COMPUTATIONAL IDENTIFICATION OF PUTATIVE DRUG TARGETS IN MALASSEZIA GLOBOSA BY SUBTRACTIVE GENOMICS AND PROTEIN CLUSTER NETWORK APPROACH
Objective: Yeast commonly causes superficial mycoses similar to the dermatophytes. Superficial mycoses were reported with an estimated incidence of âˆ¼140,000,000 cases/year worldwide and most frequently caused by Malassezia globosa and Malassezia furfur. Treatment available for these conditions is limited and with side effects. Moreover, termination of the treatment may result in the reoccurrence of the disease. The objective of this research was to identify the putative drug targets using computational approaches.
Methods: The analysis of genome sequence improves the understanding of diseases which leads to better treatment. Comparison of the genome of the pathogen with the host at the molecular level is suitable for performing the sequence based prediction of protein-protein interaction network, which also forms the basis of drug target identification leading to the discovery of new drugs for the improved treatment.
Results: Out of 100 pathways of M. globosa, 95 were common to the host and 5 were unique to the pathogen. Total common and unique targets from common pathways are 1704 and 300, respectively. A unique target from unique pathways and 147 from common pathways were non-homologous targets. From this, 46 targets were screened out as essential and processed in the next phase to identify the clustered targets which resulted with three clusters based on their biological role and subcellular location.
Conclusion: In this study, putative drug targets were identified in M. globosa using in silico approaches of subtractive genomics and cluster network which will help in the next level of drug discovery such as lead identification for the novel targets.
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