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|Title:||Identification of Novel Cholinesterase and Other Biological Target Inhibitors Using Computer Aided Drug Design (CADD) Strategy|
|Publisher:||University of Karachi, Karachi.|
|Abstract:||The cholinesterase enzyme consists of two family members acetylcholinesterase (AChE, EC 188.8.131.52) and butyrylcholinesterase (BChE, EC 184.108.40.206), functioning as terminators of the cholinergic neurotransmission. These (AChE & BChE) enzymes were selected as a receptor to identify effective inhibitors by computational techniques towards Alzheimer disease. Computational techniques like molecular docking simulation, molecular dynamic (MD) simulation, three dimension quantitative structure–activity relationship (3D-QSAR) and virtual screening (VS) techniques were applied on targeted enzyme to understand the binding mechanism and get diverse hit to lead compounds by using different datasets. Physostigmine analogues as AChE inhibitor were found to increase the long term memory process. Due to this reason 3D-QSAR modeling applied on forty inhibitors of physostigmine to explore their structure activity correlation with AChE. The 3D-QSAR modeling use to developed two type of satisfactory models, comparative molecular field analysis (CoMFA) (r2 = 0.989, q2 = 0.762) and comparative molecular similarity indices analysis (CoMSIA) (r2 = 0.988, q2 = 0.754). The correlation coefficient values of CoMFA & CoMSIA test sets were 0.730 and 0.720, respectively. In molecular docking simulation, four different datasets including isolated steroidal, adamentyl and oxatrizine derivatives were used to explore the binding modes of all selected diverse compounds inside binding pocket of BChE. Theoretical results of these inhibitors were in good agreement with the experimental results. In the next phase, MD simulations were applied to correlate the generated docking (vacuum) results with dynamic conditions. This study was applied on three models (apo structure of BChE, highest and lowest active compounds of oxatriazine derivatives series) to examine the active site residues fluctuations. MD simulation studies were carried out in an explicit solvent model using the AMBER 12.0 package. The generated results of simulation were monitored till 10ns for three different selected models of BChE. Furthermore, structure-based virtual screening study was applied to explore the hit compounds of different core structure for BChE. In this study, ten million compounds were retrieved from freely available different databases like ZINC, NCI, MayBridge and ChemBridge databases. This study focused small scale structure-based virtual screening against BChE. Sybyl software was the appropriate choice for VS of BChE among the GOLD, Sybyl, and MOE software. Selection criteria was based on re-docking, cross docking, Enrichment Factor (EF) and Area under the curve (AUC). On the basis of different filters twelve compounds were identified as potential hits. Additionally, Vascular Endothelial Growth Factor (VEGF) and B-RAF kinase (member of Ras Activating Factor (RAF) family) target proteins were selected to compare two different types (ligand-based & structure-based) of 3D-QSAR technique, respectively. A diaryl-acylsulfonamide derivative is reported as VEGF inhibitors which were used by means of CoMFA studies to find the relation between biological activities of inhibitors and their structures of VEGF. These derivatives showed q2 values up to 0.417. The obtained model was found satisfactory in terms of excellent external predictivity 0.8. According to these results, we concluded that CoMFA technique may have some predictive power for the analysis of the generated model for VEGF. In V600EB-RAF three different datasets of inhibitors (pyrazine, pyridoimidazolones and central phenyl core of pyridoimidazolones derivatives) were used for CoMFA and CoMSIA study. Among database and receptor-guided alignment methods, receptor-guided alignment with most active conformers produced satisfactory results for both the 3D-QSAR models (CoMFA & CoMSIA) with sufficient statistical validation with y-randomization test. On the basis of these two studies few new structures were designed. The newly predicted structure (IIIa) showed higher inhibitory potency (pIC50 6.826) that indicated most active compound of the 2, 6- disubstituted pyrazine series.|
|Gov't Doc #:||16913|
|Appears in Collections:||PhD Thesis of All Public / Private Sector Universities / DAIs.|
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