Integrative Protein–Protein Interaction Network Analysis and Druggability Assessment of the Candidozyma auris Proteome for Novel Drug Target Identification

Background: In pathogenic fungi, protein–protein interaction (PPI) analysis has high potential to identify proteins central to virulence and adaptation, which is particularly important for Candidozyma auris, a multidrug-resistant pathogen with high mortality, underscoring the urgent need for novel antifungal targets. Methods: The C. auris proteome was retrieved from the STRING database to construct a PPI network. Proteins were ranked based on topological metrics including degree, betweenness centrality, closeness centrality, and eccentricity. The top 100 proteins were further filtered for non-homology to human and gut microbiome proteins. Essentiality was assessed via homologs in C. albicans, and proteins longer than 200 amino acids were evaluated for druggability using 3D structures through CavityPlus. Results: The PPI network comprised 4214 nodes and 86 872 edges, exhibiting small-world properties and organized modular subnetworks. Analysis of the top 100 proteins showed a wide range of betweenness centrality, closeness centrality, and eccentricity, with divergent amino acid lengths. Subcellular localization was determined for 53% of the proteins. After filtering for non-homology and length, 15 novel proteins were prioritized for druggability, of which 3 (A0A2H0ZDG1, A0A2H0ZLN5, and A0A2H0ZVI9) were predicted as druggable based on available 3D structures. None of the selected proteins were essential according to C. albicans homologs. These results demonstrate that combining PPI topology, homology, and structural criteria can effectively narrow down potential antifungal targets. Conclusion: Integrating PPI network analysis with homology filtering and 3D druggability assessment effectively identifies potential antifungal targets in C. auris. Nevertheless, the accuracy of such predictions highly depends on database completeness and interaction reliability.