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3D modeling and molecular dynamics simulation of an immune-regulatory cytokine, interleukin-10, from the Indian major carp, Catla catla

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Abstract

Interleukin-10 (IL-10) is a pleiotropic immune-regulatory cytokine that is expressed in various species of fish and higher vertebrates, and is activated during infection. In spite of its important role, IL-10 has not been well characterized either functionally or structurally in fish. To analyze its properties and function, we constructed a 3D model of IL-10 in the Indian major carp, the catla (Catla catla), which is a highly preferred fish species and the most commercially important one in the Indian subcontinent. The catla IL-10 model was constructed by comparative modeling using human IL-10 (2ILK) as the template, and a 5 ns molecular dynamics (MD) simulation was carried out to characterize its structural and dynamical features, which was validated by the SAVES, WHAT IF and MolProbity servers. Analysis using the VAST server revealed a comparatively low level of homology between catla and human IL-10 amino acids at the N-terminal (22.7%) compared to the C-terminal (38.29%). Six conserved domains (A–F) were predicted in catla that threaded well with human IL-10, but their putative interaction sites varied significantly. The amino acid residues in helices A and F differed in length between catla and human IL-10, which may lead to the differences in the IL-10/IL-10R complexes of these two species. The existence of two highly conserved amino acid residues (Cys5 and Cys10) in fish IL-10 but not in higher vertebrate (including human) IL-10 was analyzed in this 3D model. CastP, cons-PPISP and InterProSurf server identified several binding pockets with various probe radii, but Cys5 and Cys10 did not form any significant bonds relating to structural stabilization or protein–protein interactions.

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References

  1. Moore KW, O’Garra A, de Waal MR et al (1993) Interleukin-10. Annu Rev Immunol 11:165–190

    Article  CAS  Google Scholar 

  2. Mosmann TR (1994) Properties and function of interleukin-10. Adv Immunol 56:1–26

    Article  CAS  Google Scholar 

  3. Fiorentino DF, Bond MW, Mosmann TR (1989) Two types of mouse helper T cell. IV. Th2 clones secrete a factor that inhibits cytokine production by Th1 clones. J Exp Med 170:2081–2095

    Article  CAS  Google Scholar 

  4. Zdanov A (2004) Structural features of the interleukin-10 family of cytokines. Curr Pharm Des 10:3873–3884

    Article  CAS  Google Scholar 

  5. Rousset F, Garcia E, Defrance T, Peronne C, Vezzio N, Hsu DH, Kastelein R, Moore KW, Banchereau J (1992) Interleukin 10 is a potent growth and differentiation factor for activated human B lymphocytes. Proc Natl Acad Sci USA 89:1890–1893

    Article  CAS  Google Scholar 

  6. Josephson K, Logsdon JN, Walter RM (2001) Crystal structure of the IL-10/IL-10R1 complex reveals a shared receptor binding site. Immunity 14:35–46

    Article  Google Scholar 

  7. Tan JC, Indelicato SR, Narula SK, Zavodny PJ, Chou CC (1993) Characterization of interleukin-10 receptors on human and mouse cells. J Biol Chem 268:21053–21059

    CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  9. Gouet P, Courcelle E, Stuart DI, Metoz F (1999) ESPript: analysis of multiple sequence alignments in PostScript. Bioinformatics 15:305–308

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  12. Eisenberg D, Luthy R, Bowie JU (1997) VERIFY3D: assessment of protein models with three-dimensional profiles. Methods Enzymol 277:396–404

    Article  CAS  Google Scholar 

  13. Colovos C, Yeates TO (1993) Verification of protein structures: patterns of non bonded atomic interactions. Protein Sci 2:1511–1519

    Article  CAS  Google Scholar 

  14. Pontius J, Richelle J, Wodak SJ (1996) Deviations from standard atomic volumes as a quality measure for protein crystal structures. J Mol Biol 264:121–136

    Article  CAS  Google Scholar 

  15. Vriend G (1990) WHAT IF: a molecular modeling and drug design program. J Mol Graph 8:52–6,29

    Google Scholar 

  16. Chen VB, Arendall WB 3rd, Headd JJ, Keedy DA, Immormino RM, Kapral GJ, Murray LW, Richardson JS, Richardson DC (2010) MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr D 66:12–21

    Google Scholar 

  17. Bendtsen JD, Nielsen H, von Heijne G, Brunak S (2004) Improved prediction of signal peptides: SignalP 3.0. J Mol Biol 340:783–795

    Article  Google Scholar 

  18. Kurowski MA, Bujnicki JM (2003) GeneSilico protein structure prediction meta-server. Nucleic Acids Res 31:3305–3307

    Article  CAS  Google Scholar 

  19. Ginalski K, Elofsson A, Fischer D, Rychlewski L (2003) 3D-Jury: a simple approach to improve protein structure predictions. Bioinformatics 19:1015–1018

    Article  CAS  Google Scholar 

  20. Wallner B, Elofsson A (2005) Pcons5: combining consensus, structural evaluation and fold recognition scores. Bioinformatics 21:4248–4254

    Article  CAS  Google Scholar 

  21. Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 9:40

    Article  Google Scholar 

  22. Shi J, Blundell TL, Mizuguchi K (2001) FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. J Mol Biol 310:243–257

    Article  CAS  Google Scholar 

  23. Mizuguchi K, Deane CM, Blundell TL, Overington JP (1998) HOMSTRAD: a database of protein structure alignments for homologous families. Protein Sci 7:2469–2471

    Article  CAS  Google Scholar 

  24. Mizuguchi K, Deane CM, Blundell TL, Overington JP (1998) JOY: protein sequence–structure representation and analysis. Bioinformatics 14:617–623

    Google Scholar 

  25. Guex N, Peitsch MC (1997) SWISS-MODEL and the Swiss-Pdb Viewer: an environment for comparative protein modeling. Electrophoresis 18:2714–2723

    Article  CAS  Google Scholar 

  26. Walter RP, Scott PH et al (1999) The GROMOS biomolecular simulation program package. J Phys Chem 103:3596–3607

    Article  Google Scholar 

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

    Article  Google Scholar 

  28. Jones S, Thornton JM (1996) Principles of protein–protein interactions. Proc Natl Acad Sci USA 93:13–20

    Article  CAS  Google Scholar 

  29. Hubbard SJ, Thornton JM (1993) NACCESS. University College, London

  30. Dundas J, Ouyang Z, Tseng J, Binkowski A, Turpaz 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 

  31. Chen H, Zhou HX (2005) Prediction of interface residues in protein–protein complexes by a consensus neural network method: test against NMR data. Proteins 61:21–35

    Google Scholar 

  32. Negi SS, Schein CH, Oezquen N, Power TD, Braun W (2007) InterProSurf: a web server for predicting interacting sites on protein surfaces. Bioinformatics 23:3397–3399

    Article  CAS  Google Scholar 

  33. Pinto RD, Nascimento DS, Reis MIR, do vale A, dos Santos NM (2007) Molecular characterization, 3D modelling and expression analysis of sea bass (Dicentrarchus labrax L.) interleukin-10. Mol Immunol 44:2066–2075

    Google Scholar 

  34. Zhang Y, Skolnick J (2005) TM-align: a protein structure alignment algorithm based on the TM score. Nucleic Acids Res 33:2302–2309

    Google Scholar 

  35. McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16:404–405

    Article  CAS  Google Scholar 

  36. Heinig M, Frishman D (2004) STRIDE: a web server for secondary structure assignment from known atomic coordinates of proteins. Nucleic Acids Res 32:500–502

    Article  Google Scholar 

  37. Gibrat JF, Madej T, Bryant SH (1996) Surprising similarities in structure comparison. Curr Opin Struct Biol 6:377–385

    Article  CAS  Google Scholar 

  38. Holm L, Sander C (1995) Dali: a network tool for protein structure comparison. Trends Biochem Sci 20:478–480

    Article  CAS  Google Scholar 

  39. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(33–8):27–28

    Google Scholar 

Download references

Acknowledgments

The work was financially supported by a grant from the National Agricultural Innovation Project, Indian Council of Agricultural Research (NAIP-ICAR; project code: C4-C30018). The authors gratefully acknowledge the aid of the Bioinformatics Resources and Applications Facility (BRAF) at the Center for Development of Advanced Computing (C-DAC), Pune, India, for the MD simulation studies. We thank the Director of the Central Institute of Freshwater Aquaculture (CIFA) for providing the necessary facilities to carry out this study.

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Correspondence to Mrinal Samanta.

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Fig. S1

Time profile of the secondary structure changes in the catla IL-10 model. The secondary structure during the trajectory (5 ns MD simulation) was generated by the VMD program. Secondary structure codes are as follows: T turn, E extended conformation, B isolated bridges, H alpha-helix, G 3–10 helix, I pi-helix, C coil. A key to the colors used is given in the figure. (JPEG 204 kb)

High-resolution image (EPS 1302 kb)

Supplementary video SV1

Time profile of structural changes during the trajectory in the catla IL-10 model. Additional cysteine residues (Cys5 and Cys10) that are present in fish (catla IL-10) are labeled. (M1V 11769 kb)

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Sahoo, B.R., Swain, B., Basu, M. et al. 3D modeling and molecular dynamics simulation of an immune-regulatory cytokine, interleukin-10, from the Indian major carp, Catla catla . J Mol Model 18, 1713–1722 (2012). https://doi.org/10.1007/s00894-011-1194-1

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  • DOI: https://doi.org/10.1007/s00894-011-1194-1

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