---
title: RFD4-Proteina Diffusers
emoji: 🧬
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 6.31.0
app_file: app.py
python_version: "3.12"
startup_duration_timeout: 1h
short_description: Joint sequence-structure protein design from annotated CIF
---

# RFD4-Proteína · Diffusers

Interactive demo of [`nvidia/RFD4-Proteina-Diffusers`](https://huggingface.co/nvidia/RFD4-Proteina-Diffusers) — the
[Diffusers](https://huggingface.co/docs/diffusers/api/pipelines/rfd4_proteina) release of the RFD4-Proteína
flow-matching model (Baker Lab × NVIDIA GenAIR), which designs protein **sequences and all-atom structures together**,
conditioned on the molecular context you want to preserve: a binding target, an enzyme motif, a ligand, or selected
structural features.

Upload an **annotated CIF** describing what to hold fixed and what to design (or pick one of the author examples below),
choose a design mode, and press **Design**. The output is a designed structure rendered in 3D plus a downloadable
annotated CIF written back with the generated sequence and coordinates.

## Design modes

- **Full design** — the tokens marked as designable are generated from pure noise: sequence and structure jointly.
- **Inverse folding** — the input backbone is kept and only the sequence is redesigned.
- **Partial diffusion** — the input is re-noised to the point where a chosen fraction of the schedule remains, then all
  60 steps run over that range (the released example uses 0.4). Useful for relaxed re-designs of an existing structure.

## Inputs and outputs

The input CIF uses [AtomWorks Studio](https://github.com/atlas-labinit/atomworks) annotations to say which residues,
atoms, ligands or motifs are context (`is_in_context`) and which are to be designed. The demo prepares it with the
authors' [`rfd4-proteina-prep`](https://huggingface.co/nvidia/RFD4-Proteina-prep) adapter (vendored verbatim into this
Space as `rfd4_proteina_prep/`, because the Hub's git server does not serve partial clones to pip) and samples with
`RFD4ProteinaPipeline` (the released `thinking_medium` recipe: 60 steps, 180 trunk passes, BF16).

## Examples

The bundled example inputs are the authors' own, taken from the
[`examples/` folder](https://huggingface.co/nvidia/RFD4-Proteina-Diffusers/tree/main/examples) of the model repository
(protein binder · PD-L1, enzyme catalytic motif, motif scaffolding · 1QJG, small-molecule binder · SAM, unconditional,
inverse folding · 2KL8). They demonstrate the conditioning workflow and are not experimental binding or
catalytic-activity results.

## Biosafety note

The official RFD4-Proteína deployment applies [SecureDNA](https://secure-dna.org) screening to every design before it is
returned. **This demo does not screen designs.** Use it for computational biomolecular design research only, and follow
designs with appropriate structural and experimental evaluation.

## Acknowledgements

Model, weights and example inputs: RFD4-Proteína, developed by the Baker Lab (Institute for Protein Design,
University of Washington) and NVIDIA GenAIR — [BSD-3-Clause](https://huggingface.co/nvidia/RFD4-Proteina-Diffusers/blob/main/LICENSE).
When reporting results, cite the RFD4-Proteína project, the exact model and code revisions, and any source datasets or
evaluation tools used.