git clone git@github.com:PKUfjh/progen.git
cd progen/progen2
# checkpoint
model=progen2-large
wget -P checkpoints/${model} https://storage.googleapis.com/sfr-progen-research/checkpoints/${model}.tar.gz
tar -xvf checkpoints/${model}/${model}.tar.gz -C checkpoints/${model}/
conda create -n progen python=3.9
pip3 install --upgrade pip setuptools
pip3 install -r requirements.txt
pip install numpy==1.25.1python3 sample.py --model ${model} --t 0.8 --p 0.9 --max-length 1024 --num-samples 2 --context "1"
# log-likelihood (GenBank: TMF32756.1)
python3 likelihood.py --model ${model} --context "1MGHGVSRPPVVTLRPAVLDDCPVLWRWRNDPETRQASVDEREIPVDTHTRWFEETLKRFDRKLFIVSADGVDAGMVRLDIQDRDAAVSVNIAPEWRGRGVGPRALGCLSREAFGPLALLRMSAVVKRENAASRIAFERAGFTVVDTGGPLLHSSKARLHVVAAIQARMGSTRLPGKVLVSIAGRPTIQRIAERLAVCQELDAVAVSTSVENRDDAIADLAAHLGLVCVRGSETDLIERLGRTAARTGADALVRITADCPLVDPALVDRVVGVWRRSAGRLEYVSNVFPPTFPDGLDVEVLSRTVLERLDREVSDPFFRESLTAYVREHPAAFEIANVEHPEDLSRLRWTMDYPEDLAFVEAVYRRLGNQGEIFGMDDLLRLLEWSPELRDLNRCREDVTVERGIRGTGYHAALRARGQAP2"Suite of open-sourced projects and models for protein engineering and design.
Our code and models are BSD-3 licensed. See LICENSE.txt for details.
Predicting the fitness of a protein sequence and capturing the distribution of natural proteins for generative purposes could be a powerful tool for protein design. If our technique or a future iteration thereof is adopted broadly, care should be taken in terms of the end use-cases of these designed samples and downstream effects to ensure safe, non-nefarious, and ethical applications. For projects in any domain, active oversight during project initiation, experimental optimization, and deployment phases should be put in place to ensure safe usage and limitation of unintended harmful effects.