(723f) Aggregation of Amylin in the Presence of EGCG Using Coarse-Grained Simulations | AIChE

(723f) Aggregation of Amylin in the Presence of EGCG Using Coarse-Grained Simulations

Amyloid plaques are a hallmark of many protein misfolding diseases, including type 2 diabetes mellitus (T2D). In patients diagnosed with T2D, amyloid plaques formed from amylin—a 37 amino acid peptide hormone that is co-secreted with insulin—have been found. In a diabetic patient, amylin becomes insoluble, aggregates, and forms cytotoxic amyloid deposits, leading to beta-cell death. Without insulin-producing beta-cells, blood-glucose homeostasis is lost. Due to amylin’s regulatory role, the peptide is a relevant therapeutic target.

Among the many drugs that have been proposed to combat or reverse amyloid formation, polyphenols, such as resveratrol, curcumin and epigallocatechin-3-gallate (EGCG), are popular candidates. Curcumin and resveratrol have been found to significantly decrease the aggregation propensity of amylin. Previous simulations in our group have shown that EGCG, resveratrol, and curcumin can redirect Aβ(17-36) from a fibrillar aggregate to an unstructured oligomer. Here we use a similar simulations-based approach to determine EGCG’s inhibitory mechanism in amylin amyloid formation.

Our research objective is to investigate the selectivity and binding affinity of EGCG to amylin. Atomistic simulations in AMBER were used to obtain information on EGCG needed for coarse graining: structure stability, energetic parameters, and geometric parameters. Coarse-grained (CG) simulations with PRIME20—an implicit solvent intermediate-resolution protein model/force field designed for peptide aggregation—were performed on large systems of peptides and inhibitors to assess amyloid formation and inhibitor efficacy. We simulated a system of 20 amylin peptides starting from a random coil configuration and visualized the conformations of the peptides over the course of fibrillization. Afterwards, coarse-grained EGCG molecules were included in our peptide system to determine the structure-function (inhibition) relationship. From the peptide-inhibitor simulations we have determined the binding site, quantified the number of hydrophobic interactions, and calculated the interaction energy between amylin and individual CG groups on EGCG.

Our simulations have identified key features of the inhibition mechanism of EGCG for amylin. The inhibition of peptide aggregation may be an effective strategy for therapeutic interventions in other amyloid diseases such as Alzheimer’s and Parkinson’s. Knowledge of amylin’s structure-function relationship may be used to guide future studies of Aβ, α-synuclein, or other fibrillogenic peptides.