dauparas/ProteinMPNN

Health v2 · maintenance only

Adoption not part of the score

1834 stars · 491 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

ProteinMPNN is a PyTorch-based tool that designs amino acid sequences for given protein backbone structures using a message-passing neural network. It includes pretrained full-backbone and CA-only model weights, helper scripts for PDB parsing and residue fixing, and Colab notebooks.

Use cases

• design protein sequences for a fixed backbone structure
• generate novel sequences that fold into a target PDB backbone
• score an existing sequence against a protein backbone
• compute per-residue sequence probabilities from a structure
• fix specific residues and design the rest of a protein
• add amino acid biases or tie residues during design
• retrain the sequence design model on custom data

When to choose

• you need fast, accurate inverse protein folding (sequence design from backbone)
• you want a well-cited, widely used tool with pretrained weights and MIT licensing
• you need fine control over which residues to fix, bias, or tie

When to avoid

• you need full protein structure prediction rather than sequence design
• you lack a GPU or PyTorch environment and need a no-setup web tool only
• your task is unrelated to protein engineering or structural biology

Facets

library · maturity stable

machine-learning deep-learning llm-inference bioinformatics machine-learning deep-learning python windows protein-design proteinmpnn sequence-design pytorch computational-biology structure-prediction pdb linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="dauparas/ProteinMPNN")