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rfdiffusion

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Atualizado11 de junho de 2026 às 14:45

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.

Instalação

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

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Fonte

adaptyvbio

adaptyvbio/protein-design-skills

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Cientistas biológicos, todos os outrosCiências da Vida, Físicas e Sociais·SOC 19-1029

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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

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

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

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

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

Motif Scaffolding

Extract motif coordinates
Use /0 to fix motif in contigmap
Generate surrounding scaffold
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.

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

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.