The 3D-QSAR study of 110 diverse, dual binding, acetylcholinesterase inhibitors based on alignment independent descriptors (GRIND-2). The effects of conformation on predictive power and interpretability of the models

Enzyme ChE, cholinergic therapy and molecular docking: Significant considerations and future perspectives

Enzyme Che plays an essential role in cholinergic and non-cholinergic functions. It is present in the fertilized/unfertilized eggs and sperm of different species. Inclusion criteria for data collection from electronic databases NCBI and Google Scholar are enzyme AChE/BChE, cholinergic therapy, genomic organization and gene transcription, enzyme structure, biogenesis, transport, processing and localization, molecular signaling and biological function, polymorphism and influencing factors. Enzyme Che acts as a signaling receptor during hematopoiesis, protein adhesion, amyloid fiber formation, neurite outgrowth, bone development, and maturation, explaining the activity out of synaptic neurotransmission. Polymorphism in the Che genes correlates to various diseases and diverse drug responses. In particular, change accompanies cancer, neurodegenerative, and cardiovascular disease. Literature knowledge indicates the importance of Che inhibitors that influence biochemical and molecular pathways in disease treatment, genomic organization, gene transcription, structure, biogenesis, transport, processing, and localization of Che enzyme. Enzyme Che polymorphism changes indicate the possibility of efficient and new inhibitor drug target mechanisms in diverse research areas.

A comparison between 2D and 3D descriptors in QSAR modeling based on bio-active conformations

QSAR models are widely and successfully used in many research areas. The success of such models highly depends on molecular descriptors typically classified as 1D, 2D, 3D, or 4D. While 3D information is likely important, e. g., for modeling ligand-protein binding, previous comparisons between the performances of 2D and 3D descriptors were inconclusive. Yet in such comparisons the modeled ligands were not necessarily represented by their bioactive conformations. With this in mind, we mined the PDB for sets of protein-ligand complexes sharing the same protein for which uniform activity data were reported. The results, totaling 461 structures spread across six series were compiled into a carefully curated, first of its kind dataset in which each ligand is represented by its bioactive conformation. Next, each set was characterized by 2D, 3D and 2D + 3D descriptors and modeled using three machine learning algorithms, namely, k-Nearest Neighbors, Random Forest and Lasso Regression. Models' performances were evaluated on external test sets derived from the parent datasets either randomly or in a rational manner. We found that many more significant models were obtained when combining 2D and 3D descriptors. We attribute these improvements to the ability of 2D and 3D descriptors to code for different, yet complementary molecular properties.

Dual Inhibitors of AChE and BACE-1 for Reducing Aβ in Alzheimer's Disease: From In Silico to In Vivo

Alzheimer's disease (AD) is a complex and widespread condition, still not fully understood and with no cure yet. Amyloid beta (Aβ) peptide is suspected to be a major cause of AD, and therefore, simultaneously blocking its formation and aggregation by inhibition of the enzymes BACE-1 (β-secretase) and AChE (acetylcholinesterase) by a single inhibitor may be an effective therapeutic approach, as compared to blocking one of these targets or by combining two drugs, one for each of these targets. We used our ISE algorithm to model each of the AChE peripheral site inhibitors and BACE-1 inhibitors, on the basis of published data, and constructed classification models for each. Subsequently, we screened large molecular databases with both models. Top scored molecules were docked into AChE and BACE-1 crystal structures, and 36 Molecules with the best weighted scores (based on ISE indexes and docking results) were sent for inhibition studies on the two enzymes. Two of them inhibited both AChE (IC₅₀ between 4-7 μM) and BACE-1 (IC₅₀ between 50-65 μM). Two additional molecules inhibited only AChE, and another two molecules inhibited only BACE-1. Preliminary testing of inhibition by F681-0222 (molecule 2) on APPswe/PS1dE9 transgenic mice shows a reduction in brain tissue of soluble Aβ42.

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.

Three Dimensional Quantitative Structure Activity Relationship and Pharmacophore Modeling of Tacrine Derivatives as Acetylcholinesterase Inhibitors in Alzheimer's Treatment

Background:

Three dimensional quantitative structure activity relationship and pharmacophore modeling are studied for tacrine derivatives as acetylcholinesterase inhibitors.

Methods:

The three dimensional quantitative structure–activity relationship and pharmacophore methods were used to model the 68 derivatives of tacrine as human acetylcholinesterase inhibitors. The effect of the docked conformer of each molecule in the enzyme cavity was investigated on the predictive ability and statistical quality of the produced models.

Results:

The whole data set was divided into two training and test sets using hierarchical clustering method. 3D-QSAR model, based on the comparative molecular field analysis has good statistical parameters as indicated by q2 =0.613, r2 =0.876, and r2pred =0.75. In the case of comparative molecular similarity index analysis, q2, r2 and r2pred values were 0.807, 0.96, and 0.865 respectively. The statistical parameters of the models proved that the inhibition data are well fitted and they have satisfactory predictive abilities.

Conclusion :

The results from this study illustrate the reliability of using techniques in exploring the likely bonded conformations of the ligands in the active site of the protein target and improve the understanding over the structural and chemical features of AChE.

Molecular Docking Guided Grid-Independent Descriptor Analysis to Probe the Impact of Water Molecules on Conformational Changes of hERG Inhibitors in Drug Trapping Phenomenon

Human ether a-go-go related gene (hERG) or KV11.1 potassium channels mediate the rapid delayed rectifier current (I_Kr) in cardiac myocytes. Drug-induced inhibition of hERG channels has been implicated in the development of acquired long QT syndrome type (aLQTS) and fatal arrhythmias. Several marketed drugs have been withdrawn for this reason. Therefore, there is considerable interest in developing better tests for predicting drugs which can block the hERG channel. The drug-binding pocket in hERG channels, which lies below the selectivity filter, normally contains K⁺ ions and water molecules. In this study, we test the hypothesis that these water molecules impact drug binding to hERG. We developed 3D QSAR models based on alignment independent descriptors (GRIND) using docked ligands in open and closed conformations of hERG in the presence (solvated) and absence (non-solvated) of water molecules. The ligand–protein interaction fingerprints (PLIF) scheme was used to summarize and compare the interactions. All models delineated similar 3D hERG binding features, however, small deviations of about ~0.4 Å were observed between important hotspots of molecular interaction fields (MIFs) between solvated and non-solvated hERG models. These small changes in conformations do not affect the performance and predictive power of the model to any significant extent. The model that exhibits the best statistical values was attained with a cryo_EM structure of the hERG channel in open state without water. This model also showed the best R² of 0.58 and 0.51 for the internal and external validation test sets respectively. Our results suggest that the inclusion of water molecules during the docking process has little effect on conformations and this conformational change does not impact the predictive ability of the 3D QSAR models.

The in vitro protective effects of the three novel nanomolar reversible inhibitors of human cholinesterases against irreversible inhibition by organophosphorous chemical warfare agents

Acetylcholinesterase (AChE) is an enzyme which terminates the cholinergic neurotransmission, by hydrolyzing acetylcholine at the nerve and nerve-muscle junctions. The reversible inhibition of AChE was suggested as the pre-treatment option of the intoxications caused by nerve agents. Based on our derived 3D-QSAR model for the reversible AChE inhibitors, we designed and synthesized three novel compounds 8-10, joining the tacrine and aroylacrylic acid phenylamide moieties, with a longer methylene chain to target two distinct, toplogically separated anionic areas on the AChE. The targeted compounds exerted low nanomolar to subnanomolar potency toward the E. eel and human AChE's as well as the human BChE and showed mixed inhibition type in kinetic studies. All compounds were able to slow down the irreversible inhibition of the human AChE by several nerve agents including tabun, soman and VX, with the estimated protective indices around 5, indicating a valuable level of protection. Putative noncovalent interactions of the selected ligand 10 with AChE active site gorge were finally explored by molecular dynamics simulation suggesting a formation of the salt bridge between the protonated linker amino group and the negatively charged Asp74 carboxylate side chain as a significant player for the successful molecular recognition in line with the design strategy. The designed compounds may represent a new class of promising leads for the development of more effective pre-treatment options.

Linarin improves the dyskinesia recovery in Alzheimer's disease zebrafish by inhibiting the acetylcholinesterase activity

BACKGROUND

Due to complex pathogenesis of Alzheimer's disease (AD), currently there is no effective disease-modifying treatment. Acetylcholinesterase (AChE) has introduced itself as an important target for AD therapy. Linarin as the representative active ingredient of flavonoid glycoside in Flos chrysanthemi indici has been found to have anti-acetylcholinesterase effect.

AIMS

The present study intended to explore the potential effect of linarin for treatment of AD.

MAIN METHODS

In this study, molecular docking simulation was used to evaluate whether linarin could dock with AChE and decipher the mechanism of linarin as an AChE inhibitor. After molecular docking simulation, AlCl₃-induced Alzheimer's disease zebrafish model was established. Effects of linarin on treating AD zebrafish dyskinesia and AChE inhibition were compared with donepezil (DPZ) which was used as a positive control drug.

KEY FINDINGS

Molecular docking simulation showed that linarin plays a critical role in AChE inhibition by binding AChE active sites. The experiments illustrated that the dyskinesia recovery rate of AD zebrafish could be significantly improved by linarin. The dyskinesia recovery and AChE inhibition rate were 88.0% and 74.5% respectively, while those of DPZ were 79.3% and 43.6%.

SIGNIFICANCE

These findings provide evidences for supporting linarin to be developed into an AD drug by inhibiting the activity of AChE.

Molecular docking, 3D-QSAR and structural optimization on imidazo-pyridine derivatives dually targeting AT1 and PPARg

Telmisartan, a bifunctional agent of blood pressure lowering and glycemia reduction, was previously reported to antagonize angiotensin II type 1 (AT1) receptor and partially activate peroxisome proliferator-activated receptor γ (PPARγ) simultaneously. Through the modification to telmisartan, researchers designed and obtained imidazo-\pyridine derivatives with the IC₅₀s of 0.49∼94.1 nM against AT1 and EC₅₀s of 20∼3640 nM towards PPARγ partial activation. For minutely inquiring the interaction modes with the relevant receptor and analyzing the structure-activity relationships, molecular docking and 3D-QSAR (Quantitative structure-activity relationships) analysis of these imidazo-\pyridines on dual targets were conducted in this work. Docking approaches of these derivatives with both receptors provided explicit interaction behaviors and excellent matching degree with the binding pockets. The best CoMFA (Comparative Molecular Field Analysis) models exhibited predictive results of q²=0.553, r²=0.954, SEE=0.127, r²_pred=0.779 for AT1 and q²=0.503, r²=1.00, SEE=0.019, r²_pred=0.604 for PPARγ, respectively. The contour maps from the optimal model showed detailed information of structural features (steric and electrostatic fields) towards the biological activity. Combining the bioisosterism with the valuable information from above studies, we designed six molecules with better predicted activities towards AT1 and PPARγ partial activation. Overall, these results could be useful for designing potential dual AT1 antagonists and partial PPARγ agonists.

Molecular Docking at a Glance

The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.

Multiple Linear Regressions by Maximizing the Likelihood under Assumption of Generalized Gauss-Laplace Distribution of the Error

Multiple linear regression analysis is widely used to link an outcome with predictors for better understanding of the behaviour of the outcome of interest. Usually, under the assumption that the errors follow a normal distribution, the coefficients of the model are estimated by minimizing the sum of squared deviations. A new approach based on maximum likelihood estimation is proposed for finding the coefficients on linear models with two predictors without any constrictive assumptions on the distribution of the errors. The algorithm was developed, implemented, and tested as proof-of-concept using fourteen sets of compounds by investigating the link between activity/property (as outcome) and structural feature information incorporated by molecular descriptors (as predictors). The results on real data demonstrated that in all investigated cases the power of the error is significantly different by the convenient value of two when the Gauss-Laplace distribution was used to relax the constrictive assumption of the normal distribution of the error. Therefore, the Gauss-Laplace distribution of the error could not be rejected while the hypothesis that the power of the error from Gauss-Laplace distribution is normal distributed also failed to be rejected.

Identification of petrogenic produced water components as acetylcholine esterase inhibitors

Effect-directed analysis (EDA) was applied to identify acetylcholine esterase (AChE) inhibitors in produced water. Common produced water components from oil production activities, such as polycyclic aromatic hydrocarbons (PAHs), alkylphenols, and naphthenic acids were tested for AChE inhibition using a simple mixture of PAHs and naphthenic acids. Produced water samples collected from two offshore platforms in the Norwegian sector of the North Sea were extracted by solid phase extraction and fractionated by open-column liquid solid chromatography and high-performance liquid chromatography (HPLC) before being tested using a high-throughput and automated AChE assay. The HPLC fractions causing the strongest AChE inhibition were analysed by gas chromatography coupled to a high-resolution time-of-flight mass spectrometry (GC-HR-ToF-MS). Butylated hydroxytoluene and 4-phenyl-1,2-dihydronaphthalene were identified as two produced water components capable of inhibiting AChE at low concentrations. In order to assess the potential presence of such compounds discharged into aquatic ecosystems, AChE activity in fish tissues was measured. Saithe (Pollachius virens) caught near two offshore platforms showed lower enzymatic activity than those collected from a reference location. Target analysis of saithe did not detected the presence of these two putative AChE inhibitors and suggest that additional compounds such as PAHs, naphthenic acids and yet un-identified compounds may also contribute to the purported AChE inhibition observed in saithe.

Perspectives for the structure-based design of acetylcholinesterase reactivators

Rational design of active molecules through structure-based methods has been gaining adepts during the last decades due to the wider availability of protein structures, most of them conjugated with relevant ligands. Acetylcholinesterase (AChE) is a molecular target with a considerable amount of data related to its sequence and 3-dimensional structure. In addition, there are structural insights about the mechanism of action of the natural substrate and drugs used in Alzheimer's disease, organophosphorus compounds, among others. We looked for AChE structural data useful for in silico design of potential interacting molecules. In particular, we focused on information regarding the design of ligands aimed to reactivate AChE catalytic activity. The structures of 178 AChE were annotated and categorized on different subsets according to the nature of the ligand, source organisms and experimental details. We compared sequence homology among the active site from Torpedo californica, Mus musculus and Homo sapiens with the latter two species having the closest relationship (88.9% identity). In addition, the mechanism of organophosphorus binding and the design of effective reactivators are reviewed. A curated data collection obtained with information from several sources was included for researchers working on the field. Finally, a molecular dynamics simulation with human AChE indicated that the catalytic pocket volume stabilizes around 600 Å(3), providing additional clues for drug design.

A mechanism-based 3D-QSAR approach for classification and prediction of acetylcholinesterase inhibitory potency of organophosphate and carbamate analogs

Organophosphate (OP) and carbamate esters can inhibit acetylcholinesterase (AChE) by binding covalently to a serine residue in the enzyme active site, and their inhibitory potency depends largely on affinity for the enzyme and the reactivity of the ester. Despite this understanding, there has been no mechanism-based in silico approach for classification and prediction of the inhibitory potency of ether OPs or carbamates. This prompted us to develop a three dimensional prediction framework for OPs, carbamates, and their analogs. Inhibitory structures of a compound that can form the covalent bond were identified through analysis of docked conformations of the compound and its metabolites. Inhibitory potencies of the selected structures were then predicted using a previously developed three dimensional quantitative structure-active relationship. This approach was validated with a large number of structurally diverse OP and carbamate compounds encompassing widely used insecticides and structural analogs including OP flame retardants and thio- and dithiocarbamate pesticides. The modeling revealed that: (1) in addition to classical OP metabolic activation, the toxicity of carbamate compounds can be dependent on biotransformation, (2) OP and carbamate analogs such as OP flame retardants and thiocarbamate herbicides can act as AChEI, (3) hydrogen bonds at the oxyanion hole is critical for AChE inhibition through the covalent bond, and (4) π-π interaction with Trp86 is necessary for strong inhibition of AChE. Our combined computation approach provided detailed understanding of the mechanism of action of OP and carbamate compounds and may be useful for screening a diversity of chemical structures for AChE inhibitory potency.