Three-Dimensional Quantitative Structure-Activity Relationship Analysis and Molecular Docking of some Nonpeptidic Inhibitors of Protein Tyrosine Phosphatase as Anti-Alzheimer Drugs
Background & Objective: Alzheimer’s disease (AD) is a kind of neuropsychiatric disorder that gradually degrades the mental abilities. High level activity of protein tyrosine phosphatase (PTP) results in memory function loss and Alzheimer disease. Thus, the inhibition of PTP activity can be consi...
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| Format: | Article |
| Language: | English |
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Fasa University of Medical Sciences
2020-06-01
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| Series: | Journal of Advanced Biomedical Sciences |
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| Online Access: | http://jabs.fums.ac.ir/article-1-1617-en.pdf |
| Summary: | Background & Objective: Alzheimer’s disease (AD) is a kind of neuropsychiatric disorder that gradually degrades the mental abilities. High level activity of protein tyrosine phosphatase (PTP) results in memory function loss and Alzheimer disease. Thus, the inhibition of PTP activity can be considered as a potential target for the discovery of anti-Alzheimer drug. In this study, using computational techniques anti-alzheimer drug candidates will be designed.
Materials & Methods: The three-dimensional quantitative structure activity relationship (3D-QSAR) computational studies on PTP inhibitors were performed. Accordingly, comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods were used to determine the required factors for the activity of these compounds. Distill module was applied for the alignment of molecules. A number of new active inhibitors have been proposed using the components of the CoMFA model. Molecular attachment studies were performed to investigate the inhibitory mechanism, identify bioactive conformer, and determine key interactions. Finally, ADMET studies (absorption, distribution, metabolism, digestion and toxicity) were performed on these inhibitors in a computer environment and compared with standard ranges.
Results: The statistical parameters from the models (CoMFA: q2 =0.653,r2ncv=0.961, r2pred =0.770, and CoMSIA: q2=0.564, r2ncv = 0.933, r2pred= 0.746) indicate that the data are well fitted and have high predictive ability. Based on the information obtained from the constructed models, a novel set of tyrosine phosphatase inhibitors with new molecular frameworks have been introduced as new anti-Alzheimer's drug candidates.
Conclusion: Computational techniques play a valuable role in drug design. Optimal r2pred and q2 statistical parameters led to the logical design of a number of new inhibitors of tyrosine phosphatase protein, which were introduced as new antimicrobial drug candidates. |
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| ISSN: | 2228-5105 2783-1523 |