adaptyvbio/solublempnn

SolubleMPNN Solubility-Optimized Design

Prerequisites

| Requirement | Minimum | Recommended | |-------------|---------|-------------| | Python | 3.8+ | 3.10 | | CUDA | 11.0+ | 11.7+ | | GPU VRAM | 8GB | 16GB (T4) | | RAM | 8GB | 16GB |

How to run

First time? See Installation Guide to set up Modal and biomodals.

Option 1: Modal (recommended)

SolubleMPNN uses the ProteinMPNN Modal wrapper with soluble model:

cd biomodals
modal run modal_proteinmpnn.py \
  --pdb-path backbone.pdb \
  --num-seq-per-target 16 \
  --sampling-temp 0.1 \
  --model-name v_48_020

GPU: T4 (16GB) | Timeout: 600s default

Option 2: Local installation

git clone https://github.com/dauparas/ProteinMPNN.git
cd ProteinMPNN

# Use soluble model weights
python protein_mpnn_run.py \
  --pdb_path backbone.pdb \
  --out_folder output/ \
  --num_seq_per_target 16 \
  --sampling_temp "0.1" \
  --model_name "v_48_020"  # Soluble model

Key parameters

| Parameter | Default | Range | Description | |-----------|---------|-------|-------------| | --pdb_path | required | path | Input structure | | --num_seq_per_target | 1 | 1-1000 | Sequences per structure | | --sampling_temp | "0.1" | "0.0001-1.0" | Temperature (string!) | | --model_name | v_48_020 | string | Soluble model variant |

Model Variants

| Model | Description | Use Case | |-------|-------------|----------| | v_48_002 | Standard | General design | | v_48_020 | Soluble-trained | E. coli expression | | v_48_030 | High solubility | Difficult targets |

Output format

output/
├── seqs/backbone.fa
└── backbone_pdb/backbone_0001.pdb

Sample output

Successful run

$ python protein_mpnn_run.py --pdb_path backbone.pdb --model_name v_48_020 --num_seq_per_target 8
Loading soluble model weights (v_48_020)...
Designing sequences for backbone.pdb
Generated 8 sequences in 2.1 seconds

output/seqs/backbone.fa:
>backbone_0001, score=1.31, global_score=1.24, seq_recovery=0.78
MKTAYIAKQRQISFVKSHFSRQLE...
>backbone_0002, score=1.28, global_score=1.21, seq_recovery=0.81
MKTAYIAKQRQISFVKSQFSRQLD...

What good output looks like:

• Score: 1.0-2.0 (lower = more confident)
• Reduced hydrophobic patches compared to standard MPNN
• Improved charge distribution

Decision tree

Should I use SolubleMPNN?
│
├─ What expression system?
│  ├─ E. coli → SolubleMPNN ✓
│  ├─ Mammalian → ProteinMPNN (PTMs matter more)
│  └─ Yeast → Either
│
├─ History of expression problems?
│  ├─ Yes, aggregation → SolubleMPNN ✓
│  ├─ Yes, low yield → SolubleMPNN ✓
│  └─ No → ProteinMPNN is fine
│
├─ What's in the binding site?
│  ├─ Small molecule / ligand → Use LigandMPNN
│  └─ Nothing / protein only → SolubleMPNN ✓
│
└─ Need highest solubility?
   ├─ Yes → Use v_48_030 model
   └─ Standard → Use v_48_020 model

Typical performance

| Campaign Size | Time (T4) | Cost (Modal) | Notes | |---------------|-----------|--------------|-------| | 100 backbones × 8 seq | 15-20 min | ~$2 | Standard | | 500 backbones × 8 seq | 1-1.5h | ~$8 | Large campaign |

Expected improvement: +15-30% solubility score vs standard ProteinMPNN.

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Verify

grep -c "^>" output/seqs/*.fa  # Should match backbone_count × num_seq_per_target

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Troubleshooting

Still insoluble: Try v_48_030 (higher solubility bias) Low diversity: Increase temperature to 0.2 Poor folding: Use standard ProteinMPNN and optimize later

Error interpretation

| Error | Cause | Fix | |-------|-------|-----| | RuntimeError: CUDA out of memory | Long protein or large batch | Reduce batch_size | | FileNotFoundError: v_48_020 | Missing model weights | Download soluble weights |

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Next: Structure prediction for validation → protein-qc for filtering.