Skip to main content
Log in

Computational basis for the design of PLK-2 inhibitors

  • Original Research
  • Published:
Structural Chemistry Aims and scope Submit manuscript

Abstract

PLK-2 is a serine/threonine protein kinase and plays a crucial role in cell cycle regulation; due to its pivotal function, this enzyme is approved as cancer drug target. We used BI-2536 a PLK-1/PLK-2 inhibitor to build a pharmacophore model and applied in the virtual screening of ZINC database to retrieve new molecules that bind the active site of PLK-2 environment with a high fit value. The molecules that do not fit the enzyme active site environment were subjected to conformation enrichment by generation of conformations in the active site environment by molecular docking, and the molecules with new scaffold that did not pass into the active site from molecular docking were subjected to molecular pruning to delete bulky substituents that prevent the molecules from binding. Molecular docking was used to find the binding pose of the selected molecules into active site of PLK-2; all screened-in hit molecules make favorable non-bonding interactions with PLK-2 active site similar to the reference inhibitor. Molecular dynamics simulations, the binding free energy calculations of the complexes, and the stability of hydrogen bonding interactions further revealed the usefulness of these screened compounds as suitable hit molecules for inhibition of PLK-2.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Anand P, Kunnumakara AB, Sundaram C, Harikumar KB, Tharakan ST, Lai OS, Sung B, Aggarwal BB (2008) Cancer is a preventable disease that requires major lifestyle changes. Pharm Res 25(9):2097–2116

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Islami F, Goding Sauer A, Miller KD, Siegel RL, Fedewa SA, Jacobs EJ, McCullough ML, Patel AV, Ma J, Soerjomataram I, Flanders WD (2018) Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States. CA Cancer J Clin 68(1):31–54

    Article  PubMed  Google Scholar 

  3. Wang H, Naghavi M, Allen C, Barber RM, Bhutta ZA, Carter A, Casey DC, Charlson FJ, Chen AZ, Coates MM, Coggeshall M (2016) Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388(10053):1459–1544

    Article  Google Scholar 

  4. Sawyers C (2004) Targeted cancer therapy. Nature 432(7015):294

    Article  CAS  PubMed  Google Scholar 

  5. Noble ME, Endicott JA, Johnson LN (2004) Protein kinase inhibitors: insights into drug design from structure. Science 303(5665):1800–1805

    Article  CAS  PubMed  Google Scholar 

  6. Zhang J, Adrián FJ, Jahnke W, Cowan-Jacob SW, Li AG, Iacob RE, Sim T, Powers J, Dierks C, Sun F, Guo GR (2010) Targeting Bcr–Abl by combining allosteric with ATP-binding-site inhibitors. Nature 463(7280):501

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Manning G, Whyte DB, Martinez R, Hunter T, Sudarsanam S (2002) The protein kinase complement of the human genome. Science 298(5600):1912–1934

    Article  CAS  PubMed  Google Scholar 

  8. Barnum KJ, O’Connell MJ (2014) Cell cycle regulation by checkpoints. Cell Cycle Control. Humana Press, New York, NY, pp 29–40

    Chapter  Google Scholar 

  9. Malumbres M, Barbacid M (2009) Cell cycle, CDKs and cancer: a changing paradigm. Nat Rev Cancer 9(3):153

    Article  CAS  PubMed  Google Scholar 

  10. Barr FA, Silljé HH, Nigg EA (2004) Polo-like kinases and the orchestration of cell division. Nat Rev Mol Cell Biol 5(6):429

    Article  CAS  PubMed  Google Scholar 

  11. Holtrich U, Wolf G, Bräuninger A, Karn T, Böhme B, Rübsamen-Waigmann H, Strebhardt K (1994) Induction and down-regulation of PLK, a human serine/threonine kinase expressed in proliferating cells and tumors. Proc Natl Acad Sci U S A 91(5):1736–1740

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Steegmaier M, Hoffmann M, Baum A, Lénárt P, Petronczki M, Krššák M, Gürtler U, Garin-Chesa P, Lieb S, Quant J, Grauert M (2007) BI 2536, a potent and selective inhibitor of polo-like kinase 1, inhibits tumor growth in vivo. Curr Biol 17(4):316–322

    Article  CAS  PubMed  Google Scholar 

  13. Lénárt P, Petronczki M, Steegmaier M, Di Fiore B, Lipp JJ, Hoffmann M, Rettig WJ, Kraut N, Peters JM (2007) The small-molecule inhibitor BI 2536 reveals novel insights into mitotic roles of polo-like kinase 1. Curr Biol 17(4):304–315

    Article  PubMed  CAS  Google Scholar 

  14. Zhan MM, Yang Y, Luo J, Zhang XX, Xiao X, Li S, Cheng K, Xie Z, Tu Z, Liao C (2018) Design, synthesis, and biological evaluation of novel highly selective polo-like kinase 2 inhibitors based on the tetrahydropteridin chemical scaffold. Eur J Med Chem 143:724–731

    Article  CAS  PubMed  Google Scholar 

  15. Cizmecioglu O, Krause A, Bahtz R, Ehret L, Malek N, Hoffmann I (2012) Plk2 regulates centriole duplication through phosphorylation-mediated degradation of Fbxw7 (human Cdc4). J Cell Sci 125(4):981–992

    Article  CAS  PubMed  Google Scholar 

  16. Hu ZB, Liao XH, Xu ZY, Yang X, Dong C, Jin AM, Lu H (2016) PLK 2 phosphorylates and inhibits enriched TA p73 in human osteosarcoma cells. Cancer Med 5(1):74–87

    Article  CAS  PubMed  Google Scholar 

  17. Inglis KJ, Chereau D, Brigham EF, Chiou SS, Schöbel S, Frigon NL, Yu M, Caccavello RJ, Nelson S, Motter R, Wright S (2009) Polo-like kinase 2 (PLK2) phosphorylates α-synuclein at serine 129 in central nervous system. J Biol Chem 284(5):2598–2602

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Aubele DL, Hom RK, Adler M, Galemmo Jr RA, Bowers S, Truong AP, Pan H, Beroza P, Neitz RJ, Yao N, Lin M (2013) Selective and brain-permeable polo-like kinase-2 (Plk-2) inhibitors that reduce α-synuclein phosphorylation in rat brain. ChemMedChem 8(8):1295–1313

    Article  CAS  PubMed  Google Scholar 

  19. Reddy MR, Akula B, Jatiani S, Vasquez-Del Carpio R, Billa VK, Mallireddigari MR, Cosenza SC, Subbaiah DV, Bharathi EV, Pallela VR, Ramkumar P (2016) Discovery of 2-(1H-indol-5-ylamino)-6-(2, 4-difluorophenylsulfonyl)-8-methylpyrido [2, 3-d] pyrimidin-7 (8H)-one (7ao) as a potent selective inhibitor of Polo like kinase 2 (PLK2). Bioorg Med Chem 24(4):521–544

    Article  CAS  PubMed  Google Scholar 

  20. Lounnas V, Ritschel T, Kelder J, McGuire R, Bywater RP, Foloppe N (2013) Current progress in structure-based rational drug design marks a new mindset in drug discovery. Comput Struct Biotechnol J 5(6):e201302011

    Article  PubMed  PubMed Central  Google Scholar 

  21. Walters WP, Stahl MT, Murcko MA (1998) Virtual screening—an overview. Drug Discov Today 3(4):160–178

    Article  CAS  Google Scholar 

  22. Merz Jr KM, Ringe D, Reynolds CH (eds) (2010) Drug design: structure-and ligand-based approaches. Cambridge University Press, Cambridge

    Google Scholar 

  23. Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57(12):4977–5010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Irwin JJ (2008) Community benchmarks for virtual screening. J Comput Aided Mol Des 22(3-4):193–199

    Article  CAS  PubMed  Google Scholar 

  25. Rella M, Rushworth CA, Guy JL, Turner AJ, Langer T, Jackson RM (2006) Structure-based pharmacophore design and virtual screening for novel angiotensin converting enzyme 2 inhibitors. J Chem Inf Model 46(2):708–716

    Article  CAS  PubMed  Google Scholar 

  26. Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA (1998) Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998). Pure Appl Chem 70(5):1129–1143

    Article  CAS  Google Scholar 

  27. Kandakatla N, Ramakrishnan G. (2014). Ligand based pharmacophore modeling and virtual screening studies to design novel HDAC2 inhibitors. Adv Bioinforma 2014.

  28. Yang SY (2010) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 15(11-12):444–450

    Article  CAS  PubMed  Google Scholar 

  29. Lavecchia A (2015) Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today 20(3):318–331

    Article  PubMed  Google Scholar 

  30. Šali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234(3):779–815

    Article  PubMed  Google Scholar 

  31. Sunseri J, Koes DR (2016) Pharmit: interactive exploration of chemical space. Nucleic Acids Res 44(W1):W442–W448

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52(7):1757–1768

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Rao SN, Head MS, Kulkarni A, LaLonde JM (2007) Validation studies of the site-directed docking program LibDock. J Chem Inf Model 47(6):2159–2171

    Article  CAS  PubMed  Google Scholar 

  34. Kabsch W (1976) A solution for the best rotation to relate two sets of vectors. Acta Crystallogr Sect A 32(5):922–923

    Article  Google Scholar 

  35. Bathula SR, Akondi SM, Mainkar PS, Chandrasekhar S (2015) Pruning of biomolecules and natural products (PBNP): an innovative paradigm in drug discovery. Org Biomol Chem Royal Soc Chem 13(23):6432–6448

    Article  CAS  Google Scholar 

  36. Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan SA, Karplus M (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4(2):187–217

    Article  CAS  Google Scholar 

  37. Wu G, Robertson DH, Brooks III CL, Vieth M (2003) Detailed analysis of grid-based molecular docking: a case study of CDOCKER—A CHARMm-based MD docking algorithm. J Comput Chem 24(13):1549–1562

    Article  CAS  PubMed  Google Scholar 

  38. Muegge I (2006) PMF scoring revisited. J Med Chem 49(20):5895–5902

    Article  CAS  PubMed  Google Scholar 

  39. Hess B, Kutzner C, Van Der Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4(3):435–447

    Article  CAS  PubMed  Google Scholar 

  40. Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJ (2005) GROMACS: fast, flexible, and free. J Comput Chem 26(16):1701–1718

    Article  CAS  Google Scholar 

  41. Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C (2006) Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65(3):712–725

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. da Silva AW, Vranken WF (2012) ACPYPE-Antechamber python parser interface. BMC Res Notes 5(1):367

    Article  Google Scholar 

  43. Wang J, Wang W, Kollman PA, Case DA (2006) Automatic atom type and bond type perception in molecular mechanical calculations. Mol Graph Model 25(2):247–260

    Article  CAS  Google Scholar 

  44. Berendsen HJ, Postma JP, van Gunsteren WF, Hermans J (1981) Interaction models for water in relation to protein hydration. Intermolecular forces. Springer, Dordrecht, pp 331–342

    Chapter  Google Scholar 

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

    Article  CAS  Google Scholar 

  46. Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) A smooth particle mesh Ewald method. J Chem Phys 103(19):8577–8593

    Article  CAS  Google Scholar 

  47. Hess B, Bekker H, Berendsen HJ, Fraaije JG (1997) LINCS: a linear constraint solver for molecular simulations. J Comput Chem 18(12):1463–1472

    Article  CAS  Google Scholar 

  48. Bussi G, Donadio D, Parrinello M (2007) Canonical sampling through velocity rescaling. J Chem Phys 126(1):014101

    Article  PubMed  CAS  Google Scholar 

  49. Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 52(12):7182–7190

    Article  CAS  Google Scholar 

  50. Bakan A, Meireles LM, Bahar I (2011) ProDy: protein dynamics inferred from theory and experiments. Bioinformatics 27(11):1575–1577

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(1):33–38

    Article  CAS  PubMed  Google Scholar 

  52. Homeyer N, Gohlke H (2012) Free energy calculations by the molecular mechanics Poisson− Boltzmann surface area method. Mol Inform 31(2):114–122

    Article  CAS  PubMed  Google Scholar 

  53. Kumari R, Kumar R, Open Source Drug Discovery Consortium, Lynn A (2014) g_mmpbsa– A GROMACS tool for high-throughput MM–PBSA calculations. J Chem Inf Model 54(7):1951–1962

    Article  CAS  PubMed  Google Scholar 

  54. Kothe M, Kohls D, Low S, Coli R, Rennie GR, Feru F, Kuhn C, Ding YH (2007) Selectivity-determining residues in Plk1. Chem Biol Drug Des 70(6):540–546

    Article  CAS  PubMed  Google Scholar 

  55. Reymond JL, Awale M (2012) Exploring chemical space for drug discovery using the chemical universe database. ACS Chem Neurosci 3(9):649–657

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Wolber G, Langer T (2005) LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 45(1):160–169

    Article  CAS  PubMed  Google Scholar 

  57. Wieder M, Garon A, Perricone U, Boresch S, Seidel T, Almerico AM, Langer T (2017) Common hits approach: combining pharmacophore modeling and molecular dynamics simulations. J Chem Inf Model 57(2):365–385

    Article  CAS  PubMed  Google Scholar 

  58. Xing L, Klug-Mcleod J, Rai B, Lunney EA (2015) Kinase hinge binding scaffolds and their hydrogen bond patterns. Bioorg Med Chem 23(19):6520–6527

    Article  CAS  PubMed  Google Scholar 

  59. Archer S, Glick SD, Bidlack JM (1996) Cyclazocine revisited. Neurochem Res 21(11):1369–1373

    Article  CAS  PubMed  Google Scholar 

  60. Archer S, Seyed-Mozaffari A, Jiang Q, Bidlack JM (1994) 14. alpha., 14'. beta.-[Dithiobis [(2-oxo-2, 1-ethanediyl) imino]] bis (7, 8-dihydromorphinone) and 14. alpha., 14'. beta.-[Dithiobis [(2-oxo-2, 1-ethanediyl) imino]] bis-7, 8-dihydro-N-(cyclopropyl-methyl) normorphinone: Chemistry and Opioid Binding Properties. J Med Chem 37(11):1578–1585

    Article  CAS  PubMed  Google Scholar 

  61. Araki M, Kamiya N, Sato M, Nakatsui M, Hirokawa T, Okuno Y (2016) The effect of conformational flexibility on binding free energy estimation between kinases and their inhibitors. J Chem Inf Model 56(12):2445–2456

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

The authors thank CMSD, University of Hyderabad, for providing computational facilities. MA thanks Ministry of Higher Education & Scientific Research - Republic of Yemen.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lalitha Guruprasad.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This chapter does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdullah, M., Guruprasad, L. Computational basis for the design of PLK-2 inhibitors. Struct Chem 31, 275–292 (2020). https://doi.org/10.1007/s11224-019-01394-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11224-019-01394-x

Keywords

Navigation