This repository contains relevant information of our research article 'Pre-trained protein language models shed new light on the prediction of Arabidopsis protein-protein interactions'.
We provide a file of conda virtual environment for you to refer via conda env create -f environment.yml. After the Python environment is ready, you can run the script python MLP.py in terminal and it use GPU default.
For training and testing datasets, you need to have protein pair and their label separated by tab character like protein1 protein2 label. Please note that ESM only received protein sequences less than 1022 when we used before, and things may have changed right now.
Please follow this instruction to extracts embeddings from ESM. After that, you should extract embedding from raw result and integrate all embeddings into Python dictionary like {"protein_name": torch.tensor()}. Then save it as .pkl file.
Replace the corresponding file names in MLP.py with yours and you will get PR value of testing dataset after running the script.
We express our thanks to these researches.
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