DsAMP and DsAMPGAN: Deep Learning Networks for Antimicrobial Peptides Recognition and Generation

Overview

Journal Antibiotics (Basel)

Specialty Pharmacology

Date 2024 Oct 25

PMID 39452213

Authors

Min Zhao

Yu Zhang

Maolin Wang

Luyan Z Ma

Affiliations

Soon will be listed here.

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.

References

Dathe M, Wieprecht T . Structural features of helical antimicrobial peptides: their potential to modulate activity on model membranes and biological cells. Biochim Biophys Acta. 1999; 1462(1-2):71-87. DOI: 10.1016/s0005-2736(99)00201-1. View

Shen H, Chou K . PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Anal Biochem. 2007; 373(2):386-8. DOI: 10.1016/j.ab.2007.10.012. View

Wang G, Li X, Wang Z . APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids Res. 2015; 44(D1):D1087-93. PMC: 4702905. DOI: 10.1093/nar/gkv1278. View

DCosta V, King C, Kalan L, Morar M, Sung W, Schwarz C . Antibiotic resistance is ancient. Nature. 2011; 477(7365):457-61. DOI: 10.1038/nature10388. View

Dong G, Zheng L, Huang S, Gao J, Zuo Y . Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities. Front Genet. 2021; 12:669328. PMC: 8093877. DOI: 10.3389/fgene.2021.669328. View

Ridyard K, Overhage J . The Potential of Human Peptide LL-37 as an Antimicrobial and Anti-Biofilm Agent. Antibiotics (Basel). 2021; 10(6). PMC: 8227053. DOI: 10.3390/antibiotics10060650. View

Chung C, Kuo T, Wu L, Lee T, Horng J . Characterization and identification of antimicrobial peptides with different functional activities. Brief Bioinform. 2019; . DOI: 10.1093/bib/bbz043. View

Dean S, Walper S . Variational Autoencoder for Generation of Antimicrobial Peptides. ACS Omega. 2020; 5(33):20746-20754. PMC: 7450509. DOI: 10.1021/acsomega.0c00442. View

Gu Z, Hao Y, Wang T, Cai P, Zhang Y, Deng K . Prediction of blood-brain barrier penetrating peptides based on data augmentation with Augur. BMC Biol. 2024; 22(1):86. PMC: 11027412. DOI: 10.1186/s12915-024-01883-4. View

10.

Waghu F, Gopi L, Barai R, Ramteke P, Nizami B, Idicula-Thomas S . CAMP: Collection of sequences and structures of antimicrobial peptides. Nucleic Acids Res. 2013; 42(Database issue):D1154-8. PMC: 3964954. DOI: 10.1093/nar/gkt1157. View

11.

Lin T, Yang L, Lin C, Wang C, Lai C, Ko C . Intelligent De Novo Design of Novel Antimicrobial Peptides against Antibiotic-Resistant Bacteria Strains. Int J Mol Sci. 2023; 24(7). PMC: 10095442. DOI: 10.3390/ijms24076788. View

12.

Jhong J, Yao L, Pang Y, Li Z, Chung C, Wang R . dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data. Nucleic Acids Res. 2021; 50(D1):D460-D470. PMC: 8690246. DOI: 10.1093/nar/gkab1080. View

13.

Li W, Godzik A . Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006; 22(13):1658-9. DOI: 10.1093/bioinformatics/btl158. View

14.

Pirtskhalava M, Amstrong A, Grigolava M, Chubinidze M, Alimbarashvili E, Vishnepolsky B . DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics. Nucleic Acids Res. 2020; 49(D1):D288-D297. PMC: 7778994. DOI: 10.1093/nar/gkaa991. View

15.

Lin T, Sun Y, Wang C, Cheng W, Lu I, Lin C . AI4AVP: an antiviral peptides predictor in deep learning approach with generative adversarial network data augmentation. Bioinform Adv. 2023; 2(1):vbac080. PMC: 9710571. DOI: 10.1093/bioadv/vbac080. View

16.

Kang X, Dong F, Shi C, Liu S, Sun J, Chen J . DRAMP 2.0, an updated data repository of antimicrobial peptides. Sci Data. 2019; 6(1):148. PMC: 6692298. DOI: 10.1038/s41597-019-0154-y. View

17.

Wang P, Hu L, Liu G, Jiang N, Chen X, Xu J . Prediction of antimicrobial peptides based on sequence alignment and feature selection methods. PLoS One. 2011; 6(4):e18476. PMC: 3076375. DOI: 10.1371/journal.pone.0018476. View

18.

Xing W, Zhang J, Li C, Huo Y, Dong G . iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model. Brief Bioinform. 2023; 25(1). PMC: 10699745. DOI: 10.1093/bib/bbad443. View

19.

Lobo F, Gonzalez M, Boto A, Perez de la Lastra J . Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models. Int J Mol Sci. 2023; 24(12). PMC: 10299371. DOI: 10.3390/ijms241210270. View

20.

Zhang H, Liao L, Cai Y, Hu Y, Wang H . IVS2vec: A tool of Inverse Virtual Screening based on word2vec and deep learning techniques. Methods. 2019; 166:57-65. DOI: 10.1016/j.ymeth.2019.03.012. View