RNAmigos: RNA Small Molecule Ligand Prediction

This repository is an implementation of ligand prediction from an RNA base pairing network.

Please cite:

@article{oliver2020augmented,
  title={Augmented base pairing networks encode RNA-small molecule binding preferences},
  author={Oliver, Carlos and Mallet, Vincent and Gendron, Roman Sarrazin and Reinharz, Vladimir and Hamilton, William L and Moitessier, Nicolas and Waldisp{\"u}hl, J{\'e}r{\^o}me},
  journal={Nucleic acids research},
  volume={48},
  number={14},
  pages={7690--7699},
  year={2020},
  publisher={Oxford University Press}
}

If you just want to use the tool non-programmatically or don't need to train your own model you can check the online web-server implementation here.

We implement three major components:

• data_processor: building a training set
• learning: RGCN training
• tools: general tools such as graph drawing, loading, etc.
• post: validation and visualization of results

See README in each folder for details on how to use each component.

Requirements

• Python >= 3.6
• Pytorch
• dgl
• OpenBabel
• BioPython
• tqdm
• networkx >= 2.1

You can automatically install all the dependencies with Anaconda using the following command:

conda env create -f environment.yml

Usage

Extracting the annotated data

Extract the annotated data and place it in the annotations folder if it has not yet been created.

cd data
tar -xzvf pockets_nx_symmetric_orig.tar.gz
cd ..
mkdir data/annotated
mv data/pockets_nx_symmetric_orig data/annotated

Loading a trained model

• You can use the model used for the paper, or load a trained model you trained yourself (see next section)

Making predictions for every graph in a folder.

Create a script in the root of the repository with the following code:

from tools.learning_utils import inference_on_dir

graph_dir = "data/annotated/pockets_nx_symmetric_orig"

fp_pred,_ = inference_on_dir("rnamigos", graph_dir)

fp_pred is a N x 166 matrix where N is the number of graphs in graph_dir and each column corresponds to a fingerprint index. The raw output is a probability, so if you want a binary fingerprint, do as above and use the >0.5 filter.

Training your own model

A basic example is training on the annotated graphs inside data/annotated on default settings.

The trained model and logs will be saved under the ID specified with the -n flag.

$ python learning/main.py -da <name of annotated data folder> -n <run_id>

For a full list of command-line options:

$ python learning/main.py -h