α-Helix or β-Turn? An Investigation into N-Terminally Constrained Analogues of Glucagon-like Peptide 1 (GLP-1) and Exendin-4.
Alberto OddoSofia MortensenHenning ThøgersenLeonardo De MariaStephanie HennenJames N McGuireJacob KofoedLars LinderothSteffen Reedtz-RungePublished in: Biochemistry (2018)
Peptide agonists acting on the glucagon-like peptide 1 receptor (GLP-1R) promote glucose-dependent insulin release and therefore represent important therapeutic agents for type 2 diabetes (T2D). Previous data indicated that an N-terminal type II β-turn motif might be an important feature for agonists acting on the GLP-1R. In contrast, recent publications reporting the structure of the full-length GLP-1R have shown the N-terminus of receptor-bound agonists in an α-helical conformation. To reconcile these conflicting results, we prepared N-terminally constrained analogues of glucagon-like peptide 1 (GLP-1) and exendin-4 and evaluated their receptor affinity and functionality in vitro; we then examined their crystal structures in complex with the extracellular domain of the GLP-1R and used molecular modeling and molecular dynamics simulations for further investigations. We report that the peptides' N-termini in all determined crystal structures adopted a type II β-turn conformation, but in vitro potency varied several thousand-fold across the series. Potency correlated better with α-helicity in our computational model, although we have found that the energy barrier between the two mentioned conformations is low in our most potent analogues and the flexibility of the N-terminus is highlighted by the dynamics simulations.
Keyphrases
- molecular dynamics simulations
- type diabetes
- molecular docking
- fluorescent probe
- living cells
- sensitive detection
- magnetic resonance
- glycemic control
- machine learning
- cardiovascular disease
- magnetic resonance imaging
- metabolic syndrome
- insulin resistance
- deep learning
- mass spectrometry
- binding protein
- adipose tissue
- skeletal muscle
- contrast enhanced
- adverse drug
- artificial intelligence
- crystal structure