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Proteins are an important class of biomolecules that serve as essential building blocks of the cells. Their three-dimensional structures are responsible for their functions. In this thesis we have investigated the protein structures using a network theoretical approach. While doing so we used a coarse-grained method, viz., complex network analysis. We model protein structures at two length scales as Protein Contact Networks (PCN) and as Long-range Interaction Networks (LINs). We found that proteins by virtue of being characterised by high amount of clustering, are small-world networks. Apart from the small-world nature, we found that proteins have another general property, viz., assortativity. This is an interesting and exceptional finding as all other complex networks (except for social networks) are known to be disassortative. Importantly, we could identify one of the major topological determinant of assortativity by building appropriate controls.
Protein contacts contain important information for protein structure and functional study, but contact prediction from sequence information remains very challenging. Recently evolutionary coupling (EC) analysis, which predicts contacts by detecting c
Across many fields, a problem of interest is to predict the transition rates between nodes of a network, given limited stationary state and dynamical information. We give a solution using the principle of Maximum Caliber. We find the transition rate
The twenty protein coding amino acids are found in proteomes with different relative abundances. The most abundant amino acid, leucine, is nearly an order of magnitude more prevalent than the least abundant amino acid, cysteine. Amino acid metabolic
Are turn-on and turn-off functions in protein-protein interaction networks exact opposites of each other? To answer this question, we implement a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutati