Agent skill

rfdiffusion

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.

View SKILL.md on GitHub Repository
Stars 2,009
Forks 275

Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/rfdiffusion

SKILL.md

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)

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.

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

FreedomIntelligence/OpenClaw-Medical-Skills

vcf-annotator

Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

chemist-analyst

Analyzes events through chemistry lens using molecular structure, reaction mechanisms, thermodynamics, kinetics, and analytical techniques (spectroscopy, chromatography, mass spectrometry). Provides insights on chemical processes, material properties, reaction pathways, synthesis, and analytical methods. Use when: Chemical reactions, material analysis, synthesis planning, process optimization, environmental chemistry. Evaluates: Molecular structure, reaction mechanisms, yield, selectivity, safety, environmental impact.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-alignment-io

Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

sleep-analyzer

分析睡眠数据、识别睡眠模式、评估睡眠质量，并提供个性化睡眠改善建议。支持与其他健康数据的关联分析。

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

metabolomics-workbench-database

Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-hi-c-analysis-matrix-operations

Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.

2,009 275
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results