Evaluation and optimization of in silico designed β-Secretase modulators for the treatment of "Alzheimer's Disease"

Abstract

Alzheimer's disease affects cognitive function through formation of ß- secretase mediated extracellular cerebral protein plaques and intracellular neurofibrillary tangles, thus its antagonism could mitigate disease progression. This project aims to identify newly obtained and optimized molecules which decrease the formation of ß -amyloid plaques through inhibition of the ß- secretase enzyme. Protein databank (PDB) depositions describing the bound coordinates of 6 lead structures complexed with ß- secretase were identified (PDB ID- 2VKM, 4B05, 4IVS, 3U6A, 3IGB, 2Q11) as leads for in silico ligand based and de novo design of novel antagonist molecules. For the first part of this study, ligands extracted from the protein were used as templates for screening ViCi Hamburg‟s database. Protomols were generated for each of the ligands using the Surflex Dock suite in SYBYL-X. The molecules received through ViCi were then used as ligand sources. For the second part of the study the ligand binding affinity (LBA) of each small molecule for its cognate receptor was calculated in X-Score for baseline affinity establishment. 2D topology maps highlighting the important interactions between ligand and receptor were generated using Poseview, and noncritical moieties were computationally removed in the process of creating seed structures (n=3, 2, 3,2,2,2 respectively) on to which novel moieties were computationally introduced using the GROW module of LigBuilder. Protomol and Keysite volumes were then compared using UCSF Chimera. 1636 novel structures were generated with 253 structures being Lipinski Rule compliant. The highest ranking molecules from each pharmacophoric family were identified for optimization and in vitro validation.