Anti-CRISPR prediction using deep learning reveals an inhibitor of Cas13b nucleases

As part of the ongoing bacterial-phage arms race, CRISPR-Cas systems in bacteria clear invading phages while anti-CRISPR proteins (Acrs) in phages inhibit CRISPR defenses. Known Acrs have proven extremely diverse, complicating their identification. Here, we report a deep learning algorithm for Acr identification that revealed an Acr against CRISPR-Cas systems. The algorithm predicted numerous putative Acrs spanning almost all CRISPR-Cas types and sub-types

Installation and requirements

DeepAcr_masterscript.py has been tested with Python 3.7 To run it, we recommend installing the same library versions we used. Since we exported our classifiers following the model persistence guideline from scikit-learn, it is not guaranteed that they will work properly if loaded using other Python and/or library versions. For such, we recommend the use a conda virtual environment. They make it easy to install the correct Python and library dependencies without affecting the whole operating system (see below).

First step: download the last version of the tool and extract it

git clone git@github.com:BackofenLab/Acr.git

OR 

https://github.com/BackofenLab/Acr/archive/refs/heads/main.zip
unzip main.zip

Second step: download the deep Learning (ML) models

Due to GitHub's file size constraints, we made our DL models available in Google Drive. You can download them here. Save both tar.gz files inside modules directory.

Third step: (conda)

First we install Miniconda for python 3. Miniconda can be downloaded from here: miniconda.

Install Miniconda.

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh chmod +x Miniconda3-latest-Linux-x86_64.sh ./Miniconda3-latest-Linux-x86_64.sh

Create and activate environment for DeepAcr.

conda env create -f environment.yml -n DeepAcr-env
conda activate DeepAcr-env

Quick run with the default parameters

python3.7 DeepAcr_masterscript -f <file> -m <path_to_models> -d <path_to_data>

Citation

If you use our tool, please cite our article

Anti-CRISPR prediction using deep learning reveals an inhibitor of Cas13b nucleases Katharina G. Wandera, Omer S. Alkhnbashi, Harris V.I. Bassett, Alexander Mitrofanov, Sven Hauns, Anzhela Migur, Rolf Backofen, Chase L. Beisel, Molecular Cell, May 31, 2022, DOI(https://doi.org/10.1016/j.molcel.2022.05.003 ).

BackofenLab / DeepAcr