@article{6fdddbe1d39f40d6bc31cee46cea2833,
title = "AI4AMP: An antimicrobial peptide predictor using physicochemical property-based encoding method and deep learning",
abstract = "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.",
keywords = "Antimicrobial peptide, Deep learning, Protein-encoding method, Real-world data, Web service",
author = "Lin, {Tzu Tang} and Yang, {Li Yen} and Lu, {I. Hsuan} and Cheng, {Wen Chih} and Hsu, {Zhe Ren} and Chen, {Shu Hwa} and Lin, {Chung Yen}",
note = "Funding Information: We thank the Ministry of Science and Technology (MOST), Taiwan, and Academia Sinica, Taiwan, for supporting this research and publication through MOST 108-2314-B-001 -002, MOST 108-2321-B-038 -003, and Grand Challenge Seed Program (206d-1090107), respectively. We also thank the Council of Agriculture, Executive Yuan, Taiwan (109AgriS-12.3.1-S-a2). No conflicts of interest are declared. Funding Information: We thank the Ministry of Science and Technology (MOST), Taiwan, and Academia Sinica, Taiwan, for supporting this research and publication through MOST 108-2314-B-001-002, MOST 108-2321-B-038-003, and Grand Challenge Seed Program (206d-1090107), respectively. We also thank the Council of Agriculture, Executive Yuan, Taiwan (109AgriS-12.3.1-S-a2). Publisher Copyright: Copyright {\textcopyright} 2021 Lin et al.",
year = "2021",
month = dec,
doi = "10.1128/mSystems.00299-21",
language = "English",
volume = "6",
journal = "mSystems",
issn = "2379-5077",
publisher = "American Society for Microbiology",
number = "6",
}