scholarly journals Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2322 ◽  
Author(s):  
Saw Simeon ◽  
Nuttapat Anuwongcharoen ◽  
Watshara Shoombuatong ◽  
Aijaz Ahmad Malik ◽  
Virapong Prachayasittikul ◽  
...  

Alzheimer’s disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential for cognition and memory. A large non-redundant data set of 2,570 compounds with reported IC50values against AChE was obtained from ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 different data splits using random forest. Generated models affordedR2, ${Q}_{\mathrm{CV }}^{2}$ and ${Q}_{\mathrm{Ext}}^{2}$ values in ranges of 0.66–0.93, 0.55–0.79 and 0.56–0.81 for the training set, 10-fold cross-validated set and external set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it affordedR2, ${Q}_{\mathrm{CV }}^{2}$ and ${Q}_{\mathrm{Ext}}^{2}$ values of 0.92 ± 0.01, 0.78 ± 0.06 and 0.78 ± 0.05, respectively. Furthermore, Y-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover, Kennard–Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed that compounds13,5and28exhibited the lowest binding energies of −12.2, −12.0 and −12.0 kcal/mol, respectively, against human AChE, which is modulated by hydrogen bonding,π–πstacking and hydrophobic interaction inside the binding pocket. These information may be used as guidelines for the design of novel and robust AChE inhibitors.

2021 ◽  
Author(s):  
Sait SARI ◽  
Mehmet YILMAZ

Abstract Novel acrylamide and methacryloyl carrying piperazine-dihydrofuran derivatives ( 3a-p ) were designed and obtained from radical cyclizations of unsaturated piperazine derivatives ( 1a-f ) with 1,3-dicarbonyl compounds ( 2a-c ) mediated by Mn(OAc) 3 . Structures of obtained compounds were confirmed with 1 H NMR (proton nuclear magnetic resonance), 13 C NMR (Carbon-13 nuclear magnetic resonance), HRMS (High resolution mass spectrometry), FTIR (Fourier-transform infrared spectroscopy and melting point analysis. Inhibitory activites of all piperazine-dihydrofuran compounds were evaluated against AChE (Acetylcholinesterase) by Ellman method and test results showed that 3a , 3c , 3j and 3l are most active AChEI’s (AChE inhibitors) of our work with IC 50 (half-maximal inhibitory concentration) values of 2.62, 5.29, 1.17 and 3.90 µM, respectively. Furthermore, ligand-protein interactions and inhibitory activity mechanisms of 3a and 3j were investigated by molecular docking. Finally, in silico molecular property and ADME (absorption, distribution, metabolism and excretion) of potential AChEI’s (AChE inhibitor) were predicted by PreADMET and Molinspiration webservers. It can be concluded that the lead compound 3j show excellent inhibiton and satisfactory druglike characteristics.


RSC Advances ◽  
2018 ◽  
Vol 8 (69) ◽  
pp. 39477-39495 ◽  
Author(s):  
Srabanti Jana ◽  
Ankit Ganeshpurkar ◽  
Sushil Kumar Singh

Ligand-based and energy-optimized structure-based approaches were helpful to obtain excellent candidates as non-toxic, PAS site selective, non-competitive AChE inhibitors.


2020 ◽  
Vol 16 (7) ◽  
pp. 903-927 ◽  
Author(s):  
Rahman Abdizadeh ◽  
Farzin Hadizadeh ◽  
Tooba Abdizadeh

Background: Acetylcholinesterase (AChE), a serine hydrolase, is an important drug target in the treatment of Alzheimer's disease (AD). Thus, novel AChE inhibitors were designed and developed as potential drug candidates, for significant therapy of AD. Objective: In this work, molecular modeling studies, including CoMFA, CoMFA-RF, CoMSIA, HQSAR and molecular docking and molecular dynamics simulations were performed on a series of AChE inhibitors to get more potent anti-Alzheimer drugs. Methods: 2D/3D-QSAR models including CoMFA, CoMFA-RF, CoMSIA, and HQSAR methods were carried out on 40 pyrimidinylthiourea derivatives as data set by the Sybylx1.2 program. Molecular docking and molecular dynamics simulations were performed using the MOE software and the Sybyl program, respectively. Partial least squares (PLS) model as descriptors was used for QSAR model generation. Results: The CoMFA (q2, 0.629; r2ncv, 0.901; r2pred, 0.773), CoMFA-RF (q2, 0.775; r2ncv, 0.910; r2pred, 0.824), CoMSIA (q2, 0.754; r2ncv, 0.919; r2pred, 0.874) and HQSAR models (q2, 0.823; r2ncv, 0.976; r2pred, 0.854) for training and test set yielded significant statistical results. Conclusion: These QSAR models were excellent, robust and had good predictive capability. Contour maps obtained from the QSAR models were validated by molecular dynamics simulationassisted molecular docking study. The resulted QSAR models could be useful for the rational design of novel potent AChE inhibitors in Alzheimer's treatment.


2021 ◽  
Author(s):  
Nemanja Djokovic ◽  
◽  
Ana Postolovic ◽  
Katarina Nikolic

The group of 5‐[(amidobenzyl)oxy]‐nicotinamides represents promising group of sirtuin 2 (SIRT2) inhibitors. Despite structural similarity, representatives of this group of inhibitors displayed versatile mechanisms of inhibition which hamper rational drug design. The aim of this research was to form a 3D-QSAR (3D-Quantitative Structure-Activity Relationship) model, define the pharmacophore of this subgroup of SIRT2 inhibitors, define the mode of protein-ligand interactions and design new compounds with improved predicted activity and pharmacokinetics. For the 3D-QSAR study, data set was generated using structures and activities of 166 5‐[(amidobenzyl)oxy]‐nicotinamides. 3D-conformations of compounds were optimized, alignment-independent GRIND2 descriptors were calculated and 3D-QSAR PLS models were generated using 70% of data set. To investigate bioactive conformations of inhibitors, molecular docking was used. Molecular docking analysis identified two clusters of predicted bioactive conformations which is in alignment with experimental observations. The defined pharmacophoric features were used to design novel inhibitors with improved predicted potency and ADMET profiles.


2020 ◽  
Vol 17 (10) ◽  
pp. 1293-1308 ◽  
Author(s):  
Sapna Jain Dabade ◽  
Dheeraj Mandloi ◽  
Amritlal Bajaj

Background: Treatments of fungal diseases, including Candidiasis, remain not up to scratch in spite of the mounting catalog of synthetic antifungal agents. These have served as the impetus for investigating new antifungal agents based on natural products. Consequently, genetic algorithm-multiple linear regression (GA-MLR) based QSAR (Quantitative Structure-Activity Relationship) studies of coumarin analogues along with molecular docking were carried out. Methods: Coumarin analogues with their MIC values were used to generate the training and test sets of compounds for QSAR models development; the analogues were also docked into the binding pocket of NMT (MyristoylCoA: protein N-myristoyltransferase). Results and Discussion: The statistical parameters for internal and external validation of QSAR analysis (R2 = 0.830, Q2 = 0.758, R2Pred = 0.610 and R2m overall = 0.683 ), Y Randomization, Ridge trace, VIF, tolerance and model criteria of Golbraikh and Tropsha data illustrate the robustness of the best proposed QSAR model. Most of the analogues bind to the electrostatic, hydrophobic clamp and display hydrogen bonding with amino acid residues of NMT. Interestingly, the most active coumarin analogue (MolDock score of -189.257) was docked deeply within the binding pocket of NMT, thereby displaying hydrogen bonding with Tyr107, Leu451, Leu450, Gln226, Cys393 and Leu394 amino acid residues. Conclusion: The combinations of descriptors from various descriptor subsets in QSAR analysis have highlighted the role of atomic properties such as polarizability and atomic van der Waals volume to explain the inhibitory activity. The models and related information may pave the way for important insight into the designing of putative NMT inhibitors for Candida albicans.


Author(s):  
Dayena J. Christian ◽  
Rajesh H. Vekariya ◽  
Kinjal D. Patel ◽  
Dhanji P. Rajani ◽  
Smita D. Rajani ◽  
...  

A data set of chalcone and pyrimidine derivatives with anti-malarial activity against Plasmodium falciparum was employed in investigating the quantitative structure-activity relationship (QSAR). Molecular docking study was performed for plasmodium falciparum dihydrofolate reductase (PfDHFR-TS). Genetic function approximation (GFA) technique was used to identify the descriptors that have influence on anti-malarial activity. The most influencing molecular descriptors identified include thermodynamics, structural and physical descriptors. Generated model was found to be good based on correlation coefficient, LOF, rm2 and rcv2 values. Nrotb, solubility, polarizibility may have negative influence on antimalarial activity or play an important role in growth inhibition of Plasmodium falciparum. The QSAR models so constructed provide fruitful insights for the future development of anti-malarial agents.


Author(s):  
Amit Joshi ◽  
G. Sunil Krishnan ◽  
Vikas Kaushik

Abstract Background At present, viral diseases become major concern for the world. SARS-CoV2 and SFTS viruses are deadly in nature, and there is a need for developing best treatments for them. Modern in silico approaches were found to be very handy in determining putative drug molecules. In this study, we analyze interaction of beta-sesquiphellandrene (compound belongs to ginger) with spike protein (Sp) and membrane glycoprotein polyprotein (MPp). Results Our molecular docking and simulation study reveals the perfect binding pocket of Sp and MPp holding beta-sesquiphellandrene (bS). Binding energies for MPp-bS and Sp-bS were found to be − 9.5 kcal/mol and − 10.3 kcal/mol respectively. RMSD and RMSF values for docked complexes were found to be in selectable range, i.e., 1 to 3 Å and 1 to 8 Å respectively. Modern computational tools were used here to make this investigation fast and effective. Further, ADME analysis reveals the therapeutic validations for beta-sesquiphellandrene to act as a useful pharmacoactive compound. Beta-sesquiphellandrene provides not only inhibitory effect on spike protein of SARS-CoV2 but also similar inhibitory effects on membrane glycoprotein polyprotein complex of SFTS virus, which hampers the pathological initiation of the diseases caused by both the viruses, i.e., COVID-19 and severe fever with thrombocytopenia syndrome. Conclusion This method of computational analysis was found to be rapid and effective, and opens new doors in the domain of in silico drug discovery. Beta-sesquiphellandrene can be used as effective medicine to control these harmful pathogens after wet lab validations.


2018 ◽  
Vol 2018 ◽  
pp. 1-24 ◽  
Author(s):  
Shola Elijah Adeniji ◽  
Sani Uba ◽  
Adamu Uzairu

A quantitative structure-activity relationship (QSAR) study was performed to develop a model that relates the structures of 50 compounds to their activities against M. tuberculosis. The compounds were optimized by employing density functional theory (DFT) with B3LYP/6-31G⁎. The Genetic Function Algorithm (GFA) was used to select the descriptors and to generate the correlation model that relates the structural features of the compounds to their biological activities. The optimum model has squared correlation coefficient (R2) of 0.9202, adjusted squared correlation coefficient (Radj) of 0.91012, and leave-one-out (LOO) cross-validation coefficient (Qcv2) value of 0.8954. The external validation test used for confirming the predictive power of the built model has R2pred value of 0.8842. These parameters confirm the stability and robustness of the model. Docking analysis showed the best compound with high docking affinity of −14.6 kcal/mol which formed hydrophobic interaction and hydrogen bond with amino acid residues of M. tuberculosis cytochromes (Mtb CYP121). QSAR and molecular docking studies provide valuable approach for pharmaceutical and medicinal chemists to design and synthesize new anti-Mycobacterium tuberculosis compounds.


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