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More UA, Patel S, Rahevar V, Noolvi MN, Aminabhavi TM, Joshi SD. In Silico ADME and QSAR Studies on a Set of Coumarin Derivatives As Acetylcholinesterase Inhibitors Against Alzheimer’s Disease: CoMFA, CoMSIA, Topomer CoMFA, and HQSAR. LETT DRUG DES DISCOV 2020. [DOI: 10.2174/1570180816666190712095907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:Alzheimer’s disease (AD) is increasingly being recognized as one of the lethal diseases in older people. Acetylcholinesterase (AChE) has proven to be the most promising target in AD, used for designing drugs against AD.Methods:In silico studies, 2D- or 3D-QSAR like hologram QSAR (HQSAR), Topomer comparative molecular field analysis (Topomer CoMFA), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods were used to generate QSAR models for acetylcholinesterase inhibitors.Results:Acetylcholinesterase inhibitors used for the present study contain a series of 7- hydroxycoumarin derivatives connected by piperidine, piperazine, tacrine, triazole, or benzyl fragments through alkyl or amide spacer training set compounds were used to generate best model with a HQSAR q2 value of 0.916 and r2 value of 0.940; a Topomer CoMFA q2 value of 0.907 and r2 value of 0.959, CoMFA q2 value of 0.880 and r2 value of 0.960; and a CoMSIA q2 value of 0.865 and r2 value of 0.941. In addition, contour plots obtained from QSAR models suggested the significant regions that influenced the AChE inhibitory activity.Conclusion:In light of these results, this study provides knowledge about the structural requirements for the development of more active acetylcholinesterase inhibitors. In addition, the predicted ADME profile helps us to find CNS active molecules, the obtained prediction compared with well-known AChE inhibitors viz., ensaculin, tacrine, galantamine, rivastigmine, and donepezil. Based on the knowledge obtained from these studies, the hybridization approach is one of the best ways to find lead compounds and these findings can be useful in the treatment of Alzheimer's disease.
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Affiliation(s)
- Uttam Ashok More
- Department of Pharmaceutical Chemistry, Shree Dhanvantary Pharmacy College, Kim, Surat 394110, India
| | - Sameera Patel
- Department of Pharmaceutical Chemistry, Shree Dhanvantary Pharmacy College, Kim, Surat 394110, India
| | - Vidhi Rahevar
- Department of Pharmaceutical Chemistry, Shree Dhanvantary Pharmacy College, Kim, Surat 394110, India
| | | | - Tejraj M. Aminabhavi
- Department of Pharmaceutical Chemistry, Shree Dhanvantary Pharmacy College, Kim, Surat 394110, India
| | - Shrinivas D. Joshi
- Department of Pharmaceutical Chemistry, SET’s College of Pharmacy, Dharwad 580002, India
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Pan C, Meng H, Zhang S, Zuo Z, Shen Y, Wang L, Chang KJ. Homology modeling and 3D-QSAR study of benzhydrylpiperazine δ opioid receptor agonists. Comput Biol Chem 2019; 83:107109. [PMID: 31445419 DOI: 10.1016/j.compbiolchem.2019.107109] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 08/04/2019] [Accepted: 08/13/2019] [Indexed: 12/16/2022]
Abstract
The binding affinity of a series of benzhydrylpiperazine δ opioid receptor agonists were pooled and evaluated by using 3D-QSAR and homology modeling/molecular docking methods. Ligand-based CoMFA and CoMSIA 3D-QSAR analyses with 46 compounds were performed on benzhydrylpiperazine analogues by taking the most active compound BW373U86 as the template. The models were generated successfully with q2 value of 0.508 and r2 value of 0.964 for CoMFA, and q2 value of 0.530 and r2 value of 0.927 for CoMSIA. The predictive capabilities of the two models were validated on the test set with R2pred value of 0.720 and 0.814, respectively. The CoMSIA model appeared to work better in this case. A homology model of active form of δ opioid receptor was established by Swiss-Model using a reported crystal structure of active μ opioid receptor as a template, and was further optimized using nanosecond scale molecular dynamics simulation. The most active compound BW373U86 was docked to the active site of δ opioid receptor and the lowest energy binding pose was then used to identify binding residues such as s Gln105, Lys108, Leu125, Asp128, Tyr129, Leu200, Met132, Met199, Lys214, Trp274, Ile277, Ile304 and Tyr308. The docking and 3D-QSAR results showed that hydrogen bond and hydrophobic interactions played major roles in ligand-receptor interactions. Our results highlight that an approach combining structure-based homology modeling/molecular docking and ligand-based 3D-QSAR methods could be useful in designing of new opioid receptor agonists.
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Affiliation(s)
- Chenling Pan
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming 650500, China
| | - Hao Meng
- Beijing Beike Deyuan Bio-Pharm Technology Co., Ltd., Beijing, 100094, China
| | - Shuqun Zhang
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, 650201, China
| | - Zhili Zuo
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, 650201, China
| | - Yuehai Shen
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming 650500, China; State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, 650201, China.
| | - Liangliang Wang
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, 650201, China.
| | - Kwen-Jen Chang
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming 650500, China
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Yousefinejad S, Mahboubifar M, Rasekh S. Prediction of different antibacterial activity in a new set of formyl hydroxyamino derivatives with potent action on peptide deformylase using structural information. Struct Chem 2018. [DOI: 10.1007/s11224-018-1242-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Zhang S, Lin Z, Pu Y, Zhang Y, Zhang L, Zuo Z. Comparative QSAR studies using HQSAR, CoMFA, and CoMSIA methods on cyclic sulfone hydroxyethylamines as BACE1 inhibitors. Comput Biol Chem 2017; 67:38-47. [DOI: 10.1016/j.compbiolchem.2016.12.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Revised: 11/02/2016] [Accepted: 12/16/2016] [Indexed: 10/20/2022]
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Combined 3D-QSAR modeling and molecular docking study on spiro-derivatives as inhibitors of acetyl-CoA carboxylase. Med Chem Res 2016. [DOI: 10.1007/s00044-016-1743-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Zhang S, Hou B, Yang H, Zuo Z. Design and prediction of new acetylcholinesterase inhibitor via quantitative structure activity relationship of huprines derivatives. Arch Pharm Res 2016; 39:591-602. [PMID: 26832327 DOI: 10.1007/s12272-016-0709-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Accepted: 01/14/2016] [Indexed: 11/26/2022]
Abstract
Acetylcholinesterase (AChE) is an important enzyme in the pathogenesis of Alzheimer's disease (AD). Comparative quantitative structure-activity relationship (QSAR) analyses on some huprines inhibitors against AChE were carried out using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR) methods. Three highly predictive QSAR models were constructed successfully based on the training set. The CoMFA, CoMSIA, and HQSAR models have values of r (2) = 0.988, q (2) = 0.757, ONC = 6; r (2) = 0.966, q (2) = 0.645, ONC = 5; and r (2) = 0.957, q (2) = 0.736, ONC = 6. The predictabilities were validated using an external test sets, and the predictive r (2) values obtained by the three models were 0.984, 0.973, and 0.783, respectively. The analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the AChE to further understand the vital interactions between huprines and the protease. On the basis of the QSAR study, 14 new potent molecules have been designed and six of them are predicted to be more active than the best active compound 24 described in the literature. The final QSAR models could be helpful in design and development of novel active AChE inhibitors.
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Affiliation(s)
- Shuqun Zhang
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China
| | - Bo Hou
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China
| | - Huaiyu Yang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Zhili Zuo
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China.
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
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Figueroa-Valverde L, Díaz-Cedillo F, López-Ramos M, Garcia-Cervera E, Rivero-Rosado V. Synthesis and theoretical QSAR study of a naphthalene–androsterone derivative. MONATSHEFTE FUR CHEMIE 2011. [DOI: 10.1007/s00706-011-0584-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Gao J, Cheng Y, Cui W, Chen Q, Zhang F, Du Y, Ji M. 3D-QSAR and molecular docking studies of hydroxamic acids as peptide deformylase inhibitors. Med Chem Res 2011. [DOI: 10.1007/s00044-011-9672-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Sharma A, Khuller GK, Sharma S. Peptide deformylase--a promising therapeutic target for tuberculosis and antibacterial drug discovery. Expert Opin Ther Targets 2009; 13:753-65. [PMID: 19530983 DOI: 10.1517/14728220903005590] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND Tuberculosis (TB) remains the most important infectious disease causing morbidity and death, due to the human pathogen Mycobacterium tuberculosis. The emergence of multi-drug-resistant and extensively-drug-resistant forms of TB have resulted in an increase in the number of TB cases. Thus, there is an urgent need to identify new drugs with novel targets to ensure future therapeutic success. Studies have indicated that peptide deformylase is an interesting potential candidate for discovering antimicrobial agents. OBJECTIVE To explore the role of peptide deformylase, a highly conserved metalloprotease and an essential enzyme in bacterial life cycle, as a target for antibacterial as well as antimycobacterial drug development. METHODS This review is based on recent published literature and online resources related to peptide deformylase inhibitors and their antibacterial potential. RESULTS/CONCLUSION Peptide deformylase is an emerging therapeutic target for the treatment of tuberculosis and peptide deformylase inhibitors can act as potential future antibacterial agents.
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Affiliation(s)
- Anshika Sharma
- University of Notre Dame, Department of Biological Sciences, Notre Dame, IN-46556, USA
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