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Structural insight into the role of Gln293Met mutation on the Peloruside A/Laulimalide association with αβ-tubulin from molecular dynamics simulations, binding free energy calculations and weak interactions analysis

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Abstract

Peloruside A (PLA) and Laulimalide (LAU) are novel microtubule-stabilizing agents with promising properties against different cancer types. These ligands share a non-taxoid binding site at the outer surface of β-tubulin and promote microtubule stabilization by bridging two adjacent αβ-tubulin dimers from parallel protofilaments. Recent site-directed mutagenesis experiments confirmed the existence of a unique β-tubulin site mutation (Gln293Met) that specifically increased the activity of PLA and caused resistance to LAU, without affecting the stability of microtubules in the absence of the ligands. In this work, fully atomistic molecular dynamics simulations were carried out to examine the PLA and LAU association with native and mutated αβ-tubulin in the search for structural and energetic evidence to explain the role of Gln293Met mutation on determining the activity of these ligands. Our results revealed that Gln293Met mutation induced the loss of relevant LAU–tubulin contacts but exerted negligible changes in the interaction networks responsible for PLA–tubulin association. Binding free energy calculations (MM/GBSA and MM/PBSA), and weak interaction analysis (aNCI) predicted an increased affinity for PLA, and a weakened association for LAU after mutation, thus suggesting that Gln293Met mutation exerts its action by a modulation of drug–tubulin interactions. These results are valuable to increase understanding about PLA and LAU activity and to assist the future design of novel agents targeting the PLA/LAU binding pocket.

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References

  1. Dumontet C, Sikic BI (1999) Mechanisms of action of and resistance to antitubulin agents: microtubule dynamics, drug transport, and cell death. J Clin Oncol 17:1061–1070

    Article  CAS  Google Scholar 

  2. Dumontet C, Jordan MA (2010) Microtubule-binding agents: a dynamic field of cancer therapeutics. Nat Rev Drug Discov 9:790–803

    Article  CAS  Google Scholar 

  3. Jordan A, Hadfield JA, Lawrence NJ, McGown AT (1998) Tubulin as a target for anticancer drugs: agents which interact with the mitotic spindle. Med Res Rev 18:259–296

    Article  CAS  Google Scholar 

  4. Hood KA, West LM, Rouwe B, Northcote PT, Berridge MV, Wakefield SJ, Miller JH (2002) Peloruside A, a novel antimitotic agent with paclitaxel-like microtubule-stabilizing activity. Cancer Res 62(12):3356–3360

    CAS  Google Scholar 

  5. Mooberry SL, Tien G, Hernandez AH, Plubrukarn A, Davidson BS (1999) Laulimalide and isolaulimalide, new paclitaxel-like microtubule stabilizing agents. Cancer Res 59(3):653–660

    CAS  Google Scholar 

  6. Zhao Y, Mu X, Du GH (2016) Microtubule-stabilizing agents: new drug discovery and cancer therapy. Pharmacol Therapeut 162:134–143

    Article  CAS  Google Scholar 

  7. Gaitanos TN, Buey RM, Diaz JF, Northcote PT, Teesdale-Spittle P, Andreu JM, Miller JH (2004) Peloruside A does not bind to the taxoid site on beta-tubulin and retains its activity in multidrug-resistant cell lines. Cancer Res 64(15):5063–5067

    Article  CAS  Google Scholar 

  8. Pryor DE, O’Brate A, Bilcer G, Diaz JF, Wang YF, Wang Y, Kabaki M, Jung MK, Andreu JM, Ghosh AK, Giannakakou P, Hamel E (2002) The microtubule stabilizing agent Laulimalide does not bind in the taxoid site, kills cells resistant to paclitaxel and epothilones, and may not require its epoxide moiety for activity. Biochem US 41:9109–9115

    Article  CAS  Google Scholar 

  9. Prota AE, Bargsten K, Northcote PT, Marsh M, Altmann KH, Miller JH, Diaz JF, Steinmetz MO (2014) Structural basis of microtubule stabilization by Laulimalide and Peloruside A. Angew Chem Int Edit 53:1621–1625

    Article  CAS  Google Scholar 

  10. Bennett MJ, Barakat K, Huzil JT, Tuszynski J, Schriemer DC (2010) Discovery and characterization of the Laulimalide-microtubule binding mode by mass shift perturbation mapping. Chem Biol 17:725–734

    Article  CAS  Google Scholar 

  11. Kanakkanthara A, Wilmes A, O’Brate A, Escuin D, Chan A, Gjyrezi A, Crawford J, Rawson P, Kivell B, Northcote PT, Hamel E, Giannakakou P, Miller JH (2011) Peloruside- and Laulimalide-resistant human ovarian carcinoma cells have βI-tubulin mutations and altered expression of βII- and βIII-tubulin isotypes. Mol Cancer Ther 10:1419–1429

    Article  CAS  Google Scholar 

  12. Begaye A, Trostel S, Zhao Z, Taylor RE, Schriemer DC, Sackett DL (2011) Mutations in the β-tubulin binding site for Peloruside A confer resistance by targeting a cleft significant in side chain binding. Cell Cycle 10:3387–3396

    Article  CAS  Google Scholar 

  13. Kanakkanthara A, Rowe MR, Field JJ, Northcote PT, Teesdale-Spittle PH, Miller JH (2015) Beta I-tubulin mutations in the Laulimalide/Peloruside binding site mediate drug sensitivity by altering drug-tubulin interactions and microtubule stability. Cancer Lett 365:251–260

    Article  CAS  Google Scholar 

  14. Hassanzadeh M, Bagherzadeh K, Amanlou M (2016) A comparative study based on docking and molecular dynamics simulations over HDAC-tubulin dual inhibitors. J Mol Graph Model 70:170–180

    Article  CAS  Google Scholar 

  15. Kumbhar BV, Borogaon A, Panda D, Kunwar A (2016) Exploring the origin of differential binding affinities of human tubulin isotypes alpha beta II, alpha beta III and alpha beta IV for DAMA-colchicine using homology modelling, molecular docking and molecular dynamics simulations. PLoS ONE. doi:10.1371/journal.pone.0156048

    Google Scholar 

  16. Costa KM, Alves CN, Silva JRA, Lameira J (2016) A Computational analysis of indomethacin derivative as tubulin inhibitor: insights into development of chemotherapeutic agents. Comb Chem High Throughput Screen J 19:431–436

    Article  CAS  Google Scholar 

  17. Churchill CDM, Klobukowski M, Tuszynski JA (2015) Elucidating the mechanism of action of the clinically approved taxanes: a comprehensive comparison of local and allosteric effects. Chem Biol Drug Des 86:1253–1266

    Article  CAS  Google Scholar 

  18. Li DD, Qin YJ, Zhang X, Yin Y, Zhu HL, Zhao LG (2015) Combined molecular docking, 3D-QSAR, and pharmacophore model: design of novel tubulin polymerization inhibitors by binding to colchicine-binding site. Chem Biol Drug Des 86:731–745

    Article  CAS  Google Scholar 

  19. Navarrete KR, Alderete JB, Jimenez VA (2015) Structural basis for drug resistance conferred by beta-tubulin mutations: a molecular modeling study on native and mutated tubulin complexes with epothilone B. J Biomol Struct Dyn 33:2530–2540

    Article  CAS  Google Scholar 

  20. Churchill CDM, Klobukowski M, Tuszynski JA (2015) The unique binding mode of Laulimalide to two tubulin protofilaments. Chem Biol Drug Des 86:190–199

    Article  CAS  Google Scholar 

  21. Churchill CDM, Klobukowski M, Tuszynski JA (2016) Analysis of the binding mode of Laulimalide to microtubules: establishing a Laulimalide-tubulin pharmacophore. J Biomol Struct Dyn 34:1455–1469

    Article  CAS  Google Scholar 

  22. Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G, Schmidt T, Kiefer F, Cassarino TG, Bertoni M, Bordoli L, Schwede T (2014) SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res 42:W252–W258

    Article  CAS  Google Scholar 

  23. Kiefer F, Arnold K, Kunzli M, Bordoli L, Schwede T (2009) The SWISS-MODEL repository and associated resources. Nucleic Acids Res 37:D387–D392

    Article  CAS  Google Scholar 

  24. Trott O, Olson AJ (2010) Software news and update autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461

    CAS  Google Scholar 

  25. Anandakrishnan R, Aguilar B, Onufriev AV (2012) H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations. Nucleic Acids Res 40:W537–W541

    Article  CAS  Google Scholar 

  26. Gordon JC, Myers JB, Folta T, Shoja V, Heath LS, Onufriev A (2005) H++: a server for estimating pK(a)s and adding missing hydrogens to macromolecules. Nucleic Acids Res 33:W368–W371

    Article  CAS  Google Scholar 

  27. Gilson MK, Zhou HX (2007). Calculation of protein-ligand binding affinities. Annu Rev Biophys Biomol 36:21–42

    Article  CAS  Google Scholar 

  28. Kumbhar BV, Borogaon A, Panda D, Kunwar A (2016) Exploring the origin of differential binding affinities of human tubulin isotypes alpha beta II, alpha beta III and alpha beta IV for DAMA-colchicine using homology modelling, molecular docking and molecular dynamics simulations. PLoS ONE 11(5):22

    Article  Google Scholar 

  29. Tripathi S, Kumar A, Kumar BS, Negi AS, Sharma A (2016) Structural investigations into the binding mode of novel neolignans Cmp10 and Cmp19 microtubule stabilizers by in silico molecular docking, molecular dynamics, and binding free energy calculations. J Biomol Struct Dyn 34(6):1232–1240

    Article  CAS  Google Scholar 

  30. Tripathi S, Srivastava G, Sharma A (2016) Molecular dynamics simulation and free energy landscape methods in probing L215H, L217R and L225M βI-tubulin mutations causing paclitaxel resistance in cancer cells. Biochem Biophys Res Commun 476(4):273–279

    Article  CAS  Google Scholar 

  31. Mobley DL, Dill KA (2009) Binding of small-molecule ligands to proteins: “what you see” is not always “what you get”. Structure 17(4):489–498

    Article  CAS  Google Scholar 

  32. Bai Q, Yao X (2016) Investigation of allosteric modulation mechanism of metabotropic glutamate receptor 1 by molecular dynamics simulations, free energy and weak interaction analysis. Sci. Rep. UK 6:21763

    Article  CAS  Google Scholar 

  33. Bian C, Wang SJ, Liu YH, Se KH, Jing XL (2016) Role of nonbond interactions in the glass transition of novolac-type phenolic resin: a molecular dynamics study. Ind Eng Chem Res 55:9440–9451

    Article  CAS  Google Scholar 

  34. Wu P, Chaudret R, Hu XQ, Yang WT (2013) Noncovalent interaction analysis in fluctuating environments. J Chem Theory Comput 9:2226–2234

    Article  CAS  Google Scholar 

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Acknowledgements

The authors thank FONDECYT Grant No. 1160060.

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Correspondence to Matías A. Zúñiga or Verónica A. Jiménez.

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Zúñiga, M.A., Alderete, J.B., Jaña, G.A. et al. Structural insight into the role of Gln293Met mutation on the Peloruside A/Laulimalide association with αβ-tubulin from molecular dynamics simulations, binding free energy calculations and weak interactions analysis. J Comput Aided Mol Des 31, 643–652 (2017). https://doi.org/10.1007/s10822-017-0029-2

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