Identification and Analysis of Disease Target Network of Human MicroRNA and Predicting Promising Leads for ZNF439, a Potential Target for Breast Cancer

Abstract

It has become increasingly evident that many diseases are linked to microRNAs and the role these biomolecules play in the development of numerous cancers. Thus, Biological network analysis offers novel approach in understanding basic mechanisms controlling normal biological processes and disease pathologies. Computational study of the disease-associated networks of human microRNAs is one of the means that provide complementary information. In the present study we predicted the disease association of all human microRNA predicted targets and the most promising leads were identified for the most potential target in breast cancer. The comprehensive list of human microRNAs was downloaded and the target genes of individual microRNA were retrieved based on miTG score using target prediction program. The resultant network was investigated based on the association of nodes with a relevant pathway, disease and pathological event. Further all the targets in the network for subjected for phylogenetic analysis and a comparative study was performed for these targets. The pattern of the conserved region was derived by Prosite analysis. Structure-based virtual screening involves docking of screened compounds and the protein target and the leads were identified based on the affinity and free energy of binding values. After extensive research, breast cancer was identified as the most significant disease and ZNF439 was identified as the most promising drug target in the network that was under study and the compounds such as Indolocarbozole, Camptothecin, Lucidenic Acid, Quercetin and Staurosporine were predicted as promising leads for ZNF439.