AntiVPP 1.0: A Portable Tool for Prediction of Antiviral Peptides

Overview

Journal Comput Biol Med

Publisher Elsevier

Specialties Biology
General Medicine
Medical Informatics

Date 2019 Feb 26

PMID 30802694

Citations 24

Authors

Jorge Felix Beltran Lissabet

Lisandra Herrera Belen

Jorge G Farias

Affiliations

Soon will be listed here.

Abstract

Viruses are worldwide pathogens with a high impact on the human population. Despite the constant efforts to fight viral infections, there is a need to discover and design new drug candidates. Antiviral peptides are molecules with confirmed activity and constitute excellent alternatives for the treatment of viral infections. In the present study, we developed AntiVPP 1.0, an accurate bioinformatic tool that uses the Random Forest algorithm for antiviral peptide predictions. The model of AntiVPP 1.0 for antiviral peptide predictions uses several features of 1088 peptides for training and validation. During the validation of the model we achieved the TPR = 0.87, SPC = 0.97, ACC = 0.93 and MCC = 0.87 performance measures, which were indicative of a robust model. AntiVPP 1.0 is a fast, accurate and intuitive software focused on the assessment of antiviral peptides candidates. AntiVPP 1.0 is available at https://github.com/bio-coding/AntiVPP.

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