Machine learning-assisted substrate binding pocket engineering based on structural information
Xinglong Wang,
Kangjie Xu,
Xuan Zeng
et al.
Abstract:Engineering enzyme–substrate binding pockets is the most efficient approach for modifying catalytic activity, but is limited if the substrate binding sites are indistinct. Here, we developed a 3D convolutional neural network for predicting protein–ligand binding sites. The network was integrated by DenseNet, UNet, and self-attention for extracting features and recovering sample size. We attempted to enlarge the dataset by data augmentation, and the model achieved success rates of 48.4%, 35.5%, and 43.6% at a p… Show more
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