Deep learning enables accurate design of functional transmembrane proteins
Deep learning enables accurate design of functional transmembrane proteins
NeMO is a conditional deep learning model for environment-aware protein sequence design. By employing specific condition tags during inference, the model allows users to strictly dictate whether the generated sequence is tailored for a transmembrane or water-soluble context. Installation: conda create -n nemo python=3.10 conda activate nemo pip install torch torchvision fair-esm biotite biopython wandb pandas scipy Usage: Navigate to the examples/ directory and run the shell scripts for different design tasks: BR_TMP.sh / BR_WSP.sh: Monomer design with fixed residues (transmembrane / water-soluble) WSHC6_TMP.sh / WSHC6_WSP.sh: Homo-oligomer design with tied chain positions (transmembrane / water-soluble) See README.md for detailed usage instructions.
- Tsinghua University China (People's Republic of)
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