Protein–peptide docking with a rational and accurate diffusion generative model

Abstract

Therapeutic peptides represent the forefront of drug discovery, offering potent and safe alternatives to traditional small molecules. However, their weak and context-dependent nature complicates the efficient virtual screening and structural characterization of protein–peptide patterns. Here we introduce RAPiDock, a diffusion generative model designed for rational, accurate and rapid protein–peptide docking at an all-atomic level. RAPiDock efficiently reduces the sampling space by incorporating physical constraints and uses a bi-scale graph to effectively capture multidimensional structural information while balancing efficiency. In addition, the model uses a Clebsch–Gordan tensor product-based architecture to ensure physical symmetry. RAPiDock outperforms existing tools in prediction of protein–peptide-binding patterns, achieving a 93.7% success rate at top-25 predictions (13.4% higher than AlphaFold2-Multimer), with an execution speed of 0.35 seconds per complex (~270 times faster than AlphaFold2-Multimer). Extensive experiments demonstrate RAPiDock’s remarkable ability to handle 92 types of residue including posttranslational modifications, accurately predict subtle docking patterns, successfully identify multiple potential peptide-binding sites in global docking and serve as a powerful tool for high-throughput virtual screening with structural precision. All these push the boundaries of efficient protein–peptide docking in multiple real-application scenarios. Zhao et al. present RAPiDock, an all-atom diffusion model that predicts peptide–protein binding patterns across 92 amino acid types, enabling high-throughput virtual screening for advancing therapeutic peptide design.