Skip to main content
SpringerLink
Log in

Targeting the ubiquitin-conjugating enzyme E2D4 for cancer drug discovery–a structure-based approach

  • Original Article
  • Published:
Journal of Chemical Biology

Abstract

Cancer progression is a global burden. The incidence and mortality now reach 30 million deaths per year. Several pathways of cancer are under investigation for the discovery of effective therapeutics. The present study highlights the structural details of the ubiquitin protein ‘Ubiquitin-conjugating enzyme E2D4’ (UBE2D4) for the novel lead structure identification in cancer drug discovery process. The evaluation of 3D structure of UBE2D4 was carried out using homology modelling techniques. The optimized structure was validated by standard computational protocols. The active site region of the UBE2D4 was identified using computational tools like CASTp, Q-site Finder and SiteMap. The hydrophobic pocket which is responsible for binding with its natural receptor ubiquitin ligase CHIP (C-terminal of Hsp 70 interacting protein) was identified through protein-protein docking study. Corroborating the results obtained from active site prediction tools and protein-protein docking study, the domain of UBE2D4 which is responsible for cancer cell progression is sorted out for further docking study. Virtual screening with large structural database like CB_Div Set and Asinex BioDesign small molecular structural database was carried out. The obtained new ligand molecules that have shown affinity towards UBE2D4 were considered for ADME prediction studies. The identified new ligand molecules with acceptable parameters of docking, ADME are considered as potent UBE2D4 enzyme inhibitors for cancer therapy.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Engblom C, Pfirschke C, Pittet JM (2016) The role of myeloid cells in cancer therapies. Nat Rev Cancer 16:447–462

    Article  CAS  Google Scholar 

  2. Cheng M, Chen Y, Xiao W, Sun R, Tian Z (2013) NK cell-based immunotherapy for malignant diseases. Cell Mol Immunol 10:230–252

    Article  CAS  Google Scholar 

  3. Ciechnover A (2015) The unravelling of the ubiquitin system. Nat Rev Mol Cell Biol 16:322–324

    Article  Google Scholar 

  4. Nalepa G, Rofle M, Harper WJ (2006) Drug discovery in the ubiquitin-proteasome system. Nat Rev Drug Discov 5:596–613

    Article  CAS  Google Scholar 

  5. Ravid T, Hochstrasser M (2008) Diversity of degradation signals in the ubiquitin-proteasome system. Nat Rev Mol Cell Biol 9:679–689

    Article  CAS  Google Scholar 

  6. Sarita Rajender P, Ramasree D, Bhargavi K, Vasavi M, Uma V (2010) Selective inhibition of proteins regulating CDK/cyclin complexes: strategy against cancer—a review. J Recept Signal Transduct Res 30:206–213

    Article  CAS  Google Scholar 

  7. Ciechanover A (2005) Proteolysis: from the lysosome to ubiquitin and the proteasome. Nat Rev Mol Cell Biol 6:79–87

    Article  CAS  Google Scholar 

  8. Muratani M, Tansey PT (2003) How the ubiquitin-proteasome system controls transcription. Nat Rev Mol Cell Biol 4:192–201

    Article  CAS  Google Scholar 

  9. Bedford L, Lowe J, Dick LR, Mayer RJ, Brownell JE (2011) Ubiquitin-like protein conjugation and the ubiquitin-proteasome system as drug targets. Nat Rev Drug Discov 10:29–46

    Article  CAS  Google Scholar 

  10. Hu R, Hochstrasser M (2016) Recent progress in ubiquitin and ubiquitin-like protein (Ubl) signalling. Cell Res 26:389–390

    Article  CAS  Google Scholar 

  11. Mani A, Gelmann EP (2005) The ubiquitin-proteasome pathway and its role in cancer. J Clin Oncol 23:4776–4789

    Article  CAS  Google Scholar 

  12. Ye Y, Rape M (2009) Building ubiquitin: E2 enzymes at work. Nat Rev Mol Cell Biol 10:755–764

    Article  CAS  Google Scholar 

  13. Berndsen CE, Wolberger C (2014) New insights into ubiquitin E3 ligase mechanism. Nat Struct Mol Biol 21:301–307

    Article  CAS  Google Scholar 

  14. Severe N, Dieudonne FX, Marie PJ (2013) E3 ubiquitin ligase-mediated regulation of bone formation and tumorigenesis. Cell Death Disease 4:1–10

    Article  Google Scholar 

  15. Xu Z, Kohli E, Devlin IK, Bold M, Nix CJ, Misra S (2008) Interactions between the quality control ubiquitin ligase CHIP and ubiquitin conjugating enzymes. BMC Struct Biol 8:1–13

    Article  Google Scholar 

  16. Jiang J, Ballinger CA, Wu Y, Dai Q, Cyr DM, Hohfeld J, Patterson C (2001) CHIP is a U-box-dependent E3 ubiquitin ligase identification of Hsc70 as a target for ubiquitinylation. J Biol Chem 276:42938–42944

    Article  CAS  Google Scholar 

  17. Kajiro M, Hirota R, Nakajima Y, Kawanowa K, So-ma K, Ito I, Yamaguchi Y, Ohie S, Kobayashi Y, Seino Y, Kawano M, Kawabe Y, Takei H, Hayashi S, Kurosumi M, Murayama A, Kimura K, Yanagisawa J (2009) The ubiquitin ligase CHIP acts as an upstream regulator of oncogenic pathways. Nat Cell Biol 11:312–319

    Article  CAS  Google Scholar 

  18. Cavasotto NC, Phatak SS (2009) Homology modeling in drug discovery: current trends and applications. Drug Discov Today 14:676–683

    Article  CAS  Google Scholar 

  19. Hillisch A, Pineda FL, Hilgenfeld R (2004) Utility of homology models in the drug discovery process. Drug Discov Today 9:659–669

    Article  CAS  Google Scholar 

  20. Alpi E, Griss J, da Silva AW, Bely B, Antunes R, Zellner H, Rios D, O’Donovan C, Vizcaino JA, Martin MJ (2015) Analysis of the tryptic search space in UniProt databases. Proteomics 15:48–57

    Article  CAS  Google Scholar 

  21. Gasteiger E, Gattiker A, Hoogland C, Ivanyi I, Appel RD, Bairoch A (2003) ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res 31:3784–3788

    Article  CAS  Google Scholar 

  22. McGinnis S, Madden TL (2004) BLAST: at the core of a powerful and diverse set of sequence analysis tools. Nucleic Acids Res 32:20–25

    Article  Google Scholar 

  23. Ye J, McGinnis S, Madden TL (2006) BLAST: improvements for better sequences analysis. Nucleic Acids Res 34:6–9

    Article  Google Scholar 

  24. Christian C, Jonathan DB, Geoffrey JB (2008) The Jpred 3 secondary structure prediction server. Nucleic Acids Res 36:197–201

    Article  Google Scholar 

  25. Contreras-Moreira B, Bates PA (2002) Domain fishing: a first step in protein comparative modeling. Bioinformatics 18:1141–1142

    Article  CAS  Google Scholar 

  26. Thompson JD, Higgins DG, Gibson TJ (1994) Clustal W: improving the sensitivity of sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680

    Article  CAS  Google Scholar 

  27. Gonnet GH, Cohen MA, Benner SA (1992) Exhaustive matching of the entire protein sequence database. Science 256:1443–1445

    Article  CAS  Google Scholar 

  28. Webb B, Sali A (2014) Comparative protein structure modeling using MODELLER. Curr Protoc Bioinformatics 47:5.6.1–5.6.32

    Article  Google Scholar 

  29. Jacobson M, Sali A (2004) Comparative protein structure modeling and its applications to drug discovery. Annu Rep Med Chem 39:259–276

    Article  CAS  Google Scholar 

  30. Sali A, Blundell TL (1993) Comparative modeling by satisfaction of spatial restraints. J Mol Biol 234:779–815

    Article  CAS  Google Scholar 

  31. Banks JL, Beard HS, Cao Y, Cho AE, Damm W, Farid R, Felts AK, Halgren TA, Mainz DT, Maple JR, Murphy R, Philipp DM, Repasky MP, Zhang LY, Berne BJ, Friesner RA, Gallicchio E, Levy RM (2005) Integrated modeling program, applied chemical theory (IMPACT). J Comput Chem 26:1752–1780

  32. Shivakumar D, Williams J, Wu Y, Damm W, Shelley J, Sherman W (2010) Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. J Chem Theory Comput 6:1509–1519

    Article  CAS  Google Scholar 

  33. Jorgensen WL, Tirado-Rives J (1996) The OPLS (optimized potentials for liquid simulations) potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J Am Chem Soc 110:1657–1666

    Article  Google Scholar 

  34. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHEK: a program to check the stereo chemical quality of protein structures. J Appl Crystallogr 26:283–291

    Article  CAS  Google Scholar 

  35. Zhou AQ, O’Hern CS, Regan L (2011) Revisiting the Ramachandran plot from a new angle. Prot Sci 20:1166–1171

    Article  CAS  Google Scholar 

  36. Wiederstein M, Sippl MJ (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 35:407–441

    Article  Google Scholar 

  37. Sippl MJ (1993) Recognition of errors in three-dimensional structures of proteins. Proteins 17:355–362

    Article  CAS  Google Scholar 

  38. Kalman M, Ben-Tal N (2010) Quality assessment of protein model-structures using evolutionary conservation. Bioinformatics 26:1299–1307

    Article  CAS  Google Scholar 

  39. Luthy R, Bowie JU, Eisenberg D (1992) Assessment of protein models with three dimensional profiles. Nature 356:83–85

    Article  CAS  Google Scholar 

  40. Morris AL, MacArthur MW, Hutchinson EG, Thornton JM (1992) Stereochemical quality of protein structure coordinates. Proteins 12:345–364

    Article  CAS  Google Scholar 

  41. Jiang F, Han W, YD W (2010) Influence of side chain conformations on local conformational features of amino acids implication for force field development. J Phys Chem 114:5840–5850

    Article  CAS  Google Scholar 

  42. Sippl MJ (1995) Knowledge-based potentials for proteins. Curr Opin Struct Biol 5:229–235

    Article  CAS  Google Scholar 

  43. Bowie JU, Luthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253:164–170

    Article  CAS  Google Scholar 

  44. Guex N, Peitsch MC, Schwede T (2009) Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: a historical perspective. Electrophoresis 30:162–173

    Article  Google Scholar 

  45. Dundas J, Ouyang Z, Seng TJ, Binkowski A, Trupaz Y, Liang J (2006) CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res 34:116–118

    Article  Google Scholar 

  46. Laurie AT, Jackson RM (2005) Q-site finder: an energy-based method for the prediction of protein-ligand sites. Bioinformatics 21:1908–1916

    Article  CAS  Google Scholar 

  47. Halgren T (2009) Identifying and characterizing binding sites and assessing druggability. J Chem Inf Mod 49:377–389

    Article  CAS  Google Scholar 

  48. Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ (2005) PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res 33:363–367

    Article  Google Scholar 

  49. Binkowski TA, Naghibzadeh S, Liang J (2003) CASTp: computed atlas of surface topography of proteins. Nucleic Acids Res 31:3352–3355

    Article  CAS  Google Scholar 

  50. Liang J, Edelsbrunner H, Woodward C (1998) Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design. Prot Sci 7:1884–1897

    Article  CAS  Google Scholar 

  51. Halgren TA (2007) New method for fast and accurate binding site identification and analysis. Chem Biol Drug Des 69:146–148

    Article  CAS  Google Scholar 

  52. Reddy AS, Priyadarshini PS, Kumar PP, Pradeep HN, Sastry GN (2007) Virtual screening in drug discovery—a computational perspective. Curr Protein Pept Sci 8:329–351

    Article  CAS  Google Scholar 

  53. Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949

    Article  CAS  Google Scholar 

  54. Girke T, Cheng LC, Raikhel N (2005) ChemMine. A compound mining database for chemical genomics. Plant Physiol 138:573–577

    Article  CAS  Google Scholar 

  55. Chen IJ, Folopee N (2010) Drug-like bioactive structures and conformational coverage with the LigPrep/ConfGen suite: comparison to programs MOE and catalyst. J Chem Inf Model 50:822–839

    Article  CAS  Google Scholar 

  56. Elokely MK, Doerksen JR (2013) Docking challenge: protein sampling and molecular docking performance. J Chem Inf Model 53:1934–1945

    Article  CAS  Google Scholar 

  57. Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic encloser for protein-ligand complexes. J Med Chem 49:6177–6196

    Article  CAS  Google Scholar 

  58. Durham E, Dorr B, Woetzel N, Staritzbichler R, Meiler J (2009) Solvent accessible surface area approximations for rapid and accurate protein structure prediction. J Mol Model 15:1093–1108

    Article  CAS  Google Scholar 

  59. Lill MA, Danielson ML (2011) Computer-aided drug design platform using PyMOL. J Comput Aid Mol Des 25:13–19

    Article  CAS  Google Scholar 

  60. 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 

  61. Ioakimids L, Thoukydidis L, Mirza A, Naeem S, Reynisson J (2008) Benchmarketing the reliability of QikProp. Correlation between experimental and predicted values. QSAR Comb Sci 27:445–456

    Article  Google Scholar 

  62. Schwede T (2013) Protein modelling: what happened to the “protein structure gap”. Structure 21:1531–1540

    Article  CAS  Google Scholar 

  63. Lee D, Redfern O, Orengo C (2007) Predicting protein function from sequence and structure. Nat Rev Mol Cell Biol 8:995–1005

    Article  CAS  Google Scholar 

  64. Whisstock JC, Lesk AM (2003) Prediction of protein function from protein sequence and structure. Q Rev Biophys 36:307–340

    Article  CAS  Google Scholar 

  65. Watson JD, Laskowski RA, Thornton JM (2005) Predicting protein function from sequence and structural data. Curr Opin Struct Biol 15:275–284

    Article  CAS  Google Scholar 

  66. Dumpati R, Dulapalli R, Kondagari B, Ramatenki V, Vellanki S, Vadija R, Vuruputuri U (2016) Suppressor of cytokine signalling-3 as a drug target for type 2 diabetes mellitus: a structure-guided approach. Chemistry Select 1:2502–2514

    CAS  Google Scholar 

  67. Sasikala D, Jeyakanthan J, Srinivasan R (2016) Structural insights on identification of potential lead compounds targeting WbpP in vibrio vulnificus through structure-based approaches. J Recept Signal Transduct Res 36:515–530

    Article  CAS  Google Scholar 

  68. Vadija R, Mustyala KK, Niambigari N, Dulapalli R, Dumpati RK, Ramatenki V, Vellanki SP, Vuruputuri U (2016) Homology modeling and virtual screening studies of FGF-7 protein—a structure-based approach to design new molecules against tumor angiogenesis. J Chem Biol 9:69–78

    Article  Google Scholar 

  69. Yahalom R, Reshef D, Wiener A, Frankel S, Kalisman N, Lerner B, Keasar C (2011) Structure-based identification of catalytic residues. Proteins 79:1952–1963

    Article  CAS  Google Scholar 

  70. Bartlett GJ, Porter CT, Borkakoti N, Thornton JM (2002) Analysis of catalytic residues in enzyme active sites. J Mol Biol 324:105–121

    Article  CAS  Google Scholar 

  71. Mustyala KK, Malkhed V, Chittireddy VRR, Vuruputuri U (2016) Identification of small molecular inhibitors for efflux protein: DrrA of Mycobacterium tuberculosis. Cell Mol Bioeng 9:190–202

    Article  CAS  Google Scholar 

  72. Singh T, Biswas D, Jayaram B (2011) AADS—an automated active site identification, docking and scoring protocol for protein targets based on physicochemical descriptors. J Chem Inf Model 51:2515–2527

    Article  CAS  Google Scholar 

  73. Ramatenki V, Potlapally SR, Dumpati RK, Vadija R, Vuruputuri U (2015) Homology modeling and virtual screening of ubiquitin conjugation enzyme E2A for designing a novel selective antagonist against cancer. J Recept Signal Transduct Res 35:536–549

    Article  CAS  Google Scholar 

  74. Malkhed V, Mustyala KK, Potlapally SR, Vuruputuri U (2014) Identification of novel leads applying in silico studies for mycobacterium multidrug resistant (MMR) protein. J Biomol Struct Dyn 32:1889–1906

    Article  CAS  Google Scholar 

  75. Sliwoski G, Kothiwale S, Meiler J, Lowe EW Jr (2014) Computational methods in drug discovery. Pharmacol Rev 66:334–395

    Article  Google Scholar 

  76. Lionta E, Spyrou G, Vassilatis DK, Cournia Z (2014) Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr Top Med Chem 14:1923–1938

    Article  CAS  Google Scholar 

  77. Martins P, Jesus J, Santos S, Raposo LR, Roma-Rodrigues C, Baptista PV, Fernandes AR (2015) Heterocyclic anticancer compounds: recent advances and the paradigm shift towards the use of nanomedicine’s tool box. Molecules 20:16852–16891

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  79. Congreve M, Carr R, Murray C, Jhoti H (2003) A ‘rule of three’ for fragment-based lead discovery? Drug Discov Today 8:876–877

    Article  Google Scholar 

Download references

Acknowledgements

The author VR acknowledges the Council of Scientific and Industrial Research, India, for the financial support (File No: 09/132(0821)/2012-EMR-I). The authors VR, RKD, RV, SPV and RR acknowledge the Principal and Head, Department of Chemistry, University College of Science, Osmania University, Hyderabad, for providing facilities to carry out this work.

Author information

Authors and Affiliations

  1. Department of Chemistry, University College of Science, Osmania University, Hyderabad, Telangana, 500007, India

    Vishwanath Ramatenki, Ramakrishna Dumpati, Rajender Vadija, Santhiprada Vellanki, Rohini Rondla & Uma Vuruputuri

  2. Department of Chemistry, Nizam College, Osmania University, Basheerbagh, Hyderabad, Telangana, 500001, India

    Sarita Rajender Potlapally

Authors
  1. Vishwanath Ramatenki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ramakrishna Dumpati
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rajender Vadija
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Santhiprada Vellanki
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sarita Rajender Potlapally
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rohini Rondla
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Uma Vuruputuri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Uma Vuruputuri.

Ethics declarations

Ethical standards

The authors state that no human studies and no animal studies were carried out for this article.

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Supplementary fig S1

Alignment of the UBE2D4 sequence with the other E2 sequences. The alignment file was generated using CLUSTALW server tool. The UBC domain of UBE2D4 is conserved in all the E2s. The UBE2D4 conserved domain sequence starts from LYS 4 and ends with THR 142 amino acid residue. (GIF 7718 kb)

High Resolution (TIFF 13149 kb)

Supplementary Table S1

(DOCX 17 kb)

Supplementary Table S2

(DOCX 100 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ramatenki, V., Dumpati, R., Vadija, R. et al. Targeting the ubiquitin-conjugating enzyme E2D4 for cancer drug discovery–a structure-based approach. J Chem Biol 10, 51–67 (2017). https://doi.org/10.1007/s12154-016-0164-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12154-016-0164-6

Keywords

Search

Navigation