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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

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Abstract

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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Acknowledgments

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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Authors and Affiliations

  1. Department of Bioinformatics, Orissa University of Agriculture and Technology, Bhubaneswar, 751001, India

    Bikash Ranjan Sahoo, Gopal Krushna Bhoi, Jitendra Maharana & Santosh Kumar Lenka

  2. Laboratory of Molecular Biophysics, Institute for Protein Research, Osaka University, Osaka, 5650871, Japan

    Bikash Ranjan Sahoo

  3. Biomedical Informatics Center, Rajendra Memorial Research Institute of Medical Sciences, Patna, 800007, India

    Manas Ranjan Dikhit

  4. WPI-Immunology Frontier Research Centre, Osaka University, Osaka, 5650871, Japan

    Praveen Kumar Dubey

  5. Department of Biomolecular Science and Engineering, Institute of Scientific and Industrial Research, Osaka University, Osaka, 5650871, Japan

    Dharmendra Kumar Tiwari

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  1. Bikash Ranjan Sahoo
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  3. Gopal Krushna Bhoi
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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

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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

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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

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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

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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

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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

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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

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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). https://doi.org/10.1007/s00726-014-1872-2

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