Characterization of multi-targeted insulin-mimetic antidiabetic peptides using in silico approaches

Diabetes is a metabolic disease that can affect people at any age. Despite being one of the leading causes of death, the treatment of diabetes is extremely difficult. For the treatment of diabetes mellitus various synthetic oral hypoglycemic drugs and insulin is available. However, insulin cannot be taken orally and the synthetic agents used can have harmful side effects. Bioactive peptides are particular protein fragments that have a beneficial impact on human health and physiological processes. These peptides can be applied as antidiabetic agents in the treatment of diabetes. The aim of the current study was to develop hypoglycemic peptides (HGPs) from plant sources and to investigate their binding interactions with selected diabetic protein receptors using peptide-protein docking, in order to identify potential peptide candidates for the treatment of diabetes mellitus. The binding patterns of top seven hypoglycemic peptides with ten selected receptor proteins were explored using peptide-protein docking. The peptides P2 and P6 showed best haddock scores and binding pattern against target receptors. The P2 showed strong interactions with maltase-glucoamylase (binding energy of -108.7 + /- 9.3 kcal/mol and with six hydrogen bonds), glucose transporter 1 (GLUT1) (binding energy of -85.3 + /- 1.4 kcal/mol and with three hydrogen bonds). Peptide 6 exhibited highest score against insulin-like growth factor 1 receptor (binding energy of -107.5 + /- 5.4 kcal/mol and with seven hydrogen bonds) and pancreatic alpha-amylase (binding energy of -121.3 + /- 3.1 kcal/mol and with seven hydrogen bonds), while against Jun N-terminal kinase1 receptor peptide, the peptides P5 and P6 showed same haddock energy of -92.1 + /- 4.1 kcal/mol having different number of hydrogen bond interactions in both complexes. The molecular dynamics simulation revealed that P6 and P2 were firmly bound to insulin-like growth factor 1 receptor and maltase-glucoamylase, respectively for the simulation time of 100 ns. The findings of this computational study support the data showing that these hypoglycemic peptides are effective against selected diabetic proteins. The insulin-mimetic antidiabetic peptides would be integrated into active packaging systems for the development of multi-functional materials to regulate glucose absorption. However, further assessment and validation of the particular peptides as potential therapeutic candidates for diabetes mellitus are required.