Networking Protein-Ligand Binding Sites

Sagar Bhayye, N. Sukumar
International Conference on Drug Discovery (ICDD) 2020

BITS Hyderabad, February 2020

Proteins are complex macromolecules that play a critical role in various body functions. Various functional proteins require small molecules (also known as ligands) to initiate various bio-chemical processes. The affinity of a ligand towards the specific protein is attributed to physicochemical properties of the protein-ligand binding site such as size, shape, surface charge distribution, etc. Because of this, a single ligand can bind to two or more structurally and functionally different proteins, leading to decrease in potency of drug molecules due to increase in distribution, and adverse drug reactions due to non-specific ligand binding to various proteins. In this study, a set of 4105 protein crystal structures belonging to different classes and species were used to construct a similarity network based on protein-ligand binding site properties. The Property-Encoded Shape Distributions (PESD) method was utilized to calculate protein-ligand binding site signatures and hence pair-wise similarities between ligand binding sites of the selected set of proteins. Metrics such as Euclidean, Chi-Square and Manhattan distances were used to calculate similarities between ligand binding sites of proteins, leading to quantitative understanding of their similarity relationships. Adjacency matrices calculated using these three different metrics were then used for construction of similarity networks. The networks were analyzed for properties such as vertex degree, average path length, degree distribution, average clustering coefficient, degree assortativity, modularity and different centrality measures. Properties of the three similarity networks were compared with the Erdӧs-Renyi random network. The Euclidean network shows higher average clustering coefficient and lower average path length than the Erdӧs-Renyi random network, indicating small world behavior.