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Fungal virulence factors datasets for inflammatory bowel disease-specific antifungal drug discovery

Shuo Feng1, Yi-Jia Hou1, Ao-Bo Zhang1

  • 1School of Life Science and Technology, China Pharmaceutical University, Nanjing, 211198, China.

Scientific Data
|November 17, 2025
PubMed

Insights

This study identifies 390 potential fungal virulence factors (VFs) in Candida albicans using machine learning, creating a valuable resource for developing new IBD therapeutics and drug discovery.

Area of Science:

  • Mycology
  • Computational Biology
  • Drug Discovery

Background:

  • Fungi, particularly Candida albicans (C. albicans), are significant pathogens in inflammatory bowel disease (IBD).
  • Fungal virulence factors (VFs) are key to pathogenicity, making their identification crucial for therapeutic development.
  • Existing VF databases are limited, lacking predictive tools and structural data for drug discovery.

Purpose of the Study:

  • To construct a comprehensive dataset of fungal VFs.
  • To develop and apply machine learning models for predicting novel VFs in C. albicans.
  • To identify IBD-associated VFs and provide structural data for drug screening.

Main Methods:

  • Compiled a positive dataset of 18,072 known fungal VFs.
  • Employed machine learning algorithms to predict VFs from C. albicans proteomic data.
  • Analyzed 8,081 protein sequences from 99 C. albicans strains.
  • Included protein structural data for identified VFs.

Main Results:

  • Generated a dedicated C. albicans VF dataset containing 390 predicted VFs.
  • Identified five specific VFs associated with IBD pathogenicity.
  • Successfully integrated protein structural information for drug screening.

Conclusions:

  • This study establishes a comprehensive resource for fungal VF identification.
  • The developed dataset and methods provide a strong foundation for C. albicans research and IBD therapeutic strategies.
  • Facilitates targeted drug development by providing structural insights into pathogenic VFs.

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