Combined in silico approaches for the identification of novel inhibitors of human islet amyloid polypeptide (hIAPP) fibrillation

Abstract

Human islet amyloid polypeptide (hIAPP) is a natively unfolded polypeptide hormone of glucose metabolism, which is co-secreted with insulin by the -cells of the pancreas. In patients with type 2 diabetes, IAPP forms amyloid fibrils because of diabetes-associated -cells dysfunction and increasing fibrillation, in turn, lead to failure of secretory function of -cells. This provides a target for the discovery of small organic molecules against protein aggregation diseases. However, the binding mechanism of these molecules with monomers, oligomers and fibrils to inhibit fibrillation is still an open question. In this work, ligand and structure-based in silico approaches were used to identify novel fibrillation inhibitors and/or fibril binding compounds. The best pharmacophore model was used as a 3D search query for virtual screening of a compound database to identify novel molecules having the potential to be therapeutic agents against protein aggregation diseases. Docking and molecular dynamics simulation studies were used to explore the interaction pattern and mechanism of the identified novel small molecules with predicted hIAPP structure, its aggregation prone conformation and fibril forming segments. We show that catechins with galloyl group and molecules having two to three planar apolar rings bind to hIAPP structures and fibril forming segments with greater affinity. The differences in binding affinities of different compounds against several fibril forming segments of the peptide suggest that a mixture of active compounds may be required for treatment of aggregation diseases