โก Quick Summary
This study presents a comprehensive dataset of 18,072 fungal virulence factors (VFs) specifically for inflammatory bowel disease (IBD), enhancing the potential for targeted antifungal drug discovery. Utilizing machine learning, researchers identified 390 potential VFs from 99 C. albicans strains, paving the way for improved therapeutic strategies.
๐ Key Details
- ๐ Dataset: 18,072 fungal VFs, 8,081 protein sequences
- ๐งฌ Organism: Candida albicans (C. albicans)
- โ๏ธ Technology: Machine learning for VF prediction
- ๐ Focus: IBD-associated pathogenic VFs
๐ Key Takeaways
- ๐ฆ Fungal pathogenicity is largely driven by virulence factors.
- ๐ป Machine learning was employed to predict and identify new VFs.
- ๐ Five IBD-associated pathogenic VFs were specifically highlighted.
- ๐ฌ Structural data included to aid in small-molecule compound screening.
- ๐ Comprehensive dataset serves as a resource for antifungal drug discovery.
- ๐ Study published in Sci Data, 2025.
- ๐ฉโ๐ฌ Collaborative effort by a team of researchers from various institutions.
๐ Background
Inflammatory bowel disease (IBD) is a chronic condition that significantly impacts the quality of life for many individuals. Among the various pathogens associated with IBD, Candida albicans stands out due to its ability to exploit the compromised gut environment. Understanding the virulence factors that contribute to its pathogenicity is essential for developing targeted therapies.
๐๏ธ Study
The researchers aimed to construct a robust dataset of fungal virulence factors to facilitate antifungal drug discovery specifically for IBD. By analyzing the proteomes of 99 C. albicans strains, they utilized machine learning techniques to predict and identify a total of 390 potential VFs, significantly expanding the existing knowledge base.
๐ Results
The study successfully compiled a dataset of 18,072 fungal VFs, which includes the newly identified 390 potential VFs. Additionally, the identification of five IBD-associated pathogenic VFs provides a targeted approach for future drug development. The inclusion of protein structural data enhances the dataset’s utility for small-molecule compound screening.
๐ Impact and Implications
This research has the potential to transform the landscape of antifungal drug discovery for IBD. By providing a comprehensive dataset and employing advanced machine learning techniques, the study lays a solid foundation for the identification of new therapeutic targets. The implications extend beyond IBD, as understanding fungal virulence factors can inform treatment strategies for other fungal infections as well.
๐ฎ Conclusion
The construction of a dedicated dataset for fungal virulence factors marks a significant advancement in the field of antifungal drug discovery. By leveraging machine learning and structural data, researchers can now pursue more effective therapeutic strategies against C. albicans in the context of IBD. This study highlights the importance of integrating data science with biomedical research to tackle complex health challenges.
๐ฌ Your comments
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Fungal virulence factors datasets for inflammatory bowel disease-specific antifungal drug discovery.
Abstract
Fungi are closely associated with various diseases, among which Candida albicans (C. albicans) is recognized as an important pathogen in inflammatory bowel disease (IBD). Fungal pathogenicity is primarily mediated by virulence factors (VFs); therefore, comprehensive identification of fungal virulence factors is critical for targeted drug development and disease treatment. However, current databases contain limited numbers of fungal VFs, lack effective predictive algorithms, and do not directly provide protein structural information relevant for drug discovery. In this study, we constructed a positive dataset comprising 18,072 fungal VFs. Utilizing machine learning approaches, we further predicted and identified 390 potential VFs from 8,081 representative protein sequences across the proteomes of 99โC. albicans strains, generating a dedicated C. albicans VF dataset. Additionally, five IBD-associated pathogenic VFs were identified, and their protein structural data included in the dataset were leveraged to facilitate small-molecule compound screening. Collectively, this study provides a comprehensive data resource and theoretical foundation for the identification of fungal VFs and the development of related therapeutics.
Author: [‘Feng S’, ‘Hou YJ’, ‘Zhang AB’, ‘Wang ZT’, ‘Gu M’, ‘Si ZL’, ‘Zheng X’, ‘Li J’, ‘Lao XZ’]
Journal: Sci Data
Citation: Feng S, et al. Fungal virulence factors datasets for inflammatory bowel disease-specific antifungal drug discovery. Fungal virulence factors datasets for inflammatory bowel disease-specific antifungal drug discovery. 2025; 12:1796. doi: 10.1038/s41597-025-06087-1