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Title: Meta-iavp: A sequence-based meta-predictor for improving the prediction of antiviral peptides using effective feature representation
Authors: Nalini Schaduangrat
Chanin Nantasenamat
Virapong Prachayasittikul
Watshara Shoombuatong
Mahidol University
Keywords: Biochemistry, Genetics and Molecular Biology;Chemical Engineering;Chemistry;Computer Science
Issue Date: 2-Nov-2019
Citation: International Journal of Molecular Sciences. Vol.20, No.22 (2019)
Abstract: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. 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 where users can submit query peptide sequences for determining the likelihood of whether or not these peptides are AVPs.
ISSN: 14220067
Appears in Collections:Scopus 2019

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