AntiVPP 1.0: A portable tool for prediction of antiviral peptides

Jorge Félix Beltrán Lissabet; Lisandra Herrera Belén; Jorge G Farias

doi:10.1016/j.compbiomed.2019.02.011

AntiVPP 1.0: A portable tool for prediction of antiviral peptides

Comput Biol Med. 2019 Apr:107:127-130. doi: 10.1016/j.compbiomed.2019.02.011. Epub 2019 Feb 19.

Authors

Jorge Félix Beltrán Lissabet¹, Lisandra Herrera Belén¹, Jorge G Farias²

Affiliations

¹ Universidad de La Frontera, Department of Chemical Engineering, Faculty of Engineering and Science, Temuco, Chile.
² Universidad de La Frontera, Department of Chemical Engineering, Faculty of Engineering and Science, Temuco, Chile. Electronic address: jorge.farias@ufrontera.cl.

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.

Keywords: Antiviral; Machine learning; Peptide; Prediction; Python; Software.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Antiviral Agents*
Computational Biology / methods
Databases, Protein
Decision Trees
Machine Learning*
Peptides*
Software*
User-Computer Interface

Substances

Antiviral Agents
Peptides