Abstract
MC4R is a G protein-coupled receptor that regulates energy homeostasis through the leptin-melanocortin pathway. MC4R genetic defects are a known cause of morbid obesity. Therefore, the present study aims to explore how the MC4R genotype influences protein phenotype, stability, and ligand binding characteristics. Thus, obesity causative MC4R variants were tested with different computational pathogenicity classifier tools, and then their effects on structural features and ligand binding properties were further investigated by secondary structural annotation, 3D protein mapping, structural drift analysis, stability analysis, and molecular dynamics simulation analysis methods. Our results suggest that CADD, ClinPred, and REVEL computational methods show superior sensitivity, specificity, and accuracy in detecting MC4R clinical missense variants (T101I, S136P, A259D, C271F, P272L, M281V, P299S, R305Q, and L309Q) over the Polyphen-2 method. We have also noticed that C271F, P272L, and P299S variants could potentially induce changes in secondary structure elements (alpha-helices, beta-strands, and coils) in MC4R protein. Residue level structure drifts caused by these variants not only decrease the structural stability of the receptor, but they create rigid local structures inside the protein, comprising its conformational flexibility. Molecular docking findings showed that M281V, C271F, A259D, S136P, and T101I variants interrupt MC4R's interaction with the AgRP ligand molecule, thereby reduce MC4R functionality. Taken together, computational approaches could effectively distinguish MC4R clinical pathogenic and neutral missense variants both in structural and functional contexts. Our findings may expand MC4R genotype-protein phenotype knowledge at the molecular level and pave the way to developing personalized medicine for the morbid obese phenotype.