Structure-Based Design and Molecular Dynamics Simulations of Pentapeptide AEYTR as a Potential Acetylcholinesterase Inhibitor

Abstract

Structure-based virtual screening protocol to identify potent acetylcholinesterase inhibitors was retrospectively validated. The protocol could be employed to examine the potential of designed compounds as novel acetylcholinesterase inhibitors. In a research project designing short peptides as acetylcholinesterase inhibitors, peptide AEYTR emerged as one of the potential inhibitors. This article presents the design of AEYTR assisted by the validated protocol and guided by literature reviews followed by molecular dynamics studies to examine the interactions of the pentapeptide in the binding pocket of the acetylcholinesterase enzyme. The molecular dynamics simulations were performed using YASARA Structure in Google Cloud Platform. The peptide AEYTR was identified in silico as a potent acetylcholinesterase inhibitor with the average free energy of binding (DG) of -19.138 kcal/mol.

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

“…(Santa Cruz) as the positive control; ddH2O and phosphate buffer saline (PBS). The materials used for MD simulations were the YASARA scene input file for MD simulations obtained from Prasasty and Istyastono [23] and the YANACONDA macro file to perform MD simulations in YASARA-Structure [24].…”

“…The MD simulations were performed by employing the macro md_run.mcr in YASARA-Structure in the VPS [23][24]. The complex resulted from the virtual screening obtained from Prasasty, and Istyastono [23] was used as the input file. The module "Clean" in YASARA-Structure was then used to add hydrogens and other missing atoms.…”

“…On the other hand, we have also developed databases of short peptides readily for SBVS campaigns [22]. SBVS campaigns to discover potent AChE inhibitors on some short peptides [22] equipped with the knowledge obtained from the literature review [21] and the retrospectively validated SBVS protocol [20] identified four tetrapeptides (i.e., AEKY, AERW, AEYQ, and AEYT) and a pentapeptide AEYTR as potential AChE inhibitors [23]. Short molecular dynamics (MD) simulations of 10 ns were used to virtually confirm the discovery of AEYTR as a potential AChE [23].…”

“…SBVS campaigns to discover potent AChE inhibitors on some short peptides [22] equipped with the knowledge obtained from the literature review [21] and the retrospectively validated SBVS protocol [20] identified four tetrapeptides (i.e., AEKY, AERW, AEYQ, and AEYT) and a pentapeptide AEYTR as potential AChE inhibitors [23]. Short molecular dynamics (MD) simulations of 10 ns were used to virtually confirm the discovery of AEYTR as a potential AChE [23]. Therefore, in vitro verification of the virtually discovered AEYRT as a potent AChE inhibitor has become of timely and considerable interest.…”

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 ].…”