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Research and study of 2-((4,6 dimethyl pyrimidine-2-yle) thio)-N-phenyl acetamide derivatives as inhibitors of sirtuin 2 protein for the treatment of cancer using QSAR, molecular docking and molecular dynamic simulation

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Abstract 

Phenyl acetamide derivatives have a wide range of biological activities, so their research and development can be useful and effective for the design production of new drugs. In this project, quantitative structure–activity relationship (QSAR) was performed. For modeling two methods of multiple linear regression (MLR) and nonlinear regression of support vector machine (SVR) were used. In the MLR stage, the best model with the values of R2train = 0.913 and R2test = 0.881 was selected by stepwise method. In this model, 4 descriptors of BELV2, GATS8p, GATS6e and RDF080m were included, which were used as input for the nonlinear support vector regression method. In the SVR model, the best results were obtained using the radial Gaussian kernel function (RBF) with R2train = 0.978 and R2test = 0.990. In the next step, using molecular docking and molecular dynamic simulation methods, the interaction between phenyl acetamide derivatives and the sirtuin 2 protein was investigated. Examining the results of molecular docking, it was observed that these derivatives formed complexes by forming hydrogen and hydrophobic bonds with the sirtuin 2 protein. Also, the results of molecular dynamic simulation show that phenyl acetamide compounds form stable complex with the sirtuin 2 protein, and it was found that the compounds with more activity have formed a number of hydrogen bonds with the protein.

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References 

  1. Seyfried TN, Flores RE, Poff AM, D’Agostino DP (2014) Cancer as a metabolic disease: implications for novel therapeutics. Carcinogenesis. https://doi.org/10.1093/carcin/bgt480

    Article  PubMed  Google Scholar 

  2. Seyfried TN, Shelton LM (2010) Cancer as a metabolic disease. Nutr Metab. https://doi.org/10.1186/1743-7075-7-7

    Article  Google Scholar 

  3. Mei Zh, Zhang X, Yi J, Huang J, He J, Tao Y (2016) Sirtuins in metabolism, DNA repair, and cancer. J Exp Clin Cancer Res. https://doi.org/10.1186/s13046-016-0461-5

    Article  PubMed  PubMed Central  Google Scholar 

  4. Hoffmann G, Breitenbücher F, Schuler M, Ehrenhofer-Murray AE (2014) A Novel Sirtuin 2 (SIRT2) Inhibitor with p53-dependent Pro-apoptotic Activity in Non-small Cell Lung Cancer. J Biol Chem. https://doi.org/10.1074/jbc.M113.487736

    Article  PubMed  PubMed Central  Google Scholar 

  5. Lee IH (2019) Mechanisms and disease implications of sirtuin-mediated autophagic regulation. Exp Mol Med. https://doi.org/10.1038/s12276-019-0302-7

    Article  PubMed  PubMed Central  Google Scholar 

  6. Bosch-Presegué L, Vaquero A (2011) The Dual Role of Sirtuins in cancer. SAGE. https://doi.org/10.1177/1947601911417862

    Article  Google Scholar 

  7. Gomes P, Fleming Outeiro T, Cavadas C (2015) Emerging Role of Sirtuin 2 in the Regulation of Mammalian Metabolism. Trends Pharmacol Sci. https://doi.org/10.1016/j.tips.2015.08.001

    Article  PubMed  Google Scholar 

  8. Yang L, Xi Ma, Yuan Ch, He Y, Li L, Fang S, Xia W, He T, Qian S, Xu Zh, Li G, Wang Zh (2017) Discovery of 2-((dimethylpyrimidin-2-yl) thio)-N- phenylacetamide derivatives as new potent and selective human sirtuin 2 inhibitors. Eur J Med Chem. https://doi.org/10.1016/j.ejmech.2017.04.010

    Article  PubMed  PubMed Central  Google Scholar 

  9. Aki-Sener E, Bingol KK, Temiz-Arpaci O, Yalcin I, Altanlar N (2002) Synthesis and microbiological activity of some N-(2-hydroxy-4-substitutedphennyl) benzamides, phenyl acetamides and furamides as the possible metabolites of antimicrobial active benzoxazoles. IL FARMACO. https://doi.org/10.1016/S0014-827X(02)01226-0

    Article  PubMed  Google Scholar 

  10. SahuN P, Pal Ch, Mandal NB, Banerjee S, Raha M, Kundu AP, Basu A, Ghosh M, Roy K, Bandyopadhyay S (2002) Synthesis of a Novel Quinoline Derivatives, 2-(2-Methylquinolin-4-ylamino)-N-phenylacetamide—A potential Antileishmanial agent. Bioorg Med Chem. https://doi.org/10.1016/S0968-0896(02)00046-9

    Article  Google Scholar 

  11. Soyer Z, Kilic FS, Erol K, Pabuccuoglu V (2004) Synthesis and anticonvulsant activity of some ?-(1H-imidazol-1-yl)-N-phenylacetamide and propionamide derivatives. ILFARMACO. https://doi.org/10.1016/j.farmac.2003.07.011

    Article  Google Scholar 

  12. Ertan T, Yildiz I, Ozkan S, Temiz-Arpaci O, Kaynak F, Yalcin I, Aki-Sener E, Abbasoglu U (2007) Synthesis and biological evaluation of new N-(2-hydroxy-4(or 5)- nitro/aminophenyl)benzamides and phenylacetamides as antimicrobial agents. Bioorg Med Chem. https://doi.org/10.1016/J.BMC.2006.12.035

    Article  PubMed  Google Scholar 

  13. Bu M, Cao T, Li H, Guo M, Yang BB, Zeng Ch, Zhou Y, Zhang N, Hu L (2017) Synthesis and biological evaluation of novel steroidal 5a,8a-epidioxyandrost-6-ene-3ß-ol-17-(O-phenylacetamide)oxime derivatives as potential anticancer agents. Bioorg Med Chem Lett. https://doi.org/10.1016/j.bmcl.2017.06.048

    Article  PubMed  Google Scholar 

  14. Farshad S, Darvish Ganji M (2020) Theoretical study of interaction between aspirine drug and Al-soped graphene nanostructure toward designing of suitable nanocarrier for drug delivery. Medical Sciences. https://doi.org/10.29252/iau.30.2.141

  15. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools 4:automated docking with selective receptor flexibility. J Comput Chem. https://doi.org/10.1002/jcc.21256

    Article  PubMed  PubMed Central  Google Scholar 

  16. P. Norgan A, Coffman PK, Kocher JPA, Katzmann DPJ, Sosa C (2011) Multilevel parallelization of Autodock 4.2. J Cheminform. https://doi.org/10.1186/1758-2946-3-12

  17. Safarizadeh H, Garkani-Nejad Z (2019) Molecular docking, Molecular dynamics simulations and QSAR studies on some of 2-arylethenylquinoline derivatives for inhibition of Alzheime’s amyloid-beta aggregation: Insight into mechanism of interactions and parameters for design of new inhibitors. J Mol Graph Model. https://doi.org/10.1016/j.jmgm.2018.11.019

    Article  PubMed  Google Scholar 

  18. Mortier J, Rakers C, Bermudez MM, Murgueitio MS, Riniker S, Wolber G (2015) The impact of molecular dynamics on drug design: applications for the characterization of ligand-macromolecule complexes. Drug Discov Today. https://doi.org/10.1016/j.drudis.2015.01.003

    Article  PubMed  Google Scholar 

  19. Durrant JD, McCammon JA (2011) Molecular dynamics simulations and drug discovery. J Biol. https://doi.org/10.1186/1741-7007-9-71

    Article  Google Scholar 

  20. Pronk S, Páll S, SchulzR, Larsson P, Bjelkmar P, Apostolov R, R. Shirts M, C. Smith J, M. Kasson P, Van der Spoel D, Hess B, Lindahl E (2013) GROMACS 4.5: a high throughput and highly parallel open source molecular simulation toolkit. Bioinformatics. https://doi.org/10.1093/bioinformatics/btt055

  21. Lins RD, Hünenberger PhH (2005) A new GROMOS force field for hexopyranose-based carbohydrates. J Comput Chem. https://doi.org/10.1002/jcc.20275

    Article  PubMed  Google Scholar 

  22. Van der Spoel D, Lindahl E, Hess B, Groenhof G, Mark E, A, J. C. Berendsen H, (2005) GROMACS:Fast, flexible, and free. J Comput Chem. https://doi.org/10.1002/jcc.20291

    Article  Google Scholar 

  23. SafarizadehH G-N (2019) Investigation of MI-2 analogues as MALT1 inhibitors to treat of diffuse large B-Cell lymphoma through combined molecular dynamics simulation, molecular docking and QSAR techniques and design of new inhibitors. J Mol Struct. https://doi.org/10.1016/j.molstruc.2018.12.022

    Article  Google Scholar 

  24. Chen S, Wang H, Zhang J, Lu S, Xiang Y (2020) Effect of side chain on the electrochemical performance of poly (ether ether ketone) based anion-exchange membrane: A molecular dynamics study. J Membr Sci. https://doi.org/10.1016/j.memsci.2020.118105

    Article  Google Scholar 

  25. Xi L, Wang Y, He Q, Zhang Q, Du L (2016) Interaction between pin 1 and its natural product inhibitor epigallocatechin-3- gallate by spectroscopy and molecular dynamics simulations. Spectrochim Acta. https://doi.org/10.1016/j.saa.2016.06.036

    Article  Google Scholar 

  26. Onufriev A, Bashford D, Case AD (2004) Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins. https://doi.org/10.1002/PROT.20033

    Article  PubMed  Google Scholar 

  27. Humphrey W, DalkeA SK (1995) VMD: visual molecular dynamics. J Mol Graph. https://doi.org/10.1016/0263-7855(96)00018-5

    Article  Google Scholar 

  28. Pettersen FED, Goddard DT, Huang CC, CouchGreenblatt SGMD, Meng CE, Ferrin ET (2004) UCSF Chimera- A visualization system for exploratory research and analysis. J Comput Chem. https://doi.org/10.1002/jcc.20084

    Article  PubMed  Google Scholar 

  29. Kubinyi H (1997) QSAR and 3D QSAR in drug design part 1: methodology. Drug Discov Today. https://doi.org/10.1016/S1359-6446(97)01079-9

    Article  Google Scholar 

  30. Einax WJ (2008) Chemometrics in analytical chemistry. Anal Bioanal Chem. https://doi.org/10.1007/s00216-007-1786-x

    Article  PubMed  Google Scholar 

  31. Shafieyoon P, Mehdipour E, Mary Y.S (2019) Synthesis, characterization and biological investigation of glycine-based sulfonamide derivatives and its complex: Vibration assignment, HOMO-LUMO analysis, MEP and molecular docking. J Mol Struct. https://doi.org/10.1016/j.molstruc.2018.12.067

  32. Kumer A, Sarker N, Paul S, Zannat A (2019) The Theoretical Prediction of Thermophysical properties, HOMO, LUMO, QSAR and Biological Indics of Cannabinoids (CBD) and Tetrahhdrocannabinol (THC) by Computational Chemistry. Adv J Chem A. https://doi.org/10.33945/SAMI/AJCA.2019.2.190202

  33. Mouri A, Consonni V, Pavan M, Todeschini R (2006) DRAGON SOFTWARE: ANEASY APPROACH TO MOLECULAR DESCRIPTOR CALCULATIONS. MATCH Commun Math Comput Chem 56:237–248

    Google Scholar 

  34. Gharagheizi F (2008) Quantitative structure-property relationship for prediction of the lower flammability limit of pure compounds. Energy Fuels. https://doi.org/10.1021/ef800375b

    Article  Google Scholar 

  35. Luu QH, Lau MF, Ng PHS, Yueh Chen T (2021) Testing multiple linear regression systems with metamorphic testing. J Syst Softw. https://doi.org/10.1016/j.jss.2021.111062

    Article  Google Scholar 

  36. Preacher JK, Curran JP, Bauer JD (2006) Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. J Educ Behav Stat. https://doi.org/10.3102/10769986031004437

    Article  Google Scholar 

  37. Zhi-qiang J, Han-guang F, Ling-jun L (2005) Support Vector Machine for mechanical faults classification. J Zhejiang Univ SCI. https://doi.org/10.1631/jzus.2005.A0433

    Article  Google Scholar 

  38. Smola JA, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput. https://doi.org/10.1023/B:STCO.0000035301.49549.88

    Article  Google Scholar 

  39. Üstün B, Melssen WJ, Oudenhuijzen M, Buydens LMC (2005) Determination of Optimal support vector Regression parameters by Genetic Algorithms and Simplex Optimization. Anal Chim Acta. https://doi.org/10.1016/j.aca.2004.12.024

    Article  Google Scholar 

  40. M. Balabin R, I. Lomakina E (2011) Support vector machine regression (SVR/LS-SVM)- an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data. Analyst. https://doi.org/10.1039/c0an00387e

  41. Sánches VD (2003) Advanced Support Vector Machines and kernel methods. Neurocomputing. https://doi.org/10.1016/S0925-2312(03)00373-4

    Article  Google Scholar 

  42. Yu L, Yau X, Wang S, Lai KK (2011) Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2011.06.023

    Article  Google Scholar 

  43. Todeschini R, Cosonni V (2000) Handbook of Molecular Descriptors. Weinheim. New York. Chichester. Brisbane Singapore Toronto.

  44. Asadollahi-Baboli M, Mani-Varnosfaderani A (2015) Therapeutic index modeling and predictive QSAR of novel thiazolidin-4-one analogs against Toxoplasma gondii. Eur J Pharm Sci. https://doi.org/10.1016/j.ejps.2015.01.014

    Article  PubMed  Google Scholar 

  45. Mahmud A. W, Shallangwa G. A, Uzairu A (2019) Quantitative structure –activity relationships (QSAR) study on novel 4-amidinoquinoline and 10-amidinobenzonaphthyridine derivatives as potent antimalaria agent. The journal of engineering and exact sciences. https://doi.org/10.18540/jcecvl5iss3pp0271-0282

  46. Pourbasheer E, Aalizadeh R, Ganjali MR (2019) QSAR study of CK2 inhibitors by GA-MLR and GA-SVM methods. Arab J Chem. https://doi.org/10.1016/j.arabjc.2014.12.021

    Article  Google Scholar 

  47. Kumar Gupta A, A. Gupta R, Kumar Soni L, Kaskhedikar S. G, (2008) Exploration of physicochemical properties and molecular modeling studies of 2-sulfonyl-phenyl-3-phenyl-indole analogs as cyclooxygenase-2 inhibitors. Eur J Med Chem. https://doi.org/10.1016/j.ejmech.2007.06.022

    Article  Google Scholar 

  48. Wang T, Tang L, Luan F, D. S. Cordeiro M. N, (2018) Prediction of the toxicity of binary mixtures by QSAR approach using the hypothetical descriptors. Int J Mol Sci. https://doi.org/10.3390/ijms19113423

    Article  PubMed  PubMed Central  Google Scholar 

  49. Riniker S, P. Eichenberger A, F. van Gunsteren W, (2012) Solvating atomic level fine-grained proteins in supra-molecular level coarse-grained water for molecular dynamics simulations. Eur Biophys J. https://doi.org/10.1007/s00249-012-0837-1

    Article  PubMed  Google Scholar 

  50. Skariyachan S, Khangwal I, Niranjan V, Kango N, Shukla P (2020) Deciphering effectual binding potential of xylo-substrates towards xylose isomerase and xylokinase through molecular docking and molecular dynamic simulation. J Biomol Struct Dyn. https://doi.org/10.1080/07391102.2020.1772882

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge the Shahid Bahonar University of Kerman.

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S. I. contributed to QSAR analysis, molecular docking and molecular dynamic simulation analysis, and writing the first draft of the manuscript. Z. G. contributed to project administration, supervision, methodology, interpretation of the results, reviewing, and editing.

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Correspondence to Zahra Garkani-Nejad.

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Ilaghi-Hoseini, S., Garkani-Nejad, Z. Research and study of 2-((4,6 dimethyl pyrimidine-2-yle) thio)-N-phenyl acetamide derivatives as inhibitors of sirtuin 2 protein for the treatment of cancer using QSAR, molecular docking and molecular dynamic simulation. J Mol Model 28, 343 (2022). https://doi.org/10.1007/s00894-022-05288-4

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