Skip to main content

Advertisement

Log in

Characterization of the adipogenic protein E4orf1 from adenovirus 36 through an in silico approach

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

Abstract

Adenovirus 36 (Ad-36) is related to human obesity due to its adipogenic activity mediated by the early 4 open reading frame 1 (E4orf1) protein. Mechanisms underlying the adipogenic effect of E4orf1 are not completely understood; however, the proliferation and differentiation of fat cells are increased through the activation of the phosphatidyl inositol 3 kinase pathway by binding proteins containing PDZ domain. This study characterized E4orf1 tridimensional structure and analyzed its interactions with PDZ domain-containing proteins in order to provide new information about the behavior of this viral protein and its targets, which could provide an interesting druggable target for obesity-related cardiometabolic alterations. In silico strategies such as homology modeling, docking, and molecular dynamics (MD) were used to study the interaction of E4orf1 with five PDZ domains of disk large homolog 1 (PDZ-1 and PDZ-2), membrane-associated guanylate kinase 1 (PDZ-3), and multi-PDZ domain protein 1 (PDZ-7 and PDZ-10). Mutagenesis analysis of selected residues was performed to evaluate their effects on the stabilization of E4orf1:PDZ complexes. MD simulations showed that the E4orf1:PDZ10 complex was more stable than the others ones. The highly hydrophobic residues at the C-terminal region (114–125) of the E4orf1 are essential in the initial phase stabilization of the complexes. Moreover, the residues 80–85 in the core region contribute to longer stabilization of the E4orf1:PDZ10 complex, a result that was confirmed by in silico mutagenesis. In conclusion, E4orf1 forms a stable complex with PDZ10 domain, and the residues 80–85 are of particular importance. The characterization of E4orf1 interactions with PDZ domains provides an initial approach to discover druggable targets for Ad-36-induced obesity.

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

Similar content being viewed by others

Abbreviations

Ad-36:

Adenovirus 36

cAMP:

Cyclic adenosine monophosphate

DLG1:

Disk large homolog 1

DOPE:

Discrete optimized protein energy

E4orf1:

Early 4 open reading frame 1

PI3K:

Phosphatidyl inositol 3 kinase

MAGI-1:

Membrane-associated guanylate kinase 1

MD:

Molecular dynamics

MUPP1:

Multi-PDZ domain protein 1

PATJ:

PALS1-associated tight junction protein

PBM:

PDZ-binding motif

ZO-2:

Zona occludens 2

References

  1. Ponterio E, Gnessi L (2015) Adenovirus 36 and obesity: an overview. Viruses 7(7):3719–3740. https://doi.org/10.3390/v7072787

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Chen RF, Lee CY (2014) Adenoviruses types, cell receptors and local innate cytokines in adenovirus infection. Int Rev Immunol 33(1):45–53. https://doi.org/10.3109/08830185.2013.823420

    Article  CAS  PubMed  Google Scholar 

  3. Lion T (2014) Adenovirus infections in immunocompetent and immunocompromised patients. Clin Microbiol Rev 27(3):441–462. https://doi.org/10.1128/CMR.00116-13

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Giberson AN, Davidson AR, Parks RJ (2012) Chromatin structure of adenovirus DNA throughout infection. Nucleic Acids Res 40(6):2369–2376. https://doi.org/10.1093/nar/gkr1076

    Article  CAS  PubMed  Google Scholar 

  5. Saha B, Wong CM, Parks RJ (2014) The adenovirus genome contributes to the structural stability of the virion. Viruses. 6(9):3563–3583. https://doi.org/10.3390/v6093563

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. McMurphy TB et al (2017) Hepatic expression of adenovirus 36 E4ORF1 improves glycemic control and promotes glucose metabolism through AKT activation. Diabetes. 66(2):358–371. https://doi.org/10.2337/db16-0876

    Article  CAS  PubMed  Google Scholar 

  7. Thai M et al (2014) Adenovirus E4ORF1-induced MYC activation promotes host cell anabolic glucose metabolism and virus replication. Cell Metab 19(4):694–701. https://doi.org/10.1016/j.cmet.2014.03.009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kumar M, Kong K, Javier RT (2014) Hijacking Dlg1 for oncogenic phosphatidylinositol 3-kinase activation in human epithelial cells is a conserved mechanism of human adenovirus E4-ORF1 proteins. J Virol 88(24):14268–14277. https://doi.org/10.1128/JVI.02324-14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hur SJ et al (2013) Effect of adenovirus and influenza virus infection on obesity. Life Sci 93(16):531–535. https://doi.org/10.1016/j.lfs.2013.08.016

    Article  CAS  PubMed  Google Scholar 

  10. Genoni G et al (2014) Obesity and infection: two sides of one coin. Eur J Pediatr 173(1):25–32. https://doi.org/10.1007/s00431-013-2178-1

    Article  PubMed  Google Scholar 

  11. Sabin MA et al (2015) Longitudinal investigation of adenovirus 36 seropositivity and human obesity: the cardiovascular risk in young finns study. Int J Obes (Lond) 39(11):1644–1650. https://doi.org/10.1038/ijo.2015.108

    Article  CAS  Google Scholar 

  12. Sohrab SS et al (2017) Viral infection and obesity: current status and future prospective. Curr Drug Metab 18(9):798–807. https://doi.org/10.2174/1389200218666170116110443

    Article  CAS  PubMed  Google Scholar 

  13. Sapunar J et al (2020) Adenovirus 36 seropositivity is related to obesity risk, glycemic control, and leptin levels in Chilean subjects. Int J Obes (Lond) 44(1):159–166. https://doi.org/10.1038/s41366-019-0321-4

    Article  Google Scholar 

  14. Akheruzzaman M, Hegde V, Dhurandhar NV (2019) Twenty-five years of research about adipogenic adenoviruses: a systematic review. Obes Rev 20(4):499–509. https://doi.org/10.1111/obr.12808

    Article  PubMed  Google Scholar 

  15. Dhurandhar NV (2013) Insulin sparing action of adenovirus 36 and its E4orf1 protein. J Diabetes Complicat 27(2):191–199. https://doi.org/10.1016/j.jdiacomp.2012.09.006

    Article  Google Scholar 

  16. Rogers PM et al (2008) Human adenovirus Ad-36 induces adipogenesis via its E4 orf-1 gene. Int J Obes (Lond) 32(3):397–406. https://doi.org/10.1038/sj.ijo.0803748

    Article  CAS  Google Scholar 

  17. Dhurandhar EJ et al (2011) E4orf1: a novel ligand that improves glucose disposal in cell culture. PLoS One 6(8):e23394. https://doi.org/10.1371/journal.pone.0023394

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Weiss RS et al (1997) Human adenovirus early region 4 open reading frame 1 genes encode growth-transforming proteins that may be distantly related to dUTP pyrophosphatase enzymes. J Virol 71(3):1857–1870. https://doi.org/10.1128/JVI.71.3.1857-1870.1997

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Chung SH et al (2007) A new crucial protein interaction element that targets the adenovirus E4-ORF1 oncoprotein to membrane vesicles. J Virol 81(9):4787–4797. https://doi.org/10.1128/JVI.02855-06

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kong K et al (2014) The human adenovirus E4-ORF1 protein subverts discs large 1 to mediate membrane recruitment and dysregulation of phosphatidylinositol 3-kinase. PLoS Pathog 10(5):e1004102. https://doi.org/10.1371/journal.ppat.1004102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Javier RT (2008) Cell polarity proteins: common targets for tumorigenic human viruses. Oncogene. 27(55):7031–7046. https://doi.org/10.1038/onc.2008.352

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Waterhouse A et al (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46(W1):W296–W303. https://doi.org/10.1093/nar/gky427

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Altschul SF et al (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410. https://doi.org/10.1016/S0022-2836(05)80360-2

    Article  CAS  PubMed  Google Scholar 

  24. Berman HM et al (2000) The Protein Data Bank. Nucleic Acids Res 28(1):235–242. https://doi.org/10.1093/nar/28.1.235

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Berman H, Henrick K, Nakamura H (2003) Announcing the worldwide protein data Bank. Nat Struct Biol 10(12):980. https://doi.org/10.1038/nsb1203-980

    Article  CAS  PubMed  Google Scholar 

  26. Yang J et al (2015) The I-TASSER suite: protein structure and function prediction. Nat Methods:7–8. https://doi.org/10.1038/nmeth.3213

  27. Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5(4):725–738. https://doi.org/10.1038/nprot.2010.5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 9(40). https://doi.org/10.1186/1471-2105-9-40

  29. Webb B, Sali A (2016) Comparative protein structure modeling using MODELLER. Curr Protoc Protein Sci 86(54):2.9.1–2.9.37. https://doi.org/10.1002/cpbi.3

    Article  Google Scholar 

  30. Webb B, Sali A (2017) Protein structure modeling with MODELLER. Methods Mol Biol 1654:39–54. https://doi.org/10.1007/978-1-4939-0366-5_1

    Article  CAS  PubMed  Google Scholar 

  31. Shen M-y, Sali A (2006) Statistical potential for assessment and prediction of protein structures. Protein Sci 15(11):2507–2524. https://doi.org/10.1110/ps.062416606

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Lovell SC et al (2003) Structure validation by Cα geometry: ϕ,ψ and Cβ deviation. Proteins 50(3):437–450. https://doi.org/10.1002/prot.10286

    Article  CAS  PubMed  Google Scholar 

  33. James CD, Roberts S (2016) Viral interactions with PDZ domain-containing proteins-an oncogenic trait? Pathogens. 5(1):8. https://doi.org/10.3390/pathogens5010008

    Article  CAS  PubMed Central  Google Scholar 

  34. Tian W et al (2018) CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res 46(W1):W363–W367. https://doi.org/10.1093/nar/gky473

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kozakov D, Grove LE, Hall DR, Bohnuud T, Mottarella SE, Luo L, Xia B, Beglov D, Vajda S (2015) The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins 10(5):733–755. https://doi.org/10.1038/nprot.2015.043

  36. Kozakov D et al (2013) How good is automated protein docking? Proteins. 81(12):2159–2166. https://doi.org/10.1002/prot.24403

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kozakov D et al (2017) The ClusPro web server for protein-protein docking. Nat Protoc 12(2):255–278. https://doi.org/10.1038/nprot.2016.169

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Vajda S et al (2017) New additions to the ClusPro server motivated by CAPRI. Proteins. 85(3):435–444. https://doi.org/10.1002/prot.25219

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79(2):926–935. https://doi.org/10.1063/1.445869

    Article  CAS  Google Scholar 

  40. Phillips JC et al (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26(16):1781–1802. https://doi.org/10.1002/jcc.20289

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Best R et al (2012) Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone ϕ, ψ and side-chain χ1 and χ2 dihedral angles. J Chem Theory Comput 11(9):3257–3273. https://doi.org/10.1021/ct300400x

    Article  CAS  Google Scholar 

  42. Vanommeslaeghe K et al (2010) CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem 26(16):671–690

    Google Scholar 

  43. Huang X, Zheng W, Pearce R, Zhang Y (2020) SSIPe: accurately estimating protein-protein binding affinity change upon mutations using evolutionary profiles in combination with an optimized physical energy function. Bioinformatics. 36(8):2429–2437. https://doi.org/10.1093/bioinformatics/btz926

    Article  PubMed  Google Scholar 

  44. Huang X, Pearce R, Zhang Y (2020) EvoEF2: accurate and fast energy function for computational protein design. Bioinformatics. 36(4):1135–1142. https://doi.org/10.1093/bioinformatics/btz740

    Article  CAS  PubMed  Google Scholar 

  45. Pearce R, Huang X, Setiawan D, Zhang Y (2019) EvoDesign: designing protein-protein binding interactions using evolutionary interface profiles in conjunction with an optimized physical energy function. J Mol Biol 431(13):2467–2476

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Zhang Y, Skolnick J (2004) Scoring function for automated assessment of protein structure template quality. Proteins. 57(4):702–710. https://doi.org/10.1002/prot.20264

    Article  CAS  PubMed  Google Scholar 

  47. Wallner B, Elofsson A (2003) Can correct protein models be identified? Protein Sci 12(5):1073–1086

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Ladner RD (2001) The role of dUTPase and uracil-DNA repair in cancer chemotherapy. Curr Protein Pept Sci 2(4):361–370. https://doi.org/10.1110/ps.0236803

    Article  CAS  PubMed  Google Scholar 

  49. Javier RT, Rice AP (2011) Emerging theme: cellular PDZ proteins as common targets of pathogenic viruses. J Virol 85(22):11544–11556. https://doi.org/10.1128/JVI.05410-11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Merino-Gracia J et al (2016) Insights into the c-terminal peptide binding specificity of the pdz domain of neuronal nitric-oxide synthase: characterization of the interaction with the tight junction protein CLAUDIN-3. J Biol Chem 291(22):11581–11595. https://doi.org/10.1074/jbc.M116.724427

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Chung SH et al (2008) Functionally distinct monomers and trimers produced by a viral oncoprotein. Oncogene. 27(10):1412–1420. https://doi.org/10.1038/sj.onc.1210784

    Article  CAS  PubMed  Google Scholar 

  52. Thomas MA et al (2009) E4orfs1 limits the oncolytic potential of the E1B-55K deletion mutant adenovirus. J Virol 83(6):2406–2416. https://doi.org/10.1128/JVI.01972-08

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Huber RG et al (2017) Multiscale molecular dynamics simulation approaches to the structure and dynamics of viruses. Prog Biophys Mol Biol 128:121–132. https://doi.org/10.1016/j.pbiomolbio.2016.09.010

    Article  CAS  PubMed  Google Scholar 

  54. Biagini T et al (2018) Molecular dynamics recipes for genome research. Brief Bioinform 19(5):853–862. https://doi.org/10.1093/bib/bbx006

    Article  CAS  PubMed  Google Scholar 

  55. Krüger A et al (2018) Molecular modeling applied to nucleic acid-based molecule development. Biomolecules 8(3):83. https://doi.org/10.3390/biom8030083

    Article  CAS  PubMed Central  Google Scholar 

  56. Lee HJ, Zheng JJ (2010) PDZ domains and their binding partners: structure, specificity, and modification. Cell Commun Signal 8:8. https://doi.org/10.1186/1478-811X-8-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Berendsen HJC, van der Spoel D, van Drunen R (1995) GROMACS: a message-passing parallel molecular dynamics implementation. Comput Phys Commun 91(1–3):43–56. https://doi.org/10.1016/0010-4655(95)00042-E

    Article  CAS  Google Scholar 

  58. Lindahl E, Hess B, van der Spoel D (2001) GROMACS 3.0: a package for molecular simulation and trajectory analysis. J Mol Model 7:306–317. https://doi.org/10.1007/s008940100045

    Article  CAS  Google Scholar 

Download references

Acknowledgments

Graphics were obtained from GraphPad Prims v.7. Pictures of models and atoms interactions were obtained from PyMOL v2.3. All images were edited with Inkscape 0.92.4.

Funding

This study was supported by Fondecyt-Chile (Grant number #11150445) and PCI-Conicyt (Grant number #REDI170632). We thank the Centro de Modelación y Computación Cientifica at the Universidad de la Frontera (CMCC-UFRO) and the Laboratory of Advanced Scientific Computing at the University of Sao Paulo by providing the supercomputing infrastructure where MD was performed. MH Hirata and RDC Hirata are recipients of fellowships from CNPq. GM Ferreira is a fellowship recipient from FAPESP-Brazil (Grant number #2019/06172-4).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alvaro Cerda.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

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

Electronic supplementary material

ESM 1

(DOCX 2151 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gutiérrez, A., Ferreira, G.M., Machuca, J. et al. Characterization of the adipogenic protein E4orf1 from adenovirus 36 through an in silico approach. J Mol Model 26, 285 (2020). https://doi.org/10.1007/s00894-020-04531-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00894-020-04531-0

Keywords

Navigation