ProteinMPNN is a computational framework for designing protein sequences. It uses a neural network-based approach to predict protein sequences. The primary goal is to design new proteins with specific shapes and functions, which has numerous applications in biotechnology, medicine, and research. 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 use case: Design protein sequences

Technology: Message Passing Neural Network (MPNN)

Limitation:

• Some of the parameters were kept default. Please see this page for more details.
• Some of the examples are currently not implemented. Please see this page for more details.

Parameters Guideline:

1) ProteinMPNN Task: Simple Monomer Task

• Chain List: None
• Fix or Design Specific Residue: None
• Tie Specific Residues: None

2) ProteinMPNN Task: Simple Multi-Chain Task

• Chain List: A C
• Fix or Design Specific Residue: None
• Tie Specific Residues: None

3) ProteinMPNN Task: Fixed Specific Residue Task or Design Specific Residue Task

• Chain List: A C
• Fix or Design Specific Residue: 1 2 3 4 5 6 7 8 23 25, 10 11 12 13 14 15 16 17 18 19 20 40
• Tie Specific Residues: None

4) ProteinMPNN Task: Tie Some Position Together Task

• Chain List: A C
• Fix or Design Specific Residue: None
• Tie Specific Residues: 1 2 3 4 5 6 7 8, 1 2 3 4 5 6 7 8

5) ProteinMPNN Task: Homooligomer Task

• Chain List: None
• Fix or Design Specific Residue: None
• Tie Specific Residues: None