RESUMO
Mycotic keratitis is a fungal infection of corneal epithelium. It is more common in tropical and subtropical countries and is one of the leading causes of blindness. Many of the antifungal drugs have been less effective in treating this condition and certain drugs which are efficient and yet limited in use due to its extreme side effects. Hence, in this study an attempt is made to identify potential and least toxic antifungal inhibitors that targets thiamine thiazole synthase, a novel target for suppressing Fusarium solani subsp.pisi (Nectria haematococca MPVI) infections, to combat mycotic keratitis. Integrative computational approaches involving model refinement, molecular dynamics simulation and High throughput virtual screening (HTVS) were applied through integrative multi precision mode in order to identify potential inhibitors. Moreover, machine learning approach was also implemented to prioritize potential inhibitors that are ophthalmic adaptive, as well as antifungal molecule. From the NCI and Maybridge datasets, for HTVS only 209,872 compounds that surpassed ligand property filtration were considered, which resulted in 209 compounds after XP docking. Among the top 5 compounds from XP docking, on cumulative analysis only 2-(1-hydroxyethyl)-1,3-thiazole-4-carboxamide was prioritized as the most potential hit, as it showed higher order of significance in terms of binding affinity, structural stability and therapeutic relevance for the treatment of Mycotic keratitis. Thus, widening the scope for novel antifungal therapy in ophthalmic infections.
