LigandMPNN
Baker Lab / IPD (University of Washington)
Extension of ProteinMPNN that conditions sequence design on bound ligands, small molecules, metals, and nucleotides.
Best For
Designing binding pockets and cofactor-dependent proteins
License
Open Source (check repo)
Strengths
- +Ligand-aware sequence design
- +Significantly outperforms ProteinMPNN near ligands
Limitations
- −Requires pre-defined ligand pose
- −Less validated than ProteinMPNN
R&D Pipeline Coverage
Related Tools
ProteinMPNN
Baker Lab / IPD (University of Washington)
Inverse folding model: generates amino acid sequences predicted to fold into a target 3D backbone structure. Standard component of all modern protein design pipelines.
RFdiffusion
Baker Lab / IPD (University of Washington)
Diffusion-based generative model for de novo protein backbone design. Generates novel protein structures conditioned on binding targets, symmetry, or functional sites.
More in Generative Design
RFdiffusion
Baker Lab / IPD (University of Washington)
Diffusion-based generative model for de novo protein backbone design. Generates novel protein structures conditioned on binding targets, symmetry, or functional sites.
RFdiffusion2
Baker Lab / IPD (University of Washington)
Successor to RFdiffusion using flow matching. Designs enzymes directly from active site geometry (theozyme) specifications.
ProteinMPNN
Baker Lab / IPD (University of Washington)
Inverse folding model: generates amino acid sequences predicted to fold into a target 3D backbone structure. Standard component of all modern protein design pipelines.
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