Binding prediction of multi-domain cellulases with a dual-CNN

2022/07/06 by Kay S. Schaller, Schaller, Kay S., Jeppe Kari +7
Physics and Astronomy · #Biological Physics (physics.bio-ph) #FOS: Physical sciences #physics.bio-ph

paper · pdf · doi:10.48550/arxiv.2207.02698

arxiv created 2022/07/06 · arxiv updated 2022/07/25

Abstract

Cellulases hold great promise for the production of biofuels and biochemicals. However, they are modular enzymes acting on a complex heterogeneous substrate. Because of this complexity, the computational prediction of their catalytic properties remains scarce, which restricts both enzyme discovery and enzyme design. Here, we present a dual-input convolutional neural network to predict the binding of multi-domain enzymes. This regression model outperformed previous molecular dynamics-based methods for binding prediction for cellulases in a fraction of the time. Also, we show that when changed to a classification problem, the same network can be back-propagated to suggest mutations to improve enzyme binding. A similar approach could increase our understanding of the structure-activity relationship of enzymes, and suggest new promising mutations for enzyme design using explainable artificial intelligence.

Related

• Enzyme promiscuity prediction using hierarchy-informed multi-label classification
• Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives
• Boosting Convolutional Neural Networks' Protein Binding Site Prediction Capacity Using SE(3)-invariant transformers, Transfer Learning and Homology-based Augmentation
• A Benchmark Dataset for Multimodal Prediction of Enzymatic Function Coupling DNA Sequences and Natural Language
• OmniESI: A unified framework for enzyme-substrate interaction prediction with progressive conditional deep learning