Comparative molecular field analysis and comparative molecular similarity indices analysis of hydroxyethylamine derivatives as selective human BACE-1 inhibitor

Machine learning models for predicting the activity of AChE and BACE1 dual inhibitors for the treatment of Alzheimer's disease

Multi-target directed ligand-based 2D-QSAR models were developed using different N-benzyl piperidine derivatives showing inhibitory activity toward acetylcholinesterase (AChE) and β-Site amyloid precursor protein cleaving enzyme (BACE1). Five different classes of molecular descriptors belonging to spatial, structural, thermodynamics, electro-topological and E-state indices were used for machine learning by linear method, genetic function approximation (GFA) and nonlinear method, support vector machine (SVM) and artificial neural network (ANN). Dataset used for QSAR model development includes 57 AChE and 53 BACE1 inhibitors. Statistically significant models were developed for AChE (R² = 0.8688, q² = 0.8600) and BACE1 (R² = 0.8177, q² = 0.7888) enzyme inhibitors. Each model was generated with an optimum five significant molecular descriptors such as electro-topological (ES_Count_aaCH and ES_Count_dssC), structural (QED_HBD, Num_TerminalRotomers), spatial (JURS_FNSA_1) for AChE and structural (Cl_Count, Num_Terminal Rotomers), electro-topological (ES_Count_dO), electronic (Dipole_Z) and spatial (Shadow_nu) for BACE1 enzyme, determining the key role in its enzyme inhibitory activity. The predictive ability of the generated machine learning models was validated using the leave-one-out, Fischer (F) statistics and predictions based on the test set of 11 AChE (r² = 0.8469, r²_pred = 0.8138) and BACE1 (r² = 0.7805, r²_pred = 0.7128) inhibitors. Further, nonlinear machine learning methods such as ANN and SVM predicted better than the linear method GFA. These molecular descriptors are very important in describing the inhibitory activity of AChE and BACE1 enzymes and should be used further for the rational design of multi-targeted anti-Alzheimer's lead molecules.

Recent Studies on Design and Development of Drugs Against Alzheimer's Disease (AD) Based on Inhibition of BACE-1 and Other AD-causative Agents

BACKGROUND

Alzheimer's disease (AD) is one of the neurodegenerative diseases and has been hypothesized to be a protein misfolding disease. In the generation of AD, β-secretase, γ-secretase, and tau protein play an important role. A literature search reflects ever increasing interest in the design and development of anti-AD drugs targeting β-secretase, γ-secretase, and tau protein.

OBJECTIVE

The objective is to explore the structural aspects and role of β-secretase, γ-secretase, and tau protein in AD and the efforts made to exploit them for the design of effective anti-AD drugs.

METHODS

The manuscript covers the recent studies on design and development of anti-AD drugs exploiting amyloid and cholinergic hypotheses.

RESULTS

Based on amyloid and cholinergic hypotheses, effective anti-AD drugs have been searched out in which non-peptidic BACE1 inhibitors have been most prominent.

CONCLUSION

Further exploitation of the structural aspects and the inhibition mechanism for β-secretase, γ-secretase, and tau protein and the use of cholinergic hypothesis may lead still more potent anti-AD drugs.

QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer's Disease

Alzheimer’s disease is one of the most common neurodegenerative disorders in elder population. The β-site amyloid cleavage enzyme 1 (BACE1) is the major constituent of amyloid plaques and plays a central role in this brain pathogenesis, thus it constitutes an auspicious pharmacological target for its treatment. In this paper, a QSAR model for identification of potential inhibitors of BACE1 protein is designed by using classification methods. For building this model, a database with 215 molecules collected from different sources has been assembled. This dataset contains diverse compounds with different scaffolds and physical-chemical properties, covering a wide chemical space in the drug-like range. The most distinctive aspect of the applied QSAR strategy is the combination of hybridization with backward elimination of models, which contributes to improve the quality of the final QSAR model. Another relevant step is the visual analysis of the molecular descriptors that allows guaranteeing the absence of information redundancy in the model. The QSAR model performances have been assessed by traditional metrics, and the final proposed model has low cardinality, and reaches a high percentage of chemical compounds correctly classified.

3D-QSAR and docking studies on adenosine A2A receptor antagonists by the CoMFA method

Parkinson's disease affects millions of people around the world. Recently, adenosine A2A receptor antagonists have been identified as a drug target for the treatment of Parkinson's disease. Consequently, there is an immediate need to develop new classes of A2A receptor antagonists. In the present analysis, three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed on a series of pyrimidines, using comparative molecular field analysis (CoMFA). The best prediction was obtained with a CoMFA standard model (q(2) = 0.475, r(2) = 0.977) and a CoMFA region focusing model (q(2) = 0.637, r(2) = 0.976) combined with steric and electrostatic fields. The structural insights derived from the contour maps helped to better interpret the structure-activity relationships. Also, to understand the structure-activity correlation of A2A receptor antagonists, we have carried out molecular docking analysis. Based on the results obtained from the present 3D-QSAR and docking studies, we have identified some key features for increasing the activity of compounds, which have been used to design new A2A receptor antagonists. The newly designed molecules showed high activity with the obtained models.

Encompassing receptor flexibility in virtual screening using ensemble docking-based hybrid QSAR: discovery of novel phytochemicals for BACE1 inhibition

Mimicking receptor flexibility during receptor-ligand binding is a challenging task in computational drug design since it is associated with a large increase in the conformational search space. In the present study, we have devised an in silico design strategy incorporating receptor flexibility in virtual screening to identify potential lead compounds as inhibitors for flexible proteins. We have considered BACE1 (β-secretase), a key target protease from a therapeutic perspective for Alzheimer's disease, as the highly flexible receptor. The protein undergoes significant conformational transitions from open to closed form upon ligand binding, which makes it a difficult target for inhibitor design. We have designed a hybrid structure-activity model containing both ligand based descriptors and energetic descriptors obtained from molecular docking based on a dataset of structurally diverse BACE1 inhibitors. An ensemble of receptor conformations have been used in the docking study, further improving the prediction ability of the model. The designed model that shows significant prediction ability judged by several statistical parameters has been used to screen an in house developed 3-D structural library of 731 phytochemicals. 24 highly potent, novel BACE1 inhibitors with predicted activity (Ki) ≤ 50 nM have been identified. Detailed analysis reveals pharmacophoric features of these novel inhibitors required to inhibit BACE1.

Study on the active mechanism of β-secretase inhibitors by molecular simulations

The proteolytic enzyme β-secretase (BACE-1) is one of potential drug targets for treating Alzheimers's disease. First, the reliable and accurate models of three-dimensional quantitative structure-activity relationship for the BACE-1 inhibitors were established, and the several important structural factors that mainly influence the inhibitory activity were obtained. Second, the results of molecular docking presented the binding mode between BACE-1 and its inhibitors, and molecular dynamic simulations provided the details of the receptor-ligand interactions. Furthermore, several new derivatives were designed and validated based on these theoretical analyses. Our studies revealed the binding mechanism between BACE-1 and its inhibitors, and provide some insights into the further structural modification and the design of new inhibitors with higher activity.