Using Chou’s Pseudo Amino Acid Composition and Machine Learning Method to Predict the Antiviral Peptides
Maryam Zare1, Hassan Mohabatkar1, *, Fateme Kazemi Faramarzi2, Majid Mohammad Beigi2, Mandana Behbahani1
Identifiers and Pagination:Year: 2015
First Page: 13
Last Page: 19
Publisher ID: TOBIOIJ-9-13
Article History:Received Date: 18/09/2014
Revision Received Date: 05/12/2014
Acceptance Date: 23/12/2014
Electronic publication date: 31/03/2015
Collection year: 2015
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Traditional antiviral therapies are expensive, limitedly available, and cause several side effects. Currently, designing antiviral peptides is very important, because these peptides interfere with the key stage of virus life cycle. Most of the antiviral peptides are derived from viral proteins for example peptide derived from HIV-1 capsid protein. Because of the importance of these peptides, in this study the concept of pseudo-amino acid composition (PseAAC) and machine learning methods are used to classify or identify antiviral peptides.