PocketGen: Generating Full-Atom Ligand-Binding Protein Pockets.

Designing small-molecule-binding proteins, such as enzymes and biosensors, is essential in protein biology and bioengineering. Generating high-fidelity protein pockets-areas where proteins interact with ligand molecules-is challenging due to the complex interactions between ligand molecules and proteins, the flexibility of ligand molecules and amino acid side chains, and intricate sequence-structure dependencies. We introduce PocketGen, a deep generative method that produces the residue sequence and the full-atom structure within the protein pocket region, leveraging sequence-structure consistency. PocketGen comprises a bilevel graph transformer for structural encoding and a sequence refinement module utilizing a protein language model (pLM) for sequence prediction. The bilevel graph transformer captures interactions at multiple granularities (atom-level and residue/ligand-level) and aspects (intra-protein and protein-ligand) through bilevel attention mechanisms. A structural adapter employing cross-attention is integrated into the pLM for sequence refinement to ensure consistency between structure-based and sequence-based prediction. During training, only the adapter is fine-tuned, while the other layers of the pLM remain unchanged. Experiments demonstrate that PocketGen can efficiently generate protein pockets with higher binding affinity and validity than state-of-the-art methods. PocketGen is ten times faster than physics-based methods and achieves a 95% success rate (percentage of generated pockets with higher binding affinity than reference pockets) with an amino acid recovery rate exceeding 64%.