This repo contains the code used to generate the results from Parkinson / Hard et al. 2022. The main purpose of this repo is to reproduce key experiments from the paper. If you are interested in using the RESP pipeline to generate sequences based on your own protein data, consider using the resp_protein_toolkit instead.
This code was originally run on a Linux x86_64 machine with a GTX1070 Nvidia GPU and has not been tested on other platforms at this time. Although it should in principle work on other platforms, it is primarily intended for use on Linux.
To install, first create and activate a Python virtual environment, then run the following from your terminal:
git clone https://github.com/jlparkI/In_silico_directed_evolution
cd In_silico_directed_evolution
pip install -r requirements.txt
The most common cause of issues in our experience involves problems with the CUDA installation and/or the version of PyTorch. You may need to install a different version of PyTorch than the one we have here depending on your CUDA version etc.
To reproduce any of the experiments of interest from the pipeline, first retrieve the raw data (see SETUP below), then run the run_experiments.py script. If you do so without specifying any arguments, you'll see the following screen:
which essentially provides a menu of experiments you can run. Note that some of these obviously must be run before others (for example, data must be encoded before a model can be trained etc). Pretrained models are provided so that the models do not need to be retrained unless desired.
Note that processing and encoding the raw read data is time-consuming and (for some of the encoding types we tested) computationally expensive. We highly recommend downloading the pre-encoded data instead.
The raw read data and pre-encoded data can most easily be retrieved from
Zenodo. When accessing the
Zenodo link, download the file "RESP_data.zip". Once you have downloaded
the "RESP_data.zip" file, extract it and you will find a tarball
containing encoded data plus three folders marked rh01, rh02 and rh03.
Extract the encoded data file and move the contents of the resulting
folder to the encoded_data folder in this directory. Move the rh01,
rh02 and rh03 folders to the raw_data folder in this directory. You
should then be able to proceed (and do not need to run
downloadencodings or setup under run_experiments. You
can also access these instructions by running the --setup command
under run_experiments if needed.
The raw read data is also available under SRA (see the paper for details).
If using any of the code in this repository, please cite:
Parkinson, J., Hard, R. & Wang, W. The RESP AI model accelerates the identification of tight-binding antibodies. Nat Commun 14, 454 (2023). https://doi.org/10.1038/s41467-023-36028-8