Design protein sequences considering ligands, metals, and nucleotides. Perfect for enzyme engineering and binding site optimization with superior accuracy at interaction interfaces.
Input
50
Output
Configure input settings on the left, then click "Submit"
What is LigandMPNN?
LigandMPNN is an inverse folding model that designs protein sequences while accounting for non-protein atoms—ligands, metals, nucleotides, and cofactors. Standard inverse folding methods like ProteinMPNN only see the protein backbone, which limits their accuracy at binding sites where interactions with small molecules matter most. LigandMPNN solves this by incorporating atomic context from heteroatoms during sequence prediction.
The improvement is substantial. At small molecule binding sites, LigandMPNN achieves 63.3% sequence recovery compared to 50.5% for ProteinMPNN. Metal coordination sites show an even larger gap: 77.5% versus 36.0%. These gains translate to better experimental success rates—over 100 designs have been validated, with some showing 100-fold affinity improvements over conventional methods.
Developed by Dauparas, Lee, and colleagues at the Institute for Protein Design, LigandMPNN was published in Nature Methods (2025).
How does LigandMPNN work?
LigandMPNN extends ProteinMPNN's graph neural network by processing three interconnected graphs instead of one: a protein-only graph (residues as nodes), a ligand-only graph (heteroatoms as nodes), and a protein-ligand graph that captures residue-atom interactions. This architecture allows the model to learn chemical element identities and geometric constraints at binding interfaces.
The model consists of a protein backbone encoder (3 layers), a protein-ligand encoder (2 layers), and an autoregressive decoder that generates both amino acid sequences and sidechain torsion angles. Training used 8–16 context atoms per residue and added 0.1 Å Gaussian noise to coordinates, improving generalization to novel structures.
How to use LigandMPNN online
ProteinIQ hosts LigandMPNN on cloud GPU infrastructure, eliminating the need to install Python dependencies or configure the model locally.
Inputs
Input
Description
Protein
PDB file (up to 50 MB), .ent, .cif, or RCSB PDB ID. Ligand atoms are read from HETATM records already present in the structure—no separate ligand file is needed for structures fetched from RCSB.
Ligand
Optional. SMILES string, SDF/MOL file, or PubChem CID for reference. LigandMPNN reads atomic context directly from HETATM records in the PDB; the ligand input is informational context only.
Core settings
Setting
Description
Number of sequences
How many sequence variants to generate (1–48, default 8). More sequences give better coverage of sequence space but increase runtime. Start with 8–10 for initial testing; use 20–40 for comprehensive exploration.
Sampling temperature
Controls diversity (0.05–1.0, default 0.1). Lower values produce conservative designs with high recovery; higher values explore more diverse solutions. For metal sites, 0.1–0.15 is recommended.
Use atom context
Include ligands, metals, and nucleotides during design (default on). Disabling this makes LigandMPNN behave like ProteinMPNN—only useful as a control.
Use side chain context
Include fixed residue sidechains as geometric constraints. Enable when catalytic or binding residues are fixed and their geometry should influence neighboring positions.
Random seed
Integer for reproducible results (0–99999, default 111). Same seed and settings produce identical output.
Design options
Setting
Description
Chains to design
Specify which chains to redesign (e.g., A,B); all others stay fixed. Simpler than listing every fixed residue for multi-chain proteins.
Homo-oligomer
Enable symmetric design for proteins with identical chains. All chains receive the same sequence.
Fixed positions
Residues to keep unchanged. Format: A15, A1-10, B1-20, or C for an entire chain. Comma-separate multiple entries.
Redesigned positions
Inverse of fixed positions—specify what to design and fix everything else. Cannot be used simultaneously with Fixed positions.
Parse chains only
Parse only specified chains from the PDB, ignoring all others. Useful for large assemblies where only a subset of chains is relevant.
Output
Each designed sequence includes:
Column
Description
Sequence ID
Unique identifier for the design (seq_1, seq_2, …)
Sequence
Designed amino acid sequence, with chains separated by / in FASTA output
Length
Total residue count across all designed chains
Overall confidence
Model confidence (0–1). Computed as exp[−mean(logp over residues. Higher values indicate stronger sequence-structure compatibility.
Results are available as FASTA, CSV, JSON, and backbone PDB files.
LigandMPNN vs ProteinMPNN
Feature
ProteinMPNN
LigandMPNN
Input context
Protein backbone only
Protein + ligands, metals, nucleotides
Binding site recovery
50.5%
63.3%
Metal coordination recovery
36.0%
77.5%
Nucleotide binding recovery
35.2%
50.5%
Model size
1.66M parameters
2.62M parameters
Architecture
Single protein graph
Three-graph (protein, ligand, protein-ligand)
Sidechain prediction
No
Yes (torsion angles)
Speed
Faster
~2× slower
When to use each
Use ProteinMPNN for general protein design where no ligands or cofactors are involved—de novo protein scaffolds, antibody frameworks away from CDRs, or soluble protein cores.
Use LigandMPNN whenever the design involves binding sites: enzyme active sites, cofactor-binding pockets (NAD, FAD, heme), metal coordination spheres, or nucleic acid interfaces. The sequence recovery improvements at these sites translate directly to higher experimental success rates.
Limitations
Ligand context comes from HETATM records already present in the PDB. Structures without these records default to ProteinMPNN-like behavior regardless of whether a separate ligand is provided.
Computational cost is roughly double that of ProteinMPNN due to the additional encoder layers.
Designed sequences require experimental validation—high confidence scores improve success rates but do not guarantee function.
Related tools
ProteinMPNN: Backbone-only inverse folding for general protein design
SolubleMPNN: ProteinMPNN variant optimized for soluble expression
ESM-IF1: Language model approach to inverse folding
ESMFold: Predict structures from designed sequences
Scoring cutoff (Å)
Distance cutoff for selecting residues scored with ligand context (default 8.0 Å). Decrease to focus design on residues very close to the ligand; increase for broader context.
Include zero-occupancy atoms
Include atoms with zero occupancy from crystal structures, which indicate partially occupied or disordered positions. Off by default.
Exclude amino acids
Globally exclude one or more amino acids from all designed positions. Enter one-letter codes without separators (e.g., CW to exclude cysteine and tryptophan).
Amino acid biases
Adjust sampling frequency per amino acid. Positive values (0 to +5) increase likelihood; negative values (down to −25) decrease it. Setting a value of −25 effectively excludes that amino acid entirely.
i
)]
Ligand confidence
Confidence specifically at residues within the scoring cutoff distance of ligand atoms. Distinct from overall confidence; provides a focused view of binding site quality.
