Efficient generation of protein pockets with PocketGen

作者：Zaixi Zhang, Wan Xiang Shen, Qi Liu, Marinka Žitnik · 发表于：Nature Machine Intelligence · 年份：2024 · DOI：10.1038/s42256-024-00920-9 · 被引用次数：25 · 研究领域：Advanced Proteomics Techniques and Applications、Protein Structure and Dynamics、Genetics, Bioinformatics, and Biomedical Research

Abstract Designing protein-binding proteins is critical for drug discovery. However, artificial-intelligence-based design of such proteins is challenging due to the complexity of protein–ligand interactions, the flexibility of ligand molecules and amino acid side chains, and sequence–structure dependencies. We introduce PocketGen, a deep generative model that produces residue sequence and atomic structure of the protein regions in which ligand interactions occur. PocketGen promotes consistency between protein sequence and structure by using a graph transformer for structural encoding and a sequence refinement module based on a protein language model. The graph transformer captures interactions at multiple scales, including atom, residue and ligand levels. For sequence refinement, PocketGen integrates a structural adapter into the protein language model, ensuring that structure-based predictions align with sequence-based predictions. PocketGen can generate high-fidelity protein pockets with enhanced binding affinity and structural validity. It operates ten times faster than physics-based methods and achieves a 97% success rate, defined as the percentage of generated pockets with higher binding affinity than reference pockets. Additionally, it attains an amino acid recovery rate exceeding 63%.