Discovery of Novel Acetylcholinesterase Inhibitors by Virtual Screening, In Vitro Screening, and Molecular Dynamics Simulations

Combining Deep Learning and Structural Modeling to Identify Potential Acetylcholinesterase Inhibitors from Hericium erinaceus

Alzheimer's disease (AD) is the most common type of dementia, affecting over 50 million people worldwide. Currently, most approved medications for AD inhibit the activity of acetylcholinesterase (AChE), but these treatments often come with harmful side effects. There is growing interest in the use of natural compounds for disease prevention, alleviation, and treatment. This trend is driven by the anticipation that these substances may incur fewer side effects than existing medications. This research presents a computational approach combining machine learning with structural modeling to discover compounds from medicinal mushrooms with a high potential to inhibit the activity of AChE. First, we developed a deep neural network capable of rapidly screening a vast number of compounds to indicate their potential to inhibit AChE activity. Subsequently, we applied deep learning models to screen the compounds in the BACMUSHBASE database, which catalogs the bioactive compounds from cultivated and wild mushroom varieties local to Thailand, resulting in the identification of five promising compounds. Next, the five identified compounds underwent molecular docking techniques to calculate the binding energy between the compounds and AChE. This allowed us to refine the selection to two compounds, erinacerin A and hericenone B. Further analysis of the binding energy patterns between these compounds and the target protein revealed that both compounds displayed binding energy profiles similar to the combined characteristics of donepezil and galanthamine, the prescription drugs for AD. We propose that these two compounds, derived from Hericium erinaceus (also known as lion's mane mushroom), are suitable candidates for further research and development into symptom-alleviating AD medications.

Noncovalent binding of carbofuran to acetylcholinesterase from Homo sapiens, Danio rerio, Apis mellifera and Caenorhabditis elegans: Homology modelling, molecular docking and dynamics, and quantum biochemistry description

Although various regulatory agencies have banned or severely restricted the use of carbofuran (CAR), recent reports indicate the presence of CAR residues in both cultivated and wild areas. This pesticide is a potent inhibitor of acetylcholinesterase (AChE), which acts by preventing the hydrolysis of acetylcholine (ACh). Given the critical role of AChE::ACh in the proper functioning of the nervous system, we thought it appropriate to investigate the binding of CAR to AChEs from Homo sapiens, Danio rerio, Apis mellifera, and Caenorhabditis elegans using homology modelling, molecular docking, molecular dynamics, and quantum biochemistry. Molecular docking and dynamics results indicated peculiar structural behavior in each AChE::CAR system. Quantum biochemistry results showed similar affinities for all complexes, confirming the description of carbofuran as a broad-spectrum pesticide, and have a limited correlation with IC50 values. We found the following decreasing affinity order of AChE species: H. sapiens > A. mellifera > C. elegans > D. rerio. The computational results suggest that CAR occupies different pockets in the AChEs studied. In addition, our results showed that CAR binds to hsAChE and ceAChE in a very similar manner: it has high affinities for the same subsites in both species and forms hydrogen bonds with residues (hsTYR124 and ceTRP107) occupying homologous positions in the peripheral site. This suggests that this nematode is a potential model to evaluate the toxicity of carbamates, even though the sequence identity between them is only 41 %. Interestingly, we also observed that the catalytic histidines of drAChE and amAChE exhibited favorable contacts with carbofuran, suggesting that the non-covalent binding of carbofuran to these proteins may promote faster carbamylation rates than the binding modes to human and worm acetylcholinesterases. Our computational results provide a better understanding of the binding mechanisms in these complexes, as well as new insights into the mechanism of carbamylation.

Derivatives of 9-phosphorylated acridine as butyrylcholinesterase inhibitors with antioxidant activity and the ability to inhibit β-amyloid self-aggregation: potential therapeutic agents for Alzheimer's disease

We investigated the inhibitory activities of novel 9-phosphoryl-9,10-dihydroacridines and 9-phosphorylacridines against acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and carboxylesterase (CES). We also studied the abilities of the new compounds to interfere with the self-aggregation of β-amyloid (Aβ42) in the thioflavin test as well as their antioxidant activities in the ABTS and FRAP assays. We used molecular docking, molecular dynamics simulations, and quantum-chemical calculations to explain experimental results. All new compounds weakly inhibited AChE and off-target CES. Dihydroacridines with aryl substituents in the phosphoryl moiety inhibited BChE; the most active were the dibenzyloxy derivative 1d and its diphenethyl bioisostere 1e (IC50 = 2.90 ± 0.23 µM and 3.22 ± 0.25 µM, respectively). Only one acridine, 2d, an analog of dihydroacridine, 1d, was an effective BChE inhibitor (IC50 = 6.90 ± 0.55 μM), consistent with docking results. Dihydroacridines inhibited Aβ42 self-aggregation; 1d and 1e were the most active (58.9% ± 4.7% and 46.9% ± 4.2%, respectively). All dihydroacridines 1 demonstrated high ABTS•+-scavenging and iron-reducing activities comparable to Trolox, but acridines 2 were almost inactive. Observed features were well explained by quantum-chemical calculations. ADMET parameters calculated for all compounds predicted favorable intestinal absorption, good blood-brain barrier permeability, and low cardiac toxicity. Overall, the best results were obtained for two dihydroacridine derivatives 1d and 1e with dibenzyloxy and diphenethyl substituents in the phosphoryl moiety. These compounds displayed high inhibition of BChE activity and Aβ42 self-aggregation, high antioxidant activity, and favorable predicted ADMET profiles. Therefore, we consider 1d and 1e as lead compounds for further in-depth studies as potential anti-AD preparations.

Advances of computer-aided drug design (CADD) in the development of anti-Azheimer's-disease drugs

Alzheimer's disease (AD) is a degenerative disease of the nervous system that progressively destroys memory and thinking skills. Currently there is no treatment to prevent or cure AD; targeting the direct cause of neuronal degeneration would constitute a rational strategy and hopefully offer better options for the treatment of AD. This paper first summarizes the physiological and pathological pathogenesis of AD and then discusses the representative drug candidates for targeted therapy of AD and their binding mode with their targets. Finally, the applications of computer-aided drug design in discovering anti-AD drugs are reviewed. Teaser.

PyPLIF HIPPOS and Receptor Ensemble Docking Increase the Prediction Accuracy of the Structure-Based Virtual Screening Protocol Targeting Acetylcholinesterase

In this article, the upgrading process of the structure-based virtual screening (SBVS) protocol targeting acetylcholinesterase (AChE) previously published in 2017 is presented. The upgraded version of PyPLIF called PyPLIF HIPPOS and the receptor ensemble docking (RED) method using AutoDock Vina were employed to calculate the ensemble protein–ligand interaction fingerprints (ensPLIF) in a retrospective SBVS campaign targeting AChE. A machine learning technique called recursive partitioning and regression trees (RPART) was then used to optimize the prediction accuracy of the protocol by using the ensPLIF values as the descriptors. The best protocol resulting from this research outperformed the previously published SBVS protocol targeting AChE.