Prediction and analyses of disease causing mutations through protein protein interaction data

Abstract

Protein-Protein-Interactions (PPIs) are at the core ofalmost all of the cellular processes. Thus, understanding of the structural basis of PPIs is an important endeavor. The identification of the amino acid residues at the PPI interface may help in the analyses of different biochemical phenomena, like drug development, elucidation of molecular pathways, and generation of protein mimetic and understanding of disease mechanisms as well as buildingof docking methodologies to generatestructural models of protein complexes. Over the past few years, advances in high-throughput PPI identification techniques, such as yeast two-hybrid analysis and affinity purification coupled with mass spectrometry, have helpedthe researchers to identify the sets of interacting proteins in yeast, Drosophila and other organisms. Unfortunately, these experimental methods do not provide the necessary residue level insight into the interactions between the proteins. The uses of X-Ray crystallography and Nuclear Magnetic Resonance (NMR) spectroscopy to determine the structural basis of an interaction are time consuming and expensive. In response to these difficulties, a number of different bioinformatic algorithms with varying degrees of accuracies, have been developed over the years as alternative approaches to the aforementioned experimental techniques. These bioinformatic approaches use a wide variety of data sources to predict PPIs and the modes of binding between protein pairs. The present thesis is aimed to analyze the PPIs from a bioinformatics perspective. Introduction in thesis mainly deals with the basics of PPIs and the methods of their identifications and also some preliminary discussions about machine learning methodologies. newline