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• W2097834518 abstract "In the battle against viruses, antiviral peptides (AVPs) had demonstrated the immense potential. Presently, more than 15 peptide-based drugs are in various stages of clinical trials. Emerging and re-emerging viruses further emphasize the efforts to accelerate antiviral drug discovery efforts. Despite, huge importance of the field, no dedicated AVP resource is available. In the present study, we have collected 1245 peptides which were experimentally checked for antiviral activity targeting important human viruses like influenza, HIV, HCV and SARS, etc. After removing redundant peptides, 1056 peptides were divided into 951 training and 105 validation data sets. We have exploited various peptides sequence features, i.e. motifs and alignment followed by amino acid composition and physicochemical properties during 5-fold cross validation using Support Vector Machine. Physiochemical properties-based model achieved maximum 85% accuracy and 0.70 Matthew's Correlation Coefficient (MCC). Performance of this model on the experimental validation data set showed 86% accuracy and 0.71 MCC which is far better than the general antimicrobial peptides prediction methods. Therefore, AVPpred-the first web server for predicting the highly effective AVPs would certainly be helpful to researchers working on peptide-based antiviral development. The web server is freely available at http://crdd.osdd.net/servers/avppred." @default.
• W2097834518 created "2016-06-24" @default.
• W2097834518 creator A5047954288 @default.
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• W2097834518 date "2012-05-24" @default.
• W2097834518 modified "2023-10-18" @default.
• W2097834518 title "AVPpred: collection and prediction of highly effective antiviral peptides" @default.
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• W2097834518 doi "https://doi.org/10.1093/nar/gks450" @default.
• W2097834518 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3394244" @default.
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