> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tamarind.bio/llms.txt
> Use this file to discover all available pages before exploring further.

# ProteinMPNN

> Design sequences given a structure

### Design a sequence to fold into a protein structure

ProteinMPNN significantly outperforms traditional approaches like Rosetta, achieving 52.4% sequence recovery. It can design sequences for single or multiple chains and has been experimentally validated through X-ray crystallography, cryoEM, and functional studies. The method has successfully designed various protein types including monomers, cyclic homo-oligomers, and target binding proteins, representing a major advancement in computational protein design.

ProteinMPNN is often used after RFdiffusion to generate sequences for a given designed structure, since RFdiffusion/RFantibody will design structures with poly-Gs as placeholders for designed residues. It can also be used directly from a starting structure to generate stabilizing mutations.

### Inputs

* PDB File
* Designed Residues - select residues on each chain to be designed
* Temperature: adjust the amount of diversity in your sequences. Higher value will generate more mutations.

### Outputs

* Overall Confidence: Average over all redesigned residues (exp\[-mean\_over\_residues(log\_probs)]) - higher means more confident

### Alternative weights

Others have finetuned ProteinMPNN for different use cases. You can use the following weights by changing the "Model Type" parameter:

* SolubleMPNN - trained on soluble proteins
* AbMPNN - trained on antibodies
* HyperMPNN - trained on hyperthermophilic proteins
* LigandMPNN - takes ligand atoms into account

You can also check out ThermoMPNN (uses ProteinMPNN embeddings to identify thermostable point mutations).

[Try proteinmpnn](https://app.tamarind.bio/tools/proteinmpnn)