alterlab-rfdiffusion

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

# RosettaCommons/RFdiffusion — run_inference.py drives generation (Hydra config).
# It lives in the repo's scripts directory; TODO(verify) config keys/version.
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