Subtractive proteomics-assisted putative drug target and inhibitor identification: a study based on six Candida...

Fungal infection is an ongoing public health concern due to its severity and associated complications. Exploring putative targets is needed to overcome this to enhance the therapeutic design. Therefore, a systematic computational-assisted subtractive proteomics approach was implemented to find the putative drug target and its promising inhibitor, examining the 6 Candida species. Considering 6 species, a total of 501,366 sequences were curated from NCBI and UniProt. Furthermore, based on subsequent steps such as non-orthologous, paralogous, and non-homologous, a total of 352 targets were obtained. Of which, 118 were identified as putative based on essential and target screening. Moreover, the target revealed various similar metabolic pathways toward the host organism, of which 5 proteins were found to be unique based on the non-significant similarity. The subcellular localization demonstrated that out of 5 proteins, only histidine kinase was found to be suitable based on its localization in the cytoplasm. Subsequently, the target structures were enhanced and validated, and significant structural proteins were found. The docking analysis following the high-throughput virtual screening was performed which resulted hyaluronic acid (ID: DB08818) and gadopiclenol (ID: DB17084), with docking scores of - 11.975 kcal/mol and - 11.442 kcal/mol as the most promising based on computational hits and high docking score. Moreover, the stability of these complexes was analyzed over 100 ns and found significant stability of the docked complex, based on the analyzed trajectories such as RMSD, RMSF, and PCA, along with the MMPBSA analysis. Based on the implemented strategy, the overall findings suggest that the identified drug target, together with the selected inhibitors, may enable the treatment of candidiasis-causing fungi and their associated infections.