Identification of Novel Cathepsin B Inhibitors with Implications in Alzheimer's Disease: Computational Refining and Biochemical Evaluation.

Nitin Chitranshi,Ting Shen,Samran Sheriff,Kam Y J Zhang,Vivek Gupta,Sachin K Singh,Angela Godinez,Soumalya Sarkar,Mehdi Mirzaei,Kamal Dua,Stuart L Graham,Devaraj Basavarajappa,Morteza Abyadeh,Veer Gupta,Ashutosh Kumar,Danit Saks

doi:10.3390/cells10081946

Abstract

Amyloid precursor protein (APP), upon proteolytic degradation, forms aggregates of amyloid β (Aβ) and plaques in the brain, which are pathological hallmarks of Alzheimer’s disease (AD). Cathepsin B is a cysteine protease enzyme that catalyzes the proteolytic degradation of APP in the brain. Thus, cathepsin B inhibition is a crucial therapeutic aspect for the discovery of new anti-Alzheimer’s drugs. In this study, we have employed mixed-feature ligand-based virtual screening (LBVS) by integrating pharmacophore mapping, docking, and molecular dynamics to detect small, potent molecules that act as cathepsin B inhibitors. The LBVS model was generated by using hydrophobic (HY), hydrogen bond acceptor (HBA), and hydrogen bond donor (HBD) features, using a dataset of 24 known cathepsin B inhibitors of both natural and synthetic origins. A validated eight-feature pharmacophore hypothesis (Hypo III) was utilized to screen the Maybridge chemical database. The docking score, MM-PBSA, and MM-GBSA methodology was applied to prioritize the lead compounds as virtual screening hits. These compounds share a common amide scaffold, and showed important interactions with Gln23, Cys29, His110, His111, Glu122, His199, and Trp221. The identified inhibitors were further evaluated for cathepsin-B-inhibitory activity. Our study suggests that pyridine, acetamide, and benzohydrazide compounds could be used as a starting point for the development of novel therapeutics.

Highlights

Alzheimer’s disease (AD) is the most common type of dementia [1], associated with decline in memory and cognition [2,3]
The current study employed the power of a computational approach to generate a three-dimensional pharmacophore hypothesis, refine the pharmacophore model through structural interaction fingerprinting, and validate the novel hits from the Maybridge database using molecular dynamics
We carried out ligand-based virtual screening (LBVS) of 60,538 Maybridge drug-like molecules by screening these compounds through a 3D pharmacophore model generated from novel inhibitors of cathepsin B, of both natural and synthetic origins

Summary

Introduction

Alzheimer’s disease (AD) is the most common type of dementia [1], associated with decline in memory and cognition [2,3]. Brain atrophy and toxicity associated with Aβ aggregation are key features of the disease in the vast majority of patients [7]. AD and other forms of dementia are caused by damage to neurons, and most of the current drugs have failed to meet the primary endpoints in phase 3 clinical trials. The disease remains open to potential genetic and pharmacological therapies which, in addition to the strategy of clearance of Aβ aggregates, include therapies to slow down or prevent the loss of brain neurons and maintain their function [8,9]

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Conclusion