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<p>The human skin microbiota, comprising a diverse range of microorganisms, including bacteria, viruses, and fungi, plays an important role in maintaining skin health and protecting against pathogenic invasions. Among these microorganisms, certain bacteria produce bacteriocins, which are ribosomal peptides with potent antimicrobial properties. This study presents a novel computational approach to identify and predict bacteriocins from microbial genomes comprising sebaceous region of the skin, aiming to explore their therapeutic potential. Through genome analysis using advanced bioinformatics tools, we identified potential genes, operons, open reading frames (ORFs), and promoter regions linked to bacteriocin production. The BAGEL4 platform was employed to detect structural bacteriocin genes, while modelling bacterial growth and bacteriocin expression under various environmental conditions was conducted using MATLAB’s SimBiology application. The results revealed the optimal conditions for bacteriocin production and highlighted promising candidates for further experimental validation. These findings underscore the significance of skin microbiota as a source of novel bacteriocins, offering potential alternatives to traditional antibiotics amidst rising antimicrobial resistance.</p>

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Bioinformatics-driven discovery of skin microbiota bacteriocins as potential antibiotics and probiotics

  • 期刊论文

  • 发表日期：

    2025年7月24日

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  • Anum Munir,
  • Alan Janbey,
  • Bilawal Sajjad,
  • Kowsalya V. Perumal,
  • Hajra Qayyum,
  • Marriam Bakhtiar

摘要

The human skin microbiota, comprising a diverse range of microorganisms, including bacteria, viruses, and fungi, plays an important role in maintaining skin health and protecting against pathogenic invasions. Among these microorganisms, certain bacteria produce bacteriocins, which are ribosomal peptides with potent antimicrobial properties. This study presents a novel computational approach to identify and predict bacteriocins from microbial genomes comprising sebaceous region of the skin, aiming to explore their therapeutic potential. Through genome analysis using advanced bioinformatics tools, we identified potential genes, operons, open reading frames (ORFs), and promoter regions linked to bacteriocin production. The BAGEL4 platform was employed to detect structural bacteriocin genes, while modelling bacterial growth and bacteriocin expression under various environmental conditions was conducted using MATLAB’s SimBiology application. The results revealed the optimal conditions for bacteriocin production and highlighted promising candidates for further experimental validation. These findings underscore the significance of skin microbiota as a source of novel bacteriocins, offering potential alternatives to traditional antibiotics amidst rising antimicrobial resistance.