lamm-mit SolubleMPNN Solubility-Optimized Sequence Design
ProteinMPNN variant trained to design sequences with higher solubility in aqueous solution. Reduces aggregation propensity and improves expression yields in E. coli and cell-free systems.
Installation
git clone https://github.com/dauparas/LigandMPNN
cd LigandMPNN
pip install -e .
bash get_model_params.sh
When to Use SolubleMPNN vs ProteinMPNN
| Scenario | Use |
|---|---|
| E. coli expression optimization | SolubleMPNN |
| Reducing inclusion body formation | SolubleMPNN |
| High-yield cell-free synthesis | SolubleMPNN |
| Standard binder design (expression not bottleneck) | ProteinMPNN |
| Membrane proteins | LigandMPNN (membrane model) |
| Ligand-binding sites | LigandMPNN |
Basic Usage
python3 LigandMPNN/run.py \
--model_type "soluble_mpnn" \
--checkpoint_path "model_params/solublempnn_v_48_002.pt" \
--pdb_path structure.pdb \
--out_folder output/ \
--number_of_batches 8 \
--batch_size 4 \
--temperature 0.1
Multi-Chain with Fixed Target
python3 LigandMPNN/run.py \
--model_type "soluble_mpnn" \
--checkpoint_path "model_params/solublempnn_v_48_002.pt" \
--pdb_path complex.pdb \
--out_folder output/ \
--chains_to_design "B" \
--fixed_chains "A" \
--number_of_batches 16
Python Wrapper
import subprocess
from pathlib import Path
def design_soluble(pdb_path: str, out_folder: str, n_seqs: int = 64,
chains_to_design: list = None, fixed_chains: list = None,
temperature: float = 0.1) -> str:
"""Run SolubleMPNN and return output folder path."""
cmd = [
"python3", "LigandMPNN/run.py",
"--model_type", "soluble_mpnn",
"--checkpoint_path", "model_params/solublempnn_v_48_002.pt",
"--pdb_path", pdb_path,
"--out_folder", out_folder,
"--number_of_batches", str(n_seqs // 4),
"--batch_size", "4",
"--temperature", str(temperature),
]
if chains_to_design:
cmd += ["--chains_to_design", " ".join(chains_to_design)]
if fixed_chains:
cmd += ["--fixed_chains", " ".join(fixed_chains)]
result = subprocess.run(cmd, capture_output=True, text=True)
if result.returncode != 0:
raise RuntimeError(f"SolubleMPNN failed: {result.stderr}")
return out_folder
Post-Processing: Predict Solubility
Filter designed sequences using biophysical predictors:
from Bio.SeqUtils.ProtParam import ProteinAnalysis
def analyze_sequence(seq: str) -> dict:
analysis = ProteinAnalysis(seq)
return {
"instability_index": analysis.instability_index(), # <40 = stable
"gravy": analysis.gravy(), # <0 = hydrophilic
"isoelectric_point": analysis.isoelectric_point(),
"molecular_weight": analysis.molecular_weight(),
}
# Flag likely insoluble sequences
def is_likely_soluble(seq: str) -> bool:
props = analyze_sequence(seq)
return (
props["instability_index"] < 40 and
props["gravy"] < 0.1 and # not too hydrophobic
not any(seq[i:i+5].count("C") >= 3 for i in range(len(seq)-4)) # no cysteine clusters
)
Sequence Liabilities to Check
import re
def check_liabilities(seq: str) -> list:
liabilities = []
if re.search(r"N[^P][ST]", seq):
liabilities.append("N-glycosylation site")
if re.search(r"NG|DG", seq):
liabilities.append("Deamidation motif")
if re.search(r"[KR]{3,}", seq):
liabilities.append("Polybasic cluster (proteolysis risk)")
if seq.count("C") > 3 and seq.count("C") % 2 != 0:
liabilities.append("Odd number of cysteines (unpaired disulfide)")
if re.search(r"[FWYI]{4,}", seq):
liabilities.append("Hydrophobic cluster (aggregation risk)")
return liabilities
Recommended Workflow for E. coli Expression
- Generate backbone (RFdiffusion or BoltzGen)
- Design sequences with SolubleMPNN (temperature 0.1, 64+ sequences)
- Filter: GRAVY < 0, instability index < 40, no liabilities
- Validate fold with ColabFold/Chai
- Codon-optimize for E. coli (use online tools or
python-codon-tables) - Order synthetic genes (200 bp–2 kb: IDT, Twist Bioscience)
Benchmark: SolubleMPNN vs ProteinMPNN Expression
Published results (Dauparas et al., 2023):
- SolubleMPNN designs expressed solubly in E. coli at 72% rate
- ProteinMPNN designs: 54% rate
- ~18 percentage point improvement in soluble expression