Identification of cluster of proteins in the network of MAPK pathways as cancer drug targets

Abstract

The quest to develop computational drug target identification methods in complex diseases like cancer is growing in recent years. Feedback, feed-forward loops and cross-talks observed among the MAPK pathways led to the definition of a network of MAPK pathways and considered for single or multiple therapeutic interventions. We developed a computational method to identify clusters of drug targets by analysing the directed network's topological properties and the individual node's functional roles. We aim to identify the primary drug target nodes possessing more cancerous properties and less number of cellular functional roles. For every primary drug targets, we collect the alternate substrate activating nodes for local resistance analysis. Alternate substrate activation free nodes identified as single drug target are SOS, ATF1, BAD, GAB1, LAD, NFAT4, ATF2, MEF2, eEF2K, 4EBP1 and HSP27. Among the remaining identified nodes and their corresponding alternate substrate activating nodes with their cancer retaining and side effects causing properties studied as three different classes-single, multiple and dangerous targets. C-Raf1 and MAPKAP-K observed as a single efficient target due to the absence of resistance mechanism. Due to the resistance mechanism observed among the targeted M3/6, GADD45, and MKK6 multiple target intervention of their corresponding alternate nodes might prove to be the efficient targets. Targeted effect on MLK3, ZAK, DLK and MLTKa/b will impair the network due to intertwined and proximity nature among themselves.

Keywords

• Network of MAPK pathways
• Clusters
• Drug targets identification
• Drug resistance mechanism