Atomic context-conditioned protein sequence design using LigandMPNN

Protein sequence design in the context of small molecules, nucleotides, and metals is critical to enzyme and small molecule binder and sensor design, but current state-of-the-art deep learning-based sequence design methods are unable to model non-protein atoms and molecules. Here, we describe a deep learning-based protein sequence design method called LigandMPNN that explicitly models all non-protein components of biomolecular systems. LigandMPNN significantly outperforms Rosetta and ProteinMPNN on native backbone sequence recovery for residues interacting with small molecules (63.3% vs. 50.4% & 50.5%), nucleotides (50.5% vs. 35.2% & 34.0%), and metals (77.5% vs. 36.0% & 40.6%). LigandMPNN generates not only sequences but also sidechain conformations to allow detailed evaluation of binding interactions. Experimental characterization demonstrates that LigandMPNN can generate small molecule and DNA-binding proteins with high affinity and specificity. One-sentence summary We present a deep learning-based protein sequence design method that allows explicit modeling of small molecule, nucleotide, metal, and other atomic contexts.