Computational modeling and study of the anti-cancer activity of novel NSAID 1-acyl-4-cycloalkyl/arylsemicarbazide and 1-acyl-5-benzyloxy/hydroxy carbamoylcarbazide derivatives using molecular docking and molecular dynamics simulations
- 19 Downloads
In this study, a series of NSAID 1-acyl-4-cycloalkyl/arylsemicarbazides and 1-acyl-5-benzyloxy/hydroxy carbamoylcarbazides possess anti-cancer activity against three human cancer cell lines L1210, CEM, and HeLa have been investigated using a combined approach including quantitative structure activity relationship (QSAR) study, molecular docking, and molecular dynamics (MD) simulations. First, different molecular descriptors were calculated for these compounds. Stepwise multiple linear regression (MLR) method was performed for selecting some common descriptors, including VEA1, hydration energy (HE), log P, and binding energy for all three cell lines. Then, other QSAR models were constructed using support vector regression (SVR) method. According to the results, SVR models were more efficient in predicting the anti-cancer activity. To better understand the mechanism of the binding interactions of NSAID 1-acyl-4-cycloalkyl/arylsemicarbazide and 1-acyl-5-benzyloxy/hydroxy carbamoylcarbazide derivatives with 5-lipoxygenase (5-LOX) protein, molecular docking studies were conducted. These studies have also been used to explore the effects of HE, log P, and binding energy on anti-cancer activity of the studied compounds. The results of molecular docking suggest that hydrophobic interactions of ligands with the active site of 5-LOX are responsible for their anti-cancer activities. Finally, molecular dynamics (MD) simulations using GROMACS package were used for evaluating the stability of 5-LOX in complex with NSAID 1-acyl-4-cycloalkyl/arylsemicarbazide and 1-acyl-5-benzyloxy/hydroxy carbamoylcarbazide derivatives. The results of MD simulations demonstrate stability of protein structure in complex with active compounds, 12 and 27.
KeywordsQuantitative structure activity relationship (QSAR) Molecular docking Molecular dynamics (MD) simulations Nonsteroidal anti-inflammatory drug (NSAID) Lipoxygenase Semicarbazide
Shahid Bahonar University of Kerman is acknowledged by the authors.
Compliance with ethical standards
Conflict of interest
The authors declare that there are no conflicts of interest.
- 5.Perkovic I, Butula I, Kralj M, Martin-Kleiner I, Balzarini J, Hadjipavlou-Litina D, Katsori AM, Zorc B (2012) Novel NSAID 1-acyl-4-cycloalkyl/arylsemicarbazides and 1-acyl-5-benzyloxy/hydroxy carbamoyl carbazides as potential anticancer agents and antioxidants. Eur J Med Chem 51:227–238. https://doi.org/10.1016/j.ejmech.2012.02.046 CrossRefPubMedGoogle Scholar
- 6.Shureiqi I, Chen D, Lotan R, Yang P, Newman RA, Fischer SM, Lippman SM (2000) 15-Lipoxygenase-1 mediates nonsteroidal anti-inflammatory drug-induced apoptosis independently of cyclooxygenase-2 in colon cancer cells. Cancer Res 60:6846–6850. https://doi.org/10.1073/pnas.1631086100 CrossRefPubMedGoogle Scholar
- 8.Cetenko WA, Connor DT, Flynn DL, Sircar JC (1990) Hydroxamate derivatives of selected nonsteroidal antiinflammatory acyl residues and their use for cyclooxygenase and 5-lipoxygenase inhibition. US Patent 4943587, filled May 19, 1988, issued JulyGoogle Scholar
- 11.Sally E, Wenzel MD (1997) Arachidonic acid metabolites: mediators of inflammation in asthma. Pharmacotherapy 17:3S–12S. https://doi.org/10.1002/j.1875-9114.1997.tb03696.x CrossRefGoogle Scholar
- 12.Steele VE, Holmes CA, Hawk ET, Kopelovich L, Lubet RA, Crowell JA, Sigman CC, Kelloff GJ (1999) Lipoxygenase inhibitors as potential cancer chemopreventives. Cancer Epidemiol Biomark Prev 8:467–483Google Scholar
- 14.Mauri A, Consonni V, Pavan M, Todeschini R (2006) DRAGON software: an easy approach to molecular descriptor calculations MATCH. Commun Math Comput Chem 56:237–248Google Scholar
- 16.Cortes C, Vapnik V, Mach J (1995) Support-vector networks. Mach Learn 20:273–297Google Scholar
- 19.Scholkopf B, Smola A (2002) Learning with kernel. MIT Press, CambridgeGoogle Scholar
- 20.Herbrich R (2002) Learning kernel classifiers. MIT Press, CambridgeGoogle Scholar