Quantum chemistryQuantum biology beautifully captures the strength, the energies, and the geometries of small molecules from the overlap of atomic orbitals. However, the shape of biopolymers, like proteins, is determined largely by weak non-covalent interactionsNon-covalent interactions, such as hydrogen bonds, salt bridges, and so on. Regular secondary structures like α-helix and β-sheet are governed by intra-chain hydrogen bonds between the backbone atoms of the protein, as elucidated by G N Ramachandran, about six decades ago. Within the backdrop of backbone secondary structures, the spatial interactions of the amino acid side chains, pin down the resultant three-dimensional structure of proteins. Although, extensive characterization of side chain interactions was available at pairwise level, our lab realized the need for a formal method of identifying and quantifying collective interactions, unveiling the global architecture of side chain interactions in protein structures. In this article, we present our network approaches for the characterization of side chain interactions, from pairwise to global level. This has become possible by adapting the concepts from other disciplines in the past few decades, leading to the methods of construction and analyses of the protein structure network, with explicit focus on side chain interactions. Such a network (named as PScNProtein Side chain Network (PScN)) formalism has the advantage of connecting local side chain interactions at the atomic level to the global mesoscopic level. A brief account of the construction of PScNProtein Side chain Network (PScN) and the analysis of topological metrics such as cliques/communities, largest cluster, etc., that can be extracted from PScNProtein Side chain Network (PScN) are presented. Furthermore, maps of communication between two points or domains within and across protein structures can explicitly be evaluated at the side chain connectivity level from PScNProtein Side chain Network (PScN). These features provide a wealth of information to explore protein sequence-structure–function relationships. In this article, through our network approach we elucidate the phenomenon of allosteryAllostery, which is a powerful concept in biochemistry and refers specifically to action at distance. We demonstrate the methods of identifying paths of communication, crucial amino acids which influence the transmission of information. These paths are highly relevant to understand the detailed mechanism of the functions of enzymes. Rigorous method of protein structure network comparison is another application presented here. Such a comparison is helpful in grouping structures with similar networks. It is noteworthy that the method presented here is not restricted only to protein structures, it can also be utilized in other domains. It should be noted that currently several computational methods, similar to the one presented here are developed by many other labs and widely used to unravel the functioning of complex biological systems.