lamm-mit ProteinMPNN Sequence Design
Inverse folding: design protein sequences that fold into a given backbone structure. Use after RFdiffusion backbone generation or to redesign existing protein sequences.
Installation
pip install proteinmpnn
# Or from source (preferred for full control)
git clone https://github.com/dauparas/ProteinMPNN
cd ProteinMPNN
pip install -e .
Basic Usage
Single Chain Design
python3 protein_mpnn_run.py \
--pdb_path designs/binder_0.pdb \
--out_folder output/ \
--num_seq_per_target 8 \
--sampling_temp "0.1" \
--seed 37 \
--batch_size 1
Fixed Residues (Partial Design)
Lock specific positions while designing the rest:
python3 protein_mpnn_run.py \
--pdb_path structure.pdb \
--out_folder output/ \
--num_seq_per_target 8 \
--sampling_temp "0.1" \
--fixed_residues "A1 A2 A3 A45 A46" # keep these unchanged
Multi-Chain Design (Complex)
Design binder (chain B) while fixing target (chain A):
python3 protein_mpnn_run.py \
--pdb_path complex.pdb \
--out_folder output/ \
--num_seq_per_target 8 \
--sampling_temp "0.1" \
--chain_id_jsonl chains.jsonl \
--fixed_chains_jsonl fixed.jsonl # fix chain A (target)
# Generate chains.jsonl
import json
with open("chains.jsonl", "w") as f:
json.dump({"complex": ["A", "B"]}, f)
f.write("\n")
# Generate fixed.jsonl (fix target chain A)
with open("fixed.jsonl", "w") as f:
json.dump({"complex": ["A"]}, f)
f.write("\n")
Python API
from protein_mpnn_utils import ProteinMPNN, parse_PDB
# Load model
model = ProteinMPNN(
num_letters=21,
node_features=128,
edge_features=128,
hidden_dim=128,
num_encoder_layers=3,
num_decoder_layers=3,
augment_eps=0,
k_neighbors=48,
)
model.load_state_dict(torch.load("vanilla_model_weights/v_48_020.pt"))
model.eval()
# Parse structure
X, S, mask, chain_M, chain_encoding_all, residue_idx, chain_list = parse_PDB(
"structure.pdb",
input_chain_list=["A", "B"]
)
# Sample sequences
with torch.no_grad():
sample_dict = model.sample(
X, randn, S, chain_M, chain_encoding_all,
residue_idx, mask, temperature=0.1,
num_samples=8
)
sequences = sample_dict["S"]
Temperature Parameter
Controls sequence diversity vs. nativeness:
| Temperature | Effect | Use When |
|---|---|---|
| 0.05–0.1 | Conservative, high identity to native | Redesigning functional proteins |
| 0.1–0.2 | Balanced diversity | Standard binder design |
| 0.3–0.5 | High diversity | Exploring sequence space |
Output Format (FASTA)
>design_0, score=1.234, global_score=1.189, seq_recovery=0.45
EVQLVESGGGLVQPGGSLRLSCAASGFTFSDYYMSWVRQAPGKGLEWVSYITYSGSTAYYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARDYYGSGSYFDYWGQGTLVTVSS
>design_1, score=1.198, global_score=1.145, seq_recovery=0.43
EVQLVESGGGLVQPGGSLRLSCAASGFTFS...
Lower score = better fit to backbone.
Scoring Existing Sequences
python3 protein_mpnn_run.py \
--pdb_path structure.pdb \
--out_folder scores/ \
--score_only 1 \
--path_to_fasta sequences.fasta
Multi-State Design
Design sequences that fold well in multiple conformations:
python3 protein_mpnn_run.py \
--pdb_path_multi '{"state1": "conf1.pdb", "state2": "conf2.pdb"}' \
--out_folder output/ \
--num_seq_per_target 8
Recommended Workflow
- Generate backbone with RFdiffusion (50–500 designs)
- Run ProteinMPNN with
--num_seq_per_target 8 --sampling_temp 0.1 - Total: 400–4000 sequences per backbone set
- Predict structures with ColabFold/Chai
- Filter by ipTM > 0.7, pLDDT > 75
- Rank survivors with ipSAE