4.7 Article

AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches

期刊

BRIEFINGS IN BIOINFORMATICS
卷 22, 期 6, 页码 -

出版社

OXFORD UNIV PRESS
DOI: 10.1093/bib/bbab263

关键词

machine learning; imbalanced learning; antimicrobial peptide; antiviral peptide

资金

  1. NationalNatural Science Foundation of China [32070659]
  2. Guangdong Province Basic and Applied Basic Research Fund [2021A1515012447]
  3. Ganghong Young Scholar Development Fund [2021E007]
  4. Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China

向作者/读者索取更多资源

Through the AVPIden model, multiple descriptors are utilized to accurately demonstrate peptide properties and explainable machine learning strategies based on Shapley value are adopted to show how the descriptors impact antiviral activities. The evaluation performance of the model indicates its ability to predict antivirus activities and their potential functions against six virus families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight kinds of virus (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). AVPIden provides an option for strengthening the development of AVPs with a computer-aided method, and it has been deployed at http://awi.cuhk.edu.cn/AVPIden/.
Antiviral peptide (AVP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against virus infection. Machine learning-based prediction with a computational biology approach can facilitate the development of the novel therapeutic agents. In this study, we proposed a double-stage classification scheme, named AVPIden, for predicting the AVPs and their functional activities against different viruses. The first stage is to distinguish the AVP from a broad-spectrum peptide collection, including not only the regular peptides (non-AMP) but also the AMPs without antiviral functions (non-AVP). The second stage is responsible for characterizing one or more virus families or species that the AVP targets. Imbalanced learning is utilized to improve the performance of prediction. The AVPIden uses multiple descriptors to precisely demonstrate the peptide properties and adopts explainable machine learning strategies based on Shapley value to exploit how the descriptors impact the antiviral activities. Finally, the evaluation performance of the proposed model suggests its ability to predict the antivirus activities and their potential functions against six virus families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight kinds of virus (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). The AVPIden gives an option for reinforcing the development of AVPs with the computer-aided method and has been deployed at http://awi.cuhk.edu.cn/AVPIden/.

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