ArticleNature methods2025
Atomic context-conditioned protein sequence design using LigandMPNN.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers.
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Who cites it
130 citing papers in PubMed.
- Graph-based representations in modern protein science.The Biochemical journal · 2026Review
- Multistate Enzyme Design Enables Efficient and Stereoselective Multistep Catalysis.bioRxiv : the preprint server for biology · 2026Article
- Inverse FoldDir: Structure-conditioned Protein Sequence Design by Dirichlet Flow Matching.bioRxiv : the preprint server for biology · 2026Article
- Integrative machine learning approaches for enzyme kinetic parameter prediction.Briefings in bioinformatics · 2026Review
- HFGuidedDesign:Chemical science · 2026Article
- Improved Stability and Brightness Following Iterative Redesign of a De Novo Biliprotein.Biochemistry · 2026Article
- Article
- Applications and limitations of AI tools in enzyme design.Protein science : a publication of the Protein Society · 2026Review
- Engineering Antivenom: Research Progress and Future Directions in Snakebite Envenoming Therapy.Annals of the New York Academy of Sciences · 2026Review
- A Generative Neuro-Symbolic AI for Protein Sequence Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AbioRxiv : the preprint server for biology · 2026Article
- Structure and evolution-guided design of minimal RNA-guided nucleases.Science (New York, N.Y.) · 2026Article
- Epigenetic editing approaches maturity: AI-driven precision design, delivery innovation, and the road to clinical translation.Clinical epigenetics · 2026Review
- Structure-aware artificial intelligence for next-generation drug discovery: from protein-ligand modeling to generative biomolecular design.Briefings in bioinformatics · 2026Review
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- Redesign of the Ferritin Ferroxidase Center for Universal Molecular Binding or Specific Recognition.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Emergence of specific binding and catalysis from a designed generalist binding protein.Nature chemistry · 2026Article
- Benchmarking and behavioral characterization of LLM agents for protein design.bioRxiv : the preprint server for biology · 2026Article
- Targeting the Undruggable: Deep Learning-Driven Design of Peptide Therapeutics in Cancer.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Modular Input-Output Biosensor Design UsingACS sensors · 2026Article
70 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
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 not only sequences but also sidechain conformations to allow detailed evaluation of binding interactions. LigandMPNN has been used to design over 100 experimentally validated small-molecule and DNA-binding proteins with high affinity and high structural accuracy (as indicated by four X-ray crystal structures), and redesign of Rosetta small-molecule binder designs has increased binding affinity by as much as 100-fold. We anticipate that LigandMPNN will be widely useful for designing new binding proteins, sensors and enzymes.
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