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MuPIPR - Mutation effect estimation on protein-protein interactions using deep contextualized representation learning

This is the repository for the NAR Genom. Bioinform. paper "Mutation effect estimation on protein-protein interactions using deep contextualized representation learning" (MuPIPR). This repository contains the source code and links to some datasets used in our paper. (to be updated)

Pre-requisite

MuPIPR can be run under Linux. The following packages are required: python 3.6, h5py, Tensorflow 1.7 (with GPU support), Keras 2.2.4 and bilm-tf.

Installing

If you don't have python 3.6, please download from here.

Then you can use pip install to install the following packages:

h5py
Tensorflow 1.7 (with GPU support)
Keras 2.2.4

Make sure tensorflow and h5py has been installed successfully before you install bilm-tf. To install bilm-tf, please download the package from [here] (https://github.com/allenai/bilm-tf) and run: python setup.python

Contents

• biLM: contains the pre-trained language models for contextualized amino acid representations.
• data: contains the datasets and processing scripts for the two tasks.
• model: contains the implementation for MuPIPR.

Using the pre-trained amino acid language model for contextualized representation

We obtain the corpus to pre-train the BiLSTM language model from the STRING database. A total of 66235 protein sequences of four most frequent species from the SKEMPI database are extracted, i.e. Homo sapiens, Bos taurus, Mus musculus and Escherichia coli. These are the four most frequent species in the SKP1402m dataset.

To serve the pre-trained contextualized embedding model to MuPIPR, please download and unzip the model.zip file in the biLM folder.

Data processing and model running

Please refer to the readme in the data folder and the model folder, respectively.

Reference

This work has been published in the NAR Genomics and Bioinformatics journal.

DOI: https://doi.org/10.1093/nargab/lqaa015
Bibtex:

@article{zhou2020mupipr,
    title={Mutation Effect Estimation on Protein–protein Interactions Using Deep Contextualized Representation Learning},
    author={Zhou, Guangyu and Chen, Muhao and Ju, Chelsea and Wang, Zheng and Jiang Jyun-yu and Wang, Wei},
    journal={NAR Genomics and Bioinformatics},
    volume = {2},
    number = {2},
    year = {2020},
    month = {03},
    publisher={Oxford University Press}
}

PIPR (ISMB 2019)

Also check out the follow up work in the Bioinformatics (Procs of ISMB) paper Multifaceted Protein-Protein Interaction Prediction Based on Siamese Residual RCNN, in which we provide an end-to-end neural learning system to predict multifaceted PPI information.
The released software is available at muhaochen/seq_ppi.