challenging.
- Existing machine learning models struggle to capture temperature-dependent enzyme kinetics.
Purpose of the Study:
- To develop a machine learning framework for predicting beta-glucosidase kcat/Km using protein sequence and temperature.
- To model the complex, nonlinear relationship between enzyme sequence, temperature, and catalytic efficiency.
- To improve quantitative prediction of enzyme activity across varying temperatures.
Main Methods:
- Developed a unique three-module machine learning framework.
- Each module captures distinct aspects of sequence, temperature, and kcat/Km interplay.
- Integrated modules to map sequence and temperature spaces for beta-glucosidase activity.
Main Results:
- The modular framework achieved notable generalization performance on unseen protein sequences.
- Predicted temperature-dependent kcat/Km values with reduced variability and overfitting.
- Demonstrated superior performance compared to traditional single-module machine learning methods.
Conclusions:
- The three-module ML framework effectively predicts temperature-dependent enzyme activity.
- Modular design offers advantages in model optimization and prediction accuracy.
- This approach is applicable to other complex biological systems for property exploration.
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