Le Fu, Xu Zheng, Jiawen Luo, Yiyu Zhang, Xue Gao, Li Jin, Wenting Liu, Chaoqun Zhang, Dongyu Gao, Bocheng Xu, Qingru Jiang, Shuli Chou, Liang Luo
{"title":"Machine Learning Accelerates the Discovery of Epitope-based Dual-bioactive Peptides Against Skin Infections.","authors":"Le Fu, Xu Zheng, Jiawen Luo, Yiyu Zhang, Xue Gao, Li Jin, Wenting Liu, Chaoqun Zhang, Dongyu Gao, Bocheng Xu, Qingru Jiang, Shuli Chou, Liang Luo","doi":"10.1016/j.ijantimicag.2024.107371","DOIUrl":null,"url":null,"abstract":"<p><p>Skin injuries and infections are an inevitable part of daily human life, particularly with chronic wounds, becoming an increasing socioeconomic burden. In treating skin infections and promoting wound healing, bioactive peptides may hold significant potential, particularly those possessing antimicrobial and anti-inflammatory properties. However, obtaining these peptides solely through traditional wet laboratory experiments is costly and time-consuming, and peptides identified by current computer-assisted predictive models largely lack validation of their effects via wet laboratory experiments. Consequently, this study aimed to integrate computer-assisted methods and traditional wet laboratory experiments to identify anti-inflammatory and antimicrobial peptides. We developed a computer-assisted mining pipeline to screen potential peptides from the epitopes of the major histocompatibility complex class II (MHC-II). The peptide AIMP1 was identified, with the ability to physically damage Escherichia coli by increasing bacterial cell membrane permeability and with the ability to inhibit inflammation by binding to endotoxin-lipopolysaccharide. Additionally, in an LPS-induced inflammation animal model, AIMP1 slightly increased levels of proinflammatory cytokines (TNF-α, IL-1β, and IL-6), and in a skin wound infection animal model, AIMP1 effectively accelerated healing, reduced levels of these pro-inflammatory cytokines, and showed no acute hepatotoxicity or nephrotoxicity. In conclusion, this study not only developed a computer-assisted mining pipeline for identifying anti-inflammatory and antimicrobial peptides but also successfully pinpointed the peptide AIMP1, demonstrating its therapeutic potential for skin injury treatment.</p>","PeriodicalId":13818,"journal":{"name":"International Journal of Antimicrobial Agents","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Antimicrobial Agents","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.ijantimicag.2024.107371","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}
引用次数: 0
Abstract
Skin injuries and infections are an inevitable part of daily human life, particularly with chronic wounds, becoming an increasing socioeconomic burden. In treating skin infections and promoting wound healing, bioactive peptides may hold significant potential, particularly those possessing antimicrobial and anti-inflammatory properties. However, obtaining these peptides solely through traditional wet laboratory experiments is costly and time-consuming, and peptides identified by current computer-assisted predictive models largely lack validation of their effects via wet laboratory experiments. Consequently, this study aimed to integrate computer-assisted methods and traditional wet laboratory experiments to identify anti-inflammatory and antimicrobial peptides. We developed a computer-assisted mining pipeline to screen potential peptides from the epitopes of the major histocompatibility complex class II (MHC-II). The peptide AIMP1 was identified, with the ability to physically damage Escherichia coli by increasing bacterial cell membrane permeability and with the ability to inhibit inflammation by binding to endotoxin-lipopolysaccharide. Additionally, in an LPS-induced inflammation animal model, AIMP1 slightly increased levels of proinflammatory cytokines (TNF-α, IL-1β, and IL-6), and in a skin wound infection animal model, AIMP1 effectively accelerated healing, reduced levels of these pro-inflammatory cytokines, and showed no acute hepatotoxicity or nephrotoxicity. In conclusion, this study not only developed a computer-assisted mining pipeline for identifying anti-inflammatory and antimicrobial peptides but also successfully pinpointed the peptide AIMP1, demonstrating its therapeutic potential for skin injury treatment.
期刊介绍:
The International Journal of Antimicrobial Agents is a peer-reviewed publication offering comprehensive and current reference information on the physical, pharmacological, in vitro, and clinical properties of individual antimicrobial agents, covering antiviral, antiparasitic, antibacterial, and antifungal agents. The journal not only communicates new trends and developments through authoritative review articles but also addresses the critical issue of antimicrobial resistance, both in hospital and community settings. Published content includes solicited reviews by leading experts and high-quality original research papers in the specified fields.