Computer-Aided Discovery of Pentapeptide AEYTR as a Potent Acetylcholinesterase Inhibitor

Abstract

One of the key targets in the drug development for potential Alzheimer’s disease (AD) therapeutics is the search for acetylcholinesterase enzyme (AChE) inhibitors. Very recently, a pentapeptide AEYTR was reported as a potential inhibitor for AChE. The peptide was identified in a retrospectively validated virtual screening campaign, which was subsequently followed by 10 ns molecular dynamics (MD) simulations. The study aimed to characterize the structure and identify in vitro of AEYTR peptide as a potent acetylcholinesterase inhibitor. This article presents the structure characterization and the in vitro examination of the peptide as an AChE inhibitor, followed by MD simulations for 100 ns. The results show that the pentapeptide is a potent AChE inhibitor with an IC50 value in the picomolar range and stabilizes the enzyme during MD simulations.

PyPLIF HIPPOS and Receptor Ensemble Docking Increase the Prediction Accuracy of the Structure-Based Virtual Screening Protocol Targeting Acetylcholinesterase

“…The clustering of the MD snapshots [ 12 ] resulted in two AChE-AEYTR complexes for further construction of the SBVS protocol. The complexes were split into the protein (AChE) and the ligand (AEYTR), and then prepared for the docking simulations.…”

“…The SBVS protocol targeting AChE released in 2017 [ 7 ] played an important role in the discovery of the pentapeptide AEYTR as a potent AChE inhibitor with IC50 value of 0.462 ± 0.079 nM [ 9 , 10 , 11 , 12 ]. The backbone of the protocol is PLANTS docking software [ 28 ] and PyPLIF [ 23 ].…”

“…The prospective screening campaigns of the short peptides [ 9 ] have hit peptides AEKY, AERW, AEYQ, AEYT, and AEYTR [ 11 ]. The following molecular dynamics simulations and the in vitro test have verified the peptide AEYTR as a potent AChE inhibitor [ 12 ]. The snapshots of complex AChE-AEYTR resulting from the molecular dynamics offer the possibility to perform receptor ensemble docking (RED) [ 13 ].…”

“…Since PyPLIF HIPPOS was not developed to identify PLIF from docking poses resulting from AutoDock Vina flexible docking [ 26 , 27 ], the RED approach was selected instead of the IFD approach to consider the protein flexibility in this research. The availability of PyPLIF HIPPOS [ 26 ] and the AChE-AEYTR complexes resulting from the molecular dynamics simulations [ 12 ] were beneficial to attaining the aim.…”

PyPLIF HIPPOS-Assisted Prediction of Molecular Determinants of Ligand Binding to Receptors

“…Subsequently, the descriptor ensPLIF was introduced to cover all relevant docking poses produced during the SBVS campaign in order to mimic the lock-and-key and the induced-fit theories [ 23 ] in the RPART analysis [ 13 , 24 ]. The protocols were then employed to prospectively screen and design novel ligands for ERα [ 13 , 25 , 26 ] and inhibitors for acetylcholine esterase (AChE) [ 27 , 28 , 29 ]. Interestingly, besides increasing the prediction quality of the SBVS protocols to identify ligand for ERα [ 13 ] and inhibitor for acetylcholine esterase (AChE) [ 24 ], the combined methods could also predict the important amino acid residues that play an essential role in the ligand binding to the proteins [ 13 , 24 ].…”