Clustering protein-protein interaction network of TP53 tumor suppressor protein using Markov clustering algorithm

Abstract

The formation and proliferation of tumor cells occurs if a special protein that regulates cell division experience any changing on their function, gene expression or both of them. One of the tumor suppressor proteins that plays a significant role in controlling the cell cycle is the TP53 protein. In most of the genetic changes in the tumor, it found that mutant of TP53 is a high risk factor for cancer. Therefore, it is important to conduct studies on clustering protein-protein interactions (PPI) network of TP53. PPI networks are generally presented in the graph network with proteins as vertices and interactions as edges. Markov clustering (MCL) algorithm is a graph clustering method which based on a simulation of stochastic flow on a graph. In implementation, we applied MCL process using the Python programming language. The clustering datasets are the PPI of TP53 obtained from the STRING database. MCL algorithm consists of three main operations such as expansion, inflation, and prune. We conduct the clustering simulation using different parameter of expansion, inflation and the multiplier factor of identity matrix. As the results we found the MCL algorithm is proven to produce robust cluster with TP53 protein as a centroid for each clustering results.