Introduction: Short bowel syndrome (SBS) is a disabling condition that occurs following
the loss of substantial portions of the intestine, leading to inadequate absorption of nutrients and
fluids. Teduglutide is the only drug that has been FDA-approved for long-term treatment of SBS.
This medicine exerts its biological effects through binding to the GLP-2 receptor.
Methods: The current study aimed to use computational mutagenesis approaches to design novel
potent analogues of teduglutide. To this end, the constructed teduglutide-GLP2R 3D model was
subjected to the alanine scanning mutagenesis where ARG20, PHE22, ILE23, LEU26, ILE27 and
LYS30 were identified as the key amino acids involved in ligand-receptor interaction. In order to
design potent teduglutide analogues, using MAESTROweb machine learning method, the residues
of teduglutide were virtually mutated into all naturally occurring amino acids and the affinity improving
mutations were selected for further analysis using PDBePISA methodology which interactively
investigates the interactions established at the interfaces of macromolecules.
Results: The calculations resulted in D15I, D15L, D15M and N24M mutations, which can improve
the binding ability of the ligand to the receptor. The final evaluation of identified mutations was
performed by molecular dynamics simulations, indicating that D15I and D15M are the most reliable
mutations to increase teduglutide affinity towards its receptor.
Conclusion: The findings in the current study may facilitate designing more potent teduglutide analogues
leading to the development of novel treatments in short bowel syndrome.