Alphafold Database Fetch And Analyze
google-deepmind/science-skills
Retrieve and analyze AlphaFold predicted structures for a protein.
Solubility-optimized protein sequence design using SolubleMPNN.
Install Solublempnn
$ npx skills add adaptyvbio/protein-design-skills --skill solublempnn -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install adaptyvbio/protein-design-skills solublempnn --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/solublempnn .claude/skills/solublempnn && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "solublempnn" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/solublempnn into .claude/skills/solublempnn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "solublempnn", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/solublempnnType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add adaptyvbio/protein-design-skills --skill solublempnn -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install adaptyvbio/protein-design-skills solublempnn --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/solublempnn .agents/skills/solublempnn && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "solublempnn" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/solublempnn into .agents/skills/solublempnn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "solublempnn", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add adaptyvbio/protein-design-skills --skill solublempnn -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install adaptyvbio/protein-design-skills solublempnn --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/solublempnn .cursor/skills/solublempnn && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "solublempnn" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/solublempnn into .cursor/skills/solublempnn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "solublempnn", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/adaptyvbio/protein-design-skills.git --path skills/solublempnn--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add adaptyvbio/protein-design-skills --skill solublempnn -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install adaptyvbio/protein-design-skills solublempnn --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/solublempnn .gemini/skills/solublempnn && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "solublempnn" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/solublempnn into .gemini/skills/solublempnn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "solublempnn", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install adaptyvbio/protein-design-skills solublempnnInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add adaptyvbio/protein-design-skills --skill solublempnn -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/solublempnn .github/skills/solublempnn && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "solublempnn" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/solublempnn into .github/skills/solublempnn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "solublempnn", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add adaptyvbio/protein-design-skills --skill solublempnn -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install adaptyvbio/protein-design-skills solublempnn --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/solublempnn .opencode/skills/solublempnn && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "solublempnn" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/solublempnn into .opencode/skills/solublempnn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "solublempnn", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
solublempnn- GitHub stars
- 163
- Used in
- 4 other repos
- Token cost
- ~1.1k tokens
- SKILL.md length
- 256 words
- Files
- 1
- Skills in repo
- 24
- Repo updated
- First seen
- Licence
- MIT
At a glance
Solubility-optimized protein sequence design using SolubleMPNN.
- Designing for E
- SKILL.md covers Prerequisites, How to run, Key parameters and Model weights, plus 6 more sections
- Calls python, modal and git; reaches github.com
- Tasks that involve Protein structure and design
What it does
Solublempnn is an agent skill from adaptyvbio/protein-design-skills. 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.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Protein structure and design. The repository describes itself as: Claude Code skills for protein design. The licence is MIT.
When your agent uses it
- Designing for E
- Tasks that involve Protein structure and design
Example prompts
- “/solublempnn”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 59dd633. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
pythonmodalgit
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Solublempnn loads about 1.1k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 256 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from adaptyvbio/protein-design-skills at commit 59dd633, republished under its MIT licence (© adaptyvbio). 256 words, ~1,139 tokens.
Download SKILL.mdSave it as
.claude/skills/solublempnn/SKILL.md (or your agent's skills folder).- 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 Getting started to set up Modal and biomodals.
Option 1: Modal (recommended)
SolubleMPNN is the soluble model type within the LigandMPNN wrapper:
cd biomodals
modal run modal_ligandmpnn.py \
--input-pdb backbone.pdb \
--params-str "--model_type soluble_mpnn --number_of_batches 16 --temperature 0.1"GPU: A10G default | Timeout: 900s default
Option 2: Local installation
git clone https://github.com/dauparas/ProteinMPNN.git
cd ProteinMPNN
# The soluble weights are selected with --use_soluble_model, not a model name
python protein_mpnn_run.py \
--pdb_path backbone.pdb \
--out_folder output/ \
--num_seq_per_target 16 \
--sampling_temp "0.1" \
--use_soluble_modelKey parameters
| Parameter | Default | Description |
|---|---|---|
--pdb_path | required | Input structure |
--use_soluble_model | off | Use the solubility-trained weights |
--num_seq_per_target | 1 | Sequences per structure |
--sampling_temp | "0.1" | Temperature (string) |
--model_name | v_48_020 | Noise level (0.20 A); orthogonal to solubility |
Model weights
--model_name sets the training-noise level (v_48_002 = 0.02 A, v_48_010 = 0.10 A,
v_48_020 = 0.20 A), not a solubility tier. Solubility is a separate weight set chosen
with --use_soluble_model, available for v_48_010 and v_48_020. Higher noise gives
more sequence diversity.
Output format
output/
├── seqs/backbone.fa
└── backbone_pdb/backbone_0001.pdbSample output
Successful run
$ python protein_mpnn_run.py --pdb_path backbone.pdb --use_soluble_model --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 ✓
│
└─ Optimizing for expression?
└─ Add --use_soluble_model to ProteinMPNNTypical 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_targetTroubleshooting
Still insoluble: Confirm --use_soluble_model is set; redesign more positions or add explicit hydrophobic-residue 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.
© adaptyvbio, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/solublempnn of adaptyvbio/protein-design-skills.
Open the folder on GitHubat commit 59dd633
Used in 4 other repositories
We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 4 other GitHub owners. This page covers the copy in adaptyvbio/protein-design-skills, which our catalogue first saw on October 7, 2026.
- majiayu000/claude-skill-registryskills/ai-ml/solublempnnskills/ai-ml/solublempnn-adaptyvbio-protein-design-skill-3
- NeverSight/learn-skills.devdata/skills-md/adaptyvbio/protein-design-skills/solublempnn
- FreedomIntelligence/OpenClaw-Medical-Skillsskills/solublempnn
- BioTender-max/awesome-bio-agent-skillsskills/adaptyv/solublempnn
Compare with similar skills
Solublempnn next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Solublempnn this skilladaptyvbio/protein-design-skills | 163 | 4 repos | ~1.1k | Automated safety check: Pass | MIT | |
| Alphafold Database Fetch And Analyzegoogle-deepmind/science-skills | 3.2k | 2 repos | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Pymol VisualizationChatMol/ChatMol | 372 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Complexa Binder DesignNVIDIA-BioNeMo/bionemo-agent-toolkit | 478 | — | ~3.1k | Automated safety check: Notes | Apache-2.0 | |
| DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3k | Automated safety check: Notes | MIT | |
| Biopipelineslocbp-uzh/biopipelines | 109 | — | ~2.4k | Automated safety check: Pass | MIT |
Similar skills
Pymol Visualization
ChatMol/ChatMol
Generate publication-quality molecular visualization images using PyMOL.
Complexa Binder Design
NVIDIA-BioNeMo/bionemo-agent-toolkit
Run a complete protein binder design campaign with NVIDIA Proteina-Complexa: resolve a target structure and hotspots from a name/sequence/PDB, co-design binder sequence+structure with reward-guided…
DiffDock Molecular Docking
K-Dense-AI/scientific-agent-skills
Predicts how small molecules bind to a protein with DiffDock, covering batch docking, pose ranking by confidence and checks on the results; not for binding affinity.
Biopipelines
locbp-uzh/biopipelines
Design and run computational protein and ligand workflows on a GPU: binder and enzyme design, de novo backbone generation, inverse folding and sequence redesign, structure prediction, protein-ligand…
Protein Binder Design
NVIDIA-BioNeMo/bionemo-agent-toolkit
Orchestrate an end-to-end de novo protein binder design campaign against a protein target by composing BioNeMo NIM skills.
More from adaptyvbio/protein-design-skills
All 24 skills in this repoAlphafold
adaptyvbio/protein-design-skills
Validate protein designs using AlphaFold2 structure prediction.
Bindcraft
adaptyvbio/protein-design-skills
End-to-end binder design using BindCraft hallucination. An agent skill from adaptyvbio/protein-design-skills.
Boltzgen
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All-atom protein design using BoltzGen diffusion model. An agent skill from adaptyvbio/protein-design-skills.
Chai
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Structure prediction using Chai-1, a foundation model for molecular structure.
Protein Design Workflow
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End-to-end guidance for protein design pipelines. An agent skill from adaptyvbio/protein-design-skills.
Protein Qc
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Quality control metrics and filtering thresholds for protein design.
Related
Categories
Questions about Solublempnn
What does Solublempnn do?
Solubility-optimized protein sequence design using SolubleMPNN. Solublempnn is an agent skill from adaptyvbio/protein-design-skills. Solubility-optimized protein sequence design using SolubleMPNN.
When should I use Solublempnn?
Solublempnn fits situations like: designing for E; tasks that involve Protein structure and design.
How do I install Solublempnn in Claude Code?
Run `npx skills add adaptyvbio/protein-design-skills --skill solublempnn -a claude-code`. Or copy the skill folder (skills/solublempnn in adaptyvbio/protein-design-skills) into .claude/skills/solublempnn in your project. Claude Code loads it when a task matches its description.
How do I install Solublempnn in Codex?
Run `npx skills add adaptyvbio/protein-design-skills --skill solublempnn -a codex`. Or copy the skill folder (skills/solublempnn in adaptyvbio/protein-design-skills) into .agents/skills/solublempnn in your project. Codex loads it when a task matches its description.
Can I use Solublempnn in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add adaptyvbio/protein-design-skills --skill solublempnn -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/solublempnn, .gemini/skills/solublempnn, .github/skills/solublempnn and .opencode/skills/solublempnn in your project.
What does Solublempnn need to run?
Going by SKILL.md and its folder, Solublempnn needs the command-line tools its instructions call (python, modal and git). Our summary lists: Python 3.
Does Solublempnn access the network?
SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Is Solublempnn safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Solublempnn use?
Solublempnn is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Solublempnn use?
About 1.1k tokens (SKILL.md is roughly 4.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Solublempnn?
Skills that share tags, products or a category with Solublempnn: Alphafold Database Fetch And Analyze (google-deepmind/science-skills, 3.2k stars), Pymol Visualization (ChatMol/ChatMol, 372 stars), Complexa Binder Design (NVIDIA-BioNeMo/bionemo-agent-toolkit, 478 stars) and DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Solublempnn?
adaptyvbio (a GitHub organization) maintains it in adaptyvbio/protein-design-skills, which has 163 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on June 11, 2026.
Source: adaptyvbio/protein-design-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.