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RFDiffusion: Accurate Protein Design

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Protein Structure and Design
Step 1: Upload your data

Upload PDB File

Drag your file(s) or upload
  • Your file can be in the following formats:pdb
    • The Protein Data Bank (PDB) data format is a standard file format used to store information about the three-dimensional structures of biological macromolecules.
or
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Use our demo data to run
Use Demo Data
Step 2: Set Parameters
Unconditional Protein Generation
NaN

RFdiffusion is a deep learning framework, that refines the RoseTTAFold structure prediction network. This innovative model excels in diverse protein design challenges, including de novo binding, higher-order symmetry, and enzyme active site scaffolding. RFdiffusion's success lies in its ability to generate complex, functional proteins from basic molecular specifications, showcasing its versatility through experimental validations of hundreds of new designs. This marks a significant advancement in protein design using deep learning, overcoming previous limitations in modeling protein backbone geometry and sequence-structure relationships.

Example parameter inputs:

  • Unconditional Protein Generation:
               Contig Map Input: 150-200
               Hotspot Points Input: Empty
               Symmetry Options: NaN
  • Motif Scaffolding
               Contig Map Input: 10-40/A163-181/10-40
               Hotspot Points Input: Empty
               Symmetry Options: NaN
  • Binder Design
               Contig Map Input: A1-150/0 70-100
               Hotspot Points Input: A59,A83,A91
               Symmetry Options: NaN
  • Symmetric Oligomers Generation
               Contig Map Input: 360-360
               Hotspot Points Input: Empty
               Symmetry Options: Tetrahedral

Check the examples page for more information!

Technology: Diffusion Model

Limitations: Some metrics are kept as default. You can check here for more details.

Citation:
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach, Woody Ahern, Andrew J. Borst, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Nikita Hanikel, Samuel J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vázquez Torres, Anna Lauko, Valentin De Bortoli, Emile Mathieu, Regina Barzilay, Tommi S. Jaakkola, Frank DiMaio, Minkyung Baek, David Baker bioRxiv 2022.12.09.519842; doi: https://doi.org/10.1101/2022.12.09.519842
Released:
Nov-20-2023
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