CodonMPNN is similar to ProteinMPNN but produces a codon sequences instead of an amino acid sequence and it is conditioned on a specific organism (the organism conditioning is optional). The current version has been trained on Monomers from the AlphaFold database.
Here is one version of a conda environment that works for running the code. Keep in mind that the following assumes that you have a CUDA compatible GPU. If that is not the case you should follow the pytorch installation instructions here instead: https://pytorch.org/get-started/locally/
conda create -n codon python=3.9
pip install jupyterlab
pip install numpy==1.21.2 pandas==1.5.3
pip install torch==1.12.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html
pip install biopython==1.79 dm-tree==0.1.6 modelcif==0.7 ml-collections==0.1.0 scipy==1.7.1 absl-py einops
pip install pytorch_lightning==2.0.4 fair-esm mdtraj
pip install 'openfold @ git+https://github.com/aqlaboratory/openfold.git@5484c38'
pip install biopython==1.81
pip install modelcif foldcomp tmtools omegaconf rdkit-pypi imageio matplotlib plotly wandb torchdiffeq jupyterlab gpustat gemmi h5py deeptime A toy dataset is in afdb_small to run the code on. CodonMPNN was trained on a MMSeqs clustered version of AFDB.
Here is a link to download the .csv file for the whole training validation and test data. It contains AFDB ids for representatives of mmseqs clusters with a maximum 30% sequence identity. The train, val, test split is a random split.
Unfortunately, I cannot upload all the .pdb files somewhere due to the dataset size. You can download the pdb files from AFDB using their api and code similar to this:
import requests
url = f"https://alphafold.ebi.ac.uk/files/{afdb_id}.pdb"
response = requests.get(url)
if response.status_code == 200:
with open(f'{afdb_id}.pdb', 'wb') as pdb_file:
pdb_file.write(response.content)
We provide the weights here.
https://publbuck.s3.us-east-2.amazonaws.com/codonmpnn_afdb_taxons20k.ckpt