Protein–Peptide Binding Site Detection Using 3D Convolutional Neural Networks

Peptides and peptide-based molecules represent a promising therapeutic modality targeting intracellular protein–protein interactions, potentially combining the beneficial properties of biologics and small-molecule drugs. Protein–peptide complexes occupy a unique niche of interaction interfaces with respect to protein–protein and protein–small molecule complexes. Protein–peptide binding site identification resembles image object detection, a field that had been revolutionalized with computer vision techniques. We present a new protein–peptide binding site detection method called BiteNetPp by harnessing the power of 3D convolutional neural network. Our method employs a tensor-based representation of spatial protein structures, which is fed to 3D convolutional neural network, resulting in probability scores and coordinates of the binding “hot spots” in the input structures. We used the domain adaptation technique to fine-tune model trained on protein–small molecule complexes using a manually curated set of protein–peptide structures. BiteNetPp consistently outperforms existing state-of-the-art methods in the independent test benchmark. It takes less than a second to analyze a single-protein structure, making BiteNetPp suitable for the large-scale analysis of protein–peptide binding sites.