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