Understanding the distinguishable structural and functional features in zebrafish TLR3 and TLR22, and their binding modes with fish dsRNA viruses: an exploratory structural model analysis


Viral infections are one of the major challenges in aquaculture production, and considered as the potential threat for fish farming. Toll-like receptor (TLR) 3 and TLR22 are highly specialized innate immune receptors that recognize double-stranded (ds)-RNA of viruses resulting in the induction of innate immunity. The existence of TLR3 and TLR22 only in aquatic animals indicates their distinctive characteristics in viral infection; however, the studies in exploring their structural features and dsRNA binding mechanism are still elusive. Here, we studied the structural and functional differentiations of TLR3 and TLR22 in zebrafish by employing comparative modeling and molecular dynamics simulation. Comparative structural analysis revealed a distinct spatial arrangement of TLR22 ectodomain with a flattened horseshoe-shape conformation as compared to other TLRs. Essential dynamics studies showed that unlike TLR3, TLR22 possessed a prominent motion, elasticity and twisting at both terminus separated by a distance equivalent to the length of a short-sized dsRNA. Interaction analysis of polyinosinic:polycytidylic acid (poly I:C) and dsRNA depicted leucine-rich-repeats (LRR)2–3 and LRR18–19 (in TLR3) and LRRNT-LRR3 and LRR22–24 (in TLR22) as the potential binding sites. The short-sized dsRNA binds tightly across its full-length with TLR22-monomer, and suggested that TLR22 dimer may sense long-sized dsRNA. Binding energy (BE) calculation using MM/PBSA method from the TLR3- and TLR22-ligand complexes revealed an adequate binding affinity between TLR22-monomer and dsRNA as like as TLR3-dimer-dsRNA complex. Mutagenesis and BE computation of key residues suggested their involvement in dsRNA recognition. These findings can be helpful for therapeutic applications against viral diseases in fish.

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  1. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410

  2. Amadei A, Linssen AB, Berendsen HJ (1993) Essential dynamics of proteins. Proteins 17:412–425

  3. Amini K, Chanb NWC, Kraatz HB (2014) Toll-like receptor 3 modified Au electrodes: an investigation into the interaction of TLR3 immobilized on Au surfaces with poly (I:C). Anal Methods 6:3322–3328

  4. Bakan A, Meireles LM, Bahar I (2011) ProDy: protein dynamics inferred from theory and experiments. Bioinformatics 27:1575–1577

  5. Baker NA, Sept D, Joseph S, Holst MJ, McCammon JA (2001) Electrostatics of nanosystems: application to microtubules and the ribosome. Proc Natl Acad Sci USA 98:10037–10041

  6. Bell JK, Botos I, Hall PR, Askins J, Shiloach J, Segal DM, Davies DR (2005) The molecular structure of the toll-like receptor 3 ligand-binding domain. Proc Natl Acad Sci USA 102:10976–10980

  7. Bell JK, Askins J, Hall PR, Davies DR, Segal DM (2006) The dsRNA binding site of human toll-like receptor 3. Proc Natl Acad Sci USA 103:8792–8797

  8. Bhattacharya D, Cheng J (2013) i3Drefine software for protein 3D structure refinement and its assessment in CASP10. PLoS One 8:e69648

  9. Botos I, Segal DM, Davies DR (2011) The structural biology of toll-like receptors. Structure 19:447–459

  10. Brown SP, Muchmore SW (2006) High-throughput calculation of protein-ligand binding affinities: modification and adaptation of the MM-PBSA protocol to enterprise grid computing. J Chem Inf Model 46:999–1005

  11. Buchan DW, Minneci F, Nugent TC, Bryson K, Jones DT (2013) Scalable web services for the PSIPRED protein analysis workbench. Nucleic Acids Res 41:349–357

  12. 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 Biol Crystallogr 66:12–21

  13. Chen L, Li Q, Su J, Yang C, Li Y, Rao Y (2013) Trunk kidney of grass carp (Ctenopharyngodon idella) mediates immune responses against GCRV and viral/bacterial PAMPs in vivo and in vitro. Fish Shellfish Immunol 34:909–919

  14. Choe J, Kelker MS, Wilson IA (2005) Crystal structure of human toll-like receptor 3 (TLR3) ectodomain. Science 309:581–585

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

  16. Comeau SR, Gatchell DW, Vajda S, Camacho CJ (2004) ClusPro: an automated docking and discrimination method for the prediction of protein complexes. Bioinformatics 20:45–50

  17. Dominguez C, Boelens R, Bonvin AM (2003) HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J Am Chem Soc 125:1731–1737

  18. Duthie MS, Windish HP, Fox CB, Reed SG (2011) Use of defined TLR ligands as adjuvants within human vaccines. Immunol Rev 239:178–196

  19. Eswar N, Webb B, Marti-Renom MA, Madhusudhan MS, Eramian D, Shen MY, Pieper U, Sali A (2007) Comparative protein structure modeling using MODELLER. Curr Protoc Protein Sci 50:2.9.1–2.9.31

  20. Eyal E, Yang LW, Bahar I (2006) Anisotropic network model: systematic evaluation and a new web interface. Bioinformatics 22:2619–2627

  21. Ferre F, Clote P (2005) DiANNA: a web server for disulfide connectivity prediction. Nucleic Acids Res 33:230–232

  22. Finn RD, Mistry J, Tate J, Coggill P, Heger A, Pollington JE, Gavin OL, Gunasekaran P, Ceric G, Forslund K, Holm L, Sonnhammer EL, Eddy SR, Bateman A (2010) The Pfam protein families database. Nucleic Acids Res 38:211–222

  23. Fiser A, Sali A (2003) ModLoop: automated modeling of loops in protein structures. Bioinformatics 19:2500–2501

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

  25. Gilson MK, Zhou HX (2007) Calculation of protein-ligand binding affinities. Annu Rev Biophys Biomol Struct 36:21–42

  26. Gupta R, Brunak S (2002) Prediction of glycosylation across the human proteome and the correlation to protein function. Pac Symp Biocomput 7:310–322

  27. Hinsen K (1998) Analysis of domain motions by approximate normal mode calculations. Proteins 33:417–429

  28. Huang J, MacKerell AD Jr (2013) CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data. J Comput Chem 34:2135–2145

  29. Huang B, Schroeder M (2006) LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation. BMC Struct Biol 6:19

  30. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38

  31. Joosten RP, te Beek TA, Krieger E, Hekkelman ML, Hooft RW, Schneider R, Sander C, Vriend G (2011) A series of PDB related databases for everyday needs. Nucleic Acids Res 39:411–419

  32. Kang JY, Lee JO (2011) Structural biology of the toll-like receptor family. Annu Rev Biochem 80:917–941

  33. Kim H, Abeysirigunawarden SC, Chen K, Mayerle M, Ragunathan K, Luthey-Schulten Z, Ha T, Woodson SA (2014) Protein-guided RNA dynamics during early ribosome assembly. Nature 506:334–338

  34. Kimbrell DA, Beutler B (2001) The evolution and genetics of innate immunity. Nat Rev Genet 2:256–267

  35. Kumar A, Zhang J, Yu FS (2006) Toll-like receptor 3 aganist poly (I:C)-induced antiviral response in human corneal epithelial cells. Immunology 117:11–21

  36. Kumar H, Kawai T, Akira S (2011) Pathogen recognition by the innate immune system. Int Rev Immunol 30:16–34

  37. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Cryst 26:283–291

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

  39. Leonard JN, Ghirlando R, Askins J, Bell JK, Margulies DH, Davies DR, Segal DM (2008) The TLR3 signaling complex forms by cooperative receptor dimerization. Proc Natl Acad Sci USA 105:258–263

  40. Letunic I, Doerks T, Bork P (2012) SMART 7: recent updates to the protein domain annotation resource. Nucleic Acids Res 40:302–305

  41. Li YG, Siripanyaphinyo U, Tumkosit U, Noranate N, A-Nuegoonpipat A, Pan Y, Kameoka M, Kurosu T, Ikuta K, Takeda N, Anantapreecha S (2012) Poly (I:C), an agonist of toll-like receptor-3, inhibits replication of the Chikungunya virus in BEAS-2B cells. Virol J 9:114

  42. Liu L, Botos I, Wang Y, Leonard JN, Shiloach J, Segal DM, Davies DR (2008) Structural basis of toll-like receptor 3 signaling with double-stranded RNA. Science 320:379–381

  43. Luo R, David L, Gilson MK (2002) Accelerated Poisson–Boltzmann calculations for static and dynamic systems. J Comput Chem 23:1244–1253

  44. Luo J, Obmolova G, Malia TJ, Wu SJ, Duffy KE, Marion JD, Bell JK, Ge P, Zhou ZH, Teplyakov A, Zhao Y, Lamb RJ, Jordan JL, San Mateo LR, Sweet RW, Gilliland GL (2012) Lateral clustering of TLR3:dsRNA signaling units revealed by TLR3ecd:3Fabs quaternary structure. J Mol Biol 421:112–124

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

  46. Maharana J, Swain B, Sahoo BR, Dikhit MR, Basu M, Mahapatra AS, Jayasankar P, Samanta M (2013) Identification of MDP (muramyl dipeptide)-binding key domains in NOD2 (nucleotide-binding and oligomerization domain-2) receptor of Labeo rohita. Fish Physiol Biochem 39:1007–1023

  47. Maharana J, Patra MC, De BC, Sahoo BR, Behera BK, De S, Pradhan SK (2014) Structural insights into the MDP binding and CARD–CARD interaction in zebrafish (Danio rerio) NOD2: a molecular dynamics approach. J Mol Recognit 27:260–275

  48. Marchler-Bauer A, Lu S, Anderson JB, Chitsaz F, Derbyshire MK, DeWeese-Scott C, Fong JH, Geer LY, Geer RC, Gonzales NR, Gwadz M, Hurwitz DI, Jackson JD, Ke Z, Lanczycki CJ, Lu F, Marchler GH, Mullokandov M, Omelchenko MV, Robertson CL, Song JS, Thanki N, Yamashita RA, Zhang D, Zhang N, Zheng C, Bryant SH (2011) CDD: a conserved domain database for the functional annotation of proteins. Nucleic Acids Res 39:225–229

  49. Matsuo A, Oshiumi H, Tsujita T, Mitani H, Kasai H, Yoshimizu M, Matsumoto M, Seya T (2008) TLR22 recognizes RNA duplex to induce IFN and protect cells from Birnaviruses. J Immunol 181:3474–3485

  50. Maynard CM, Hall KB (2010) Interactions between PTB RRMs induce slow motions and increase RNA binding affinity. J Mol Biol 397:260–277

  51. Medzhitov R, Janeway C Jr (2000) Innate immune recognition: mechanisms and pathways. Immunol Rev 173:89–97

  52. Meeker ND, Trede NS (2008) Immunology and zebrafish: spawning new models of human disease. Dev Comp Immunol 32:745–757

  53. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791

  54. Naumann K, Wehner R, Schwarze A, Petzold C, Schmitz M, Rohayem J (2013) Activation of dendritic cells by the novel toll-like receptor 3 agonist RGC100. Clin Dev Immunol 2013:283649

  55. Oostenbrink C, Villa A, Mark AE, van Gunsteren WF (2004) A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6. J Comput Chem 25:1656–1676

  56. Pierce BG, Wiehe K, Hwang H, Kim BH, Vreven T, Weng Z (2014) ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers. Bioinformatics 30:1771–1773

  57. Pietretti D, Wiegertjes GF (2014) Ligand specificities of toll-like receptors in fish: indications from infection studies. Dev Comp Immunol 4:205–222

  58. Pirher N, Ivicak K, Pohar J, Bencina M, Jerala R (2008a) A second binding site for double-stranded RNA in TLR3 and consequences for interferon activation. Nat Struct Mol Biol 15:761–763

  59. Pirher N, Ivicak K, Pohar J, Bencina M, Jerala R (2008b) A second binding site for double-stranded RNA in TLR3 and consequences for interferon activation. Nat Struct Mol Biol 15:761–763

  60. Pronk S, Páll S, Schulz R, Larsson P, Bjelkmar P, Apostolov R, Shirts MR, Smith JC, Kasson PM, van der Spoel D, Hess B, Lindahl E (2013) GROMACS 4.5: a high-throughput and highly parallel open sourcemolecular simulation toolkit. Bioinformatics 29:845–854

  61. Sahoo BR, Basu M, Swain B, Maharana J, Dikhit MR, Jayasankar P, Samanta M (2012) Structural insights of rohu TLR3, its binding site analysis with fish reovirus dsRNA, poly I:C and zebrafish TRIF. Int J Biol Macromol 51:531–543

  62. Sahoo BR, Basu M, Swain B, Dikhit MR, Jayasankar P, Samanta M (2013a) Elucidation of novel structural scaffold in rohu TLR2 and its binding site analysis with peptidoglycan, lipoteichoic acid and zymosan ligands, and downstream MyD88 adaptor protein. Biomed Res Int 2013:185282

  63. Sahoo BR, Swain B, Dikhit MR, Basu M, Bej A, Jayasankar P, Samanta M (2013b) Activation of nucleotide-binding oligomerization domain 1 (NOD1) receptor signaling in Labeo rohita by iE-DAP and identification of ligand-binding key motifs in NOD1 by molecular modeling and docking. Appl Biochem Biotechnol 170:1282–1309

  64. Sahoo BR, Maharana J, Bhoi GK, Lenka SK, Patra MC, Dikhit MR, Dubey PK, Pradhan SK, Behera BK (2014) A conformational analysis of mouse Nalp3 domain structures by molecular dynamics simulations, and binding site analysis. Mol BioSyst 10:1104–1116

  65. Samanta M, Basu M, Swain B, Panda P, Jayasankar P (2013) Molecular cloning and characterization of toll-like receptor 3, and inductive expression analysis of type I IFN, Mx and pro-inflammatory cytokines in the Indian carp, rohu (Labeo rohita). Mol Biol Rep 40:225–235

  66. Samanta M, Swain B, Basu M, Mahapatra G, Sahoo BR, Paichha M, Lenka SS, Jayasankar P (2014) Toll-like receptor 22 in Labeo rohita: molecular cloning, characterization, 3D modeling, and expression analysis following ligands stimulation and bacterial infection. Appl Biochem Biotechnol 174:309–327

  67. Spiliotopoulos D, Spitaleri A, Musco G (2012) Exploring PHD fingers and H3K4me0 interactions with molecular dynamics simulations and binding free energy calculations: AIRE-PHD1, a comparative study. PLoS One 7:e46902

  68. Su J, Heng J, Huang T, Peng L, Yang C, Li Q (2012) Identification, mRNA expression and genomic structure of TLR22 and its association with GCRV susceptibility/resistance in grass carp (Ctenopharyngodon idella). Dev Comp Immunol 36:450–462

  69. Takeda K, Akira S (2005) Toll-like receptors in innate immunity. Int Immunol 17:1–14

  70. Vriend G (1990) WHAT IF: a molecular modeling and drug design program. J Mol Graph 8:52–56

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

  72. Wang Y, Liu L, Davies DR, Segal DM (2010) Dimerization of toll-like receptor 3 (TLR3) is required for ligand binding. J Biol Chem 285:36836–36841

  73. Wiederstein M, Sippl MJ (2007) ProSA-web, interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 35:407–410

  74. Yu L, Wang L, Chen S (2010) Endogenous toll-like receptor ligands and their biological significance. J Cell Mol Med 14:2592–2603

  75. Zhao Y, Kormos BL, Beveridge DL, Baranger AM (2006) Molecular dynamics simulation studies of a protein-RNA complex with a selectively modified binding interface. Biopolymers 81:256–269

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The authors are grateful to Sukanta Kumar Pradhan (HOD), Department of Bioinformatics, Orissa University of Agriculture and Technology, Bhubaneswar for their suggestive and helpful discussion during the manuscript preparation and revision

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There are no conflicts of interest.

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The manuscript does not contain clinical studies or patient data.

Author information

Correspondence to Bikash Ranjan Sahoo.

Additional information

We dedicate this work to our beloved co-author Mr. Gopal Krushna Bhoi (12/05/1984 to 20/11/2014). A special feeling of gratitude to my best and loving friend Mr. Gopal whose words of encouragement and push for tenacity ring in my ears.

Electronic supplementary material

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Supplementary material 1 (MPG 20017 kb). Fig. S1 Target-template alignment generated using Modeller v9.12. The alignment was manually refined to minimize gaps in reference to PSI-BLAST results. The significant gaps generated with single template is shown inside rectangles. “*” mark represents target residue conservation in either of the template sequence

Supplementary material 1 (MPG 20017 kb). Fig. S1 Target-template alignment generated using Modeller v9.12. The alignment was manually refined to minimize gaps in reference to PSI-BLAST results. The significant gaps generated with single template is shown inside rectangles. “*” mark represents target residue conservation in either of the template sequence

Supplementary material 2 (EPS 1813 kb). Fig. S2 Secondary structural changes in zebrafish TLR3-ECD and TLR22-ECD models during 50 ns MD simulation a zTLR3-ECD, and b zTLR22-ECD. The colors representing different secondary structural units are presented in the legends, and is given at the bottom of the figure

Supplementary material 3 (EPS 2366 kb). Fig. S3 Elasticity analysis of zebrafish TLR3-ECD and TLR22-ECD models using ProDy a Anisotropic network model of zTLR3-ECD model during 50 ns MD simulation, and b Anisotropic network model of zTLR22-ECD model during 50 ns MD simulation. The arrows represent the extent of elastic movements and direction

Supplementary material 4 (EPS 2775 kb). Fig. S4 Ramachandran plot analysis of homology models using PROCHECK program a zTLR3-ECD model, and b zTLR22-ECD model

Supplementary material 5 (EPS 5784 kb). Fig. S5 Molecular interaction of poly I:C and zebrafish TLR3-ECD and zTLR22-ECD models in ArgusLab 4.0.1 a Docking at N-terminus of zTLR3-ECD model, b at C-terminus of zTLR3-ECD model, c at N-terminus of zTLR22-ECD model, and d at N-terminus of zTLR22-ECD model. The ligand binding sites are generated using PyMOL. The protein is shown as cartoon and ligand as stick. TLR-NT and CT represents N-terminal and C-terminal, respectively

Supplementary material 6 (EPS 3797 kb). Fig. S6 Stability and interaction analysis in complexes during MD simulation a Radius of gyration in different receptor (zTLR3-ECD and zTLR22-ECD) and poly I:C complexes, and b Hydrogen bond fluctuations between receptor and poly I:C

Supplementary material 7 (EPS 1603 kb). Fig. S7 Root mean square fluctuation of amino acid residues in zebrafish TLR22-ECD and dsRNA complexes during 10 ns MD simulation

Supplementary material 8 (EPS 904 kb). ESM_1 The animation of structural rearrangements of zTLR22-ECD terminal regions during MD simulation was generated using VMD and show as cartoon

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Sahoo, B.R., Dikhit, M.R., Bhoi, G.K. et al. Understanding the distinguishable structural and functional features in zebrafish TLR3 and TLR22, and their binding modes with fish dsRNA viruses: an exploratory structural model analysis. Amino Acids 47, 381–400 (2015) doi:10.1007/s00726-014-1872-2

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  • TLR3
  • TLR22
  • Molecular docking
  • Molecular dynamics simulation
  • RNA