proteinmpnn
Design protein sequences using ProteinMPNN inverse folding. Use this skill when: (1) Designing sequences for RFdiffusion backbones, (2) Redesigning existing protein sequences, (3) Fixing specific residues while designing others, (4) Optimizing sequences for expression or stability, (5) Multi-state or negative design. For backbone generation, use rfdiffusion or bindcraft. For ligand-aware design, use ligandmpnn. For solubility optimization, use solublempnn.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill proteinmpnn --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.
# ProteinMPNN Sequence 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: Local installation (recommended) ```bash git clone https://github.com/dauparas/ProteinMPNN.git cd ProteinMPNN python protein_mpnn_run.py \ --pdb_path backbone.pdb \ --out_folder output/ \ --num_seq_per_target 16 \ --sampling_temp "0.1" ``` **GPU**: T4 (16GB) sufficient | **Time**: ~50-100 sequences/minute ### Option 2: Modal (via LigandMPNN wrapper) ```bash cd biomodals modal run modal_ligandmpnn.py \ --pdb-path backbone.pdb \ --num-seq-per-target 16 ``` Note: LigandMPNN includes ProteinMPNN functionality. ## Config Schema ### Core Parameters | Parameter | Default | Range | Description | |-----------|---------|-------|-------------| | `--pdb_path` | required | path | Single PDB input | | `--pdb_path_chains` | all | A,B | Chains to design (comma-sep) | | `--out_folder` | required | path | Output directory | | `--num_seq_per_target
- Prerequisites
- How to run
- Option 1: Local installation (recommended)
- Option 2: Modal (via LigandMPNN wrapper)
- Config Schema
- Core Parameters
- Temperature Guide
- Common mistakes
- Temperature Parameter
- Fixed Positions JSONL
- Chain Selection
- Amino Acid Biases
- Multi-Chain Design
- Variants Comparison
git clone https://github.com/dauparas/ProteinMPNN.git cd ProteinMPNN python protein_mpnn_run.py \ cd biomodals modal run modal_ligandmpnn.py \ Bias toward certain AAs (positive = favor) Omit specific AAs globally Per-position omission Design chains A and B together Tie chains (same sequence)
What does the proteinmpnn skill do?
Design protein sequences using ProteinMPNN inverse folding. Use this skill when: (1) Designing sequences for RFdiffusion backbones, (2) Redesigning existing protein sequences, (3) Fixing specific residues while designing others, (4) Optimizing sequences for expression or stability, (5) Multi-state or negative design. For backbone generation, use rfdiffusion or bindcraft. For ligand-aware design, use ligandmpnn. For solubility optimization, use solublempnn.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill proteinmpnn --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 FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 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.