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Open AccessArticle
dsAMP and dsAMPGAN: Deep Learning Networks for Antimicrobial Peptides Recognition and Generation
by
Min Zhao
Min Zhao 1,2,
Yu Zhang
Yu Zhang 1,3,
Maolin Wang
Maolin Wang 4,* and
Luyan Z. Ma
Luyan Z. Ma 1,2,*
1
State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, 100101 Beijing, China
2
University of Chinese Academy of Sciences, 100049 Beijing, China
3
Department of Bioscience and Biotechnology, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, 744 Motooka, Nishi-ku, 819-0395 Fukuoka, Japan
4
CAAC Key Laboratory of General Aviation Operation, Civil Aviation Management Institute of China, 100102 Beijing, China
*
Authors to whom correspondence should be addressed.
Antibiotics 2024, 13(10), 948; https://doi.org/10.3390/antibiotics13100948 (registering DOI)
Submission received: 10 August 2024
/
Revised: 3 October 2024
/
Accepted: 3 October 2024
/
Published: 9 October 2024
Abstract
Antibiotic resistance is a growing public health challenge. Antimicrobial peptides (AMPs) effectively target microorganisms through non-specific mechanisms, limiting their ability to develop resistance. Therefore, the prediction and design of new AMPs is crucial. Recently, deep learning has spurred interest in computational approaches to peptide drug discovery. This study presents a novel deep learning framework for AMP classification, function prediction, and generation. We developed discoverAMP (dsAMP), a robust AMP predictor using CNN Attention BiLSTM and transfer learning, which outperforms existing classifiers. In addition, dsAMPGAN, a Generative Adversarial Network (GAN)-based model, generates new AMP candidates. Our results demonstrate the superior performance of dsAMP in terms of sensitivity, specificity, Matthew correlation coefficient, accuracy, precision, F1 score, and area under the ROC curve, achieving >95% classification accuracy with transfer learning on a small dataset. Furthermore, dsAMPGAN successfully synthesizes AMPs similar to natural ones, as confirmed by comparisons of physical and chemical properties. This model serves as a reliable tool for the identification of novel AMPs in clinical settings and supports the development of AMPs to effectively combat antibiotic resistance.
Share and Cite
MDPI and ACS Style
Zhao, M.; Zhang, Y.; Wang, M.; Ma, L.Z.
dsAMP and dsAMPGAN: Deep Learning Networks for Antimicrobial Peptides Recognition and Generation. Antibiotics 2024, 13, 948.
https://doi.org/10.3390/antibiotics13100948
AMA Style
Zhao M, Zhang Y, Wang M, Ma LZ.
dsAMP and dsAMPGAN: Deep Learning Networks for Antimicrobial Peptides Recognition and Generation. Antibiotics. 2024; 13(10):948.
https://doi.org/10.3390/antibiotics13100948
Chicago/Turabian Style
Zhao, Min, Yu Zhang, Maolin Wang, and Luyan Z. Ma.
2024. "dsAMP and dsAMPGAN: Deep Learning Networks for Antimicrobial Peptides Recognition and Generation" Antibiotics 13, no. 10: 948.
https://doi.org/10.3390/antibiotics13100948
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