Abstract
In spite of the large-scale production and widespread distribution of vaccines and antiviral drugs, viruses remain a prominent human disease. Recently, the discovery of antiviral peptides (AVPs) has become an influential antiviral agent due to their extraordinary advantages. With the avalanche of newly-found peptide sequences in the post-genomic era, there is a great demand to develop a sequence-based predictor for timely identifying AVPs as this information is very useful for both basic research and drug development. In this study, we propose a novel sequence-based meta-predictor with an effective feature representation, called Meta-iAVP, for the accurate prediction of AVPs from given peptide sequences. Herein, the effective feature representation was extracted from a set of prediction scores derived from various machine learning algorithms and types of features. To the best of our knowledge, the model proposed herein represents the first meta-based approach for the prediction of AVPs. An overall accuracy and Matthews correlation coefficient of 95.20% and 0.90, respectively, was achieved from the independent test set on an objective benchmark dataset. Comparative analysis suggested that Meta-iAVP was superior to that of existing methods and therefore represents a useful tool for AVP prediction. Finally, in an effort to facilitate high-throughput prediction of AVPs, the model was deployed as the Meta-iAVP web server and is made freely available online at http://codes.bio/meta-iavp/ where users can submit query peptide sequences for determining the likelihood of whether or not these peptides are AVPs.
Highlights
Human morbidity, mortality, and economic productivity continue to be affected by viral infections and their associated diseases
The features that are beneficial for discriminating antiviral peptides (AVPs) from Non-AVPs were determined by conducting performance comparisons between five types of features, i.e., amino acid composition (AAC) (20D), dipeptide composition (DPC) (400D), gap dipeptide composition (GDC) (400D), pseudo amino acid composition (PseAAC) (20 + 2λ), and Am- PseAAC (20 + 2λ), and six commonly used machine learning (ML) algorithms
To serve easy and rapid classification of query peptide sequence, Meta-iAVP is exploited as a free prediction web server for discriminating AVPs and Non-AVPs
Summary
Mortality, and economic productivity continue to be affected by viral infections and their associated diseases. The dominance of sporadic viral outbreaks by zoonotic viruses such as Ebola and Zika in recent years have added to the prevalence of viral species with which humans are already in battle (i.e., human immunodeficiency virus (HIV), rhinoviruses, and influenza viruses). Major breakthroughs in combating viral infections by vaccine production have led to remarkable advances in modern medicine such as the eradication and control of disease such as small pox [5] and polio [6], respectively. The ever-increasing reports of antiviral resistance [8,9,10] coupled with the emergence and re-emergence of viral epidemics as observed for H1N1 [11], Ebola [12], and Zika [13] viruses, demands the production of new antiviral drugs with broad-spectrum activity [14]. Besides the advantages of peptide-based drugs, a short half-life, immunogenic potential, and low oral absorption are some of their current limitations [16]
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