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proteinmpnnlisted

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.
BioTender-max/awesome-bio-agent-skills · ★ 58 · AI & Automation · score 80
Install: claude install-skill BioTender-max/awesome-bio-agent-skills
# 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` | 1 | 1-1000 | Sequences per structure | | `--sampling_temp` | "0.1" | "0.0001-1.0" | Temperature (string!) | | `--seed` | 0 | int | Random seed | | `--batch_size` | 1 | 1-32 | Batch size | ### Temperature Guide ``` 0.1 -> Low diversity, high recovery (production) 0.2 ->