alterlab-rfdiffusion

# 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 ```bash # RosettaCommons/RFdiffusion — run_inference.py drives gene...