LigandMPNN is a deep learning-based protein sequence design method that explicitly models non-protein atomic contexts, including small molecules, nucleotides, and metals. It significantly improves native sequence recovery and side-chain conformation accuracy compared to existing methods like Rosetta and ProteinMPNN. You can use LigandMPNN after RFDiffusion-All Atom to refine protein sequences and optimize interactions with ligands for enhanced binding affinity and specificity.

Example use case:

Designing protein sequences that interact with specific small molecules, nucleotides, or metals to improve binding affinity and specificity for applications in drug discovery, biosensors, and enzyme engineering.

Technology:

Graph neural networks (GNNs) based on ProteinMPNN, with additional encoding layers for ligand-protein interactions.

Limitations:

• Performance may be limited for compounds with rare or novel chemical elements not well-represented in the training data. Hybrid approaches with physics-based modeling may be needed for low-data regimes.
• Some parameters are kept as default; please check the original GitHub repository for details.

Metrics:

• Sequence recovery near small molecules: 63.3% (vs. 50.4% for Rosetta & ProteinMPNN)
• Sequence recovery near nucleotides: 50.5% (vs. 35.2% & 34.0%)
• Sequence recovery near metals: 77.5% (vs. 36.0% & 40.6%)
• Side-chain chi1 angle recovery: 86.1% (vs. 76.0% for Rosetta)