Visualizing the topology and re-analyzing the causes of small-world property of amino acid network

Amino acid network is the reduced representation of protein structure, which benefits the investigation of protein structure. And network paradigm offers an insight into viewing global connectivity of protein and studying the interaction between secondary structures by visualizing amino acid networks. However, there exists some disadvantages in current research. Much research about protein structures are based on protein contact map, but it cannot provide an easy way to present the local regularity and the topology of protein structures. Until current research recently consider primary structure of protein cause small-world property of amino acid network and neglect the effects of secondary structures. In this paper, we present two algorithms RandomSS and RandomSC, and compare them with RandomNN algorithm. We find not only primary structures but also secondary structures play key roles in emergence of small-world property in amino acid network. The secondary structure of protein in amino acid network is regular, and the inherent regularity reinforces small-world property. Moreover, in order to reveal the characteristic patterns of the topology of protein structures and visualize the regular layout of Helix and Sheet structures and the interaction between secondary structures, we use network visualization methods to show amino acid networks. And amino acid network is visualized by macroscopic view and microscopic view to show different regions of protein, which own various roles. This paper also provides a new view to study protein structure by network paradigm.