GENETICS OF CARDIOVASCULAR DISORDERS: AN IN-SILICO ANALYSIS USING PROTEIN STRUCTURAL AND INTERACTION INFORMATION

Abstract

Proteins are the structural and functional workhouse in the cell that takes part in virtually every event within and between cells. Proteins in association with other molecules determine the ultimate behavior of biological system. Recently, network-centered approaches have been increasingly used to comprehend the fundamentals of biology. There are different databases documenting the interactions of proteins as PPI networks but they do not reveal the molecular mechanism behind the binding process occurring between molecules. This problem can only be solved by including the structural details of the complexes which includes the 3 dimensional structures of proteins, interface as well as topological properties. Interface is the region where two protein chains interact leading to formation of protein complex. The main concern of the present study is to present interface analysis of cardiovascular-disorder (CVD) related proteins to shed lights on details of interactions and to emphasize the importance of using structures in network studies. We have used interface properties as parameters to classify the CVD associated proteins and non CVD proteins. Machine learning algorithm was used to generate a classifier based on the training set which was used to predict potential CVD related proteins from a set of polymorphic proteins which are not known to be involved in any disease. The predicted CVD related proteins may not be the causing factor of particular disease but can be involved in pathways and reactions yet unknown to us thus permitting a more rational analysis of disease mechanism. Study of their interactions with other proteins can significantly improve our understanding in the molecular mechanism of diseases. The wider scope of this study is the characterization of all the hereditary disorders based on their structural properties to gain better understanding of the molecular machinery within the cells of living organism.