solublempnn

name	solublempnn
description	Solubility-optimized protein sequence design using SolubleMPNN. Use this skill when: (1) Designing for E. coli expression, (2) Optimizing solubility of designed proteins, (3) Reducing aggregation propensity, (4) Need high-yield expression, (5) Avoiding inclusion body formation. For standard design, use proteinmpnn. For ligand-aware design, use ligandmpnn.
license	MIT
category	design-tools
tags	["sequence-design","inverse-folding","solubility"]
biomodals_script	modal_ligandmpnn.py

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.

---

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

---

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

Inputs

• Backbone structures that need sequence design with a stronger solubility bias.
• Optional residue constraints and the desired number of redesigned sequences.
• Sampling settings and hardware context for batch inference.

Outputs

• Solubility-optimized designed sequences for each provided backbone.
• Sequence libraries suitable for structure validation and expression-focused triage.
• Candidates that can be compared against standard ProteinMPNN outputs in downstream QC.

Next Step

Validate the sequences structurally and compare them against standard proteinmpnn outputs using protein-design-qc and expression screens.