PrMFTP

multi-label therapeutic peptides prediction based on multi-head self-attention mechanism and class weights optimization

Introduction

Motivation:

The peptide drugs has wide applications, such as lowering blood glucose levels, blood pressure, reducing inflammation, and resistance diseases. However, it is exceedingly expensive and time-consuming for experiments to determine the function of numerous peptides. Therefore, computational methods such as machine learning are becoming more and more important for peptide function prediction. Meanwhile, there are finitude research about applying the deep learning method to address multi-label therapeutic peptide prediction. In this study, we develop a deep learning approach that determining the peptide function.

Results:

We propose a multi-label classification model, named PrMFTP, to predict peptide function. In the field of functional peptide prediction, compared with state-of-the-art single-label predictor, PrMFTP can predict multiple functions including AAP, anti-angiogenic peptide、ABP, anti-bacterial peptide、 ACP, Anti-cancer peptide、 ACVP, Anti-coronavirus peptide、 ADP, Anti-diabetic peptide、 AEP, Anti-endotoxin peptide、 AFP, Anti-fungal peptide、 Anti-HIV peptide, AHIVP、 AHP, Anti-hypertensive peptide、 AIP, Anti-inflammatory peptide、 AMRSAP, Anti-MRSA peptide、 APP, Anti-parasitic peptide、 ATP, Anti-tubercular peptide、 AVP, Anti-viral peptide、 BBP, Blood-brain Barrier peptide、 BIP, Biofilm-inhibitory peptide、 CPP, Cell-penetrating peptide、 DPPIP, Dipeptidyl Peptidase IV peptide、 QSP, Quorum-sensing peptide、 SBP, Surface-binding peptide、 THP, Tumor-homing peptide simultaneously. Meanwhile, our model takes the raw sequence vector as input to replace biological features encoding from peptide sequences, extracts convolution and sequence features from the raw sequence, and combines with recurrent neural network to extract global features of sequences, than the multi head self attention mechanism is used to optimize the extracted features, so as to provide the prediction sequence of the model, and the class weight is used to solve the problem of data imbalance. The validation experiments conducted on the dataset show that PrMFTP has superior prediction performance.

Related Files

PrMFTP

FILE NAME	DESCRIPTION
main.py	the main file of PrMFTP predictor (include data reading, encoding, and data partitioning)
train.py	train model
model.py	model construction
test.py	test model result
evaluation.py	evaluation metrics (for evaluating prediction results)
dataset	data
model	models of PrMFTP
result	result of PrMFTP

Installation

• Requirement

  OS：

  • Windows ：Windows10 or later

  • Linux：Ubuntu 16.04 LTS or later

  Python：

  • Python >= 3.6

• Download MLTPto your computer

  git clone https://github.com/xialab-ahu/PrMFTP.git

• open the dir and install requirement.txt with pip

  cd PrMFTP
  pip install -r requirement.txt
  

Run PrMFTP on a new test fasta file

python predictor.py --file test.fasta --out_path result

• --file : input the test file with fasta format

• --out_path: the output path of the predicted results

Contact

Please feel free to contact us if you need any help.