مطالعات QSAR ، داکینگ مولکولی و شبیه سازی دینامیک مولکولی بازدارنده های فلاپ برای بیماری شریان کرونری
محورهای موضوعی : شیمی کاربردی
1 - عضو هیئت علمی
کلید واژه: Migraine, QSAR, CORALSEA, Docking, Molecular dynamics simulation, GA.,
چکیده مقاله :
هدف: فعال کننده پروتینی 5-لیپوکسی ژناز(فلاپ) در سنتز لیوکوترین لازمند و میانجی های چربی هستند. در این مطالعه محاسبات فعالیت-ساختار روی 30 ترکیب انجام شده است.مطالعات داکینگ مولکولی و شبیه سازی دینامیک مولکولی انجام شده و دو ترکیب دارویی جدید پیشنهاد شده است.
مواد و روش ها: ساختار 30 ترکیب با استفاده از برنامه کم درا رسم شده است.داده های تجربی مورد نیاز از منابع موجود استخراج شده است.ساختار لیگاند با استفاده از برنامه ماروین اسکچ رسم شده و مطالعات داکینگ با استفاده از برنامه وینا برای ارزیابی برهم کنش ها انجام شده است.در این مطالعه از برنامه کورال سی برای به مدل در آوردن فعالیت 30 ترکیب بازدارنده های فلاپ استفاده شده است. در محاسبات داکینگ مولکولی برهم کنش های بین سیستم پروتینی با PDB: 1B43 و مولکول های بهینه شده ارزیابی شدند. . محاسبات شبیه سازی دینامیک مولکولی روی مولکول 9 انجام شد.
نتایج: در محاسبات مونته کارلو برای سری تست و آموزش این نتایج بدست آمدند: 10 n = , 9986/، , R²=9975/0Q²= (برای سری آموزش( و 10 , n = 99997/0, R²= 9995/0Q²= (برای سری تست). در مطالعات داکینک حداکثر افینیتی 9/10-کیلو کالری بر مول بود که نشان دهنده برهم کنش قوی است.
نتیجه گیری:. مولکول های 9 و 15 بعنوان ترکیبات پایدار برای مطالعات بیشتر بالینی پیشنهاد می شوند.
Objectiv: 5-lipoxygenase activating protein (FLAP) is necessary in synthesis of leukotriene, which are lipid mediators. In the present work, a QSAR study was conducted on 30 molecules. Accordingly, the auto molecular Docking and Molecular Dynamics Simulations were performed and 2 novel drugs were proposed.
Methods: The structures of 30 molecules of FLAP inhibitors were drawn using CHEMDRAW program. The required experimental data was obtained from the literature. Ligand input structure was drawn using Marvin Sketch software and the docking studies were performed using Vina program to evaluate the interactions .In the QSAR study, the CORALSEA software has been employed as a tool of modeling of the FLAP inhibitor activity of 30 compounds. In the molecular docking study, the binding affinity was obtained between each of enzyme systems ( PDB: 1B43) and the geometric-optimized molecules of 30 molecules. Regular and Flexible docking approaches were run. Molecular dynamics simulation was tested on 1B43 with molecule 9.
Results: The CORALSEA program results showed that n =10, R²= 0.9986, and Q²= 0.9975 (Training set); also, n =10, R²= 0.9997, and Q²= 0.9995 (test set). In the molecular docking study, the maximum binding affinity of -10.9 kcal/mol was obtained, representing a strong interaction.
Conclusion: In docking study, molecules 9 and 15 were presented as the most stable ones that may be introduced for further investigations, including clinical experiments.
Keywords: FLAP, CORALSEA, Monte Carlo method, Molecular docking, Molecular dynamics simulation
1. G. Bain , C. D. King , K Schaab , Rewolinski M, Pharmacodynamics, pharmacokinetics
and safety of GSK2190915, a novel oral antiinflammatory5-lipoxygenase-activating protein
inhibitor, British Journal of Clinical Pharmacology, 75 (3) (2013) 779-790.
2. J. C. Tardif , L. L'Allier Philippe , R. Ibrahim , C. Grégoire Jean, Treatment with5-lipoxygenase inhibitor VIA-2291 (atreleuton) in patients with recent acute coronary syndrome, Circulation: Cardiovascular Imaging 3 (3) (2010) 298-307.
3. D. Pettersen , Ö. Davidsson , C. Whatling, Recent advances for FLAP inhibitors, Bioorganic & Medicinal Chemistry Letters 25 (13) (2015) 2607-2612.
4. N. S. Stock , G. Bain , J. Zunic , Y. Li , J .Ziff, 5-lipoxygenase-activating protein (FLAP) inhibitors. part 4: development of 3-[3-tert-Butylsulfanyl-1-[4-(6-ethoxypyridin-3-yl)benzyl]- 5-(5-methylpyridin-2-ylmethoxy)-1H-indol-2-yl]-2,2-dimethylpropionic acid (AM803), a potent, oral, once daily FLAP inhibitor, Journal of Medicinal Chemistry 54 (23) (2011) 8013-8029.
5. H. Takahashi , A. Bartolozzi , T .Simpson, Discovery of the novel oxadiazole- Containing 5-lipoxygenase activating protein (FLAP) inhibitor BI 665915. In Comprehensive Accounts of Pharmaceutical Research and Development: From Discovery to Late-Stage Process Development, journal of the American Chemical Society 1239(2016) 101-119.
6. Z. T. Gür , B. Çalışkan , E. Banoglu, Drug discovery approaches targeting 5- lipoxygenase activating protein (FLAP) for inhibition of cellular leukotriene biosynthesis, European Journal of Medicinal Chemistry 153(2018) 34-48.
7. M. Lemurell , J. Ulander , S .Winiwarter , A. Dahlén , Ö Davidsson, Discovery of AZD6642, an inhibitor of 5-lipoxygenase activating protein (FLAP) for the treatment of
inflammatory diseases, Journal of Medicinal Chemistry 58 (2) (2015) 897-911.
8. E. Dere , D. J. Spade , S. J. Hall , A. Altemus, Identification of sperm mRNA biomarkers
associated with testis injury during preclinical testing of pharmaceutical compounds, Toxicology and Applied Pharmacology 320 (2017) 1-7.
9. A. A. Toropov, A. P Toropova, E. Benfenati, The definition of the molecular structure
for potential anti-malaria agents by the Monte Carlo method, Structural Chemistry, 24(2013) 1369-1381.
10. A. P. Toropova, A. A. Toropov, The index of ideality of correlation: A criterion of
predictability of QSAR models for skin permeability?, Science of the Total Environment 586 (2017)466-472.
11. A. A. Toropov, A. P. Toropova , The index of ideality of correlation: A criterion of predictive potential of QSPR/QSAR models?, Mutation Research 81 9(2017) 31-37
12. A. P. Toropova, A. A. Toropov, CORAL: Monte Carlo method to predict endpoint for medical chemistry, Mini-Reviews in Medicinal Chemistry 18(2018) 382-391.
13. Toropov AA, Carbó-Dorca R, Toropova AP, Index of ideality of correlation: New
possibilities to validate QSAR : a case study, Structural Chemistry 29(2018) 33-38
14. A. P. Toropova, A. A. Toropov, J. B. Veselinovic, QSAR models for HEPT derivates
as NNRTI inhibitors based on Monte Carlo method, European Journal of Medicinal Chemistry 77(2014) 298-305.
15. A. P. Toropova, A. A. Toropov, E. Benfenati , CORAL: quantitative structure- activity relationship models for estimating toxicity of organic compounds in rats , Journal of Computational Chem 32(2011) 2727-2733.
16. K. M. Elokely, R. J. Doerksen, Docking challenge: Protein sampling and molecular docking performance, Journal of Chemical Information and Modeling 53(8) (2013) 1934-1945.
17. W. C. D. Cheong , L. C. Zhang, Molecular dynamics simulation of phase transformations
in silicon monocrystals due to nano-indentation, Nanotechnology 11(2000) 173-180.
18. L. Zhang, H. W. Zhao, Y. Y. H. Dai, X. C. Du, P. Y. Tang, Molecular dynamics simulation of deformation accumulation in repeated nanometric cutting on single-crystal copper, RSC Advances5(2015) 12678-12685.
19. C. Y. Shih, M. V. Shugaev, C. Wu, L.V. Zhigilei, Generation of subsurface voids, incubation effect, and formation of nanoparticles in short pulse laser interactions with bulk metal targets in liquid: Molecular dynamics study, Journal of Physical Chemistry C 121(30) (2017) 16549-16567.
20. M. Lemurell , J. Ulander , H. Emtenäs , S. Winiwarter , J. Broddefalk , Novel chemical series of 5-LO activating protein (FLAP) inhibitors for treatment of Coronary Artery Disease, Journal of Medicinal Chemistry 62(9) (2019) 4325-4349 .
