Candida albicans (Candida albicans) is one of the major sources of nosocomial infections in humans which may prove fatal in 30% of cases. The hospital acquired infection is very difficult to treat affectively due to the presence of drug resistant pathogenic strains, therefore there is a need to find alternative drug targets to cure this infection. In silico and computational level frame work was used to prioritize and establish antifungal drug targets of Candida albicans. The identification of putative drug targets was based on acquiring 5090 completely annotated genes of Candida albicans from available databases which were categorized into essential and non-essential genes. The result indicated that 9% of proteins were essential and could become potential candidates for intervention which might result in pathogen eradication. We studied cluster of orthologs and the subtractive genomic analysis of these essential proteins against human genome was made as a reference to minimize the side effects. It was seen that 14% of Candida albicans proteins were evolutionary related to the human proteins while 86% are non-human homologs. In the next step of compatible drug target selections, the non-human homologs were sequentially compared to the human microbiome data to minimize the potential effects against gut flora which accumulated to 38% of the essential genome. The sub-cellular localization of these candidate proteins in fungal cellular systems indicated that 80% of them are cytoplasmic, 10% are mitochondrial and the remaining 10% are associated with the cell wall. The role of these non-human and non-gut flora putative target proteins in Candida albicans biological pathways was studied. Due to their integrated and critical role in Candida albicans replication cycle, four proteins were selected for molecular modeling. For drug designing and development, four high quality and reliable protein models with more than 70% sequence identity were constructed. These proteins are used for the docking studies of the known and new ligands (unpublished data). Our study will be an effective framework for drug target identifications of pathogenic microbial strains and development of new therapies against the infections they cause.
Keywords: Candida albicans; drug target identification; protein modeling.