Research Article Received: 15 June 2013,

Revised: 20 December 2013,

Accepted: 20 December 2013,

Published online in Wiley Online Library

(wileyonlinelibrary.com) DOI: 10.1002/jmr.2357

Structural insights into the MDP binding and CARD–CARD interaction in zebrafish (Danio rerio) NOD2: a molecular dynamics approach Jitendra Maharanaa*†, Mahesh Chandra Patrab,c†, Bidhan Chandra Dea, Bikash Ranjan Sahoob,d, Bijay Kumar Beheraa, Sachinandan Dec and Sukanta Kumar Pradhanb Nucleotide binding and oligomerization domain (NOD2) is a key component of innate immunity that is highly specific for muramyl dipeptide (MDP)—a peptidoglycan component of bacterial cell wall. MDP recognition by NOD2–leucine rich repeat (LRR) domain activates NF-κB signaling through a protein–protein interaction between caspase activating and recruitment domains (CARDs) of NOD2 and downstream receptor interacting and activating protein kinase 2 (RIP2). Due to the lack of crystal/NMR structures, MDP recognition and CARD–CARD interaction are poorly understood. Herein, we have predicted the probable MDP and CARD–CARD binding surfaces in zebrafish NOD2 (zNOD2) using various in silico methodologies. The results show that the conserved residues Phe819, Phe871, Trp875, Trp929, Trp899, and Arg845 located at the concave face of zNOD2–LRR confer MDP recognition by hydrophobic and hydrogen bond (H-bond) interactions. Molecular dynamics simulations reveal a stable association between the electropositive surface on zNOD2–CARDa and the electronegative surface on zRIP2–CARD reinforced mostly by H-bonds and electrostatic interactions. Importantly, a 3.5 Å salt bridge is observed between Arg60 of zNOD2–CARDa and Asp494 of zRIP2–CARD. Arg11 and Lys53 of zNOD2–CARDa and Ser498 and Glu508 of zRIP2–CARD are critical residues for CARD–CARD interaction and NOD2 signaling. The 2.7 Å H-bond between Lys104 of the linker and Glu508 of zRIP2–CARD suggests a possible role of the linker for shaping CARD–CARD interaction. These findings are consistent with existing mutagenesis data. We provide first insight into MDP recognition and CARD–CARD interaction in the zebrafish that will be useful to understand the molecular basis of NOD signaling in a broader perspective. Copyright © 2014 John Wiley & Sons, Ltd. Additional supporting information may be found in the online version of this article at the publisher’s web site. Keywords: zebrafish; NOD2; RIP2; muramyl dipeptide; CARD–CARD interaction; protein threading; molecular dynamics

INTRODUCTION

260

Innate immunity is the first line defense against pathogens and inflammations in higher and lower eukaryotes. In general, innate immunity is subsidized by various pattern recognition receptors (PRRs) that recognize pathogen/microbe associated molecular patterns (PAMPs/MAMPs). These PRRs include various leucine rich repeat (LRR) regions and are classified into toll-like receptors (TLRs), nucleotide binding and oligomerization domain (NOD)-like receptors (NLRs), retinoic acid-inducible gene I-like receptors (RLRs), C-type lectin receptors (CLRs), and so on. The NLR family members are categorized into five distinct subfamilies, viz., NLRA, NLRB, NLRC, NLRP, and NLRX. Further, NLRC subfamily is classified as NOD1, NOD2, NLRC3, NLRC4, and NLRC5 (Ting et al., 2008). NLRs are located in the cytoplasm and play a major role in recognizing the invading PAMPs that cross over the plasma membrane. Upon interaction with PAMPs/MAMPs, NLRs trigger signaling cascades initiating innate immune responses (Fritz et al., 2006; Meylan et al., 2006; Kumagai and Akira, 2010; Takeuchi and Akira, 2010; Swain et al., 2012a, 2012b). The pathogen-derived conserved structures recognized by NLRs are the smallest components of cell wall peptidoglycan (PGN) called muramyl dipeptide (MDP) and γ-D-glutamyl-meso-diamino-pimelic acid (iE-DAP).

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* Correspondence to: Jitendra Maharana, Biotechnology Laboratory, Central Inland Fisheries Research Institute, Barrackpore, Kolkata, 700120, West Bengal, India. E-mail: [email protected] †

These authors contributed equally to this work and are listed alphabetically.

a J. Maharana, B. C. De, B. K. Behera Biotechnology Laboratory, Central Inland Fisheries Research Institute, Kolkata, 700120, West Bengal, India b M. C. Patra, B. R. Sahoo, S. K. Pradhan BIF-Centre, Department of Bioinformatics, Orissa University of Agriculture and Technology, Bhubaneswar, 751003, Odisha, India c M. C. Patra, S. De Animal Genomics Laboratory, Animal Biotechnology Centre, National Dairy Research Institute, Karnal, 132001, Haryana, India d B. R. Sahoo Laboratory of Molecular Biophysics, Institute of Protein Research, Osaka University, Osaka Prefecture, 5650871, Japan Abbreviations: MDP, muramyl dipeptide; CARD, caspase activating and requirement domain; NOD, nucleotide oligomerization domain; PRRs, pattern recognition receptors; IBD, inflammatory bowel disease; LRR, leucine rich repeat; RIP2, receptor interacting serine–threonine kinase-2 adaptor protein; MD, molecular dynamics; PCA, principal component analysis.

Copyright © 2014 John Wiley & Sons, Ltd.

STRUCTURAL CHARACTERIZATION AND BINDING SITE ANALYSIS OF ZEBRAFISH NOD2

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In the present study, we have constructed the three-dimensional (3D) models for zNOD2–LRR, zNOD2–CARDab, and zRIP2–CARD using protein threading and comparative modeling approaches. We have predicted the low energy binding modes of MDP and identified intermolecular interactions between MDP and zNOD2–LRR using molecular docking, molecular dynamics (MD) simulations, and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) methods. Protein–protein docking followed by MD simulation of the docked complex reveal vital residues engaged in NOD2 activation and downstream signaling in the zebrafish. This study elucidates the structural and dynamic properties of NOD2 in response to MDP and the molecular basis of immune signaling through RIP2 via CARD–CARD interaction.

MATERIALS AND METHODS Domain analysis The amino acid (aa) sequences of zNOD2 (UniProt ID: F8W3K2) and zRIP2 (UniProt ID: Q8JHU4) were retrieved from UniProtKB database (http://www.uniprot.org). The functional domains were identified by Conserved Domain Database (CDD) (MarchlerBauer et al., 2011). The sequences were scanned in InterProScan (Quevillon et al., 2005), SMART (Letunic et al., 2012), and Pfam (Finn et al., 2010) databases to predict N-terminal CARD, middle NACHT, and C-terminal LRR domains. The CARD, NOD, and LRR domains of zNOD2 were also manually located by multiple sequence alignment of NOD2 sequences from human, mouse, bovine, rohu, and zebrafish using Clustal Omega (Sievers et al., 2011) tool. The LRR regions were manually identified by locating “LxxLxLxxNxL” motifs (where L = leucine/isoleucine/valine/phenylalanine; x = any amino acid; and N = asparagine/threonine/ serine/cysteine). Modeling of CARD and LRR domains The homologous templates for zNOD2–CARDa, zNOD2–CARDb, zNOD2–LRR, and zRIP2–CARD were searched using BLAST, (Altschul et al., 1990) DELTA–BLAST, and various threading/fold recognition programs including Genesilico Metaserver2 (Kurowski and Bujnicki, 2003), ModLink+ (Fornes et al., 2009), and SPARKS-X (Yang et al., 2011). Because the BLAST programs showed very low percentage of sequence identities (i.e., ¼< E MM > þ < Gsol > T < SMM >

(2)

The molecular mechanics interaction energy, EMM is defined as E MM ¼ E int þ E coul þ E vdW

(3)

where Eint denotes bond, angle, and torsion angle energies; Ecoul indicates electrostatic energy; and EvdW represents van der Waals energy. The solvation free energy term, Gsol is divided into polar and nonpolar contributions: Gsol ¼ Gpolar þ Gnonpolar

(4)

In this study, the Gpolar and Gnonpolar terms were calculated using APBS program (Baker et al., 2001). The polar term (Gpolar) was calculated by solving nonlinearized PB equation. The parameters for APBS calculation were as follows: grid spacing was set to an upper limit of 0.5 Å, the temperature was set to 296 K, and the salt concentration was 0.15 M. The nonpolar contribution Gnonpolar is computed as Gnonpolar ¼ γ SASA þ β 1

2

(5) 1

where γ = 0.0227 kJ mol Å and β = 0 kJ mol (Brown and Muchmore, 2009). The dielectric boundary was defined with 1.4 Å of probe radius.

Stability parameters of the modeled domains

RESULTS AND DISCUSSION Identification of functional domains Domain analysis using CDD, InterProScan, SMART, and Pfam database confirms that zNOD2 contains two CARD domains— CARDa (1–96 aa) and CARDb (106–198 aa) at the N-terminal region connected by a nine-residues linker, a central NACHT domain (271–594 aa), and a C-terminal LRR domain (729–975 aa). The LRR domain consists of a series of covalently linked 21–29 residues motifs with the conserved pattern “LxxLxLxxNxL”. Each of the LRR motifs is comprised of one β-sheet with the pattern “xxLxLxx” and one α-helix connected by a loop (Kajava and Kobe, 2002). The LRR prediction using SMART, Pfam, and InterProscan indicates five LRR motifs in zNOD2. However, multiple sequence alignment (Figure 1) reveals additional four LRR motifs in zNOD2 (Maharana et al., 2013). The zRIP2–CARD (471–551 aa) is present on the C-terminal end of zRIP2, identified as described earlier. The multiple sequence alignment of human, bovine, mouse, and rohu NOD2s showing delineation of domain boundaries is presented Supplementary Figure S1. Overall structure of constructed domains (zNOD2–LRR, zNOD2–CARDa, zNOD2–CARDb, and zRIP2–CARD)

The stability of the modeled LRR and CARD domains during MD simulations was ascertained by calculating the RMSD and radius of gyration (Rg) as a function of simulation time and the root mean square fluctuation (RMSF) of Cα atoms as a function of residue number. The backbone atoms of zNOD2–LRR show an average RMSD of 2.2 Å (Figure 4(a)). The RMSD increases up to 4 ns but gradually settles down after 6 ns with maximum stability between 14 and 15 ns. This indicates that the modeled zNOD2– LRR attained a successful equilibration during the MD simulation. The Rg of zNOD2–LRR is found to be ~19.5 Å throughout the simulation (Figure 4(b)), suggesting a consistent shape and size. The RMSF curve shows that the Cα atoms of the amino acids located toward N-terminal and C-terminal end as well as the intermediate loops fluctuate up to 2 Å (Figure 4(c)). The Cα deviation is least during 14–15 ns (RMSF = 0.8 Å). Nonetheless, the residues 170–250 located at the C-terminal part of the protein continue to fluctuate all-through the simulation. The total LRR domain shows an average residue fluctuation of 1.5 Å from the initial positions, indicating stability. The RMSDs of CARDa and CARDb subunits become stable after 2 ns MD simulation (Figure 4(d)), but the RMSD of CARDab complex (black line) shows an arbitrary backbone deviation. The higher RMSD of the complex is attributed to the fluctuation of the linker. The highly dynamic nature of the linker is further confirmed by DISOPRED2 (a disorder prediction server; Ward et al., 2004) server (Supplementary Figure S3). The increased

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Figure 2 shows the overall structural features of modeled zNOD2–LRR after MD simulation, which appears as a semicircle α/β protein with nine parallel β-sheets and α-helices lining its

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inner and outer circumferences, respectively. These secondary structures were consistent during the MD simulation, indicating stability (Supplementary Figure S2 (a)). The active site lies within the concave β-sheet rich surface of zNOD2–LRR (Tanabe et al., 2004; Girardin et al., 2005). Ligand recognition initiates conformational alterations of LRR and oligomerization of NACHT domains that leads to NOD2 signaling cascade (Mo et al., 2012). Figure 3 shows the overall structure of CARD domains. Each domain consists of six α-helices, where nonpolar side chains of helices 3, 5, and 6 are packed against helices 1, 2, and 4 to form a hydrophobic core. Helices 2–5 form a bundle of four antiparallel helices where helix 6 and helix 1 overlay on the top of helix 5 and helix 4, respectively. The helix 1 is broken by a kink into two smaller halves: helix 1a and helix 1b (Figure 3). After MD simulations, helix 1a of zRIP2–CARD becomes random coil (Figure 3(b) and Supplementary Figure S2(c)) and helix 1 of zNOD2–CARDa is distinctly kinked (Figure 3(a) and Supplementary Figure S2 (b)). However, helix 1 of zNOD2–CARDb does not show helical interruption except for a slight curvature (Figure 3(a) and Supplementary Figure S2(b)). The helix 6 of all the models is distinctly curved showing an atypical orientation for CARD domains. It starts unfolding shortly after 0.2 ns MD simulation in zNOD2– CARDab and zRIP2–CARD systems (Figure 3 and Supplementary Figure S2(b) and (c)). This distortion of helix 6 has also been reported in the solution structure of hNOD1–CARD, which could be due to the presence of “Proline” that is well-known to distort helices (Manon et al., 2007). The disruption of helix 6 in our modeled CARDs is not considered a major concern, because this helix does not involve in CARD–CARD interaction. The 3D models of CARD domains highly resemble the solved structures of NOD1–CARD (Manon et al., 2007), ICEBERG (Humke et al., 2000), RAIDD (Chou et al., 1998; Xiao et al., 1999), procaspase-9 (Qin et al., 1999), and Apaf-1 (Vaughn et al., 1999; Zhou et al., 1999).

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Figure 1. Multiple sequence alignment between NOD2–LRR sequences of human, mouse, bovine, and zebrafish. The leucine rich repeat (LRR) regions are highlighted in boxes and the nine LRRs of the zebrafish are indicated on top of the alignment. At the bottom of the alignment “*”, “:”, and “.”, indicate identical, conserved, and semi-conserved substitutions, respectively.

stability of individual CARDs signifies that the tandem CARDs could independently interact with each other even in the absence of the linker (Fridh and Rittinger, 2012). The average gyration radii of the complex are ~19 Å and that of CARDa and CARDb are 12 and 12.5 Å, respectively (Figure 4(e)). This implies a compact shape and size of the CARD domains. The Cα RMSF of CARDab indicates a large fluctuation at the C-terminal end during the MD simulation (Figure 4(e)). These Cα variations are totally stabilized between 18 and 19 ns. Nevertheless, the linker continues to fluctuate throughout the simulation. Together, the modeled zNOD2–CARDab is successfully equilibrated toward the end of the MD simulation. The RMSD of zRIP2–CARD is found to be stable after 200 ps of MD simulation (Figure 5(a)). The RMSD further increases around 2 ns; afterwards, it reaches a plateau of 2 Å for the rest of the simulation. The Rg curve shows a minor change in the overall shape of the model indicating rigidity of its tertiary fold (Figure 5(b)). The local fluctuations of the Cα atoms converge to equilibrium toward the end of the MD simulation, as revealed by the smaller RMSF values during last 1 ns MD simulation (Figure 5(c)). These stability parameters indicate that the equilibrated structures are reasonable for use in further analysis.

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Figure 2. Schematic representation of modeled zNOD2–LRR indicating the position of the LRRs. The α-helices are shown in red, β-sheets in yellow, and coil in green colors. The highlighted residues (transparent surface representations) are reported to bind MDP. The figure also shows the alignment of nine LRRs with the conserved pattern “LxxLxLxxNxL.” The α-helices are colored in red and β-sheets in blue fonts.

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Validation of the built models The accuracy of dihedral angles (Φ/Ψ) was checked using ramachandran plot integrated in PROCHECK program (Laskowski et al., 1993) of SAVeS metaserver. The results show that zNOD2–

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STRUCTURAL CHARACTERIZATION AND BINDING SITE ANALYSIS OF ZEBRAFISH NOD2

Figure 3. The overall structural features of (a) zNOD2–CARDab and (b) zRIP2–CARD after MD simulation. The linker connecting CARDa and CARDb is highlighted in blue color.

Figure 4. Stability parameters for the models as a function of simulation time. (a) Backbone RMSD of zNOD2–LRR; (b) Rg of zNOD2–LRR; (c) Cα RMSF of zNOD2–LRR during 20 ns (black line) and between 14–15 ns (red line); (d) backbone RMSDs of zNOD2–CARDab (black), CARDa (green), CARDb (blue), and the linker (red); and (e) Rg of zNOD2–CARDab (black), CARDa (red), and CARDb (green) (f) Cα RMSF of zNOD2–CARDab during 20 ns (black) and 18–19 ns (red). The linker spanning residues 97–106 are highlighted by a cyan colored box.

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nonbonded atomic contacts, and our predicted models have scores greater than the acceptable value, that is, 50% (Colovos and Yeates, 1993). The Z scores of the predicted models calculated by ProSA are in agreement to that of the PDB structures of similar sizes (Wiederstein and Sippl, 2007). ProQ analysis indicates that the qualities of zNOD2–LRR and zNOD2–CARDab are “extremely good” and that of zRIP2–CARD was “very good” (Wallner and Elofsson, 2003). The bond length and bond angle analysis of the predicted models using MolProbity reveals that none of the residues contains bad side chain or main chain conflicts (Table 1). Interaction between zNOD2–LRR and MDP The monomeric components of PGN include MDP in both Gram positive and Gram negative bacteria. MDP is composed of a carbohydrate (N-acetylmuramic acid) and a dipeptide (L-alanine D-isoglutamine). Upon detecting MDP, NOD2–LRR activates the transcription factor NF-κB (Girardin et al., 2003; Inohara et al., 2003) through CARD–CARD interaction with the kinase RIP2 (Ogura et al., 2001a, 2001b; Kobayashi et al., 2002). The MDP

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LRR has 77% of residues in the most favored region and 97.7% residues in the allowed region (Table 1). However 2.3% of residues (Ser769, Asp772, Thr824, Ser832, and Arg882) are located within the disallowed region of the plot. These residues neither come under structurally rigid region nor delineate the ligand binding site. Specifically, Ser832 and Arg882 are located at the end of the α-helices of LRR4 and LRR6, respectively, while Ser769, Asp772, and Thr824 are found in the loops. For zNOD2– CARDab, 86.9% of residues are found in the most favored region and 99.4% in the allowed region. A single residue (Tyr155), located at the loop connecting helices 3 and 4 of CARDb, is found in the disallowed region. The ramachandran plot of zRIP2–CARD showed that 83.3% of residues are present in the most favored region and 100% of residues are located in the allowed region. These validation scores indicate that the stereo-chemical parameters of the built models are reasonably good. The G-factors of the modeled proteins (Table 1) are well above the cutoff value of 0.5, indicating good stereo-chemical qualities (Laskowski et al., 1993). The Verify 3D scores of the models are >80% that is in agreement to the 3D–1D cutoff score (Lüthy et al., 1992). ERRAT score provides accuracy of the

J. MAHARANA ET AL. 2003). However, some follow-up studies suggested that α-helix/ turn could also detect MDP (Inohara et al., 2003; Tanabe et al., 2004). Recently, we showed that the α-helix part of rNOD2 is comparatively unstable than the β-sheet region in presence of MDP (Maharana et al., 2013). In order to extend our knowledge about the exact binding mode of MDP with the β-sheet region of zNOD2–LRR, we implemented molecular docking and MD simulations on multiple docked complexes of MDP–zNOD2–LRR (Materials and Methods). The stability parameters for the three docked complexes during MD simulations are provided in Supplementary Figure S4. The interaction between MDP and zNOD2–LRR is relatively similar in all three docking results. However, MD simulations reveal that MDP binds with greater affinity to zNOD2–LRR in complex III having seven intermolecular hydrogen bonds (H-bonds), which is two folds greater than what is observed in complexes I and II (Table 2). The larger grid box on complex III (Materials and Methods) might have allowed increased conformational freedom to the ligand. In complex III, MDP initially showed strong H-bonds with Lys815 and Arg845. However, shortly after first 0.5 ns MD simulation, the H-bonds disappear, except for an arbitrary H-bond around 2 ns (Table 3). This suggests that the side chain orientations of key amino acids of zNOD2 LRR might be unfavorable for stabilizing MDP in dynamics condition. On the other hand, the H-bond interactions between MDP and zNOD2–LRR are consistent in complexes I and II. Further, the average number of H-bonds in complex II is comparatively higher than others (Figure 6). Figure 5. The stability parameters for modeled zRIP2–CARD (a) backbone RMSD, (b) Rg, and (c) Cα RMSF during 5 ns (black) and between 4 and 5 ns (red) simulation times.

recognition is well characterized in human (Girardin et al., 2003; Tanabe et al., 2004; Mo et al., 2012), but in fish, it is poorly understood. Our recent in silico study provided some crucial insights into the binding modes of MDP with rNOD2–LRR (Maharana et al., 2013), which allowed us to define the active site for zNOD2–LRR in the present study. The modeled zNOD2–LRR contains a concave β-sheet rich region and a convex α-helical region (Figure 2). Earlier, it was believed that LRRs 5–9 of β-sheet surface predominantly recognize MDP (Inohara et al., 2001; Bonen et al., 2003; Chamaillard et al., 2003; Girardin et al.,

Essential dynamics Essential dynamics (Amadei et al., 1993) is an important method of MD simulation that is increasingly used for visualizing global motions of protein molecules. Our analysis reveals that MDP shows an anti-clockwise rotation in complex I, which is mainly governed by N-acetylmuramic acid moiety. This movement breaks some crucial H-bonds between MDP and Lys815 and Trp929 (Figure 7(a)). In complex III, MDP shows even more intense motion compared to complex I where the N-acetylmuramic acid rotates clockwise up to 180° and the dipeptide moiety displays a sharp 2D diffusion destabilizing key H-bonds (Figure 7(b)). On the other hand, the structural movement of MDP in complex II is highly restricted on the surface of the receptor, indicating a relaxed

Table 1. The structure validation scores of modeled zNOD2–LRR, zNOD2–CARDab, and zRIP2–CARD Servers PROCHECK

Verify 3D ERRAT ProSA ProQ MolProbity

266

Most favored regions Additionally allowed Regions Generously allowed Regions Disallowed regions G-factor Averaged 3D-1D Score > 0.2 Overall Quality Z-Score LGscore MaxSub Cβ deviations > 0.25 Å Residues with bad bonds Residues with bad angles

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zNOD2–LRR

zNOD2–CARDab

zRIP2–CARD

77.0% 18.9% 1.8% 2.3% 0.2 95.2% 83.6% 6.3 6.3 0.6 0 0.0% 0.0%

86.9% 12.5% 0.0% 0.6% 0.1 84.9% 93.2% 6.5 4.5 0.4 1 0.0% 0.0%

83.3% 14.1% 2.6% 0.0% 0.0 95.1% 98.6% 4.2 2.8 0.3 0 0.0% 0.0%

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STRUCTURAL CHARACTERIZATION AND BINDING SITE ANALYSIS OF ZEBRAFISH NOD2 Table 2. The MDP interacting residues of zNOD2–LRR and corresponding binding energy (kcal/mol) scores of the three docking complexes zNOD2–LRR– MDP Complex I Complex II Complex III

Grid dimension (Å) (X × Y × Z)

Binding energy

Ligand efficiency

Electrostatic energy

Number of H-bonds

Interacting residues

60 × 60 × 60 70 × 70 × 70 80 × 80 × 80

4.0 4.4 4.8

0.1 0.1 0.1

2.5 2.4 2.9

3 3 7

Tyr789, Lys815, Arg845 Lys815, Arg845, Glu927 Lys815, Arg845, Glu927

Table 3. The H-bond interactions between MDP and zNOD2–LRR in the three docking complexes at 0.5 ns intervals. Arg845, which interacts with MDP consistently throughout the simulation, has been highlighted in bold font Time (ns) After energy minimization 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Complex I

Complex II

Complex III

Lys815, Arg845, Ser901, Trp929 Arg845, Ser901 Arg845 Lys815, Arg845 Ser901 Arg845 Arg845, Ser901 Arg845, Arg959 Arg845 Arg845, Arg959, Ser901 Arg845, Ser901 Arg845

Lys815, Arg845, Ser901, Glu927 Arg845, Ser901 Arg845 Tyr789, Arg845 Arg845, Ser901 Arg845, Ser901 Arg791, Arg845 Arg791, Arg845, Ser901 Arg845 Arg845 Arg845 Arg845, Trp929

Lys815, Arg845, Glu927 Glu927 NA Glu927 Ser901 Arg791, Arg845, Ser901 Ser901, Glu927 Ser901 Ser901 Ser901 Ser901 Ser901

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Figure 6. Analysis of total number of intermolecular H-bonds formed between MDP and zNOD2–LRR in three different docking complexes: (a) complex I, (b) complex II, and (c) complex III. Complex II shows highest stability in terms of average number of H-bonds, signifying it as the most reliable interaction model.

molecular conformation. So, we find complex II a compatible ligandreceptor conformation that can explain the MDP recognition by zNOD2–LRR. The structural coordinates associated with the first eigenvector of complex II show that the H-bonds and electrostatic interactions are conserved throughout the simulation. The residues of Trp and Arg form strong H-bonds with MDP. The Nacetylmuramic acid of MDP is entangled in a hydrophobic pocket comprised of Phe819, Phe871, Trp875, Trp929, and Trp899 (Figure 8(a)). The side chain of Trp929 interacts with MDP through a short lifetime H-bond. Two stabilizing H-bonds of lengths 2.7 and 3.0 Å are always present between one of the carbonyl groups of MDP and side chain guanidine group of Arg845 (Figure 8(b)). The residues Tyr789 and Arg791 move toward the carbonyl group of D-isoglutamine of MDP, possibly because of an electrostatic attraction. This suggests that MDP requires a hydrophobic pocket for its N-acetylmuramic acid part and a positively charged pocket for its L-alanine D-isoglutamine dipeptide part. In addition to H-bonds, several nonbonded interactions are also found between MDP and zNOD2–LRR, which are summarized in Table 4. The total interacting amino acids can be classified into two distinct groups: stable and transient. The stable group includes residues Phe819, Arg845, Phe871, Trp875, Trp899, Ser901, Glu927, and Trp929, which consistently interact with MDP throughout the MD simulation. On the other hand, the transient residues are Tyr789, Arg791, Lys815, Ala817, Leu900, Trp957, and Arg959, which interact with MDP less frequently during MD simulation. Among the stable group of residues, Arg791, Arg845, Ser901, and Trp929 play an important role for stabilizing MDP with consistent H-bonds and hydrophobic interactions.

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Figure 7. Superimposition of structural coordinates associated with the principal component 1 (eigenvector 1) for three different zNOD2–LRR–MDP simulation systems: (a) complex I, (b) complex II, and (C) complex III. The initial conformations were colored in blue and the final in red while the intermediate ones were colored grey for distinction.

Figure 8. Intermolecular interactions of MDP and zNOD2–LRR in complex II. (a) Interaction of MDP with the active site amino acids of zNOD2–LRR. The hydrophobic amino acids are colored wheat, polar residues are green, and H-bond forming amino acids are represented in grey color. (b) A 2D representation of MDP interaction within zNOD2–LRR binding pocket as generated by LigPlot+ software. MDP is shown in blue, H-bonding amino acid in orange, and the hydrophobic contacts in red half circles.

MM/PBSA calculations on native and mutant zNOD2–LRR– MDP complexes

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In order to propose a more accurate interaction model of MDP and zNOD2–LRR, the binding free energies were estimated for native and mutant complexes. The components of binding and solvation free energies computed by MM/PBSA method are shown in Table 5. The overall binding free energies of complexes I, II, and III are 105.9, 111.9, and 106.9 kJ/mol, respectively. It should be noted that experimental binding affinity for MDP and NOD2– LRR has not been reported till date for comparison. As can be seen from Table 5(a), complex II appears energetically more favorable than others. Therefore, it was subjected to computational Alanine (Ala) scanning experiment. Breakdown of the binding free energy into electrostatic, van der Waals, polar, and nonpolar solvation energies reveals that electrostatic energies (ΔGcoul) are the major favorable contributor to MDP binding. Nonpolar solvation energies (ΔGnonpolar) also make important contribution to binding. Furthermore, the nonpolar solvation terms dominate over van der Waals interaction energies. This observation supports our aforementioned statement that MDP requires a polar and a hydrophobic

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part to effectively dock on the surface of zNOD2–LRR. Among the nonpolar residues, Phe871, Trp899, and Trp929 appear highly essential for MDP binding, because replacing these residues with Ala result in remarkable loss of the nonpolar solvation as well as overall binding energies (Table 5(b)). The LRR-containing C-terminus of NOD2 has been widely accepted as the bona fide cytoplasmic MDP-receptor (Tanabe et al., 2004; Girardin et al., 2005). The concave surface of NOD2–LRR containing β-sheet structures is the receptacle for bacterial cell wall components. Interestingly, using side directed mutagenesis, Tanabe and coworkers had determined that several residues forming α-helix/turn could recognize MDP. However, these findings lacked structural and genetic confirmations (Tanabe et al., 2004). The residues Gly879, Thr899, Trp907, Val935, Glu959, Lys989, and Ser991 belonging to LRRs 7–11 are reported to be essential for MDP recognition in human (Tanabe et al., 2004). Our sequence alignment using NOD2 sequences of zebrafish, rohu, and human indicates that most of these residues are conserved (Supplementary Figure S5). In zebrafish, the conserved residues predicted to interact with MDP are

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STRUCTURAL CHARACTERIZATION AND BINDING SITE ANALYSIS OF ZEBRAFISH NOD2 Table 4. The nonbonded interactions between MDP and zNOD2–LRR of complex II at 0.5 ns time intervals. The consistently interacting residues are shown in bold face Time (ns)

Hydrophobic interaction

Electrostatic interaction

Phe871, Trp875, Trp899, Trp929 Phe819, Phe871, Trp875, Trp899, Trp929 Phe871, Trp875, Trp899, Trp929 Phe871, Trp875, Trp899, Trp929 Phe871, Trp875, Trp899, Leu900,Trp929 Phe819, Phe871, Trp875, Trp899, Leu900, Trp929 Phe819, Phe871, Trp875, Trp899, Trp929 Phe819, Phe871, Trp875, Trp899, Trp929 Phe871, Trp875, Trp899 Phe871, Trp875, Trp899, Trp929 Phe819, Phe871, Trp875, Trp899, Trp957 Phe871, Trp899, Leu900, Trp929

After energy minimization 0 0.5 1 1.5 2

2.5 3 3.5 4 4.5 5

van der Waals interaction

Trp957, Arg959

Tyr789, Phe819

Arg791, Lys815, Glu927

Tyr789

Ser901,Glu927

Arg791, Phe819, Phe871 Phe819

Arg791, Lys815, Ser901, Glu927, Lys955 Arg791,Glu927

Phe819, Tyr789

Glu927

Tyr789, Ala817

Ser901

Tyr789, Ala817

Glu927

Tyr789, Ala817, Leu900 Phe819 Phe819, Trp957

Trp929, Glu927, Ser901 Glu927 Arg791, Ser901, Glu927, Trp929 Trp875, Ser901, Glu927, Trp957, Arg959

nil Phe819

Table 5. (a) MM/PBSA binding free energy (kJ/mol) calculations for the three complexes of zNOD2-LRR and MDP. (b) MM/PBSA binding free energies Ala-variants from complex II. (a)

ΔGbind

ΔGcoul Complex I Complex II Complex III

ΔGpolar

Polar contribution ΔGps

Nonpolar contribution ΔGvdW

ΔGnonpolar

ΔGnps

105.9 (2.0) 111.9 (2.4) 106.9 (2.7)

550.1 (4.7) 755.1 (4.6) 530.5 (4.7)

625.6 (4.9) 844.4 (5.0) 578.1 (5.3)

75.4 89.2 47.5

166.2 (1.6) 185.4 (2.0) 140.7 (2.0)

5.0 (0.3) 15.7 (0.1) 13.7 (0.1)

171.2 201.2 154.4

112.5 112.7 95.2 109.1 91.4 96.6

753.1 387.7 756.7 741.2 758.1 767.0

834.9 467.0 844.7 816.8 847.0 847.6

81.8 79.2 87.9 75.6 88.8 80.5

178.6 (2.0) 179.0 (1.9) 167.0 (2.2) 169.3 (2.0) 168.5 (2.0) 168.9 (1.8)

15.7 14.8 16.1 15.3 15.1 15.0

194.3 193.8 183.1 184.7 183.7 183.9

(b) Phe819 > Ala Arg845 > Ala Phe871 > Ala Trp875 > Ala Trp899 > Ala Trp929 > Ala

(2.4) (2.6) (2.5) (2.4) (3.4) (4.4)

(4.6) (4.2) (4.5) (4.7) (4.6) (4.7)

(5.0) (4.6) (5.0) (5.1) (5.0) (5.1)

(0.1) (0.1) (0.1) (0.1) (0.1) (0.1)

ΔGbind = Binding free energy. ΔGcoul = Electrostatic energy (coulombic term). ΔGps = Polar solvation energy. ΔGpolar = Polar term (sum of coulombic and polar solvation terms). ΔGvdW = van der Waals energy. ΔGnps = Nonpolar solvation energy. ΔGnonpolar = Nonpolar term (sum of van der Waals and nonpolar solvation terms). Numbers in parenthesis indicate standard errors.

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human and zebrafish. Except these, all other residues are variable. Specifically, Phe819 is predicted to interact with MDP in zebrafish, but the corresponding residue of human does not have MDP

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solvent exposed and present on the concave surface spanning LRRs 4–8 (Kobe and Deisenhofer, 1995; Papageorgiou et al., 1997). Among the MDP interacting residues, Trp875 and Glu927 are conserved in

J. MAHARANA ET AL. binding evidence. Likewise, Thr899 of human is reported to interact with MDP, but the corresponding residue of zebrafish (Gln876) does not show MDP interaction in our study. Similarly, Arg845, Phe871, and Trp875 of zebrafish are predicted by us to confer MDP responsiveness, but the corresponding residues of human are not experimentally reported to bind MDP. These observations suggest that sequence conservation of LRR domains is not linked with pathogen detection. It is also evident in plant NBS–LRR protein that most sequence variation occurs within the β-strands region of LRR domain (Dangl and Jones, 2001). Again, it has been proposed that animals and plants share the mechanism of pathogen detection. MDP interacts directly with the LRRs and particularly with the β-strand structures of the LRRs 7–11, which correspond to LRRs 4–8 of rohu and zebrafish (Supplementary Figure S5). Site directed mutagenesis data have shown that the ligand sensing domain of NOD1 lies within the concave region (β-sheet region) of LRRs 5–7, which are conserved in NOD1 sequences of different species. The amino acids Glu816 and Thr844 are reported to be critical for specific detection of DAP containing tripeptides (TriDAP). These data suggest that both NOD1 and NOD2 use the C-terminal regions of LRR to sense pathogenic components (Girardin et al., 2005). It has also been demonstrated that human and mouse NOD1 detect different muropeptides from peptidoglycan (Inohara et al., 2001). While both human and murine NOD1 sense TriDAP, murine NOD1 captures TetraDAP, indicating difference in ligand recognition may be attributed to the variation of amino acids within the LRR domains. These experimental data suggest that the interaction between pathogenic compound and NLRs is direct, except for few cases (plant protein NBS–LRR) where cofactors mediate ligand sensing (Belkhadir et al., 2004). In a recent study, using a series of NOD2 deletion mutants stably expressed in HEK293 cells, Mo et al. have found that MDP recognition takes place at the nucleotide binding domain opposing to the previously reported LRR domain (Mo et al., 2012). In the same work, they have further shown that substitution of NOD2–LRR for NOD1–LRR displayed MDP responsiveness in NOD1. Together, these data indicate a possible discrepancy in defining the exact MDP binding region on NOD2, which requires further investigation.

on fish are rather limited. So, we have performed MD simulation of the complex between zNOD2–CARDa and zRIP2–CARD to understand the CARD–CARD interaction in zebrafish. Although the predicted models of zNOD2–CARDa and zRIP2– CARD share a high degree of structural similarity, they differ in the distribution of charged residues on their surfaces. The electrostatic fields surrounding the proteins were calculated using APBS software. As shown in Figure 9, the zNOD2–CARDa surface contains an extensive acidic patch spanning the whole protein and a smaller basic patch composed of residues Lys9, Arg11, and Arg60 belonging to helices 1 and 4 (Figures 9(c) and 9(d)). In contrast, the zRIP2–CARD surface consists of a smaller negative patch composed of residues Asp494, Glu505, Glu508, Asp506, and Asp525 from helices 2, 3, and 4 (Figures 9 (a) and 9(b)) and a broader positive patch. This difference in the distribution of charged residues in the two proteins is correlated to their total charge; the net charge of zNOD2–CARDa is found to be 9, whereas that of zRIP2–CARD is +2. Mutagenesis data suggest that NOD2 uses a basic patch including two predominantly conserved residues Arg38 and Arg86 from helices 1 and 4 to interact with conserved, acidic residues Asp461, Glu472, Glu475, Asp473, and Asp4922 from helices, 3, and 4 of RIP2 (Fridh and Rittinger, 2012). Our sequence alignment shows that these residues align well with Arg11 and Arg60 of zNOD2–CARDa and Asp494, Glu505, Glu508, Asp506, and Asp525 of zRIP2–CARD, respectively (Supplementary Figure S6). However, Asp525 of zRIP2–CARD is distinctly positioned on one side of the acidic patch surrounded by a number of basic residues mainly from helix 4. To better understand the role of

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Several groups have provided strong evidences for the direct interaction between NOD2 and RIP2 CARDs using co-expression in Escherichia coli followed by pull-down experiments (Fridh and Rittinger, 2012), yeast two-hybrid analysis (Wagner et al., 2009), co-immunoprecipation experiments (Rosenstiel et al., 2006), and overexpression data (Ogura et al., 2001a, 2001b). Similar evidence for NOD1 and RIP2 interaction has also been reported using the NMR structure of NOD1 CARD and a homology model of RIP2 CARD (Manon et al., 2007). These experiments convincingly show that CARD–CARD binding in NOD2/RIP2 and NOD1/RIP2 systems has an electrostatic component. The oppositely charged surfaces of either domain necessitate initial docking through H-bonds and hydrophobic interactions. Although it is still ambiguous whether RIP2 uses either an acidic or basic patch to interact with NOD1 and NOD2, mutagenesis study and co-immunoprecipation data have convincingly demonstrated that CARDa of NOD2 mainly governs the interaction with RIP2 (Rosenstiel et al., 2006; Fridh and Rittinger, 2012). However, these critical findings have been reported for human innate immune systems. The NOD1/NOD2 signaling data

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Figure 9. (a) Electrostatic potential surface of zRIP2–CARD. Blue and red colored lobes indicate coulomb cages for positively charged and negatively charged residues, respectively. The negatively charged surface residues have been divided into two groups: Group I with residues Asp494, Asp506, Glu505, and Glu508 and Group II with residues Asp506, Glu505, Glu508, and Asp525. (b) Distribution of conserved negatively charged amino acids on zRIP2–CARD that are thought to bind with zNOD2–CARDa. (c) Electrostatic potential surface of zNOD2–CARDa. (d) Distribution of conserved positively charged residues that interact with zRIP2–CARD. The residues shown in transparent surfaces are experimentally reported to be involved in CARD–CARD interaction.

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STRUCTURAL CHARACTERIZATION AND BINDING SITE ANALYSIS OF ZEBRAFISH NOD2 negatively charged residues on zRIP2–CARD, they are divided into two groups: Group I including Asp494, Glu505, Glu508, and Asp506 and Group II with Glu505, Glu508, Asp506, and Asp525 (Figure 9 (a)). We docked the positive surface of zNOD2–CARDab first with the Group I negative surface of zRIP2–CARD (complex I) and then with Group II negative surface of zRIP2–CARD (complex II). The affinity of the projected binding surfaces on zNOD2 and zRIP2 CARDs was verified using MD simulation. Analysis of MD trajectories reveals that complex I displays more stable intermolecular interactions as compared to complex II (Figure 10). In complex I, the average number of H-bonds is 4, and the total number of contacts within 3.5 Å of the interacting surfaces is 8 (Figure 10(a)). In contrast, the H-bonds and nonbonded contacts between the two CARDs in complex II gradually decrease during simulation (Figure 10(b)). This decrease in ionic interactions could be due to the presence of a number of

Figure 10. Total number of H-bonds between zNOD2–CARDab and zRIP2–CARD as a function of simulation time: (a) complex I and (b) complex II. Black and red lines indicate number of H-bonds and number of nonbonded contacts within 3.5 Å of each atom at the interacting surface, respectively.

basic residues around Asp525 of zRIP2–CARD that probably dominate its electronegativity, thus weakening interaction with the positive patch on zNOD2–CARD. Based on this observation, complex I is considered the most probable binding modes of zNOD2–CARDab and zRIP2 CARD, which is described next. A 3.5 Å salt bridge is found between Arg60 of zNOD2–CARDa and Asp494 of zRIP2–CARD (Figures 11(a) and 11(b)). Arg60 of zNOD2–CARDa interacts with Tyr507 of zRIP2–CARD through strong H-bonds. Arg11 and Lys53 of zNOD2–CARDa interact electrostatically with Glu508 and Asp494 of zRIP2–CARD with an intermolecular distance of 6.5 Å. In addition, Lys53 of zNOD2–CARDa forms an H-bond with Ser498 of zRIP2–CARD. Another H-bond is found between Asp68 of zNOD2–CARDa and Gln513 of zRIP2–CARD. Interestingly, Lys104 of the linker forms a strong H-bond with Glu508 of zRIP2–CARD, suggesting its probable role in CARD–CARD interaction. Altogether, our docking and MD simulations indicate that Arg11, Arg60, and Lys53 of zNOD2 and Asp494, Glu508, and Tyr507 of zRIP2 are indispensable for tight anchoring of CARD domains in zebrafish. Previously, Wagner et al. had predicted that NOD2 would use an acidic surface to interact with RIP2. Using yeast two-hybrid experiments, they showed that point mutations of negatively charged residues E69K and D70A in CARDa and E69K, D70A, and E71K in tandem CARDs of NOD2 disrupted the interaction with RIP2–CARD (Wagner et al., 2009). Similarly, based on NMR, mutagenesis, and co-immunoprecipation experiments, Menon et al. concluded that NOD1 uses an acidic surface for interacting with the basic surface of RIP2 (Manon et al., 2007). These contradictory remarks about the charge–charge interactions between the CARD domains of NOD2 and RIP2 can only be resolved after the elucidation of their crystal/NMR structures. Importantly, we found that Lys104 of the linker interact with zRIP2–CARD during MD simulation, suggesting the CARD–CARD interaction is not limited to CARDa of zNOD2. Although Fridh and Rittinger (2012) had reported that the linker does not influence CARD– CARD interaction, our MD simulation shows that Lys104 forms a strong H-bond with zRIP2–CARD, indicating the linker of NOD2–CARDs might be involved in the CARD–CARD interaction. It is most likely that CARDb or the whole tandem construct of CARDab along-with the linker interact with RIP2, which requires further investigation.

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Figure 11. Intermolecular interactions between zNOD2–CARDab and RIP2–CARD. (a) Negatively charged, positively charged, and polar residues are colored in black, green, and red, respectively. H-bonds, salt bridge, and electrostatic interactions are represented as yellow, black, and blue dashes. For zNOD2–CARDab, helices are shown in red and loops are in green, whereas for zRIP2–CARD, helices are colored cyan and loops are salmon. (b) Intermolecular interaction between zNOD2–CARDab and zRIP2–CARD shown as a 2D diagram as calculated by LigPlot+ program. The amino acids of zRIP2–CARD are colored magenta, and those of zNOD2–CARDab are colored light orange, for distinction. H-bonds are shown as green dashes.

J. MAHARANA ET AL. The techniques of homology modeling, molecular docking, and MD simulations are being increasingly used to study the molecular interactions between key proteins of innate immune system. For instance, in silico studies have been carried out to study the interactions of TLR2 with peptidoglycans (Lipoteichoic Acid and Zymosan Ligands) and downstream MyD88 adaptor protein (Sahoo et al., 2013b), the interaction between TLR3 and dsRNA (poly I:C) (Sahoo et al., 2012), binding modes of iE-DAP on NOD1 (Sahoo et al., 2013a), and MDP recognition by NOD2 (Maharana et al., 2013). In addition, molecular modeling studies were performed to characterize the potential homo-dimer interface between TLR8 subunits and interaction with the antiviral drug R848 (Govindaraj et al., 2011); the homo and hetero-dimerization between TLR10, TRL1, and TLR2 (Govindaraj et al., 2010); and the mechanism of inhibition of TLR signaling pathway by a membrane bound protein ST2L (Basith et al., 2011). These computational methods offer atomistic insights into the phenomena of molecular recognition which is rather difficult without the application of X-ray crystallography and NMR spectroscopy techniques. On the other hand, these methods rely exclusively on the accuracy of inherent algorithms, scoring functions, and scalability of computer hardware. A detailed discussion regarding advantages and shortcomings of these protocols can be found in these comprehensive reviews (Yuriev et al., 2011; Yuriev and Ramsland, 2013). Furthermore, the accuracy of results obtained from docking and MD simulations varies depending upon how the initial conformations of ligands or proteins are represented. For example, Justin and coworkers have precisely suggested that PRODRG, a popularly and widely used tool for generating GROMACS compatible topology of ligands, usually fails to assign correct partial charges to the atoms in the ligands (Lemkul et al., 2010). This limitation can significantly impact the behavior of the ligand with solvent or the receptor during MD simulation. Hence, we have chosen ATB for generating GROMACS compatible topology of MDP, which uses knowledge-based approach in combination with QM calculations to select parameters consistent with a given force field (Malde et al., 2011). Moreover, a single docking or MD simulation can mislead an understanding about the interaction between binding partners. Therefore, we have performed multiple docking experiments and MD simulations to represent atomic interactions in our study. The best interacting complex has been discussed in detail. The MD simulations may significantly alter the initial conformation of macromolecules. So, it should be a validated experimental information. Conclusively, care should be taken while designing experiments based on the computational hypothesis.

CONCLUSIONS In this study, we have constructed 3D models of zNOD2–LRR, zNOD2–CARDab, and zRIP2–CARD using protein threading, molecular docking, and MD simulation methodologies. Molecular docking and MD simulation reveal that MDP binds to zNOD2 between LRRs 4 and 8 of the concave β-sheet region. Specifically, Arg845 is found to be essential for strong interaction of MDP with zNOD2–LRR. A hydrophobic pocket composed of residues Phe819, Phe871, Trp875, Trp929, and Trp899 is responsible for holding the ligand in its correct orientation. Most of the identified MDP binding residues are identical to the corresponding residues of human and rohu, suggesting the mechanism of pathogen recognition may have a similar mechanism. The PCA reveals that upon MDP binding, zNOD2 exhibits uniform displacements of all the nine LRRs, indicating a mechanism of conformational alteration of LRR domain after face-off with a pathogenic signal. The docking and MD simulation of modeled zNOD2–CARDa and zRIP2–CARD show that a positive patch of residues Arg11, Lys53, and Arg60 on zNOD2–CARDa interacts with a negative patch of residues Asp494 and Glu508 on zRIP2–CARD. A salt bridge is observed between Arg60 of zNOD2–CARD and Asp494 of zRIP2–CARD with an intermolecular distance of 3.5 Å. Lys104 of the linker is also found to interact with Glu508 of zRIP2–CARD through a strong H-bond, indicating the linker could associate with zRIP2–CARD in the more complex in vivo environment. Based on this fact, the involvement of zNOD2–CARDb in the CARD–CARD interaction cannot be ruled out as well. Altogether, this study provides novel insights to understand the NOD signaling pathway in the zebrafish, which is presumed to be a representative, well studied vertebrate model.

Acknowledgements The authors thank the Director, Central Inland Fisheries Research Institute for rendering institutional facility. JM, MCP, and BRS thank Mr Budheswar Dehury for his assistance in manuscript writing and corrections. JM thanks Mr Asim Kumar Jana, Senior Technical Assistant, CIFRI for his technical advice. We are grateful to the Department of Biotechnology, Ministry of Science & Technology, Government of India for providing infrastructure facility at the Department of Bioinformatics, Orissa University of Agriculture and Technology, Bhubaneswar Odisha, India.

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