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A combination of molecular docking, receptor-guided QSAR, and molecular dynamics simulation studies of S-trityl-l-cysteine analogues as kinesin Eg5 inhibitors

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Abstract

Kinesin Eg5 plays an essential role in the early stages of mitosis, and it is an interesting drug target for the design of potent inhibitors. In this work, combined molecular modeling studies of molecular docking, receptor-guided QSAR methodology, and molecular dynamics (MD) simulation were performed on a series of novel S-trityl-l-cysteine (STLC) analogues as Eg5 inhibitors to understand the structural features and key residues which are involved in the inhibition. Molecular docking study was used to find the actual conformations of STLC analogues in the binding site of Eg5. Multiple linear regression (MLR), artificial neural network (ANN), and support vector machine (SVM) models were developed by the conformation which was obtained by performing docking studies. The satisfactory result of the SVM model (R2 = 0.962, SE = 0.210, RMSE = 0.190, and Q2LOO = 0.930) demonstrated the superiority of this model over other models. Also, the satisfactory agreement between experiment and predicted inhibitory values suggested that the SVM model represents good correlation and predictive power. Molecular docking was used to study the functionalities of active molecular interaction between inhibitors and Eg5. Moreover, molecular dynamics (MD) simulation was performed on the best inhibitor-Eg5 complex to investigate the stability of docked conformation and to study the binding interactions in detail. The MD simulation result showed four hydrogen bond interactions with Eg5 residues including Gly117, Glu116, Gly117, and Glu118. The outcome of this study can be used as a guideline to better interpret the protein-ligand interaction and also can assist in the designing and development of more potent Eg5 inhibitors.

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References

  1. Slangy A, Lane HA, d'Hérin P, Harper M, Kress M, Niggt EA (1995) Phosphorylation by p34cdc2 regulates spindle association of human Eg5, a kinesin-related motor essential for bipolar spindle formation in vivo. Cell 83(7):1159–1169

    Article  Google Scholar 

  2. Sawin KE, LeGuellec K, Philippe M, Mitchison TJ (1992) Mitotic spindle organization by a plus-end-directed microtubule motor. Nature 359(6395):540–543

    Article  CAS  PubMed  Google Scholar 

  3. Weil D, Garcon L, Harper M, Dumenil D, Dautry F, Kress M (2002) Targeting the kinesin Eg5 to monitor siRNA transfection in mammalian cells. Biotechniques 33(6):1244–1248

    Article  CAS  PubMed  Google Scholar 

  4. Mayer TU, Kapoor TM, Haggarty SJ, King RW, Schreiber SL, Mitchison TJ (1999) Small molecule inhibitor of mitotic spindle bipolarity identified in a phenotype-based screen. Science 286(5441):971–974

    Article  CAS  PubMed  Google Scholar 

  5. Cox CD, Breslin MJ, Mariano BJ, Coleman PJ, Buser CA, Walsh ES, Hamilton K, Huber HE, Kohl NE, Torrent M (2005) Kinesin spindle protein (KSP) inhibitors. Part 1: the discovery of 3, 5-diaryl-4, 5-dihydropyrazoles as potent and selective inhibitors of the mitotic kinesin KSP. Bioorg Med Chem Lett 15(8):2041–2045

    Article  CAS  PubMed  Google Scholar 

  6. Cox CD, Torrent M, Breslin MJ, Mariano BJ, Whitman DB, Coleman PJ, Buser CA, Walsh ES, Hamilton K, Schaber MD (2006) Kinesin spindle protein (KSP) inhibitors. Part 4: structure-based design of 5-alkylamino-3, 5-diaryl-4, 5-dihydropyrazoles as potent, water-soluble inhibitors of the mitotic kinesin KSP. Bioorg Med Chem Lett 16(12):3175–3179

    Article  CAS  PubMed  Google Scholar 

  7. Fraley ME, Garbaccio RM, Arrington KL, Hoffman WF, Tasber ES, Coleman PJ, Buser CA, Walsh ES, Hamilton K, Fernandes C (2006) Kinesin spindle protein (KSP) inhibitors. Part 2: the design, synthesis, and characterization of 2, 4-diaryl-2, 5-dihydropyrrole inhibitors of the mitotic kinesin KSP. Bioorg Med Chem Lett 16(7):1775–1779

    Article  CAS  PubMed  Google Scholar 

  8. Roecker AJ, Coleman PJ, Mercer SP, Schreier JD, Buser CA, Walsh ES, Hamilton K, Lobell RB, Tao W, Diehl RE (2007) Kinesin spindle protein (KSP) inhibitors. Part 8: design and synthesis of 1, 4-diaryl-4, 5-dihydropyrazoles as potent inhibitors of the mitotic kinesin KSP. Bioorg Med Chem Lett 17(20):5677–5682

    Article  CAS  PubMed  Google Scholar 

  9. Brier S, Lemaire D, DeBonis S, Forest E, Kozielski F (2004) Identification of the protein binding region of S-trityl-L-cysteine, a new potent inhibitor of the mitotic kinesin Eg5. Biochemistry 43(41):13072–13082

    Article  CAS  PubMed  Google Scholar 

  10. Skoufias DA, DeBonis S, Saoudi Y, Lebeau L, Crevel I, Cross R, Wade RH, Hackney D, Kozielski F (2006) S-trityl-L-cysteine is a reversible, tight binding inhibitor of the human kinesin Eg5 that specifically blocks mitotic progression. J Biol Chem 281(26):17559–17569

    Article  CAS  PubMed  Google Scholar 

  11. Kaan HYK, Weiss J, Menger D, Ulaganathan V, Tkocz K, Laggner C, Popowycz F, Bt J, Kozielski F (2011) Structure−activity relationship and multidrug resistance study of new S-trityl-L-cysteine derivatives as inhibitors of Eg5. J Med Chem 54(6):1576–1586

    Article  CAS  PubMed  Google Scholar 

  12. Wang F, Good JA, Rath O, Kaan HYK, Sutcliffe OB, Mackay SP, Kozielski F (2012) Triphenylbutanamines: kinesin spindle protein inhibitors with in vivo antitumor activity. J Med Chem 55(4):1511–1525

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Gao C, Lowndes NF, Eriksson LA (2017) Analysis of biphenyl-type inhibitors targeting the Eg5 α4/α6 allosteric pocket

  14. Ambure P, Kar S, Roy K (2014) Pharmacophore mapping-based virtual screening followed by molecular docking studies in search of potential acetylcholinesterase inhibitors as anti-Alzheimer's agents. Biosystems 116:10–20

    Article  CAS  PubMed  Google Scholar 

  15. Balasubramanian PK, Balupuri A, Cho SJ (2016) Molecular modeling studies on series of Btk inhibitors using docking, structure-based 3D-QSAR and molecular dynamics simulation: a combined approach. Arch Pharm Res 39(3):328–339

    Article  CAS  PubMed  Google Scholar 

  16. Amini Z, Fatemi MH, Gharaghani S (2016) Hybrid docking-QSAR studies of DPP-IV inhibition activities of a series of aminomethyl-piperidones. Comput Biol Chem 64:335–345

    Article  CAS  PubMed  Google Scholar 

  17. Fatemi MH, Heidari A, Gharaghani S (2015) QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors. J Theor Biol 369:13–22

    Article  CAS  PubMed  Google Scholar 

  18. Balasubramanian PK, Balupuri A, Kang H-Y, Cho SJ (2017) Receptor-guided 3D-QSAR studies, molecular dynamics simulation and free energy calculations of Btk kinase inhibitors. BMC Syst Biol 11(2):6

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Zhong H, Huang W, He G, Peng C, Wu F, Ouyang L (2013) Molecular dynamics simulation of tryptophan hydroxylase-1: binding modes and free energy analysis to phenylalanine derivative inhibitors. Int J Mol Sci 14(5):9947–9962

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Jiang C, Chen Y, Wang X, You Q (2007) Docking studies on kinesin spindle protein inhibitors: an important cooperative ‘minor binding pocket’which increases the binding affinity significantly. J Mol Model 13(9):987–992

    Article  CAS  PubMed  Google Scholar 

  21. Luo X, Shu M, Wang Y, Liu J, Yang W, Lin Z (2012) 3D-QSAR studies of dihydropyrazole and dihydropyrrole derivatives as inhibitors of human mitotic kinesin Eg5 based on molecular docking. Molecules 17(2):2015–2029

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Release H (2002) 7.5 for windows, molecular modeling system, Hypercube. Inc http://www hyper com

  23. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19(14):1639–1662

    Article  CAS  Google Scholar 

  25. Morris GM, Huey R, Olson AJ (2008) Using autodock for ligand-receptor docking. Curr Protoc Bioinformatics:8.14. 11–18.14. 40

  26. Durrant JD, McCammon JA (2011) BINANA: a novel algorithm for ligand-binding characterization. J Mol Graph Model 29(6):888–893

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Katritzky AR, Lobanov VS, Karelson M, Murugan R, Grendze MP, Toomey J (1996) Comprehensive descriptors for structural and statistical analysis. 1: correlations between structure and physical properties of substituted pyridines. Rev Roum Chim 41(11–12):851–867

    CAS  Google Scholar 

  28. Garg A, Tewari R, Raghava GP (2010) K i DoQ: using docking based energy scores to develop ligand based model for predicting antibacterials. BMC bioinformatics 11(1):125

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kale L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26(16):1781–1802

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Zoete V, Cuendet MA, Grosdidier A, Michielin O (2011) SwissParam: a fast force field generation tool for small organic molecules. J Comput Chem 32(11):2359–2368

    Article  CAS  PubMed  Google Scholar 

  31. Ryckaert J-P, Ciccotti G, Berendsen HJ (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23(3):327–341

    Article  CAS  Google Scholar 

  32. Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N· log (N) method for Ewald sums in large systems. J Comput Phys 98(12):10089–10092

    CAS  Google Scholar 

  33. Roy K, Chakraborty P, Mitra I, Ojha PK, Kar S, Das RN (2013) Some case studies on application of “rm2” metrics for judging quality of quantitative structure–activity relationship predictions: emphasis on scaling of response data. J Comput Chem 34(12):1071–1082

    Article  CAS  PubMed  Google Scholar 

  34. Golbraikh A, Tropsha A (2002) Beware of q 2! J Mol Graph Model 20(4):269–276

    Article  CAS  PubMed  Google Scholar 

  35. Roy K, Mitra I, Kar S, Ojha PK, Das RN, Kabir H (2012) Comparative studies on some metrics for external validation of QSPR models. J Chem Inf Model 52(2):396–408

    Article  CAS  PubMed  Google Scholar 

  36. Minovski N, Župerl Š, Drgan V, Novič M (2013) Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum Euclidean distance space analysis: a case study. Anal Chim Acta 759:28–42

    Article  CAS  PubMed  Google Scholar 

  37. Papa E, Kovarich S, Gramatica P (2009) Development, validation and inspection of the applicability domain of QSPR models for physicochemical properties of polybrominated diphenyl ethers. Mol Inform 28(8):790–796

    CAS  Google Scholar 

  38. Tetko IV, Sushko I, Pandey AK, Zhu H, Tropsha A, Papa E, Oberg T, Todeschini R, Fourches D, Varnek A (2008) Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection. J Chem Inf Model 48(9):1733–1746

    Article  CAS  PubMed  Google Scholar 

  39. Gramatica P (2007) Principles of QSAR models validation: internal and external. Mol Inform 26(5):694–701

    CAS  Google Scholar 

  40. Katritzky AR, Mu L, Lobanov VS, Karelson M (1996) Correlation of boiling points with molecular structure. 1. A training set of 298 diverse organics and a test set of 9 simple inorganics. J Phys Chem 100(24):10400–10407

    Article  CAS  Google Scholar 

  41. Papa E, Luini M, Gramatica P (2009) Quantitative structure–activity relationship modelling of oral acute toxicity and cytotoxic activity of fragrance materials in rodents. SAR QSAR Environ Res 20(7–8):767–779

    Article  CAS  PubMed  Google Scholar 

  42. Luan F, Melo A, Borges F, Cordeiro MND (2011) Affinity prediction on a 3 adenosine receptor antagonists: the chemometric approach. Bioorg Med Chem 19(22):6853–6859

    Article  CAS  PubMed  Google Scholar 

  43. Todeschini R, Consonni V (2003) DRAGON software (version 1.11-2001). Milano, Italy

  44. Fornabaio M, Spyrakis F, Mozzarelli A, Cozzini P, Abraham DJ, Kellogg GE (2004) Simple, intuitive calculations of free energy of binding for protein−ligand complexes. 3. The free energy contribution of structural water molecules in HIV-1 protease complexes. J Med Chem 47(18):4507–4516

    Article  CAS  PubMed  Google Scholar 

  45. Ladbury JE (1996) Just add water! The effect of water on the specificity of protein-ligand binding sites and its potential application to drug design. Chem Biol 3(12):973–980

    Article  CAS  PubMed  Google Scholar 

  46. Fatemeh Mousavi MHF (2018) 3D-QSAR modeling of some S-trityl-L-cysteine analogues as inhibitors of mitotic kinesin Eg5 by CoMFA, CoMSIA and H-QSAR methodologies. Lett Drug Des Discov 15(9):979–987

    Article  CAS  Google Scholar 

  47. Stanton DT, Jurs PC (1990) Development and use of charged partial surface area structural descriptors in computer-assisted quantitative structure-property relationship studies. Anal Chem 62(21):2323–2329

    Article  CAS  Google Scholar 

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Mousavi, S.F., Fatemi, M.H. A combination of molecular docking, receptor-guided QSAR, and molecular dynamics simulation studies of S-trityl-l-cysteine analogues as kinesin Eg5 inhibitors. Struct Chem 30, 115–126 (2019). https://doi.org/10.1007/s11224-018-1178-1

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