Computational Approaches for Discovering Virulence Factors in Coccidioides

Abstract

Emerging respiratory dimorphic fungi, including Coccidioides, pose a growing public health threat due to their ability to cause severe disease and the limited therapeutic options. A growing gap exists between rapidly expanding computational data and slower traditional experimental methods for virulence factor identification, limiting progress in fungal pathogenesis research and therapeutic development. This review presents a framework for integrating computational and experimental methodologies to accelerate virulence discovery in Coccidioides. We examine predictive tools for adhesins, transporters, secreted effectors, carbohydrate-active enzymes (CAZymes), and secondary metabolites, plus therapeutic target prioritization strategies based on druggability, selectivity, essentiality, and precedent. Examples from Coccidioides and other World Health Organization-designated emerging fungi highlight how computational pipelines clarify pathogenic mechanisms and guide experimental design. We also assess machine learning, structural prediction, and reverse vaccinology approaches for enhance target discovery. By applying computational advances to Coccidioides research with experimental validation, this integrated approach can guide future antifungal drug and vaccine development.