---
name: RFDiffusion
slug: rfdiffusion
category: AI Engineering
description: RFDiffusion generates protein backbones for de novo design, binder scaffolds, motif scaffolding, and symmetric oligomers. Use it when you need a backbone to pass to proteinmpnn, alphafold, chai, or protein-qc.
github: "https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/adaptyv/rfdiffusion"
language: Python
stars: 174
forks: 30
install: "npx degit https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/adaptyv/rfdiffusion ~/.claude/skills/rfdiffusion"
installs_to: ~/.claude/skills/rfdiffusion
source_path: skills/adaptyv/rfdiffusion/SKILL.md
collection_size: 25
category_size: 3475
collection_url: "https://dirskills.com/collections/BioTender-max/awesome-bio-agent-skills"
added: 2026-09-07T05:21:58.620Z
last_synced: 2026-09-07T05:21:58.620Z
canonical_url: "https://dirskills.com/skills/rfdiffusion"
---

# RFDiffusion

RFDiffusion generates protein backbones for de novo design, binder scaffolds, motif scaffolding, and symmetric oligomers. Use it when you need a backbone to pass to proteinmpnn, alphafold, chai, or protein-qc.

**Install:**

```bash
npx degit https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/adaptyv/rfdiffusion ~/.claude/skills/rfdiffusion
```

## README

# 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 |
| `potentials.guide_scale` | 1.0 | 0.1-10 | Guidance strength |
| `potentials.guide_decay` | constant | string | Decay type |

#### Advanced Parameters
| Parameter | Default | Description |
|-----------|---------|-------------|
| `diffuser.partial_T` | None | Start diffusion from timestep T (partial diffusion) |
| `contigmap.inpaint_str` | None | Sequence positions to inpaint |
| `scaffoldguided.scaffoldguided` | false | Enable scaffold-guided generation |
| `scaffoldguided.target_pdb` | None | Scaffold template PDB |
| `ppi.binderlen` | None | Specify exact binder length |

#### Symmetry Parameters
| Parameter | Default | Description |
|-----------|---------|-------------|
| `symmetry.symmetry` | None | Symmetry type (C2, C3, C4, D2, etc.) |
| `symmetry.recenter` | true | Recenter symmetric assembly |
| `symmetry.radius` | None | Radius constraint for symmetric assembly |

#### Fold Conditioning
| Parameter | Default | Description |
|-----------|---------|-------------|
| `contigmap.provide_seq` | None | Provide sequence for fold conditioning |
| `contigmap.inpaint_seq` | None | Positions for sequence inpainting |

### Model Checkpoints
| Checkpoint | Use Case |
|------------|----------|
| `Complex_base_ckpt.pt` | Binder design (default) |
| `Base_ckpt.pt` | De novo monomers |
| `ActiveSite_ckpt.pt` | Active site scaffolding |
| `InpaintSeq_ckpt.pt` | Sequence inpainting |

## Common workflows

### Binder Design
1. Prepare target PDB (trim to binding region + 10A buffer)
2. Identify 3-6 hotspot residues (exposed, conserved)
3. Generate 100-500 backbones
4. Pass to proteinmpnn for sequence design

### Motif Scaffolding
1. Extract motif coordinates
2. Use `/0` to fix motif in contigmap
3. Generate surrounding scaffold
4. Validate motif preservation (RMSD < 1.5A)

### Symmetric Oligomers
```bash
# C3 symmetric trimer
python run_inference.py \
  symmetry.symmetry=C3 \
  contigmap.contigs=[100-150] \
  inference.num_designs=50

# D2 symmetric tetramer
python run_inference.py \
  symmetry.symmetry=D2 \
  contigmap.contigs=[80-120] \
  symmetry.radius=25

# Supported symmetries: C2, C3, C4, C5, C6, D2, D3, D4, tetrahedral, octahedral
```

### Partial Diffusion (Refinement)
```bash
# Start from existing structure, diffuse from timestep 10
python run_inference.py \
  inference.input_pdb=initial.pdb \
  diffuser.partial_T=10 \
  contigmap.contigs=[A1-100]
```

## Output format

```
output/
├── output_0.pdb       # Generated backbone
├── output_1.pdb
├── ...
└── output_99.pdb
```

Each PDB contains polyalanine backbone - use proteinmpnn for sequence.

## Sample output

### Successful run
```
$ python run_inference.py inference.input_pdb=target.pdb contigmap.contigs=[A1-150/0 70-100] inference.num_designs=100
[INFO] Loading model from Complex_base_ckpt.pt
[INFO] Generating design 1/100...
[INFO] Generating design 50/100...
[INFO] Generating design 100/100...
[INFO] Saved 100 designs to output/

Generated:
output/output_0.pdb (85 residues)
output/output_1.pdb (92 residues)
...
```

**What good output looks like:**
- File size: 3-8 KB per PDB (backbone only)
- Residue count within specified range
- Secondary structure visible in PyMOL (helices/sheets, not random coil)

## Decision tree

```
Should I use RFdiffusion?
│
├─ Need to generate protein backbone?
│  ├─ Yes → Continue below
│  └─ No, already have backbone → Use ProteinMPNN
│
├─ What type of design?
│  ├─ Binder for protein target → RFdiffusion ✓
│  ├─ De novo monomer → RFdiffusion ✓
│  ├─ Motif scaffolding → RFdiffusion ✓
│  └─ Symmetric assembly → RFdiffusion ✓
│
└─ Priority?
   ├─ Need highest success rate → Consider BindCraft
   ├─ Need diversity/exploration → RFdiffusion ✓
   └─ Need all-atom precision → Consider BoltzGen
```

## Typical performance

| Campaign Size | Time (A10G) | Cost (Modal) | Notes |
|---------------|-------------|--------------|-------|
| 100 backbones | 20-30 min | ~$3 | Quick exploration |
| 500 backbones | 1.5-2h | ~$12 | Standard campaign |
| 1000 backbones | 3-4h | ~$25 | Large campaign |

**Expected downstream yield**: ~10-15% of backbones pass full QC after sequence design + validation.

---

## Verify

```bash
ls output/*.pdb | wc -l  # Should match num_designs
```

## Troubleshooting

**Designs lack secondary structure**: Decrease noise_scale to 0.5-0.8
**Binder not contacting hotspots**: Verify residue numbering, increase num_designs
**OOM errors**: Reduce batch size or use A100 GPU
**Slow generation**: Reduce diffuser.T to 25-35

### Error interpretation

| Error | Cause | Fix |
|-------|-------|-----|
| `RuntimeError: CUDA out of memory` | GPU VRAM exceeded | Use A100 or reduce designs per batch |
| `KeyError: 'A'` | Chain not found in PDB | Check chain IDs with `grep ^ATOM target.pdb \| cut -c22 \| sort -u` |
| `ValueError: invalid contig` | Syntax error in contigs | Check for spaces, quotes, commas (see Common Mistakes) |
| `FileNotFoundError: ckpt` | Missing model weights | Download from IPD website |

---

**Next**: `proteinmpnn` for sequence design → structure prediction for validation → `protein-qc` for filtering.