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 nonprotein atoms and molecules. Here we describe a deep-learning-based protein sequence design method called LigandMPNN that explicitly models all nonprotein components of biomolecular systems. LigandMPNN significantly outperforms Rosetta and ProteinMPNN on native backbone sequence recovery for residues interacting with small molecules (63.3% versus 50.4% and 50.5%), nucleotides (50.5% versus 35.2% and 34.0%) and metals (77.5% versus 36.0% and 40.6%). LigandMPNN generates…

• NS
  National Science Foundation

  Award: 2226466
• BA
  Bill and Melinda Gates Foundation

  Award: INV-037981
• WR
  Washington Research Foundation
• OP
  Open Philanthropy Project
• ST
  Spark Therapeutics
• NI
  National Institute of Allergy and Infectious Diseases

  Awards: HHSN272201700059C, 75N93022C00036
• DO
  Division of Chemistry