The Computational Study of Peptide and Small Molecule GLP-1 Receptor Agonists

Katelin C. Coleman¹, Tiffani M. Holmes²

¹Department of Animal Sciences, College of Agriculture, Family Sciences and Technology Faculty & Staff, Fort Valley State University

²Department of Natural and Computational Sciences, College of Arts and Sciences, Fort Valley State University

The objective of this study is to investigate the interactions (i.e. London dispersion forces, hydrogen bonding, and pie stacking) of GLP-1 receptor agonist compounds with the GLP-1 receptor (GLP-1R) using molecular modeling techniques, further leading to better understanding of these compounds and developments of more proficient drugs. GLP-1, or glucagon-like peptide 1, is a hormone that naturally releases within the gastrointestinal tract, specifically the pancreas, in response to the intake of glucose, regulating blood glucose levels. As the body consumes glucose, GLP-1 directly binds to the GLP-1R at the surface of pancreatic beta cells, triggering the release of insulin and inhibiting glucagon secretion. Health conditions like obesity and diabetes are linked to a reduction of GLP-1 within the body, further leading to insulin resistance and metabolic complications. The developments of GLP-1 receptor agonists drugs like Wegovy, Saxenda, and Ozempic are utilized to combat these conditions. Two highly produced types of GLP-1 receptor agonists drugs include small molecule GLP-1 agonists and peptidyl GLP-1 agonists. The small molecule GLP-1 receptor agonists dangulipron, berberine, and orforglipron and the peptidyl GLP-1 receptor agonists liraglutide, tirzepatide, and exenatide are reviewed in this study. The docking of dangulipron and berberine result in a bonding affinity of -10.3 kcal via conventional hydrogen bonding and pi-type intermolecular forces, indicating the presence of strong interactions. The analysis of the remaining molecules is in progress. The interactions of these compounds will depict the degree of bonding that they have to the receptor, helping to understand which drugs are potentially most efficient.