In silico modeling for dual inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) enzymes in Alzheimer's disease. (October 2020) - Kumar, Vinay; Saha, Achintya; Roy, Kunal
Graphical abstract: Highlights: QSAR models for the dual inhibition of AChE and BuChE enzymes are developed. The models reveal important structural features for the dual inhibition of cholinesterase enzymes. Ring size, −CH2- groups, secondary aromatic amines and aromatic ketones contribute to the AChE inhibition. Distances between nitrogens, X-C(=X)-X and R–CR-X, secondary aromatic amides contribute to BuChE inhibition. The results should be of great help to design and synthesize new anti-Alzheimer's agents. Abstract: In this research, we have implemented two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling using two different datasets, namely, acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) enzyme inhibitors. A third dataset has been derived based on their selectivity and used for the development of partial least squares (PLS) based regression models. The developed models were extensively validated using various internal and external validation parameters. The features appearing in the model against AChE enzyme suggest that a small ring size, higher number of −CH2- groups, higher number of secondary aromatic amines and higher number of aromatic ketone groups may contribute to the inhibitory activity. The features obtained from the model against BuChE enzyme suggest that the sum of topological distances between two nitrogen atoms, higher number of fragments X-C(=X)-X, higher number of secondary aromatic amides, fragment R–CR-X may be more favorable for inhibition. The features obtained from selectivity based model suggest that the number of aromatic ethers, unsaturation content relative to the molecular size and molecular shape may be more specific for the inhibition of the AChE enzyme in comparison to the BuChE enzyme. Moreover, we have implemented the molecular docking studies using the most and least active molecules from the datasets in order to identify the binding pattern between ligand and target enzyme. The obtained information is then correlated with the essential structural features associated with the 2D-QSAR models.