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bioRxiv · 10.1101/2024.02.25.581968

PocketGen: Generating Full-Atom Ligand-Binding Protein Pockets

Zaixi, Z.·Shen, W.·Liu, Q.·Zitnik, M.
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Abstract

Designing protein-binding proteins is critical for drug discovery. However, the AI-based design of such proteins is challenging due to the complexity of ligand-protein interactions, the flexibility of ligand molecules and amino acid side chains, and sequence-structure dependencies. We introduce PocketGen, a deep generative model that simultaneously produces both the residue sequence and atomic structure of the protein regions where ligand interactions occur. PocketGen ensures consistency between sequence and structure by using a graph transformer for structural encoding and a sequence refinement module based on a protein language model. The bilevel graph transformer captures interactions at multiple scales, including atom, residue, and ligand levels. To enhance 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 superior binding affinity and structural validity. It operates ten times faster than physics-based methods and achieves a 95% 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 64%.

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Zaixi, Z., Shen, W., Liu, Q., Zitnik, M.. 2024-02-28. PocketGen: Generating Full-Atom Ligand-Binding Protein Pockets. https://doi.org/10.1101/2024.02.25.581968

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