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.