Efficient Generation of Protein Pockets with PocketGen

Abstract

Designing small-molecule-binding proteins, such as enzymes and biosensors, is crucial in protein biology and bioengineering. Generating high-fidelity protein pockets—areas where proteins interact with ligand molecules—is challenging due to complex interactions between ligand molecules and proteins, flexibility of ligand molecules and amino acid side chains, and complex sequence-structure dependencies. Here, we introduce PocketGen, a deep generative method for generating the residue sequence and the full-atom structure within the protein pocket region that leverages sequence-structure consistency. PocketGen consists of a bilevel graph transformer for structural encoding and a sequence refinement module that uses 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) with bilevel attention mechanisms. For sequence refinement, a structural adapter using cross-attention is integrated into a pLM to ensure structure-sequence consistency. During training, only the adapter is fine-tuned, while the other layers of the pLM remain unchanged. Experiments show 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 over 64% amino acid recovery rate.

Rationalizing diverse binding mechanisms to the same protein fold: insights for ligand recognition and biosensor design

“…Indeed, aligning the known WIN conformer to its known Hbond acceptor geometry, followed by Rosetta FastDesign 42 , is sufficient to generate designs that recognize WIN with a nanomolar limit of detection. Developing new deep learning methods to learn and design small molecule-protein interactions is in vogue 15,[43][44][45][46][47][48] . We suggest that comparable attention should be placed with the choice of ligand conformer and rigid body orientation.…”

Rationalizing Diverse Binding Mechanisms to the Same Protein Fold: Insights for Ligand Recognition and Biosensor Design

“…Indeed, aligning the known WIN conformer to its known H-bond acceptor geometry, followed by Rosetta FastDesign, is sufficient to generate designs that recognize WIN with a nanomolar limit of detection. Developing new deep-learning methods to learn and design small molecule-protein interactions is in vogue. ,− We suggest that comparable attention should be placed on the choice of ligand conformer and rigid body orientation.…”