Identification of molecular descriptors for design of novel Isoalloxazine derivatives as potential Acetylcholinesterase inhibitors against Alzheimer's disease

Use of connectivity index and simple topological parameters for estimating the inhibition potency of acetylcholinesterase

Acetylcholinesterase (AChE) has proven to be an effective drug target in the treatment of neurodegenerative diseases such as Alzheimer’s, Parkinson’s and dementia. We developed a novel QSAR regression model for estimating potency to inhibit AChE, pK_i, on a set of 75 structurally different compounds including oximes, N-hydroxyiminoacetamides, 4-aminoquinolines and flavonoids. Although the model included only three simple descriptors, the valence molecular connectivity index of the zero-order, ⁰χ^v, the number of 10-membered rings (nR10) and the number of hydroxyl groups (nOH), it yielded excellent statistics (r = 0.937, S.E. = 0.51). The stability of the model was evaluated when an initial set of 75 compounds was broadened to 165 compounds in total, with the increase of the range of pK_i (exp) from 6.0 to 10.2, yielding r = 0.882 and S.E. = 0.89. The predictive power of the model was evaluated by calculating pK_i values for 55 randomly chosen compounds (S.E._test = 0.90) from the calibration model created on other 110 compounds (S.E. = 0.89), all taken from the pool of 165 compounds.

Computational Studies on Acetylcholinesterase Inhibitors: From Biochemistry to Chemistry

Acetylcholinesterase inhibitors are the most promising therapeutics for Alzheimer's disease treatment as these prevent the loss of acetylcholine and slows the progression of the disease. The drugs approved for the management of Alzheimer's disease by the FDA are acetylcholinesterase inhibitors but are associated with side effects. Consistent and stringent efforts by the researchers with the help of computational methods opened new ways of developing novel molecules with good acetylcholinesterase inhibitory activity. In this manuscript, we reviewed the studies that identified the essential structural features of acetylcholinesterase inhibitors at the molecular level as well as the techniques like molecular docking, molecular dynamics, quantitative structure-activity relationship, virtual screening, and pharmacophore modelling that were used in designing these inhibitors.

Development of a simple QSAR model for reliable evaluation of acetylcholinesterase inhibitor potency

With the aging of the western population, more and more people are affected by the neurodegenerative Alzheimer's and Parkinson's disease. Inhibitors of acetylcholinesterase (AChE) have proven to be effective in the treatment of disease symptoms. We report the QSAR regression model for the estimation of potency of a set of 94 structurally diverse compounds (oximes, N-hydroxyiminoacetamides, 4-aminoquinolines and flavonoids) to inhibit AChE, pK_i (AChE). The model is based on three simple descriptors: the valence molecular connectivity index of the zero-order, ⁰χ^v, combined with the number of 10-membered rings (nR10) and number of hydroxyl groups in a molecule (nOH). QSAR model yielded r = 0.947, S.E. = 0.51 and S.E._cv= 0.53; the range of pK_i (exp) = 6.03. It showed its stability when the set of 94 compounds was enlarged, comprising 184 compounds in total (r = 0.886, S.E. = 0.85 and S.E._cv = 0.88; the range of pK_i (exp) = 10.21), resulting in regression parameters which were similar, although only for ⁰χ^v coefficients within the limits of S.E. (0.167(13) and 0.172(16) for the set with 94 and 184 compounds, respectively. The predictive power of the model was shown by the prediction of pK_i values for 61 randomly chosen compounds (S.E._test = 0.86) from the calibration model made on the other 123 compounds (S.E. = 0.85), all taken from the pool of 184 compounds. QSAR descriptors ⁰χ^v, nR10 and nOH were well chosen for describing the interactions of the AChE active site (amino acid interaction) with ligands through the estimation of the inhibitory potency.

A Comprehensive Review of Cholinesterase Modeling and Simulation

The present article reviews published efforts to study acetylcholinesterase and butyrylcholinesterase structure and function using computer-based modeling and simulation techniques. Structures and models of both enzymes from various organisms, including rays, mice, and humans, are discussed to highlight key structural similarities in the active site gorges of the two enzymes, such as flexibility, binding site location, and function, as well as differences, such as gorge volume and binding site residue composition. Catalytic studies are also described, with an emphasis on the mechanism of acetylcholine hydrolysis by each enzyme and novel mutants that increase catalytic efficiency. The inhibitory activities of myriad compounds have been computationally assessed, primarily through Monte Carlo-based docking calculations and molecular dynamics simulations. Pharmaceutical compounds examined herein include FDA-approved therapeutics and their derivatives, as well as several other prescription drug derivatives. Cholinesterase interactions with both narcotics and organophosphate compounds are discussed, with the latter focusing primarily on molecular recognition studies of potential therapeutic value and on improving our understanding of the reactivation of cholinesterases that are bound to toxins. This review also explores the inhibitory properties of several other organic and biological moieties, as well as advancements in virtual screening methodologies with respect to these enzymes.

Dissecting molecular network structures using a network subgraph approach

Biological processes are based on molecular networks, which exhibit biological functions through interactions of genetic elements or proteins. This study presents a graph-based method to characterize molecular networks by decomposing the networks into directed multigraphs: network subgraphs. Spectral graph theory, reciprocity and complexity measures were used to quantify the network subgraphs. Graph energy, reciprocity and cyclomatic complexity can optimally specify network subgraphs with some degree of degeneracy. Seventy-one molecular networks were analyzed from three network types: cancer networks, signal transduction networks, and cellular processes. Molecular networks are built from a finite number of subgraph patterns and subgraphs with large graph energies are not present, which implies a graph energy cutoff. In addition, certain subgraph patterns are absent from the three network types. Thus, the Shannon entropy of the subgraph frequency distribution is not maximal. Furthermore, frequently-observed subgraphs are irreducible graphs. These novel findings warrant further investigation and may lead to important applications. Finally, we observed that cancer-related cellular processes are enriched with subgraph-associated driver genes. Our study provides a systematic approach for dissecting biological networks and supports the conclusion that there are organizational principles underlying molecular networks.

In silico molecular modelling, structural dynamics simulation and characterization of antifungal nature of β-glucosidase enzyme from Sechium edule

β-glucosidase is an enzyme that has ability to cleave β-glycosidic bonds present in oligosaccharides and glycoconjugates. They are known to be present across all domains of living organism and have important roles in many biological processes including plant defense mechanism. In the present study, a β-glucosidase enzyme identified from seeds of Sechium edule was characterized using various bioinformatics tools. A homology model (SeBG) was generated using a β-glucosidase crystal structure from Oryza sativa (PDB ID: 3PTK) as template. In silico structural binding studies on putative β-glucosidase protein revealed a stable and strong interaction indicative of higher GOLD fitness score with the substrates: p-nitrophenyl-β-d-glucopyranoside (pNPG), laminarin, chitotriose, N-acetylglucosamine and N-acetylmuramic acid suggesting its possible role in broad spectrum antifungal and antimicrobial activity. Assessment of the in vitro enzyme activity with pNPG showed a K_m and V_max values of 2.7 mM and 22 µMmin^-1mL^-1mg^-1, respectively. While, the in vitro enzyme activity with laminarin showed a K_m and V_max values of 0.31 mM and 0.043 µMmin^-1mL^-1mg^-1. The broad spectrum activity of the protein shown in our result indicates SeBG as a promising biocontrol agent against phytopathogens.Communicated by Ramaswamy H. Sarma.

Dynamic structure based pharmacophore modeling of the Acetylcholinesterase reveals several potential inhibitors

Acetylcholinesterase is a critical enzyme that regulates neurotransmission by catalyzing the breakdown of neurotransmitter acetylcholine in synapses of the nervous system. It is an important target for therapeutic drugs that treat Alzheimer's disease. Since, the degree of flexibility of the side chains of the residues in the active-site gorge of Acetylcholinesterase is diverse it results in different bound ligand conformations. The side-chain conformations of Ser293, Tyr341, Leu76, and Val73 are flexible, while the side-chain conformations of Tyr72, Tyr 124, Ser125, Phe295, and Arg296 appear to be fixed. In this study, multi-conformation dynamic pharmacophore models from the donepezyl-binding pocket based on highly populated structures chosen from molecular dynamics simulations were used for screening compounds that can properly bind acetylcholinesterase. Based on these structures, three pharmacophore models were generated. Consequently, 14 hits were retrieved as final candidates by utilizing virtual screening of ZINC database and molecular docking.

NSAIDs as potential treatment option for preventing amyloid β toxicity in Alzheimer's disease: an investigation by docking, molecular dynamics, and DFT studies

Aggregation of amyloid beta (Aβ) protein considered as one of contributors in development of Alzheimer's disease (AD). Several investigations have identified the importance of non-steroidal anti-inflammatory drugs (NSAIDs) as Aβ aggregation inhibitors. Here, we have examined the binding interactions of 24 NSAIDs belonging to eight different classes, with Aβ fibrils by exploiting docking and molecular dynamics studies. Minimum energy conformation of the docked NSAIDs were further optimized by density functional theory (DFT) employing Becke's three-parameter hybrid model, Lee-Yang-Parr (B3LYP) correlation functional method. DFT-based global reactivity descriptors, such as electron affinity, hardness, softness, chemical potential, electronegativity, and electrophilicity index were calculated to inspect the expediency of these descriptors for understanding the reactive nature and sites of the molecules. Few selected NSAID-Aβ fibrils complexes were subjected to molecular dynamics simulation to illustrate the stability of these complexes and the most prominent interactions during the simulated trajectory. All of the NSAIDs exhibited potential activity against Aβ fibrils in terms of predicted binding affinity. Sulindac was found to be the most active compound underscoring the contribution of indene methylene substitution, whereas acetaminophen was observed as least active NSAID. General structural requirements for interaction of NSAIDs with Aβ fibril include: aryl/heteroaryl aromatic moiety connected through a linker of 1-2 atoms to a distal aromatic group. Considering these structural requirements and electronic features, new potent agents can be designed and developed as potential Aβ fibril inhibitors for the treatment of AD.