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.
npx skills add majiayu000/claude-skill-registry --skill solublempnn-adaptyvbio-protein-design-skill-3 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 | |
- Prerequisites
- How to run
- Option 1: Modal (recommended)
- Option 2: Local installation
- Key parameters
- Model Variants
- Output format
- Sample output
- Successful run
- Decision tree
- Typical performance
- Verify
- Troubleshooting
- Error interpretation
cd biomodals modal run modal_proteinmpnn.py \ git clone https://github.com/dauparas/ProteinMPNN.git cd ProteinMPNN Use soluble model weights python protein_mpnn_run.py \ grep -c "^>" output/seqs/*.fa # Should match backbone_count × num_seq_per_target
What does the solublempnn skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill solublempnn-adaptyvbio-protein-design-skill-3 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.