Home visGReMLIN: Graph Mining strategy to infer protein-Ligand INteraction patterns

Interactions between proteins and ligands play an important role in biological processes of living systems. For this reason, the development of computational methods to facilitate the understanding of the ligand-receptor recognition process is of fundamental importance, since this comprehension is a major step towards ligand prediction, target identification, lead discovery, among others. This article presents visGReMLIN, a visual interactive interface to explore protein-ligand interactions and their conserved substructures for a set of similar proteins. To illustrate the potential of our strategy, we used two test datasets, Ricin and human CDK2, which have their protein-ligand interface modeled as bipartite graphs, where an edge depicts an interaction between a protein node and a ligand node. Such graphs are the input to search for frequent subgraphs that are the conserved interaction patterns over the datasets. The input graphs and their patterns can be explored to find general trends and exceptions concerning types of atoms and interactions. A text search to help users to find residues/atoms of interest (for example, atoms from CDK2 hinge region and from Ricin A chain active site) is also provided. Additionally, visGReMLIN provides some visualizations of basic statistics on frequencies of atoms and interactions of specific types in the dataset. Finally, our strategy permits users to select an interaction pattern to highlight it in the context of 2D interface graphs and in a 3D molecule viewer.