How do I install the Solublempnn skill?

Run npx skillmds add majiayu000/solublempnn in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Solublempnn safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Solublempnn?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Solublempnn free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Solublempnn?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Solublempnn by majiayu000 — Agent Skill

Name: Solublempnn
Rating: 4.666666666666667 (3 reviews)
Author: majiayu000

Solublempnn

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.

majiayu000 3d9595e 2 files · 5.0 KB Updated Sep 9, 2026 567 repo stars

File contents

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

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-qc for filtering.

---name: solublempnndescription: 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--- # 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](../../docs/installation.md) to set up Modal and biomodals. ### Option 1: Modal (recommended) SolubleMPNN uses the ProteinMPNN Modal wrapper with soluble model: ```bash 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 ```bash 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 ```bash grep -c "^>" output/seqs/*.fa # Should match backbone_count × num_seq_per_target ``` --- ## 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-qc` for filtering.

Frequently asked questions