Assessing protein resilience via a complex network approach

In recent years the topological study of proteins is gaining momentum rapidly, and several studies are providing more and more insights on the structural and dynamical properties of proteins by exploiting topological indexes based on Complex Network Theory. To this end the amino acid residues play the role of graph vertices, while non-covalent contacts are the arcs. Topological structure of proteins can be imagined as resulting by folding a thread of pearls (primary sequence of aminoacids) in which amino acid (nodes) relatively distant along the sequence come into contact thanks to the folding process. The result is a configuration sharing some properties with Complex Networks. In this work we derive insights on the resilience of protein contact networks by evaluating the degradation in the size of the giant component with respect to iterated node removal. Specifically, several strategies based on topological indicators (e.g., removing nodes in descending order of clustering coefficient) are exploited, considering the human serum albumin as case study. The analysis of progressive giant component desegregation offered some interesting hints about protein folding principles and suggested some strategies to locate the amino acids most relevant for stability of the studied molecule.