RFDiffusion: Accurate Protein Design
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. Furthermore, RFDiffusion outputs can be used in ProteinMPNN to refine these backbones and design sequences that fold correctly and exhibit the desired functionalities. This combined approach allows the creation of new proteins with specific shapes and functions tailored to various needs.
Example parameter inputs:
- Unconditional Protein Generation:
Contig Map Input: 150-200
Hotspot Points Input: None
Symmetry Options: NaN
- Motif Scaffolding
Hotspot Points Input: None
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: None
Symmetry Options: Tetrahedral
- Fold Conditioning - PPI
Contig Map Input: None
Hotspot Points Input: A59,A83,A91
Symmetry Options: NaN
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