| name | alterlab-rfdiffusion |
| description | Generate de-novo protein backbones with RFdiffusion (Watson 2023) — a diffusion model for unconditional monomer generation, motif scaffolding, binder design against a target, and symmetric oligomers. Use when generating a new protein backbone from scratch, scaffolding a functional motif into a fold, designing a binder backbone to a target surface, or building symmetric assemblies; RFdiffusion produces the STRUCTURE, then alterlab-proteinmpnn designs its sequence and alterlab-alphafold validates it. For sequence design of an existing backbone prefer alterlab-proteinmpnn (or alterlab-ligandmpnn with a ligand); to fold a known sequence prefer alterlab-alphafold; for generative multimodal design prefer alterlab-esm. Part of the AlterLab Academic Skills suite. |
| license | MIT |
| allowed-tools | Read Write Edit Bash(python:*) Bash(uv:*) |
| compatibility | Runs RFdiffusion (`RosettaCommons/RFdiffusion`, PyTorch + SE(3)-transformer) under `uv run python` via `run_inference.py`. Requires a CUDA GPU for practical generation; model weights download once and cache (several GB; no account). Outputs backbone PDBs (no sequence) — pair with alterlab-proteinmpnn then alterlab-alphafold. Dispatch runs via alterlab-remote-compute. |
| metadata | {"skill-author":"AlterLab","version":"1.0.0"} |
RFdiffusion (de-novo backbone generation)
Overview
RFdiffusion (Watson et al., Nature 2023; RosettaCommons/RFdiffusion) is a diffusion
model that generates protein backbones — new 3D structures, not sequences. It supports
unconditional generation, motif scaffolding (build a fold around a fixed functional
motif), binder design (generate a backbone that binds a target surface), and symmetric
assemblies. It is the structure-generation step that starts the de-novo design pipeline;
alterlab-proteinmpnn then designs sequences for the backbone and alterlab-alphafold
validates them.
When to Use This Skill
Use this skill when the user wants to:
- Generate a novel protein backbone from scratch (unconditional).
- Scaffold a functional motif (e.g. a binding loop / catalytic geometry) into a new fold.
- Design a binder backbone against a given target protein surface / hotspots.
- Build symmetric oligomers (cyclic/dihedral) as backbones.
Does NOT Trigger
| Scenario | Use instead |
|---|
| Design the sequence for an existing backbone | alterlab-proteinmpnn |
| Design a pocket sequence with a ligand/metal present | alterlab-ligandmpnn |
| Fold a known sequence into a structure | alterlab-alphafold |
| Generative multimodal (sequence+structure) design | alterlab-esm |
Core Capabilities
1. Unconditional generation
python run_inference.py \
'contigmap.contigs=[100-100]' \
inference.output_prefix=out/uncond \
inference.num_designs=10
contigmap.contigs specifies what to build (here, a 100-residue monomer). Outputs backbone
PDBs with no sequence.
2. Motif scaffolding
