rfdiffusion

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 category: design-tools tags: [structure-design, diffusion, backbone, binder] proteinbase_slug: rfdiffusion proteinbase_url: https://proteinbase.com/design-methods/rfdiffusion

RFdiffusion Backbone Generation

Prerequisites

How to run

RFdiffusion is not in biomodals, so run it from the official RosettaCommons repo or its Docker image, not through Modal.

Local installation (official repo)

git clone https://github.com/RosettaCommons/RFdiffusion.git
cd RFdiffusion

# Conda env including the required NVIDIA SE(3)-Transformer
conda env create -f env/SE3nv.yml
conda activate SE3nv
cd env/SE3Transformer && pip install . && cd ../..
pip install -e .

# Download weights (per-file hashed paths; see the repo README for the full list)
mkdir -p models
wget -P models http://files.ipd.uw.edu/pub/RFdiffusion/e29311f6f1bf1af907f9ef9f44b8328b/Complex_base_ckpt.pt

# Binder design run; single-quote the hydra args so the shell does not split [] or ,
./scripts/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

A RosettaCommons-maintained Docker image is also available from the repo README. After backbone generation, design sequences with proteinmpnn.

Config Schema (Hydra)

Contigmap Syntax

# 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

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

Common mistakes

Contig Syntax

✅ Correct:

'contigmap.contigs=[A1-150/0 70-100]'  # Target fixed (/0), binder variable

Single-quote the whole argument so the shell does not split on the space inside the brackets.

❌ Wrong:

contigmap.contigs=[A1-150 70-100]     # Missing /0 - target will move!
contigmap.contigs=[A1-150/0 70-100]   # Unquoted: shell splits on the space
contigmap.contigs=[A1-150/0, 70-100]  # Extra comma changes the contig string

Hotspot Residues

✅ Correct:

'ppi.hotspot_res=[A45,A67,A89]'      # Chain letter + residue number, whole arg quoted

❌ Wrong:

ppi.hotspot_res=[45,67,89]           # Missing chain letter
'ppi.hotspot_res=[A45, A67, A89]'    # Spaces inside the list break parsing

Complete Parameter Reference

Core Parameters

Advanced Parameters

Symmetry Parameters

Fold Conditioning

Model Checkpoints

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

# 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)

# 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

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

Adaptyv's own tests of these models showed an RFdiffusion + sequence-design pipeline costing about $0.25 per accepted design, averaged across 7 targets, among the cheapest of the methods tested.

Verify

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

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