Skip to main content
Log in

Identification of lead BAY60-7550 analogues as potential inhibitors that utilize the hydrophobic groove in PDE2A: a molecular dynamics simulation study

  • Original Paper
  • Published:
Journal of Molecular Modeling Aims and scope Submit manuscript

Abstract

The phosphodiesterase (PDE) family of proteins are important regulators of signal transduction, which they achieve by controlling the secondary messengers cyclic AMP (cAMP) and cyclic GMP (cGMP). cAMP and cGMP are involved in many critical intracellular processes such as gene transcription, kinase activation, signal transduction in learning and memory, and channel function as secondary messengers. The involvement of PDEs in neuronal communication has made them important therapeutic targets. Considering the recent discovery that PDE2A inhibition can improve cognitive functioning, a combined molecular dynamics simulation and scoring and docking study was carried out to identify selective inhibitors of PDE2A that specifically interact with the recently discovered hydrophobic groove in PDE2A. Using the X-ray crystal structure of PDE2A (from PDB ID: 4HTX), we investigated the binding modes of a range of promising inhibitors based on the known PDE2A inhibitor BAY60-7550 to PDE2A.

The lead molecule showing highest MMPBSA binding energy with 2D and 3D binding pose in hydrophobic groove.

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

Similar content being viewed by others

References

  1. Mobashir M, Madhusudhan T, Isermann B, Beyer T, Schraven B (2014) Negative interactions and feedback regulations are required for transient cellular response. Sci Rep 4:3718

    Article  Google Scholar 

  2. Umar T, Hoda N (2015) Selective inhibitors of phosphodiesterases: therapeutic promise for neurodegenerative disorders. Med Chem Commun 6:2063–2080

    Article  CAS  Google Scholar 

  3. Fawcett L, Baxendale R, Stacey P, McGrouther C, Harrow I, Soderling S et al (2000) Molecular cloning and characterization of a distinct human phosphodiesterase gene family: PDE11A. Proc Natl Acad Sci USA 97:3702–3707

  4. Beavo JA, Brunton LL (2002) Cyclic nucleotide research—still expanding after half a century. Nat Rev Mol Cell Biol 3:710–718

    Article  CAS  Google Scholar 

  5. Manganiello VC, Degerman E (1999) Cyclic nucleotide phosphodiesterases (PDEs): diverse regulators of cyclic nucleotide signals and inviting molecular targets for novel therapeutic agents. Thromb Haemost 82:407–411

    CAS  Google Scholar 

  6. Laddha S, Wadodkar S, Meghal S (2009) cAMP-dependent phosphodiesterase inhibition and SAR studies on novel 6,8-disubstituted 2-phenyl-3-(substituted benzothiazole-2-yl)-4[3H]-quinazolinone. Med Chem Res 18:268–276

  7. Francis SH, Blount MA, Corbin JD (2011) Mammalian cyclic nucleotide phosphodiesterases: molecular mechanisms and physiological functions. Physiol Rev 91:651–690

    Article  CAS  Google Scholar 

  8. Conti M, Beavo J (2007) Biochemistry and physiology of cyclic nucleotide phosphodiesterases: essential components in cyclic nucleotide signaling. Annu Rev Biochem 76:481–511

    Article  CAS  Google Scholar 

  9. Lugnier C (2006) Cyclic nucleotide phosphodiesterase (PDE) superfamily: a new target for the development of specific therapeutic agents. Pharmacol Ther 109:366–398

    Article  CAS  Google Scholar 

  10. Iffland A, Kohls D, Low S, Luan J, Zhang Y, Kothe M et al (2005) Structural determinants for inhibitor specificity and selectivity in PDE2A using the wheat germ in vitro translation system. Biochemistry 44:8312–8325

    Article  CAS  Google Scholar 

  11. Zhu J, Yang Q, Dai D, Huang Q (2013) X-ray crystal structure of phosphodiesterase 2 in complex with a highly selective, nanomolar inhibitor reveals a binding-induced pocket important for selectivity. J Am Chem Soc 135:11708–11711

    Article  CAS  Google Scholar 

  12. Wang H, Robinson H, Ke H (2007) The molecular basis for different recognition of substrates by phosphodiesterase families 4 and 10. J Mol Biol 371:302–307

    Article  CAS  Google Scholar 

  13. Wang H, Liu Y, Hou J, Zheng M, Robinson H, Ke H (2007) Structural insight into substrate specificity of phosphodiesterase 10. Proc Natl Acad Sci USA 104:5782–5787

  14. Xu RX, Hassell AM, Vanderwall D, Lambert MH, Holmes WD, Luther MA et al (2000) Atomic structure of PDE4: insights into phosphodiesterase mechanism and specificity. Science 288:1822–1825

    Article  CAS  Google Scholar 

  15. Liu S, Mansour MN, Dillman KS, Perez JR, Danley DE, Aeed PA et al (2008) Structural basis for the catalytic mechanism of human phosphodiesterase 9. Proc Natl Acad Sci USA 105:13309–13314

  16. Gupta A, Gandhimathi A, Sharma P, Jayaram B (2007) ParDOCK: an all atom energy based Monte Carlo docking protocol for protein–ligand complexes. Protein Pept Lett 14:632–646

  17. Pearlman DA, Case DA, Caldwell JW, Ross WS, Cheathem JE III et al (1995) AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Comput Phys Commun 91:1–41

    Article  CAS  Google Scholar 

  18. van der Spoel D, Lindahl E, Hess B, Kutzner C, van Buuren AR, Apol E et al (2006) GROMACS user manual, version 4.0. GROMACS Development Team, Groningen

  19. Schüttelkopf AW, van Aalten DMF (2004) PRODRG: a tool for high-throughput crystallography of protein–ligand complexes. Acta Crystallogr D 60:1355–1363

  20. Hess B, Bekker H, Berendsen HJC, Fraaije JGEM (1997) LINCS: a linear constraint solver for molecular simulations. J Comput Chem 18:1463–1472

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  22. Kumari R, Kumar R, Lynn A (2014) g_mmpbsa—a GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inf Model 54:1951–1962

  23. He JY, Li C, Wu G (2014) Discovery of potential drugs for human-infecting H7N9 virus containing R294K mutation. Drug Des Devel Ther 8:2377–2390

    Article  CAS  Google Scholar 

  24. Kastritis PL, Bonvin AM (2010) Are scoring function in protein–protein docking ready to predict interactomes? Clues from a novel binding affinity benchmark. J Proteome Res 9(5):2216–2225

Download references

Acknowledgements

JK thanks the CSIR for a doctoral research fellowship. TU thanks the UGC for a basic scientific research fellowship. We also thank Prof. B. Jayaram, IIT Delhi, for access to the supercomputing facility. We acknowledge the support provided by the BRAF at C-DAC, Pune, India, when we were carrying out MD simulations using computational resources at that facility.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Shahid M. Nayeem or Nasimul Hoda.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 3426 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, J., Umar, T., Kausar, T. et al. Identification of lead BAY60-7550 analogues as potential inhibitors that utilize the hydrophobic groove in PDE2A: a molecular dynamics simulation study. J Mol Model 23, 7 (2017). https://doi.org/10.1007/s00894-016-3171-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00894-016-3171-1

Keywords

Navigation