Molecular Modeling and Bioinformatics Analysis of Drug-Receptor Interactions in the System Formed by Glargine, Its Metabolite M1, the Insulin Receptor, and the IGF1 Receptor

Abstract

Introduction: Insulin and insulin-like growth factor type 1 (IGF1) regulate multiple physiological functions by acting on the insulin receptor (IR) and insulin-like growth factor type 1 receptor (IGF1R). The insulin analog glargine differs from insulin in three residues (GlyA21, ArgB31, ArgB32), and it is converted to metabolite M1 (lacks residues ArgB31 and ArgB32) by in vivo processing. It is known that activation of these receptors modulates pathways related to metabolism, cell division, and growth. Though, the structures and structural basis of the glargine interaction with these receptors are not known. Aim: To generate predictive structural models, and to analyze the drug/receptor interactions in the system formed by glargine, its metabolite M1, IR, and IGF1R by using bioinformatics tools. Methods: Ligand/receptor models were built by homology modeling using SWISSMODEL, and surface interactions were analyzed using Discovery Studio® Visualizer. Target and hetero target sequences and appropriate template structures were used for modeling. Results: Our glargine/IR and metabolite M1/IR models showed an overall symmetric T-shaped conformation and full occupancy with four ligand molecules. The glargine/IR model revealed that the glargine residues ArgB31 and ArgB32 fit in a hydrophilic region formed by the α-chain C-terminal helix (αCT) and the cysteine-rich region (CR) domain of this receptor, close to the CR residues Arg270-Arg271-Gln272 and αCT residue Arg717. Regarding IGF1R, homologous ligand/receptor models were further built assuming that the receptor is in a symmetrical T-shaped conformation and is fully occupied with four ligand molecules, similar to what we described for IR. Our glargine/IGF1R model showed the interaction of the glargine residues ArgB31 and ArgB32 with Glu264 and Glu305 in the CR domain of IGF1R. Conclusion: Using bioinformatics tools and predictive modeling, our study provides a better understanding of the glargine/receptor interactions.