mACPpred2

Standalone program for the paper "mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations"

Introduction • Installation • Getting Started • Citation • References

Introduction

This repository provides the standalone program that was added to the mACPpred 2.0 web server at https://balalab-skku.org/mACPpred2/. The baseline and final models are available via Zenodo at

• Development: https://github.com/nhattruongpham/mACPpred2.git
• Release: https://github.com/cbbl-skku-org/mACPpred2.git

Installation

Software requirements

• Ubuntu 20.04.6 LTS (This source code has been already tested on Ubuntu)
• CUDA 11.7 (with GPU suport)
• cuDNN 8.6.0.163 (with GPU support)
• Python 3.9

Creating conda environment

conda create -n mACPpred2 python=3.9.12

conda activate mACPpred2

Installing TensorFlow with CUDA support

conda install -c conda-forge cudatoolkit=11.7.0

python -m pip install nvidia-cudnn-cu11==8.6.0.163 --no-cache-dir

python -m pip install tensorflow==2.11.* --no-cache-dir

python -m pip install chardet --no-cache-dir

conda install anaconda::numpy-base

mkdir -p $CONDA_PREFIX/etc/conda/activate.d

echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn; print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib:$CUDNN_PATH/lib' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

source $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

Installing bio-embeddings^[1] and re-installing PyTorch with CUDA support

python -m pip install --upgrade pip setuptools wheel --no-cache-dir

python -m pip install gensim==3.8 --use-pep517 --no-cache-dir

python -m pip install bio-embeddings[seqvec] --no-cache-dir

python -m pip install scipy==1.10.1 --no-cache-dir

python -m pip install protobuf==3.20.* --no-cache-dir

python -m pip install bio-embeddings[all] --no-cache-dir

python -m pip uninstall numpy

python -m pip install numpy==1.26.0 --no-cache-dir

conda install anaconda::numpy-base

python -m pip uninstall torch

python -m pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117 --no-cache-dir

Installing required specific packages

python -m pip install peptidy==0.0.1 --no-cache-dir

python -m pip install protlearn==0.0.3 --no-cache-dir

python -m pip install catboost==1.2 lightgbm==3.3.5 scikit-learn==0.24.2 xgboost==0.82 --no-cache-dir

Getting started

Cloning this repository

git clone https://github.com/nhattruongpham/mACPpred2.git

cd mACPpred2

Downloading basline and final models

• Please download the baseline and final models via Zenodo at
• For the baseline models, please extract and put all *.pkl files into the models/baseline_models folder.
• For the final models, please please extract and put all *.h5 files into models/final_models folder.

Running prediction

Usage

CUDA_VISIBLE_DEVICES=<GPU_NUMBER> python predictor.py --input_file <PATH_TO_INPUT_FILE> --output_file <PATH_TO_OUTPUT_FILE>

Example

CUDA_VISIBLE_DEVICES=0 python predictor.py --input_file examples/test.fasta --output_file result.csv

Citation

If you use this code or part of it, please cite the following papers:

Main

@article{sangaraju2024macppred,
  title={mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations},
  author={Sangaraju, Vinoth Kumar and Pham, Nhat Truong and Wei, Leyi and Yu, Xue and Manavalan, Balachandran},
  journal={Journal of Molecular Biology},
  volume={436},
  number={17},
  pages={168687},
  year={2024},
  publisher={Elsevier}
}

Zenodo

@software{sangaraju_2024_11350064,
  author       = {Sangaraju, Vinoth Kumar and
                  Pham, Nhat Truong and
                  Manavalan, Balachandran},
  title        = {{mACPpred 2.0: Stacked deep learning for anticancer 
                   peptide prediction with integrated spatial and
                   probabilistic feature representations}},
  month        = may,
  year         = 2024,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.11350064},
  url          = {https://doi.org/10.5281/zenodo.11350064}
}

References

[1] Dallago, C., Schütze, K., Heinzinger, M., Olenyi, T., Littmann, M., Lu, A. X., Yang, K. K., Min, S., Yoon, S., Morton, J. T., & Rost, B. (2021). Learned embeddings from deep learning to visualize and predict protein sets. Current Protocols, 1, e113.
[2] Özçelik, R., van Weesep, L., de Ruiter, S., & Grisoni, F. (2024). peptidy: A light-weight Python library for peptide representation in machine learning.
[3] Dorfer, T. (2021). protlearn: A Python package for extracting protein sequence features. (v0.0.3 on Mar 24, 2021) URL: https://github.com/tadorfer/protlearn.