Comparative modeling of retinol-binding protein-3 and retinal S-antigen in Eales’ disease and prediction of their binding sites using computational methods

  • Anshul Tiwari
  • Ashish Chandra Trivedi
  • Prachi Srivastava
  • Aditya Bhusan Pant
  • Sandeep Saxena
Article

Abstract

Retinal S-antigen and interphotoreceptor retinoid-binding protein-3 play a significant role in the etiopathogenesis of Eales' disease. Protein 3D structures are functionally very important and play a significant role in progression of the disease, hence these 3D structures are better target for further drug designing and relative studies. We developed 3D model structure of retinol-binding protein-3 and retinal S-antigen protein of human involved in Eales' disease. Functional site prediction is a very important and related step; hence, in the current course of analysis, we predicted putative functional site residues in the target proteins. Molecular models of these proteins of Eales' disease as documented in this study may provide a valuable aid for designing an inhibitor or better ligand against Eales' disease and could play a significant role in drug design.

Keywords

Eales' disease Retinol-binding protein-3 Retinal S-antigen Hemorrhages Venoocclusive Modeling Functional sites 

References

  1. 1.
    Sen A, Paine SK, Chowdhury IH, Mukherjee A, Choudhury S, Mandal LK, Bhattacharya B. Assessment of gelatinase and tumor necrosis factor-a level in the vitreous and serum of patients with Eales disease: Role of Inflammation-Mediated Angiogenesis in the Pathogenesis of Eales Disease .Retina. 2011. doi: 10.1097/IAE.0b013e318203c199.
  2. 2.
    Saxena S, Kumar D, Singh VK, Rajasingh J. Immunological studies in Eales disease: a review. Afro-Asian J Ophthalmol. 1995;13:19–22.Google Scholar
  3. 3.
    Therese KL, Deepa P, Therese J, Bagyalakshmi R, Biswas J, Madhavan HN. Association of mycobacteria with Eales’ disease. Indian J Med Res. 2007;126:56–62.PubMedGoogle Scholar
  4. 4.
    Narayanasamy A, Radhakrishnan S, Rishi P, Jyotirmoy B, Shah J, Joyce T. Ratio of the vitreous vascular endothelial growth factor and pigment epithelial-derived factor in Eales disease. J Ocular Biol Dis Informat. 2009;2:20–8.CrossRefGoogle Scholar
  5. 5.
    Das T, Biswas J, Kumar A, et al. Eales’ disease. Indian J Ophthalmol. 1994;42:3–18.PubMedGoogle Scholar
  6. 6.
    Das T, Pathengay A, Hussain N, Biswas J. Eales’ disease: diagnosis and management. Eye. 2010;24:472–82.PubMedCrossRefGoogle Scholar
  7. 7.
    Saxena S, Pant AB, Khanna VK, Singh K, Shukla RK, Meyer CH, et al. Tumor necrosis factor-a-mediated severity of idiopathic retinal periphlebitis in young adults (Eales’ disease): implication for anti-TNF-a therapy. J Ocular Biol Dis Informat. 2010;3:35–8.CrossRefGoogle Scholar
  8. 8.
    Pfister C, Chabre M, Plouet J, Tuyen VV, De Kozak Y, Faure JP, et al. Retinal S antigen identified as the 48 K protein regulating light-dependent phosphodiesterase in rods. Science. 1985;228:891–3.PubMedCrossRefGoogle Scholar
  9. 9.
    Saxena S, Rajasingh J, Biswas S, Kumar D, Shinohara T, Singh VK. Cellular immune response to retinal S-antigen and interphotoreceptor retinoid-binding protein fragments in Eales' disease patients. Pathobiology. 1999;67:39–44.PubMedCrossRefGoogle Scholar
  10. 10.
    Pathan S, Chintan C, Sriram S. Intrinsically unstructured proteins: potential targets for drug discovery. Am J Infect Dis. 2009;5:133–41.CrossRefGoogle Scholar
  11. 11.
    Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, Bairoch A. Protein identification and analysis tools on the ExPASy Server; John M. Walker, editors: The Proteomics Protocols Handbook, Humana. 2005. p. 571–607.Google Scholar
  12. 12.
    Arnold K, Bordoli L, Kopp J, Schwede T. The SWISS-MODEL Workspace: a web-based environment for protein structure homology modelling. Bioinformatics. 2006;22:195–201.PubMedCrossRefGoogle Scholar
  13. 13.
    Kiefer F, Arnold K, Künzli M, Bordoli L, Schwede T. The SWISS-MODEL repository and associated resources. Nucleic Acids Res. 2009;37:387–92.CrossRefGoogle Scholar
  14. 14.
    Peitsch MC. Protein modeling by e-mail bio/technology. Nature. 1995;13:658–60.Google Scholar
  15. 15.
    Thomas DG, Conrad CH, Thomas EF. Software extensions to UCSF Chimera for interactive visualization of large molecular assemblies. Structure. 2005;13:473–82.CrossRefGoogle Scholar
  16. 16.
    Bowie JU, Lüthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science. 1991;253:164–70.PubMedCrossRefGoogle Scholar
  17. 17.
    Lüthy R, Bowie JU, Eisenberg D. Assessment of protein models with three-dimensional profiles. Nature. 1992;356:83–5.PubMedCrossRefGoogle Scholar
  18. 18.
    Benkert P, Künzli M, Schwede T. QMEAN server for protein model quality estimation. Nucleic Acids Res. 2009;37:510–4.CrossRefGoogle Scholar
  19. 19.
    Laskowski RA, Watson JD, Thornton JM. ProFunc: a server for predicting protein function from 3D structure. Nucleic Acids Res. 2005;33:89–93.CrossRefGoogle Scholar
  20. 20.
    Alasdair TR, Richard M. Q-Site Finder: an energy-based method for the prediction of protein–ligand binding sites. Bioinformatics. 2005;21:1908–16.CrossRefGoogle Scholar
  21. 21.
    Srivastava P, Tiwari A, Trivedi AC. Computational prediction of 3D structure for the matrix protein 2 (BM2) of the influenza B virus. Intl Med Technol Univ Med J. 2010;1:22–36.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Anshul Tiwari
    • 1
  • Ashish Chandra Trivedi
    • 3
  • Prachi Srivastava
    • 3
  • Aditya Bhusan Pant
    • 2
  • Sandeep Saxena
    • 1
  1. 1.Department of OphthalmologyCSM Medical University (Erstwhile King George’s Medical University)LucknowIndia
  2. 2.Indian Institute of Toxicological ResearchLucknowIndia
  3. 3.Amity Institute of BiotechnologyAmity University, Lucknow CampusLucknowIndia

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