MAPPING OF NATURAL KAPOSI SARCOMA INHIBITOR USING NETWORK BIOLOGY APPROACH

Identification of protein-ligand interactionnetworks on a proteome scale is crucial to address a wide range of biological problems such as correlating molecular functions to physiological processes and designing safe and efficient therapeutics. In this study we have developed a novel computational strategy to identify ligand binding profiles of proteins across gene families and applied it to predicting protein functions, elucidating molecular mechanisms of drug adverse effects, and repositioning safe pharmaceuticals to treat different diseases The resultant network is then extrapolated to proteomics level to sort out the genes only expressed in the specific cancer types. The network is statistically analyzed and represented by the graphical interpretation to encounter the hub nodes. The objective of developing a biological networking is for the evaluation and validation of cancer drugs and their targets. In the field of cancer biology, the drug and their targets holds a role of paramount importance. With the work conducted here it shows the study of relation between drug target networks. Kaposi’s sarcoma (KS) is a systemic disease which can present with cutaneous lesions with or without internal involvement. Genes belonging to the group of proto-oncogenes and tumor suppressors are best targeted for cancer studies. Biological networks like gene regulatory networks, protein interaction network is usually created to simplify the studies. In the present study, 26 proteins as receptor were selected for the study; all the receptors were responsible for the cause of Kaposi’s sarcoma. Also, 121 natural anti-Kaposi Sarcoma compounds were selected from different sources the natural components were the best component for blocking of abnormal activity.