Flexible protein–ligand docking with diffusion-based side-chain packing - Runze Zhang; Xinyu Jiang; Duanhua Cao; Zhaokun Wang; Jie Yu; Mingan Chen; Zhehuan Fan; Xiangtai Kong; Jiacheng Xiong; Zimei Zhang; Wei Zhang; Shengkun Ni; et al. (Proceedings of the National Academy of Sciences, 2025)
Understanding protein structure and dynamics is crucial for basic biology and drug design. Conventional methods often provide static conformations that inadequately capture protein flexibility. We present PackDock, a framework that integrates deep learning and physics-based modeling to represent protein–ligand interactions. PackDock’s core, PackPocket, uses diffusion models to sample diverse binding pocket conformations and predict ligand-induced changes. We validate PackDock through side-chain packing, redocking, and cross-docking experiments, demonstrating its ability to address protein flexibility challenges. In a real-world application, PackDock identified nanomolar affinity compounds with unreported scaffolds for the protein of interest. Additionally, it revealed key amino acid conformational changes, offering insights into protein–ligand interactions. By accurately predicting complex conformations in various scenarios, PackDock enhances our understanding of protein dynamics and provides perspectives for both basic biological research and drug discovery efforts.