README.md

metadata

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 — the Diffusers 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 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 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 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 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. When reporting results, cite the RFD4-Proteína project, the exact model and code revisions, and any source datasets or evaluation tools used.