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Discovery of Therapeutic Lead Molecule Against β-Tubulin Using Computational Approach

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Abstract

Virtual screening strategy was performed against the target β-tubulin to overcome paclitaxel resistance in blood cancer types. In essence, A185T and A248V are two such important mutations frequently observed in clinical trials that confer paclitaxel resistance. In the present investigation, compounds from NPACT database were filtered by pharmacokinetics, toxicity and binding energy values. A total of 5 active compounds were identified from a list of 1574 bioactive compounds investigated in our study. Finally, we have compiled all the characteristic features into biologically meaningful clusters by hierarchical clustering algorithm. Overall, the results from our analysis indicate that glaucarubol, isolated from the bark of Ailanthus excelsa tree, could be the potential lead molecule for the treatment of paclitaxel-resistant cancer types. It is worth stressing that our result is the first such observation of inhibitory action of glaucarubol against β-tubulin and warrants further experimental investigation.

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References

  1. Mukhtar E, Adhami VM, Mukhtar H (2014) Targeting microtubules by natural agents for cancer therapy. Mol Cancer Ther 13:275–284

    Article  CAS  Google Scholar 

  2. Gupta ML Jr, Bode CJ, Georg GI, Himes RH (2003) Understanding tubulin–Taxol interactions: mutations that impart Taxol binding to yeast tubulin. Proc Natl Acad Sci USA 100:6394–6397

    Article  CAS  Google Scholar 

  3. Yin S, Bhattacharya R, Cabral F (2010) Human mutations that confer paclitaxel resistance. Mol Cancer Ther 9:327–335

    Article  CAS  Google Scholar 

  4. Klebe G (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 11:580–594

    Article  CAS  Google Scholar 

  5. Kiang TK, Wilby KJ, Ensom MH (2015) A critical review on the clinical pharmacokinetics, pharmacodynamics, and clinical trials of ceftaroline. Clin Pharmacokinet 54:915–931

    Article  CAS  Google Scholar 

  6. Khazir J, Bilal AM, Shabir AM, Don C (2013) Natural products as lead compounds in drug discovery. J Asian Nat Prod Res 15:764–788

    Article  CAS  Google Scholar 

  7. Kingston DG (2011) Modern natural products drug discovery and its relevance to biodiversity conservation. J Nat Prod 74:496–511

    Article  CAS  Google Scholar 

  8. Xu S, Chi S, ** Y, Shi Q, Ge M, Wang S et al (2012) Molecular dynamics simulation and density functional theory studies on the active pocket for the binding of paclitaxel to tubulin. J Mol Model 18:377–391

    Article  CAS  Google Scholar 

  9. DeLano WL (2002) The PyMOL molecular graphics system. DeLano Scientific Alto, California

    Google Scholar 

  10. Mangal M, Sagar P, Singh H, Raghava GPS, Agarwal SM (2013) NPACT: naturally occurring plant-based anti-cancer compound-activity-target database. Nucleic Acids Res 41:1124–1129

    Article  Google Scholar 

  11. Gasteiger J, Rudolph C, Sadowski J (1990) Automatic generation of 3D-atomic coordinates for organic molecules. Tetrahedron Comput Methodol Pergamon 3:537–547

    Article  CAS  Google Scholar 

  12. Orr GA, Verdier-Pinard P, McDaid H, Horwitz SB (2003) Mechanisms of Taxol resistance related to microtubules. Oncogene 22:7280–7295

    Article  CAS  Google Scholar 

  13. Guex N, Peitsch MC (1997) SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis 18:2714–2723

    Article  CAS  Google Scholar 

  14. DE Pires V, Ascher DB, Blundell TL (2014) mCSM: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics 30:335–342

    Article  CAS  Google Scholar 

  15. Raghav D, Sharma V (2013) An in silico evaluation of deleterious nonsynonymous single nucleotide polymorphisms in the ErbB3. Oncogene 2:206–211

    CAS  Google Scholar 

  16. Shoichet BK (2004) Virtual screening of chemical libraries. Nature 432:862–865

    Article  CAS  Google Scholar 

  17. Bielska E, Lucas X, Czerwoniec A, Kasprzak JM, Kaminska KH, Bujnicki JM (2011) Virtual screening strategies in drug design—methods and applications. J Biotechnol Comput Biol Bionanotechnol 92:249–264

    CAS  Google Scholar 

  18. Tondi D, Slomczynska U, Costi MP, Watterson DM, Ghelli S, Shoichet BK (1999) Structure-based discovery and in-parallel optimization of novel competitive inhibitors of thymidylate synthase. Chem Biol 6:319–331

    Article  CAS  Google Scholar 

  19. Merlot C (2010) Computational toxicology—a tool for early safety evaluation. Drug Discov Today 15:16–22

    Article  CAS  Google Scholar 

  20. Balakin KV, Ivanenkov YA, Savchuk NP, Ivashchenko AA, Ekins S (2005) Comprehensive computational assessment of ADME properties using map** techniques. Curr Drug Discov Technol 2:99–113

    Article  CAS  Google Scholar 

  21. Ertl P, Rohde B, Selzer P (2000) Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J Med Chem 43:3714–3717

    Article  CAS  Google Scholar 

  22. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25

    Article  CAS  Google Scholar 

  23. Lipinski CA (2004) Lead profiling lead- and drug-like compounds : the rule-of-five revolution. Drug Discov Today Technol 1:337–341

    Article  CAS  Google Scholar 

  24. Muegge I (2003) Selection criteria for drug-like compounds. Med Res Rev 23:302–321

    Article  CAS  Google Scholar 

  25. Buntrock RE (2002) ChemOffice Ultra 7.0. J Chem Inf Model 42:1505–1506

    CAS  Google Scholar 

  26. Tetko IV (2005) Computing chemistry on the web. Drug Discov Today 10:1497–1500

    Article  Google Scholar 

  27. Sander T, Freyss J, von Korff M, Rufener C (2015) DataWarrior: an open-source program for chemistry aware data visualization and analysis. J Chem Inf Model 5:460–473

    Article  Google Scholar 

  28. Von Korff M, Sander T (2006) Toxicity-indicating structural patterns. J Chem Inf Model 46:536–544

    Article  Google Scholar 

  29. Olaniyan JM, Muhammad HL, Makun HA, Busari MB (2016) Acute and sub-acute toxicity studies of aqueous and methanol extracts of Nelsonia campestris in rats. J Acute Dis 5:62–70

    Article  Google Scholar 

  30. Didziapetris R, Reynolds DP, Japertas P, Zmuidinavicius D, Petrauskas A (2006) In silico technology for identification of potentially toxic compounds in drug discovery. Curr Comput Aided Drug Des 2:95–103

    Article  CAS  Google Scholar 

  31. Mazzatorta P, Estevez MD, Coulet M, Schilter B (2008) Modeling oral rat chronic toxicity. J Chem Inf Model 48:1949–1954

    Article  CAS  Google Scholar 

  32. DE Pires V, Blundell TL, Ascher DB (2015) pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 58:4066–4072

    Article  CAS  Google Scholar 

  33. Brown RD, Martin YC (1996) Use of structure–activity data to compare structure-based clustering methods and descriptors for use in compound selection. J Chem Inf Comput Sci 36:572–584

    Article  CAS  Google Scholar 

  34. Pinheiro M, Afreixo V, Moura G, Freitas A, Santos MAS, Oliveira JL (2006) Statistical, computational and visualization methodologies to unveil gene primary structure features. Methods Inf Med 45:163–168

    Article  CAS  Google Scholar 

  35. Perez F, Granger BE (2007) IPython: a system for interactive scientific computing. Comput Sci Eng 9:21–29

    Article  CAS  Google Scholar 

  36. Akbari V, Moghim S, Reza Mofid M (2011) Comparison of epothilone and taxol binding in yeast tubulin using molecular modeling. Avicenna J Med Biotechnol 3:167–175

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Natarajan K, Senapati S (2012) Understanding the basis of drug resistance of the mutants of αβ-tubulin dimer via molecular dynamics simulations. PLoS One 7:e42351

    Article  CAS  Google Scholar 

  38. Ghanbarzadeh S, Ghasemi S, Shayanfar A, Ebrahimi-Najafabadi H (2015) 2D-QSAR study of some 2,5-diaminobenzophenone farnesyltransferase inhibitors by different chemometric methods. EXCLI J 14:484–495

    PubMed  PubMed Central  Google Scholar 

  39. Gopal V, Al Rashid MH, Majumder S, Maiti PP, Mandal SC (2015) Computational optimization of bioanalytical parameters for the evaluation of the toxicity of the phytomarker 1,4 naphthoquinone and its metabolite 1,2,4-trihydroxynapththalene. J Pharmacopunct 18:7–18

    Google Scholar 

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

    Article  CAS  Google Scholar 

  41. 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 30:2785–2791

    Article  CAS  Google Scholar 

  42. Weiner SJ, Kollman PA, Case DA, Singh UC, Ghio C, Alagona G et al (1984) A new force field for molecular mechanical simulation of nucleic acids and proteins. J Am Chem Soc 106:765–784

    Article  CAS  Google Scholar 

  43. Gasteiger J, Marsili M (1980) Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron 36:3219–3228

    Article  CAS  Google Scholar 

  44. Rathinasamy K, **dal B, Asthana J, Singh P, Balaji PV, Panda D (2010) Griseofulvin stabilizes microtubule dynamics, activates p53 and inhibits the proliferation of MCF-7 cells synergistically with vinblastine. BMC Cancer 10:213

    Article  Google Scholar 

  45. Iman M, Davood A, Nematollahi AR, Dehpoor AR, Shafiee A (2011) Design and synthesis of new 1,4-dihydropyridines containing 4(5)-chloro-5(4)-imidazolyl substituent as a novel calcium channel blocker. Arch Pharm Res 34:1417–1426

    Article  CAS  Google Scholar 

  46. Iman M, Saadabadi A, Davood A (2013) Docking studies of phthalimide pharmacophore as a sodium channel blocker. Iran J Basic Med Sci 16:1016–1021

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Yu L, Pan Y, Wu Y (2009) Research on data normalization methods in multi-attribute evaluation. In: International conference on computational intelligence and software engineering (CiSE). IEEE, Wuhan, pp 1–5

  48. Singhal S, Jeena M (2013) A study on WEKA tool for data preprocessing, classification and clustering. IJITEE 2:250–253

    Google Scholar 

  49. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software. ACM 16(11):10

    Google Scholar 

  50. Wallace AC, Laskowski RA, Thornton JM (1995) LIGPLOT: a program to generate schematic diagrams of protein–ligand interactions. Protein Eng 8:127–134

    Article  CAS  Google Scholar 

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Acknowledgements

The authors of the manuscript would like to thank the management of VIT University for providing the facility and support to carry out this research work.

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Correspondence to K. Ramanathan.

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Ramanathan, K., Verma, K., Gupta, N. et al. Discovery of Therapeutic Lead Molecule Against β-Tubulin Using Computational Approach. Interdiscip Sci Comput Life Sci 10, 734–747 (2018). https://doi.org/10.1007/s12539-017-0233-8

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  • DOI: https://doi.org/10.1007/s12539-017-0233-8

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