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rfdiffusion

Generate protein backbones using RFdiffusion, a diffusion-based generative model for de novo protein structure generation.

Skill by BioTender-max

IndexedMIT★ 206
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About

Generate protein backbones using RFdiffusion, a diffusion-based generative model for de novo protein structure generation. Use this skill when: (1) Designing binder scaffolds for a target protein, (2) Generating novel protein backbones from scratch, (3) Scaffolding functional motifs into new proteins, (4) Specifying hotspot residues for interface design, (5) Creating symmetric oligomers. For sequence design after backbone generation, use proteinmpnn. For structure validation, use alphafold or chai. For QC thresholds, use protein-qc.

Use cases: design

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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Network hosts it talks to 1

  • codeload.github.com · found in install command

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  • ~/.claude/skills/rfdiffusion

What's inside

Skill name
rfdiffusion
Description
Generate protein backbones using RFdiffusion, a diffusion-based generative model for de novo protein structure generation. Use this skill when: (1) Designing binder scaffolds for a target protein, (2) Generating novel protein backbones from scratch, (3) Scaffolding functional motifs into new proteins, (4) Specifying hotspot residues for interface design, (5) Creating symmetric oligomers. For sequence design after backbone generation, use proteinmpnn. For structure validation, use alphafold or chai. For QC thresholds, use protein-qc.
License
MIT
Instructions
286 lines in SKILL.md · view on GitHub

Before installing

  • Instructions call the network

Files (4)

  • SKILL.md
  • binder-walkthrough.md · examples/
  • hotspot-selection.md · references/
  • parameters.md · references/

SKILL.md (first lines)

# RFdiffusion Backbone Generation

## Prerequisites

| Requirement | Minimum | Recommended |
|-------------|---------|-------------|
| Python | 3.9+ | 3.10 |
| CUDA | 11.7+ | 12.0+ |
| GPU VRAM | 16GB | 24GB (A10G) |
| RAM | 16GB | 32GB |

## How to run

> **First time?** See [Installation Guide](../../docs/installation.md) to set up Modal and biomodals.

### Option 1: Modal (recommended)
```bash
# Clone biomodals
git clone https://github.com/hgbrian/biomodals && cd biomodals

# Basic binder design
modal run modal_rfdiffusion.py \
  --pdb target.pdb \
  --contigs "A1-150/0 70-100" \
  --hotspot "A45,A67,A89" \
  --num-designs 100

# With custom GPU/timeout
GPU=A100 TIMEOUT=60 modal run modal_rfdiffusion.py \
  --pdb target.pdb \
  --contigs "A1-150/0 70-100" \
  --num-designs 100
```

**GPU**: A10G (24GB) | **Timeout**: 30min default

### Option 2: Local installation
```bash
# Clone and install
git clone https://github.com/RosettaCommons/RFdiffusion.git
cd RFdiffusion && pip install -e .

# Download weights
wget http://files.ipd.uw.edu/pub/RFdiffusion/models/Complex_base_ckpt.pt

# Run inference
python run_inference.py \
  inference.input_pdb=target.pdb \
  contigmap.contigs=[A1-150/0 70-100] \
  ppi.hotspot_res=[A45,A67,A89] \
  inference.num_designs=100
```

## Config Schema (Hydra)

### Contigmap Syntax
```bash
# De novo single chain (50-100 residues)
contigmap.contigs=[50-100]

# Binder + target (A = target chain, fixed with /0)
contigmap.contigs=[A1-150/0 70-100]

# Motif scaffolding (preserve residues, /0 = fixed)
contigmap.contigs=[20-40/0 A10-30/0 20-40]

# Multi-chain binder
contigmap.contigs=[A1-100/0 B1-100/0 60-80]

# Variable length ranges
contigmap.contigs=[A1-150/0 50-100]  # Binder 50-100 AA
```

### Hotspot Specification
```bash
# Residues for interface (chain + resnum, no spaces)
ppi.hotspot_res=[A45,A67,A89]
```

## Common mistakes

### Contig Syntax
✅ **Correct**:
```bash
contigmap.contigs=[A1-150/0 70-100]  # Target fixed (/0), binder variable
```

❌ **Wrong**:
```bash
contigmap.contigs=[A1-150 70-100]    # Missing /0 - target will move!
contigmap.contigs="A1-150/0 70-100"  # Quotes break parsing
contigmap.contigs=[A1-150/0, 70-100] # Comma breaks parsing
```

### Hotspot Residues
✅ **Correct**:
```bash
ppi.hotspot_res=[A45,A67,A89]        # Chain letter + residue number
```

❌ **Wrong**:
```bash
ppi.hotspot_res=[45,67,89]           # Missing chain letter
ppi.hotspot_res=[A45, A67, A89]      # Spaces break parsing
ppi.hotspot_res="A45,A67,A89"        # Quotes break parsing
```

### Complete Parameter Reference

#### Core Parameters
| Parameter | Default | Range | Description |
|-----------|---------|-------|-------------|
| `inference.num_designs` | 10 | 1-10000 | Number of designs to generate |
| `inference.input_pdb` | - | path | Target structure file |
| `inference.output_prefix` | output | string | Output filename prefix |
| `diffuser.T` | 50 | 20-200 | Diffusion timesteps |
| `denoiser.noise_scale_ca` | 1.0 | 0.0-2.0 | CA atom noise (0.5-0.8 = conservative) |
| `denoiser.noise_scale_frame` | 1.0 | 0.0-2.0 | Frame noise |
| `inference.ckpt_override_path` | - | path | Model checkpoint |

From BioTender-max/awesome-bio-agent-skills, MIT.

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Last commit (this folder)
May 25, 2026 (5 months ago)
Last release
Jul 1, 2026 (3 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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rfdiffusion skill. Agent Mart. https://agentmart.dev/skills/biotender-max-rfdiffusion (last updated 2026-10-24)