Candida species are among the most impactful fungal pathogens, normally associated with
very high mortality rates. With the rise in frequency of multidrug-resistant clinical isolates, the
identification of new drug targets and new drugs is crucial to overcome the increase in
therapeutic failure.
In this study, we present the first validated genome-scale metabolic models for three
pathogenic Candida species, Candida albicans, Candida auris and Candida parapsilosis.
These models were reconstructed using the open-source software tool merlin 4.0.2 and are
provided in the well-established systems biology markup language (SBML) format, thus, being
usable in most metabolic engineering platforms, such as OptFlux or COBRA. These models
were used as a platform for the discovery of new drug targets, through the determination of
gene essentiality in conditions mimicking the human host. Using predictive computational
techniques, Homology Modelling and Molecular Docking, we were able to identify potential
inhibitory compounds for the identified drug targets, whose experimental validation is
underway. This computational approach provides a promising platform for the identification of
new drug targets and new antifungal drugs to tackle human candidiasis.info:eu-repo/semantics/publishedVersio
