Non-covalent interactions between residues play a critical role in determining protein structure and function. Within families of evolutionarily related proteins, studying these interactions can provide insights into functional specificity and evolutionary constraints placed on the sequences . However, considering the large number of non-covalent interactions in any given protein, traditional experimental techniques often lack the high-throughput capabilities or resolution required for a comprehensive analysis of these relationships. Computational sequence-based approaches such as EVcouplings (2), GREMLIN , Protein Sparse InverseCOVariance (PSICOV, (5)), Direct Coupling analysis (DCA, ) have been applied to address this gap. These techniques rely on a large, diverse sample of available sequences and are thus less suitable for characterizing small groups of evolutionarily related proteins. Structural information, particularly those based on residue interaction networks (RINs), can provide an alternative way to identify preserved interaction motifs and co-evolving groups of residues. A RIN is a graph-based representation of a protein structure where nodes represent individual residues and edges represent physico-chemical interactions between them . Combined with graph-theoretic analysis techniques, RIN-based tools have demonstrated success in analysis of stability, folding, and function of proteins .