AI4AMP: An antimicrobial peptide predictor using physicochemical property-based encoding method and deep learning

Tzu Tang Lin, Li Yen Yang, I. Hsuan Lu, Wen Chih Cheng, Zhe Ren Hsu, Shu Hwa Chen, Chung Yen Lin

研究成果: 雜誌貢獻文章同行評審

1 引文 斯高帕斯(Scopus)

摘要

Antimicrobial peptides (AMPs) are innate immune components that have recently stimulated considerable interest among drug developers due to their potential as antibiotic substitutes. AMPs are notable for their fundamental properties of microbial membrane structural interference and the biomedical applications of killing or suppressing microbes. New AMP candidates must be developed to oppose antibiotic resistance. However, the discovery of novel AMPs through wet-lab screening approaches is inefficient and expensive. The prediction model investigated in this study may help accelerate this process. We collected both the up-to-date AMP data set and unbiased negatives based on which the protein-encoding methods and deep learning model for AMPs were investigated. The external testing results indicated that our trained model achieved 90% precision, outperforming current methods. We implemented our model on a user-friendly web server, AI4AMP, to accurately predict the antimicrobial potential of a given protein sequence and perform proteome screening.

原文英語
文章編號e00299-21
期刊mSystems
6
發行號6
DOIs
出版狀態已發佈 - 12月 2021

ASJC Scopus subject areas

  • 微生物學
  • 生態學、進化論、行為學與系統學
  • 生物化學
  • 生理學
  • 建模與模擬
  • 分子生物學
  • 遺傳學
  • 電腦科學應用

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