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Microorganisms play a fundamental role in vaccine development, gene therapy, and therapeutic production. Their biological properties are harnessed to advance medicine and public health. Beyond immunization, microorganisms contribute to gut health, antibiotic synthesis, and genetic disease treatment.Live Attenuated and Inactivated VaccinesLive attenuated vaccines, such as the measles, mumps, and rubella (MMR) vaccine, utilize weakened forms of pathogens to closely resemble natural infections.
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VirulentHunter: deep learning-based virulence factor predictor illuminates pathogenicity in diverse microbial

Chen Chen1,2, Yong Xu1, Jian Ouyang1,3

  • 1Center for Bioinformatics and Computational Biology, and The Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Dongchuan Road 500, Shanghai 200241, China.

Briefings in Bioinformatics
|June 16, 2025
PubMed
Summary
This summary is machine-generated.

VirulentHunter, a deep learning tool, accurately identifies and classifies bacterial virulence factors (VFs) from protein sequences, outperforming existing methods. It aids in understanding pathogen differences and gut microbiome health.

Keywords:
deep learninghomology independentmicrobial pathogenicitymulti-purpose toolvirulence factors

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Area of Science:

  • Computational Biology
  • Bacterial Pathogenesis
  • Machine Learning in Microbiology

Background:

  • Virulence factors (VFs) are key to bacterial pathogenicity.
  • Current homology-based methods struggle with novel or divergent VFs.
  • Existing machine learning approaches often lack functional classification of VFs.

Purpose of the Study:

  • To introduce VirulentHunter, a deep learning framework for simultaneous VF identification and functional classification.
  • To leverage fine-tuned protein language models for enhanced VF prediction.
  • To provide a versatile tool for analyzing VFs across various biological contexts.

Main Methods:

  • Development of VirulentHunter, a deep learning framework utilizing fine-tuned protein language models.
  • Curation of a comprehensive VF database with expanded category annotations.
  • Benchmarking against existing VF identification methods.

Main Results:

  • VirulentHunter demonstrates superior performance, especially in identifying VFs without detectable homologs.
  • Strain-level analysis revealed distinct pathogenicity profiles between Mycobacterium tuberculosis and Mycobacterium avium.
  • Metagenomic analysis of IBD patient gut microbiota showed a depletion of VFs linked to immune homeostasis.

Conclusions:

  • VirulentHunter is a powerful and versatile tool for virulence factor analysis.
  • The framework enables nuanced understanding of bacterial pathogenicity and host-microbiome interactions.
  • A freely accessible web service is provided for broader research community access.