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 alphafold2-multimer or chai1-structure-prediction. For QC thresholds, use protein-design-qc.
license	MIT
category	design-tools
tags	["structure-design","diffusion","backbone","binder"]
proteinbase_slug	rfdiffusion
proteinbase_url	https://proteinbase.com/design-methods/rfdiffusion
biomodals_script	modal_rfdiffusion.py

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 to set up Modal and biomodals.

Option 1: Modal (recommended)

# 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

# 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

# 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

❌ Wrong:

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:

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

❌ Wrong:

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

# 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

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

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-design-qc for filtering.

Templates and Demo

• Starter flags: templates/rfdiffusion/binder_flags.txt
• Planning context: examples/minimal-binder-campaign/target-brief.md

Inputs

• A prepared target structure plus contig and hotspot definitions for the design task.
• A clear generation objective such as binder design, motif scaffolding, or symmetry.
• Sampling settings such as design count, checkpoint choice, and noise scale.

Outputs

• Generated backbone structures that satisfy the requested geometric constraints.
• A candidate backbone library for sequence design with proteinmpnn or solublempnn.
• Run metadata needed to reproduce successful generation settings.

Next Step

Hand the generated backbones to proteinmpnn for sequence design, then validate with structure prediction and protein-design-qc.