Journal article
A Conditional Denoising VAE-based Framework for Antimicrobial Peptides Generation with Preserving Desirable Properties
Bioinformatics (Oxford, England), v 41(2), btaf069
11 Feb 2025
PMID: 39932977
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The widespread use of antibiotics has led to the emergence of resistant pathogens. Antimicrobial peptides (AMPs) combat bacterial infections by disrupting the integrity of cell membranes, making it challenging for bacteria to develop resistance. Consequently, AMPs offer a promising solution to addressing antibiotic resistance. However, the limited availability of natural AMPs cannot meet the growing demand. While deep learning technologies have advanced AMP generation, conventional models often lack stability and may introduce unforeseen side effects.
This study presents a novel denoising VAE-based model guided by desirable physicochemical properties for AMPs generation. The model integrates key features (e.g., molecular weight, isoelectric point, hydrophobicity, etc.), and employs position encoding along with a Transformer architecture to enhance generation accuracy. A customized loss function, combining reconstruction loss, KL divergence, and property preserving loss, ensures effective model training. Additionally, the model incorporates a denoising mechanism, enabling it to learn from perturbed inputs, thus maintaining performance under limited training data. Experimental results demonstrate that the proposed model can generate AMPs with desirable functional properties, offering a viable approach for AMP design and analysis, which ultimately contributes to the fight against antibiotic resistance.
The data and source codes are available both in GitHub (https://github.com/David-WZhao/PPGC-DVAE) and Zenodo (DOI 10.5281/zenodo.14730711).
wzzhao@ccnu.edu.cn, and Supplementary materials are available at Bioinformatics online.
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Details
- Title
- A Conditional Denoising VAE-based Framework for Antimicrobial Peptides Generation with Preserving Desirable Properties
- Creators
- Weizhong Zhao (Corresponding Author) - Central China Normal UniversityKaijieyi Hou - Central China Normal UniversityYiting Shen - Hubei University of TechnologyXiaohua Hu - Drexel University
- Publication Details
- Bioinformatics (Oxford, England), v 41(2), btaf069
- Publisher
- Oxford University Press
- Number of pages
- 9
- Grant note
- National Natural Science Foundation of China: 62472192, 62372205, 61932008
This work was partially supported by the National Natural Science Foundation of China (62472192, 62372205, and 61932008).
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Web of Science ID
- WOS:001431845100001
- Scopus ID
- 2-s2.0-85218892259
- Other Identifier
- 991022028082104721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Statistics & Probability