What problem does it solve?
ProteinMPNN enables designers to generate amino acid sequences that fold into a specified backbone, accelerating structure-based design workflows.
Core Features & Use Cases
- Multi-chain design support, fixed positions, and amino acid biases enable complex design tasks such as interface engineering and homooligomer design.
- Docker-based execution with a prepackaged model ensures GPU-accelerated inference in scalable environments.
- Use cases include de novo protein design, binder design, and experimental sequence screening against backbone targets.
Quick Start
Run a design job using the provided Docker image ghcr.io/open-prophetdb/proteinmpnn:arm64-blackwell, mapping your backbone.pdb to output sequences under /data/output, for example:
docker run --rm --gpus all -v $(pwd):/data ghcr.io/open-prophetdb/proteinmpnn:arm64-blackwell python /app/protein_mpnn_run.py --pdb_path /data/backbone.pdb --out_folder /data/output --num_seq_per_target 10