SimDiv: A New Solution for Protein Comparison

Abstract

Proteins are the main players in the game of life. Good understanding of their structures, functions, and behaviors leads to good understanding of drugs, diseases, and thus our health. So, much effort has been done to study and categorize proteins. Nowadays, tens of thousands of proteins have been found. Moreover, the problem of comparing the proteins is hard. Therefore, efficient methods are needed to deal with this problem. In this paper, we use an important computational geometric concept and graph matching algorithm, namely, "Delaunay Tetrahedralization" and "Similarity Flooding", and propose a new idea to extract similar parts of proteins. Furthermore, we used protein fragmentation to reduce the time and storage complexity of the model for larger proteins. IntroductionThe number of known proteins is increasing every day; tens of thousands have been studied and categorized by now.To understand the functions and behaviors of a newly found protein, one should find well studied proteins with similar structure. In fact, the behavior of a protein is related to its sequence of amino acids and its 3D structure. So the comparison of proteins is a key technique not only in finding similarities in the structures of proteins but also to categorize them and define families and super-families among the proteins.