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Boosted Prediction of Antihypertensive Peptides Using Deep Learning

Authors :
Anum Rauf
Aqsa Kiran
Malik Tahir Hassan
Sajid Mahmood
Ghulam Mustafa
Moongu Jeon
Source :
Applied Sciences, Vol 11, Iss 5, p 2316 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Heart attack and other heart-related diseases are among the main causes of fatalities in the world. These diseases and some other severe problems like kidney failure and paralysis are mainly caused by hypertension. Since bioactive peptides extracted from naturally existing food substances possess antihypertensive activity, these antihypertensive peptides (AHTP) can function as prospective replacements for existing pharmacological drugs with no or fewer side effects. Such naturally existing peptides can be identified using in-silico approaches. The in-silico methods have been proven to save huge amounts of time and money in the identification of effective peptides. The proposed methodology is a deep learning-based in-silico approach for the identification of antihypertensive peptides (AHTPs). An ensemble method is proposed that combines convolutional neural network (CNN) and support vector machine (SVM) classifiers. Amino acid composition (AAC) and g-gap dipeptide composition (DPC) techniques are used for feature extraction. The proposed methodology has been evaluated on two standard antihypertensive peptide sequence datasets. The model yields 95% accuracy on the benchmarking dataset and 88.9% accuracy on the independent dataset. Comparative analysis is provided to demonstrate that the proposed method outperforms existing state-of-the-art methods on both of the benchmarking and independent datasets.

Details

Language :
English
ISSN :
20763417
Volume :
11
Issue :
5
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.9affaa49cfd4754998da95c694e0e8a
Document Type :
article
Full Text :
https://doi.org/10.3390/app11052316