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Structural analysis and insight into Zika virus NS5 mediated interferon inhibition
Accepted Manuscript Structural analysis and insight into Zika virus Ns5 mediated interferon inhibition
Hamza Arshad Dar, Tahreem Zaheer, Rehan Zafar Paracha, Amjad Ali PII: DOI: Reference:
S1567-1348(17)30110-7 doi: 10.1016/j.meegid.2017.03.027 MEEGID 3107
To appear in:
Infection, Genetics and Evolution
Received date: Revised date: Accepted date:
2 January 2017 3 March 2017 25 March 2017
Please cite this article as: Hamza Arshad Dar, Tahreem Zaheer, Rehan Zafar Paracha, Amjad Ali , Structural analysis and insight into Zika virus Ns5 mediated interferon inhibition. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Meegid(2016), doi: 10.1016/j.meegid.2017.03.027
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STRUCTURAL ANALYSIS AND INSIGHT INTO ZIKA VIRUS NS5 MEDIATED INTERFERON INHIBITION Hamza Arshad Dara 1, Tahreem Zaheera 1 , Rehan Zafar Parachab, Amjad Alia*
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a. Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
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b. Research Center for Modeling and Simulation (RCMS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
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*Corresponding author Amjad Ali, Ph.D Email: [email protected]
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These authors contribute equally to this work.
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Abstract The Zika virus outbreak in 2015-2016 is the largest of its kind for which WHO declared a Public Health Emergency of International Concerns. No FDA approved drug is available for the treatment of the viral infection. The interaction of flavivirus NS5 protein with SIAH2 ubiquitin
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ligase has been previously known. NS5 of Zika virus has been implicated in the degradation of
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STAT2 protein, which activates interferon-stimulated antiviral activity. Based on our proposition
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that NS5 utilizes SIAH2-mediated proteasomal degradation of STAT2, an in-silico study was carried out to characterize the protein-protein interactions between NS5, SIAH2 and STAT2
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proteins. The aim of our study was to identify the amino acid residues of NS5 involved in IFN
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antagonism as well as to find the association between NS5, SIAH2 and STAT2 to predict the interaction pattern of these proteins. Analysis proposed that NS5 recruits SIAH2 for the
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ubiquitination-dependent degradation of STAT2. NS5 residues involved in interaction with
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SIAH2 and/or STAT2 were found to be mostly conserved across related flaviviruses. These are novel findings regarding the Zika virus and require confirmation through experimental
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approaches.
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Keywords: Zika virus; NS5 Docking; Interferon antagonism; STAT2 degradation;
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Phylogenetic analysis;
Abbreviations2
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ZIKV, Zika Virus; NS5, Non-structural protein 5; SIAH2, Seven in absentia homologue 2; STAT2, Signal Transducer and Activator of Transcription 2; MTase, Methyltransferase; IFN, Interferon; IRF9, Interferon Regulatory Factor 9; ISGF3, Interferon-Stimulated Gene Factor 3; ISGs, Interferon-Stimulated Genes; DENV, Dengue Virus; TLR, Toll-Like Receptor; PDB, Protein Data Bank; JEV, Japanese Encephalitis Virus; DOPE, Discrete Optimized Protein Energy; RMSD, Root-Mean-Square Deviation; VMD, Visual Molecular Dynamics; GROMACS, GROningen MAchine for Chemical Simulations; MD, Molecular Dynamics; OPLS-AA, Optimized potential for Liquid simulation-All Atom; PPI, Protein-Protein Interactions; RdRp, RNA dependent RNA Polymerase;
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1 Introduction Zika virus (ZIKV) belongs to the genus flavivirus of family flaviviridae and is most closely related to Spondweni virus (Kuno et al., 1998). Similar to dengue and yellow fever, ZIKV infection spreads through Aedes mosquitoes (Cao-Lormeau et al., 2016; Enfissi et al.,
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2016; European Centre for Disease Prevention and Control, 2016). The viral infection has been associated with Guillain-Barré syndrome and microcephaly in newborn babies (Cao-Lormeau et
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al., 2016; Mlakar et al., 2016). Deaths have been reported due to the viral infection in
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immunocompromised patients. Individuals infected with ZIKV are either asymptomatic or show only mild symptoms for 2-7 days (“WHO | Zika virus,” 2016). Symptoms are similar to dengue,
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and include fever, skin rashes, muscle and joint pain, conjunctivitis, headache and malaise
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(discomfort). There is no specific FDA approved drug available in the market to treat ZIKV
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infection (Zika Virus Response Updates from FDA, 2016). In 2007, the first outbreak of ZIKV occurred in the yap islands of Micronesia (Duffy et al.,
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2009). Before that, there were no reports regarding the transmission of the virus outside Asia and
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Africa. During October 2013- April 2014, French Polynesia experienced the worst outbreak of ZIKV, with increasing cases of Guillain-Barré syndrome also being actively reported (Cao-
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Lormeau et al., 2016). The epidemiological evidence prompted the scientists to determine the role of ZIKV infection, if any, in the development of Guillain-Barré syndrome. Before the French Polynesia outbreak, the virus was believed to cause only mild diseases (Musso et al., 2015). ZIKV encodes polyprotein, which is later cleaved into three structural proteins and seven nonstructural proteins (Kuno and Chang, 2007). NS5 (Non-structural protein 5) is the largest and most conserved protein of ZIKV (Zhu et al., 2016). The N-terminal of NS5 contains a putative
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MTase (methyltransferase) domain while its C-terminal is involved in the crucial RdRp (RNA dependent RNA polymerase) activity of the virus. Type I Interferons (IFN- α and β) functions to control viral infection by the regulation of immune responses. IFN- α and β transmit signals through an autocrine and paracrine approach by
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interacting with their cognate common heterodimeric IFN-α/β receptor present on the cell surface, thus ultimately leading to the activation of the JAK/STAT pathway (Horner and Gale,
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2009). Phosphorylation of STAT (Signal Transducer and Activator of Transcription) 1 and
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STAT2 proteins and their subsequent association with IRF9 (Interferon Regulatory Factor 9) results in the formation of ISGF-3 (Interferon-stimulated Gene Factor-3) transcription factor
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complex. The complex later binds to IFN response elements to enhance the gene expression of
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ISGs (IFN-stimulated genes).
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Like other viruses, flaviviruses have developed various immune evasion strategies to counter the Type I IFN antiviral responses. Especially, flaviviruses have been found to antagonize IFN-
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signaling activity (Diamond, 2009). It has been reported that DENV NS5 leads to proteasomal
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degradation of STAT2 protein and is crucial for the potent IFN signaling antagonism (Ashour et al., 2009). Similarly, experimental studies have confirmed that ZIKV NS5 is required for the
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proteasomal degradation of the STAT2 in humans (Grant et al., 2016). The effect of ZIKV NS5 on the JAK/STAT pathway is shown in the Fig 1. Fig 1. Pathway of ZIKV NS5-dependent IFN antagonism. Normally, Type I IFNs (IFN- α and β) interact with a heterodimeric IFN receptor to trigger the activation of STAT1 and STAT2 proteins (Horner and Gale, 2009). Phosphorylation of both STAT proteins, followed by their association with IRF9 results in the formation of ISGF-3 transcription factor complex. The complex later binds to IFN response elements inside the nucleus to enhance the gene expression
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of IFN-stimulated genes. ZIKV NS5 binds to STAT2, ultimately leading towards its degradation (Grant et al., 2016). Due to the viral infection, Toll-like Receptor (TLR) 3 is also activated (Hamel et al., 2015). Through TRIF and TRAM proteins, TLR3 leads to the activation of IRF3 transcription factor (Borden et al., 2007). IRF3 later binds to specific DNA sequences inside the
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nucleus to jump-start IFN transcription. SIAH (Seven in Absentia Homolog) 2 [Uniprot ID: O43255 (human origin)] is an E3 ubiquitin
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ligase that mediates ubiquitination and subsequent proteasomal degradation of target proteins,
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such as those involved in the regulation of transcription (Hu et al., 2008). Interaction of flavivirus NS3 and NS5 proteins with SIAH2 has already been reported, later confirmed by
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Mairiang et al (Le Breton et al., 2011; Mairiang et al., 2013). In the present study, a detailed
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computational analysis was carried out to find the interaction pattern between NS5, SIAH2 and STAT2 proteins. The study was helpful to gather an in-depth understanding of the interface
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residues of each protein involved in interaction as well as to predict the underlying mechanism of
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NS5 mediated IFN antagonism and the subsequent propagation and development of ZIKV
et al., 2015).
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pathogenesis. Similar in silico work had been conducted in case of DENV by Aslam et al (Aslam
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The crystal structure of ZIKV NS5 protein has been resolved. Therefore, it was used to predict the PPIs (Protein-Protein Interactions) and to analyze the unique features of the interacting residues. According to our results, NS5 may first interact with the host SIAH2, which can later proceed to bind STAT2. These findings demonstrate that ZIKV hijacks the host proteins to destroy specific host cellular proteins described in IFN-stimulated signaling pathways, which are crucial for antiviral activity of IFN.
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2 Material and methods 2.1 Protein evaluation The crystal structure of ZIKV NS5 protein was extracted from PDB ID 5TFR and checked for
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different parameters (Longenecker et al., 2016). Firstly, the structure was assessed by using
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ERRAT score, which provides information about the residues that can be rejected above a
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certain threshold, i.e. having error scale greater than 95% (Colovos and Yeates, 1993). The Ramachandran plots of the structures were produced using RAMPAGE (Lovell et al., 2003).
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RAMPAGE also provides additional information such as the percentage of amino acids residing
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in favorable region, allowed region and outlier regions. The structures having amino acids predominantly in the favorable (and allowed region) were identified. Finally, Qmean score of the
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structure was determined (Benkert et al., 2008).
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Secondary structure prediction was performed on renumbered ZIKV NS5 structure and topology diagram was generated by using PDBsum (Laskowski, 2001). The result was visualized
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accordingly.
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2.2 Energy minimization
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For the energy minimization of the crystal structure of NS5, GROMACS (GROningen MAchine for Chemical Simulations) was used (Abraham et al., 2015). The protein was subjected to molecular simulations to mimic the biological environment faced by the protein. This exercise was required for the stability of protein. GROMACS, a command based Molecular Dynamics (MD) simulation program, was used to stabilize the structure of NS5. The PDB file was converted to gro file in order to maintain the default force field compliant topology. OPLS-AA (Optimized potential for Liquid simulation)
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force field was selected (Kaminski et al., 2001). The protein was restrained in a rhombic dodecahedron cubic box as it can accommodate the solvent molecule. The protein was placed in the center of the cube having a 1 nm distance from the edge of the cube so that the periodic image of the protein was 2 nm apart. The solvent used to simulate protein was water, spc216.gro,
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it is position restrained having a force constant (kpr) of 1000 kJ mol-1 nm-2. The charge on the
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protein was predicted to be +12. To remove the charge on protein a tool present in GROMACS
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was used, named as genion. Ions.mdp file was generated to remove the charge on the protein and the cut-off scheme used was Verlet and electrostatic forces were applied. 12 chloride ions were
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added at the place of solvent molecule. Energy minimization was carried out and at 1600 step the
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final energy minimized structure was obtained. The graph of the stages of energy minimization was obtained and analyzed using Xmgrace (Turner, 2005). NVT isothermal-isochoric ensemble
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equilibration was used at 100ps to stabilize the temperature of protein up to a certain value.
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During NVT equilibration, velocity was generated so that a number of simulation can run at a variety of initial speeds. V-rescale was used, Temperature was then analyzed from temperature
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graph. Pressure and densities of stabilized NS5 were then determined using NPT ensemble
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containing almost the same parameters, the process constituted 50000 steps. The equilibrated stabilized NS5 was then MD simulated at 1ns. RMSD of backbone of energy minimized and
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RMSD of initial structure were predicted and results were generated in form of graphs. To predict the compactness of structure, radius of gyration was also predicted.
2.3 Determination of protein-protein interactions The structures were fed into guru level interface HADDOCK (High Ambiguity Driven proteinprotein DOCKing) server using default settings (van Zundert et al., 2016; Wassenaar et al., 2012). Guru level interface is an advanced HADDOCK interface that permits the identification
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of flexible regions from the simulation perspective, as opposed to the easy level interface. The top cluster was refined for better orientation, leading to improved HADDOCK scores. PDBsum was used for intermolecular structural analysis (Laskowski, 2001). CPORT (Consensus Prediction Of Interface Residues in Transient complexes) was used for the
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prediction of active and passive residues involved in interaction (de Vries and Bonvin, 2011). CPORT uses a combination of six methods to accomplish the task (Chen and Zhou, 2005; de
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Vries et al., 2006; Kufareva et al., 2007; Liang et al., 2006; Neuvirth et al., 2004; Porollo and
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Meller, 2006). In case of SIAH2, the structure was directly submitted to CPORT to predict active residues and passive residues. HADDOCK requires comma spaced residues for its work. In case
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of STAT2, the structure was prepared in Chimera and then submitted at CPORT for the
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prediction of residues (Pettersen et al., 2004). In case of ZIKV NS5, the energy minimized
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structure was renumbered and then submitted at CPORT.
2.4 Phylogenetic analysis
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PSI-BLAST was performed for the sequence of ZIKV NS5 structure under default conditions
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(Altschul and Koonin, 1998). The sequences having greater than 60% identity were retrieved and multiple aligned. Phylogenetic tree was constructed using MEGA7 to infer the evolutionary
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relationships between the sequences (Kumar et al., 2016). VMD (Visual Molecular Dynamics) was used for the visualization of the NS5 protein and a stable frame was picked (Humphrey et al., 1996). Pymol and chimera were used to visualize the figures complexes generated by HADDOCK (DeLano, 2002; Pettersen et al., 2004). PDBsum was used for the analysis of intramolecular and intermolecular attractions.
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3 Results 3.1 Crystal structure evaluation The crystal structure was evaluated using different tools. ERRAT score was evaluated and the
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overall quality score was found to be quite significant i.e. 94.96. Similarly, the Ramachandran
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plot obtained through RAMPAGE server predicted that 854 residues were present in the favored
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region, followed by 24 in the allowed region and only 2 in the outlier region. Hence, the orientation of the protein was found to be satisfactory. Moreover, the QMean score was found to
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be significant i.e. 0.72. Hence, after checking the different parameters, the structure was
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subjected to molecular simulations later.
The secondary structure prediction was performed as visualized in Fig 2. According to analysis,
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the protein having 882 amino acids contains 42 helices, 7 beta sheets, 60 beta turns and 9 gamma
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turns. The topology of the protein was generated and analyzed as shown in Fig 3.
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Fig 2. Secondary structure prediction of ZIKV NS5.
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Fig 3. Topology diagram of ZIKV NS5.
3.2 Protein structure preparation
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The mass of the protein calculated after applying OPLS-AA was 101010.794 amu. The volume of the box enclosing protein was 1934.90 nm3 while the initial density was 999.639 g/l. Initially, 58664 water molecules were added into the system. The start terminus had Gly1-NH3+ charge and the end terminus contain Leu-903 COO- charge. The total charge on protein was +12 so 12 chloride negative ions were added in the place of solvent molecules. After this refinement, the number of water molecules left were 58652. The final potential energy of ZIKV NS5 protein was
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found to be -3.3394580e+6 KJ/mol. According to potential energy and other graphs (shown in S1 Fig), the protein was stabilized and energy of the protein was lowered by a significant value and its average value was -3.24231e+6 KJ/mol with a total drift of -331697 KJ/mol. The temperature of the stabilized structure was then analyzed from graph and predicted to be
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299.737K. The average pressure after 50000 steps of NPT was -2.64314 bar while the average
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density of the protein was 1023.19 kg/m3.
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3.3 Analysis of interactions between ZIKV NS5 and SIAH2
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Modeled structure of SIAH2 was obtained from Aslam et al. with permission and was docked with crystal structure of ZIKV NS5 using Guru level interface of HADDOCK (Aslam et al.,
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2015).
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Guru level requires active and passive residues of the proteins, so we used CPORT to predict the
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active and passive residues of SIAH2 and NS5. CPORT results are shown in S2 Fig. Default conditions were used in Guru level interface to dock ZIKV NS5 with SIAH2. 33.5 % water
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refined 67 structures were generated that were clustered in 8 clusters. The top most cluster was
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used having HADDOCK score 15.8 +/- 14.1, the Z-score, Vender’s Wall energy and other scores were observed. The HADDOCK score was refined and statistical analysis was conducted, as
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shown in S3 Fig. The chains were visualized and interacting residues were focused (Fig 4). Fig 4. (A) Figure showing interacting chains between NS5 and SIAH2. (B) Another diagram from different orientation displaying all the interacting residues between both chains. The refined structure was then analyzed for PPI in PDBsum as shown in Fig 5. According to the results, collectively 30 interface residues of ZIKV NS5 were shown to interact with 30 residues
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of SIAH2. The interface area (Å2) of NS5 was found to be 1606, while that of SIAH2 was found to be 1614. Fig 5. (A) Figure showing all interacting residues between NS5 and SIAH2. The number of H-bond lines between any two residues indicates the number of potential hydrogen bonds
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between them. For non-bonded contacts, which can be plentiful, the width of the striped line is proportional to the number of atomic contacts. Here, blue color is used to label hydrogen bonds.
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Yellow color indicates disulphide bonds and red color shows salt bridges. While inter-spaced
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lines indicate non-bonded contacts. (B) Interacting residues between NS5 and SIAH2, focused view.
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3.3.1 Hydrogen bonds
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Structural analysis revealed the presence of 11 hydrogen bonds between ZIKV NS5 and SIAH2.
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Residues Tyr2541, Lys2544 (part of helix 2) and Lys2545 of NS5 were predicted to form a hydrogen bond with Asp146, with distance being 2.65 Å, 2.74 and 2.67 Å, respectively. Arg2557
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(part of helix 3) of ZIKV NS5 was predicted to interact with Thr45, distance being 2.82 Å.
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Similarly, Lys2561 was predicted to interact with two amino acids i.e. Asp46 and Asp54, with distance 2.66 Å each. Asp2562 was found to form hydrogen bond with amino acid Lys30,
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distance being 2.67 Å. Arg2843 (part of helix 12) was found to form two hydrogen bonds with amino acid Gly63, distance being 2.81 Å and 2.95 Å. Asp2850 was predicted to form hydrogen bonds with two amino acids Cys61 and Phe62, with distance 2.91 and 2.79 Å respectively. Surprisingly, none of the residues from 2851 onwards were predicted to be involved in hydrogen bonding.
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3.3.2 Salt bridges Six salt bridges have been observed between ZIKV NS5 and SIAH2. Lys2544 (part of helix 2) and Lys2545 was shown to be linked to Asp146 with distance 2.74 Å and 2.64 Å. Similarly, Lys2561 was predicted to form salt bridges with Asp46 and Asp54 at 2.66 Å distance each.
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Asp2562 was observed to form salt bridge with Lys30 at 2.67 Å distance while Lys2847 (part of
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helix 113) was observed to form salt bridge with Glu96 at 3.60 Å distance.
3.3.3 Non-bonded contacts
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Additionally, the total number of non-bonded contacts between ZIKV NS5 and SIAH2 was
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found to be 149. However, no Disulphide bonds were predicted.
3.4 Analysis of interactions between ZIKV NS5 and STAT2
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Modeled structure of STAT2 was obtained from Aslam et al. with permission and was docked
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with prepared structure of ZIKV NS5 using Guru level interface of HADDOCK (Aslam et al., 2015). Guru level requires active and passive residues of the proteins, so we used CPORT to
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predict the active and passive residues of STAT2 and NS5. Default conditions were used in Guru
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level interface to dock ZIKV NS5 with STAT2. 16.0% water refined 32 structures were generated in 4 cluster. The top most cluster was used for further analysis. The cluster had
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HADDOCK score 65.9 +/- 5.7, the Z-score, Vender’s Wall energy and other parameters were observed. The HADDOCK score was refined and analysis was conducted, as shown in S4 Fig. The chains were visualized and interacting residues were noted (Fig 6). Fig 6. (A) Figure showing interacting chains between NS5 and STAT2. (B) Another figure from different orientation displaying all the interacting residues between both chains.
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The refined docked structure was then analyzed for PPI in PDB sum as shown in Fig 7. According to the results collectively 29 interface residues of ZIKV NS5 were shown to interact with 24 interface residues of STAT2. The Interface area (Å2) of NS5 was found to be 1383 while that of STAT2 was found to be 1394.
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Fig 7. (A) Figure visualizing all interacting residues between NS5 and STAT2. The number of H-bond lines between any two residues indicates the number of potential hydrogen bonds
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between them. For non-bonded contacts, the width of the striped line is proportional to the
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number of atomic contacts. Here, blue color is used to label hydrogen bonds. Yellow color indicates disulphide bonds and red color shows salt bridges. While inter-spaced lines indicate
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non-bonded contacts. (B) Interacting residues between NS5 and STAT2, focused view.
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3.4.1 Hydrogen bonds
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15 hydrogen bonds were predicted between NS5 and STAT2. Residues Lys2544 (part of helix 2) and Arg2729 were predicted to form a hydrogen bond with Asp52 at a distance of 2.65 Å and
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3.02 Å respectively. Gly2625 was observed to form hydrogen bond with Gln42, with distance
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being 2.93 Å. Glu2665 formed a hydrogen bond with Lys55 at a distance of 2.69 Å. Two residues Asn2839 and Gly2840 (part of helix 12) were found to make hydrogen bond with
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Asp77 at a distance of 2.76 Å. Moreover, Ser3259 (part of beta sheet D) was also predicted to form hydrogen bond with Asp77 at a distance of 2.68 Å. Arg2843 (part of helix 12) was found to form two hydrogen bonds with Gln33 at 2.77 Å and 2.90 Å. Lys2847 (part of helix 13) was predicted to form hydrogen bond with Glu5 and Gln8 at 2.69 Å and 3.08 Å respectively. Likewise, Gly3263 formed hydrogen bond with Gln76 at 2.78 Å while Ile3266 (part of helix 36) was predicted to make hydrogen bond with Glu79, with distance being 2.75 Å. Arg3372 (part of helix 41) formed two hydrogen bonds with Gln126 at a distance of 2.98 Å and 3.08 Å.
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3.4.2 Salt bridge Similar to SIAH2, six salt bridges were predicted between ZIKV NS5 and STAT2. Lys2544 (part of helix 2) was predicted to form a salt bridge with Asp52 at a distance of 2.65 Å. Similarly, Glu2627 and Glu2665 were predicted to form salt bridge with His62 and Lys55 at
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distance of 3.34 and 2.69 Å respectively. Likewise, Arg2729 formed salt bridge with Asp52,
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with distance being 3.02 Å. Lys2847 (part of helix 13) was predicted to form salt bridge with
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Glu5 with distance 2.69 Å while Arg3372 (part of helix 41) was predicted to form it with Glu131
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at a distance of 3.43 Å.
3.4.3 Non-bonded contacts
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Additionally, 161 total number of non-bonded contacts were observed between NS5 and STAT2.
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However, no disulphide bonds were predicted similar to SIAH2 in our in-silico analysis.
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Phylogenetic analysis
Multiple sequence alignment was generated using the MEGA7 (Kumar et al., 2016) built-in
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MUSCLE program (Edgar, 2004) to analyze the conservation of the various interface residues of
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the ZIKV NS5 involved in interaction with SIAH2 and/or STAT2 proteins. Sequence alignment in combination with evolutionary phylogenetic analysis (Fig 8 and Fig 9) confirmed that the
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predicted residues were mostly conserved across the different flaviviral sequences as shown. Our own ZIKV NS5 residue-level conservation analysis suggested that these interface residues were conserved significantly within ZIKV, with change in a residue often maintaining the physicochemical property of the amino acids. Fig 8. Multiple sequence alignment of flavivirus NS5 sequences. Multiple sequence alignment was generated to analyze the conservation of interface residues across related flaviviral NS5
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sequences. The NS5 sequences were multiple-aligned using MEGA7 built-in MUSCLE tool (Edgar, 2004; Kumar et al., 2016). The residues were renumbered as per the genomic position of the query sequence. Here, only the interface residues are shown as observed in the alignment. Jalview was used to show the alignment in a better way (Waterhouse et al., 2009).
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Fig 9. Evolutionary relationships of NS5 sequences. The evolutionary history was inferred using the Neighbor-Joining method (Saitou and Nei, 1987). The optimal tree with the sum of
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branch length = 6.17891434 is shown. The tree is drawn to scale, with branch lengths in the same
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units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Poisson correction method and are in the units of the number
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of amino acid substitutions per site (Zuckerkandl and Pauling, 1965). The analysis involved 64
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amino acid sequences. All positions containing gaps and missing data were eliminated. There were a total of 862 positions in the final dataset. Evolutionary analyses were conducted in
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MEGA7 (Kumar et al., 2016). The query sequence is highlighted in red while the most related
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sequences are highlighted in pink. The other sequences showing comparatively more variation in
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their interacting residues are shown in black.
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4 Discussion
Flaviviruses are similar viruses reported to have similar features that can be exploited. ZIKV is a member of the flavivirus most closely related to Spondweni virus. However, unlike Spondweni virus NS5 and like DENV, ZIKV NS5 expression led to the decrease in STAT2 levels in human cell lines, thus implying that it binds and subsequently degrades STAT2 protein (Grant et al., 2016). Moreover, both ZIKV and DENV-mediated IFN antagonism behavior was
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observed in human but not in mice, suggesting a host-specific infection pattern of both viruses. However, unlike DENV, ZIKV does not appear to require E3 ubiquitin ligase URB4 to degrade STAT2. Moreover, experimental evidence shows that proteolytic processing of the N terminal of DENV NS5 is required for DENV-mediated STAT2 degradation (Ashour et al., 2009). In
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contrast, ZIKV NS5 does not require an authentic N terminus to degrade STAT2. It is believed
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that UBR4 functions specifically in the degradation of proteins containing destabilizing N
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termini or N-degrons, hence it is necessary for DENV-mediated IFN-inhibition (Tasaki et al.,
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2005).
Experiments have shown that the RdRP domain of DENV NS5 is capable of inhibiting IFN-α
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signaling alone, suggesting that the residues responsible for the inhibitory activity are present
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within this domain (Mazzon et al., 2009). However, less inhibition was observed in cells expressing the RdRp domain alone compared with full-length DENV NS5. Nevertheless,
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according to our in-silico analysis, only a few interacting residues were found to be in the MTase
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domain; Hence, majority of the residues of the protein were present in the RdRP domain, thus
protein.
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suggesting that the RdRP domain of ZIKV NS5 is involved majorly in interaction with STAT2
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The crystal structure was analyzed to check and evaluate different parameters. ERRAT score helped determine the quality of the overall structures. ERRAT server provided an overview of the different residues of NS5 that can be rejected above 95% error scale. The Ramachandran plots assessed by RAMPAGE provided further information about the bond angles and orientation of protein molecules within the permitted constraints due to peptide bond. Moreover, it provided crucial information such as the percentage of amino acid residues in the favorable, allowed and outlier regions. QMean score of structure is based on multiple parameters, including Carbon beta
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interaction energy, All-atom pairwise energy, Solvation energy, Torsion angle energy, Secondary structure agreement and Solvent accessibility agreement. Structural analysis of the crystal structure revealed that it had bond angles, dihedrals, lengths and interaction energies within the required threshold range; Moreover, the structure was stable as atomic collisions were not
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present. Simulations were carried out to prepare the NS5 protein before docking. During energy minimization in GROMACS an important parameter emtol = 1000 was used in
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the mdp file so that the final structure obtained is stable and its force is not greater than 1000 kJ
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mol-1 nm-1. To equilibrate the ions and solvent molecules around the protein, the protein was simulated at the required temperature in order to properly orient the protein in the solvent
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molecule.
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Guru level interface in HADDOCK was used to get more refined results in terms of flexibility
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and stability of protein complexes. Default setting of histidine residues, radius of gyration and distance of atoms were selected to generate the final clusters.
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HADDOCK system provides meaningful results based on the integration of the biochemical
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and/or biophysical interaction data (Liang et al., 2006) in addition to ab initio PPIs. HADDOCK uses the docking protocol, which incorporates useful features such as flexibility of specified
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protein regions, solvated docking, and use of modified amino acids. Refinements were carried out to further characterize the PPIs. Notably, the buried surface area 3241.7 +/- 269.9 was further increased to 3290.5 +/- 37.8 in case of NS5-SIAH2 docking while RMSD from the overall lowest-energy structure declined from 15.3 +/- 0.1 to 0.2 +/- 0.1. Overall, HADDOCK score improved from 15.8 +/- 14.1 to -195.8 +/- 1.9, thus implying a significant interaction between NS5 and SIAH2 proteins.
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Comparatively, Buried Surface Area decreased from 3065.6 +/- 119.1 to 2861.4 +/- 40.3 while the RMSD from the overall lowest-energy structure declined from 37.6 +/- 0.2 to 0.3 +/- 0.2 after refinements in case of NS5-STAT2 interactions. Overall, HADDOCK score improved significantly i.e. from 65.9 +/- 5.7 to -138.8 +/- 3.9. Based on the docking results, a logical
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conclusion was drawn that ZIKV NS5 interacted strongly with both SIAH2 and STAT2 proteins,
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with more significant interaction found in case of former.
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On the basis of HADDOCK score it is proposed that ZIKV NS5 first recruits SIAH2 and then
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uses the protein to degrade STAT2 thus antagonizing the responses of IFN. Similar study had been conducted for DENV NS5, the only other flavivirus implicated in STAT2 degradation
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(Aslam et al., 2015). Although Spondweni virus is more closely related to ZIKV yet it only binds
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weakly with STAT2 (Grant et al., 2016). Spondweni virus NS5 has been proposed to inhibit the
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other protein involved in JAK/STAT pathway.
Identification of amino acid residues of ZIKV NS5 involved in IFN antagonism can permit the
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design of mutant ZIKVs that are attenuated due to their failure to prevent IFN-stimulated
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signaling. It is possible to grow these modified viruses in IFN-deficient cell lines and use them as live attenuated vaccines provided that their safety and immunogenicity is confirmed (Grant et al.,
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2016). Moreover, identification of crucial amino acid residues of ZIKV NS5 would permit the design of therapeutic interventions to control the epidemic infection. One of the objectives of this study was to ascertain whether ZIKV NS5 facilitated the interaction of SIAH2 E3 ubiquitin ligase with STAT2, possibly leading to its degradation. Identification and characterization of potential PPIs between NS5, SIAH2 and STAT2 are required for understanding ZIKV mediated STAT2 degradation and may set the guidelines for therapies directed specifically to target NS5-SIAH2 and/or NS5-STAT2 interaction. Structural evaluation
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as well as PPIs were performed to get a comprehensive overview and physico-chemical features of interactions were determined. Our study provides a starting point for the development of ZIKV-specific therapeutic interventions. However, in-vivo and animal model studies needs to be carried out to further
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confirm these useful results.
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5 Acknowledgements We want to thank Dr Babar Aslam for providing us with models of SIAH2 and STAT2 proteins. We want to acknowledge Afreenish Hassan for help with GROMACS simulations. We also want to acknowledge the help of Taha Nemat from the Institute of Geographical Information System,
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NUST, in operating Linux system. This work was performed in supervision of Dr Amjad Ali and
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Dr Rehan Zafar and the administrative support of Atta-ur-Rahman School of Applied Biosciences. This research did not receive any specific grant from funding agencies in the public,
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commercial, or not-for-profit sectors.
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6 References
M
Abraham, M.J., Murtola, T., Schulz, R., Páll, S., Smith, J.C., Hess, B., Lindahl, E., 2015. GROMACS: High performance molecular simulations through multi-level parallelism from
ED
laptops to supercomputers. SoftwareX 1, 19–25. doi:10.1016/j.softx.2015.06.001
PT
Altschul, S.F., Koonin, E. V, 1998. Iterated profile searches with PSI-BLAST--a tool for
CE
discovery in protein databases. Trends Biochem. Sci. 23, 444–7. Ashour, J., Laurent-Rolle, M., Shi, P.-Y., García-Sastre, A., 2009. NS5 of dengue virus mediates
AC
STAT2 binding and degradation. J. Virol. 83, 5408–18. doi:10.1128/JVI.02188-08 Aslam, B., Ahmad, J., Ali, A., Paracha, R., Tareen, S., Khusro, S., Ahmad, T., Muhammad, S., Niazi, U., Azevedo, V., 2015. Structural modeling and analysis of dengue-mediated inhibition of interferon signaling pathway. funpecrp.com.br Genet. Mol. Res. Mol. Res 14, 4215–4237. doi:10.4238/2015.April.28.4 Benkert, P., Tosatto, S.C.E., Schomburg, D., 2008. QMEAN: A comprehensive scoring function
ACCEPTED MANUSCRIPT 21
for model quality assessment. Proteins Struct. Funct. Bioinforma. 71, 261–277. doi:10.1002/prot.21715 Borden, E.C., Sen, G.C., Uze, G., Silverman, R.H., Ransohoff, R.M., Foster, G.R., Stark, G.R., 2007. Interferons at age 50: past, current and future impact on biomedicine. Nat. Rev. Drug
IP
T
Discov. 6, 975–990. doi:10.1038/nrd2422
CR
Cao-Lormeau, V.-M., Blake, A., Mons, S., Lastère, S., Roche, C., Vanhomwegen, J., Dub, T., Baudouin, L., Teissier, A., Larre, P., Vial, A.-L., Decam, C., Choumet, V., Halstead, S.K.,
US
Willison, H.J., Musset, L., Manuguerra, J.-C., Despres, P., Fournier, E., Mallet, H.-P., Musso, D., Fontanet, A., Neil, J., Ghawché, F., 2016. Guillain-Barré Syndrome outbreak
AN
associated with Zika virus infection in French Polynesia: a case-control study. Lancet 387,
M
1531–1539. doi:10.1016/S0140-6736(16)00562-6
ED
Chen, H., Zhou, H.-X., 2005. Prediction of interface residues in protein-protein complexes by a consensus neural network method: Test against NMR data. Proteins Struct. Funct.
PT
Bioinforma. 61, 21–35. doi:10.1002/prot.20514
CE
Colovos, C., Yeates, T., 1993. ERRAT: an empirical atom-based method for validating protein
AC
structures. Protein Sci. 2, 1511--1519. de Vries, S.J., Bonvin, A.M.J.J., 2011. CPORT: A Consensus Interface Predictor and Its Performance in Prediction-Driven Docking with HADDOCK. PLoS One 6, e17695. doi:10.1371/journal.pone.0017695 de Vries, S.J., van Dijk, A.D.J., Bonvin, A.M.J.J., 2006. WHISCY: What information does surface conservation yield? Application to data-driven docking. Proteins Struct. Funct. Bioinforma. 63, 479–489. doi:10.1002/prot.20842
ACCEPTED MANUSCRIPT 22
DeLano, W.L., 2002. The PyMOL molecular graphics system. Diamond, M.S., 2009. Mechanisms of Evasion of the Type I Interferon Antiviral Response by Flaviviruses. J. Interf. Cytokine Res. 29, 521–530. doi:10.1089/jir.2009.0069
T
Duffy, M.R., Chen, T.-H., Hancock, W.T., Powers, A.M., Kool, J.L., Lanciotti, R.S., Pretrick,
IP
M., Marfel, M., Holzbauer, S., Dubray, C., Guillaumot, L., Griggs, A., Bel, M., Lambert,
CR
A.J., Laven, J., Kosoy, O., Panella, A., Biggerstaff, B.J., Fischer, M., Hayes, E.B., 2009. Zika Virus Outbreak on Yap Island, Federated States of Micronesia. N. Engl. J. Med. 360,
US
2536–2543. doi:10.1056/NEJMoa0805715
AN
Edgar, R.C., 2004. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–7. doi:10.1093/nar/gkh340
M
Enfissi, A., Codrington, J., Roosblad, J., Kazanji, M., Rousset, D., Lanciotti, R., Kosoy, O.,
ED
Laven, J., al., et, Cao-Lormeau, V., Musso, D., Oehler, E., Watrin, L., Larre, P., al., et,
PT
Zanluca, C., Melo, V. de, Mosimann, A., Santos, G. Dos, Santos, C. Dos, Luz, K., WHO, ECDC, 2016. Zika virus genome from the Americas. Lancet (London, England) 387, 227–8.
CE
doi:10.1016/S0140-6736(16)00003-9
AC
European Centre for Disease Prevention and Control, 2016. Zika Virus Infection [WWW Document]. URL http://ecdc.europa.eu (accessed 11.24.16). Grant, A., Ponia, S.S., Tripathi, S., Balasubramaniam, V., Miorin, L., Sourisseau, M., Schwarz, M.C., Sánchez-Seco, M.P., Evans, M.J., Best, S.M., García-Sastre, A., 2016. Zika Virus Targets Human STAT2 to Inhibit Type I Interferon Signaling, Cell Host & Microbe. doi:10.1016/j.chom.2016.05.009
ACCEPTED MANUSCRIPT 23
Hamel, R., Dejarnac, O., Wichit, S., Ekchariyawat, P., Neyret, A., Luplertlop, N., Perera-Lecoin, M., Surasombatpattana, P., Talignani, L., Thomas, F., Cao-Lormeau, V.-M., Choumet, V., Briant, L., Desprès, P., Amara, A., Yssel, H., Missé, D., 2015. Biology of Zika Virus Infection in Human Skin Cells. J. Virol. 89, 8880–96. doi:10.1128/JVI.00354-15
IP
T
Horner, S.M., Gale, M., 2009. Intracellular Innate Immune Cascades and Interferon Defenses That Control Hepatitis C Virus. J. Interf. Cytokine Res. 29, 489–498.
CR
doi:10.1089/jir.2009.0063
US
Hu, X., Chakravarty, S.D., Ivashkiv, L.B., 2008. Regulation of interferon and Toll-like receptor signaling during macrophage activation by opposing feedforward and feedback inhibition
AN
mechanisms. Immunol. Rev. 226, 41–56. doi:10.1111/j.1600-065X.2008.00707.x
M
Humphrey, W., Dalke, A., Schulten, K., 1996. VMD: Visual molecular dynamics. J. Mol. Graph.
ED
14, 33–38. doi:10.1016/0263-7855(96)00018-5
PT
Kaminski, G.A., Friesner*, R.A., Tirado-Rives, J., Jorgensen, W.L., 2001. Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate
CE
Quantum Chemical Calculations on Peptides†. doi:10.1021/JP003919D
AC
Kufareva, I., Budagyan, L., Raush, E., Totrov, M., Abagyan, R., 2007. PIER: Protein interface recognition for structural proteomics. Proteins Struct. Funct. Bioinforma. 67, 400–417. doi:10.1002/prot.21233 Kumar, S., Stecher, G., Tamura, K., 2016. MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets. Mol. Biol. Evol. 33, 1870–4. doi:10.1093/molbev/msw054 Kuno, G., Chang, G.J., Tsuchiya, K.R., Karabatsos, N., Cropp, C.B., 1998. Phylogeny of the
ACCEPTED MANUSCRIPT 24
genus Flavivirus. J. Virol. 72, 73–83. Kuno, G., Chang, G.-J.J., 2007. Full-length sequencing and genomic characterization of Bagaza, Kedougou, and Zika viruses. Arch. Virol. 152, 687–696. doi:10.1007/s00705-006-0903-z
T
Laskowski, R.A., 2001. PDBsum: summaries and analyses of PDB structures. Nucleic Acids
IP
Res. 29, 221–2. doi:10.1093/NAR/29.1.221
CR
Le Breton, M., Meyniel-Schicklin, L., Deloire, A., Coutard, B., Canard, B., de Lamballerie, X., Andre, P., Rabourdin-Combe, C., Lotteau, V., Davoust, N., 2011. Flavivirus NS3 and NS5
AN
11, 234. doi:10.1186/1471-2180-11-234
US
proteins interaction network: a high-throughput yeast two-hybrid screen. BMC Microbiol.
Liang, S., Zhang, C., Liu, S., Zhou, Y., 2006. Protein binding site prediction using an empirical
M
scoring function. Nucleic Acids Res. 34, 3698–707. doi:10.1093/nar/gkl454
ED
Longenecker, K.., Upadhyay, A.., Cyr, M., 2016. Crystal structure of Zika Virus NS5 protein. To
PT
Be Publ. doi:10.2210/PDB5TFR/PDB
CE
Lovell, S.C., Davis, I.W., Arendall, W.B., de Bakker, P.I.W., Word, J.M., Prisant, M.G., Richardson, J.S., Richardson, D.C., 2003. Structure validation by Cα geometry: ϕ,ψ and Cβ
AC
deviation. Proteins Struct. Funct. Bioinforma. 50, 437–450. doi:10.1002/prot.10286 Mairiang, D., Zhang, H., Sodja, A., Murali, T., Suriyaphol, P., Malasit, P., Limjindaporn, T., Finley, R.L., 2013. Identification of New Protein Interactions between Dengue Fever Virus and Its Hosts, Human and Mosquito. PLoS One 8, e53535. doi:10.1371/journal.pone.0053535 Mazzon, M., Jones, M., Davidson, A., Chain, B., Jacobs, M., 2009. Dengue virus NS5 inhibits
ACCEPTED MANUSCRIPT 25
interferon-alpha signaling by blocking signal transducer and activator of transcription 2 phosphorylation. J. Infect. Dis. 200, 1261–70. doi:10.1086/605847 Mlakar, J., Korva, M., Tul, N., Popović, M., Poljšak-Prijatelj, M., Mraz, J., Kolenc, M., Resman Rus, K., Vesnaver Vipotnik, T., Fabjan Vodušek, V., Vizjak, A., Pižem, J., Petrovec, M.,
IP
T
Avšič Županc, T., 2016. Zika Virus Associated with Microcephaly. N. Engl. J. Med. 374,
CR
951–958. doi:10.1056/NEJMoa1600651
Musso, D., Cao-Lormeau, V.M., Gubler, D.J., Organization, P.A.H., Duffy, M., Chen, T.,
US
Hancock, al., et, Cao-Lormeau, V., Musso, D., Musso, D., Nilles, E., Cao-Lormeau, V., Weaver, S., Lecuit, M., Nhan, T., Musso, D., Musso, D., Nilles, E., Cao-Lormeau, V., 2015.
AN
Zika virus: following the path of dengue and chikungunya? Lancet (London, England) 386,
M
243–4. doi:10.1016/S0140-6736(15)61273-9
ED
Neuvirth, H., Raz, R., Schreiber, G., 2004. ProMate: A Structure Based Prediction Program to Identify the Location of Protein–Protein Binding Sites. J. Mol. Biol. 338, 181–199.
PT
doi:10.1016/j.jmb.2004.02.040
CE
Pettersen, E.F., Goddard, T.D., Huang, C.C., Couch, G.S., Greenblatt, D.M., Meng, E.C., Ferrin,
AC
T.E., 2004. UCSF Chimera?A visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612. doi:10.1002/jcc.20084 Porollo, A., Meller, J., 2006. Prediction-based fingerprints of protein-protein interactions. Proteins Struct. Funct. Bioinforma. 66, 630–645. doi:10.1002/prot.21248 Saitou, N., Nei, M., 1987. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425. doi:10.1093/OXFORDJOURNALS.MOLBEV.A040454
ACCEPTED MANUSCRIPT 26
Tasaki, T., Mulder, L.C.F., Iwamatsu, A., Lee, M.J., Davydov, I. V, Varshavsky, A., Muesing, M., Kwon, Y.T., 2005. A family of mammalian E3 ubiquitin ligases that contain the UBR box motif and recognize N-degrons. Mol. Cell. Biol. 25, 7120–36. doi:10.1128/MCB.25.16.7120-7136.2005
IP
T
Turner, P., 2005. XMGRACE, Version 5.1. 19. Cent. Coast. Land-Margin Res. Oregon Grad.
CR
Inst. Sci. Technol. Beaverton, OR.
van Zundert, G.C.P., Rodrigues, J.P.G.L.M., Trellet, M., Schmitz, C., Kastritis, P.L., Karaca, E.,
US
Melquiond, A.S.J., van Dijk, M., de Vries, S.J., Bonvin, A.M.J.J., 2016. The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular
AN
Complexes, Journal of Molecular Biology. doi:10.1016/j.jmb.2015.09.014
M
Wassenaar, T.A., van Dijk, M., Loureiro-Ferreira, N., van der Schot, G., de Vries, S.J., Schmitz,
ED
C., van der Zwan, J., Boelens, R., Giachetti, A., Ferella, L., Rosato, A., Bertini, I., Herrmann, T., Jonker, H.R.A., Bagaria, A., Jaravine, V., Güntert, P., Schwalbe, H.,
PT
Vranken, W.F., Doreleijers, J.F., Vriend, G., Vuister, G.W., Franke, D., Kikhney, A.,
CE
Svergun, D.I., Fogh, R.H., Ionides, J., Laue, E.D., Spronk, C., Jurkša, S., Verlato, M., Badoer, S., Dal Pra, S., Mazzucato, M., Frizziero, E., Bonvin, A.M.J.J., 2012. WeNMR:
AC
Structural Biology on the Grid. J. Grid Comput. 10, 743–767. doi:10.1007/s10723-0129246-z
Waterhouse, A.M., Procter, J.B., Martin, D.M.A., Clamp, M., Barton, G.J., 2009. Jalview Version 2--a multiple sequence alignment editor and analysis workbench. Bioinformatics 25, 1189–91. doi:10.1093/bioinformatics/btp033 WHO | Zika virus [WWW Document], 2016. . WHO. URL
ACCEPTED MANUSCRIPT 27
http://www.who.int/mediacentre/factsheets/zika/en/ Zhu, Z., Chan, J.F.-W., Tee, K.-M., Choi, G.K.-Y., Lau, S.K.-P., Woo, P.C.-Y., Tse, H., Yuen, K.-Y., 2016. Comparative genomic analysis of pre-epidemic and epidemic Zika virus strains for virological factors potentially associated with the rapidly expanding epidemic.
IP
T
Emerg. Microbes Infect. 5, e22. doi:10.1038/emi.2016.48
CR
Zika Virus Response Updates from FDA, 2016.
Zuckerkandl, E., Pauling, L., 1965. Evolutionary divergence and convergence in proteins. Evol.
AC
CE
PT
ED
M
AN
US
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Highlights
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A detailed computational study was carried out to understand the dynamics of Zika virus NS5-mediated IFN antagonism. Our docking results suggest that NS5 protein of Zika virus recruits SIAH2 ligase for the proteasomal degradation of STAT2 protein that is crucial for Type I interferon production. Similar mechanism has been proposed previously for Dengue virus NS5-mediated STAT2 degradation. The residues of Zika virus NS5 involved in interaction with SIAH2 and STAT2 proteins were found to be conserved considerably among flaviviruses.
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Hamza Arshad Dar, Tahreem Zaheer, Rehan Zafar Paracha, Amjad Ali PII: DOI: Reference:
S1567-1348(17)30110-7 doi: 10.1016/j.meegid.2017.03.027 MEEGID 3107
To appear in:
Infection, Genetics and Evolution
Received date: Revised date: Accepted date:
2 January 2017 3 March 2017 25 March 2017
Please cite this article as: Hamza Arshad Dar, Tahreem Zaheer, Rehan Zafar Paracha, Amjad Ali , Structural analysis and insight into Zika virus Ns5 mediated interferon inhibition. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Meegid(2016), doi: 10.1016/j.meegid.2017.03.027
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STRUCTURAL ANALYSIS AND INSIGHT INTO ZIKA VIRUS NS5 MEDIATED INTERFERON INHIBITION Hamza Arshad Dara 1, Tahreem Zaheera 1 , Rehan Zafar Parachab, Amjad Alia*
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a. Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
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b. Research Center for Modeling and Simulation (RCMS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
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*Corresponding author Amjad Ali, Ph.D Email: [email protected]
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These authors contribute equally to this work.
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Abstract The Zika virus outbreak in 2015-2016 is the largest of its kind for which WHO declared a Public Health Emergency of International Concerns. No FDA approved drug is available for the treatment of the viral infection. The interaction of flavivirus NS5 protein with SIAH2 ubiquitin
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ligase has been previously known. NS5 of Zika virus has been implicated in the degradation of
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STAT2 protein, which activates interferon-stimulated antiviral activity. Based on our proposition
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that NS5 utilizes SIAH2-mediated proteasomal degradation of STAT2, an in-silico study was carried out to characterize the protein-protein interactions between NS5, SIAH2 and STAT2
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proteins. The aim of our study was to identify the amino acid residues of NS5 involved in IFN
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antagonism as well as to find the association between NS5, SIAH2 and STAT2 to predict the interaction pattern of these proteins. Analysis proposed that NS5 recruits SIAH2 for the
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ubiquitination-dependent degradation of STAT2. NS5 residues involved in interaction with
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SIAH2 and/or STAT2 were found to be mostly conserved across related flaviviruses. These are novel findings regarding the Zika virus and require confirmation through experimental
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approaches.
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Keywords: Zika virus; NS5 Docking; Interferon antagonism; STAT2 degradation;
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Phylogenetic analysis;
Abbreviations2
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ZIKV, Zika Virus; NS5, Non-structural protein 5; SIAH2, Seven in absentia homologue 2; STAT2, Signal Transducer and Activator of Transcription 2; MTase, Methyltransferase; IFN, Interferon; IRF9, Interferon Regulatory Factor 9; ISGF3, Interferon-Stimulated Gene Factor 3; ISGs, Interferon-Stimulated Genes; DENV, Dengue Virus; TLR, Toll-Like Receptor; PDB, Protein Data Bank; JEV, Japanese Encephalitis Virus; DOPE, Discrete Optimized Protein Energy; RMSD, Root-Mean-Square Deviation; VMD, Visual Molecular Dynamics; GROMACS, GROningen MAchine for Chemical Simulations; MD, Molecular Dynamics; OPLS-AA, Optimized potential for Liquid simulation-All Atom; PPI, Protein-Protein Interactions; RdRp, RNA dependent RNA Polymerase;
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1 Introduction Zika virus (ZIKV) belongs to the genus flavivirus of family flaviviridae and is most closely related to Spondweni virus (Kuno et al., 1998). Similar to dengue and yellow fever, ZIKV infection spreads through Aedes mosquitoes (Cao-Lormeau et al., 2016; Enfissi et al.,
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2016; European Centre for Disease Prevention and Control, 2016). The viral infection has been associated with Guillain-Barré syndrome and microcephaly in newborn babies (Cao-Lormeau et
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al., 2016; Mlakar et al., 2016). Deaths have been reported due to the viral infection in
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immunocompromised patients. Individuals infected with ZIKV are either asymptomatic or show only mild symptoms for 2-7 days (“WHO | Zika virus,” 2016). Symptoms are similar to dengue,
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and include fever, skin rashes, muscle and joint pain, conjunctivitis, headache and malaise
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(discomfort). There is no specific FDA approved drug available in the market to treat ZIKV
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infection (Zika Virus Response Updates from FDA, 2016). In 2007, the first outbreak of ZIKV occurred in the yap islands of Micronesia (Duffy et al.,
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2009). Before that, there were no reports regarding the transmission of the virus outside Asia and
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Africa. During October 2013- April 2014, French Polynesia experienced the worst outbreak of ZIKV, with increasing cases of Guillain-Barré syndrome also being actively reported (Cao-
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Lormeau et al., 2016). The epidemiological evidence prompted the scientists to determine the role of ZIKV infection, if any, in the development of Guillain-Barré syndrome. Before the French Polynesia outbreak, the virus was believed to cause only mild diseases (Musso et al., 2015). ZIKV encodes polyprotein, which is later cleaved into three structural proteins and seven nonstructural proteins (Kuno and Chang, 2007). NS5 (Non-structural protein 5) is the largest and most conserved protein of ZIKV (Zhu et al., 2016). The N-terminal of NS5 contains a putative
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MTase (methyltransferase) domain while its C-terminal is involved in the crucial RdRp (RNA dependent RNA polymerase) activity of the virus. Type I Interferons (IFN- α and β) functions to control viral infection by the regulation of immune responses. IFN- α and β transmit signals through an autocrine and paracrine approach by
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interacting with their cognate common heterodimeric IFN-α/β receptor present on the cell surface, thus ultimately leading to the activation of the JAK/STAT pathway (Horner and Gale,
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2009). Phosphorylation of STAT (Signal Transducer and Activator of Transcription) 1 and
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STAT2 proteins and their subsequent association with IRF9 (Interferon Regulatory Factor 9) results in the formation of ISGF-3 (Interferon-stimulated Gene Factor-3) transcription factor
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complex. The complex later binds to IFN response elements to enhance the gene expression of
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ISGs (IFN-stimulated genes).
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Like other viruses, flaviviruses have developed various immune evasion strategies to counter the Type I IFN antiviral responses. Especially, flaviviruses have been found to antagonize IFN-
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signaling activity (Diamond, 2009). It has been reported that DENV NS5 leads to proteasomal
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degradation of STAT2 protein and is crucial for the potent IFN signaling antagonism (Ashour et al., 2009). Similarly, experimental studies have confirmed that ZIKV NS5 is required for the
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proteasomal degradation of the STAT2 in humans (Grant et al., 2016). The effect of ZIKV NS5 on the JAK/STAT pathway is shown in the Fig 1. Fig 1. Pathway of ZIKV NS5-dependent IFN antagonism. Normally, Type I IFNs (IFN- α and β) interact with a heterodimeric IFN receptor to trigger the activation of STAT1 and STAT2 proteins (Horner and Gale, 2009). Phosphorylation of both STAT proteins, followed by their association with IRF9 results in the formation of ISGF-3 transcription factor complex. The complex later binds to IFN response elements inside the nucleus to enhance the gene expression
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of IFN-stimulated genes. ZIKV NS5 binds to STAT2, ultimately leading towards its degradation (Grant et al., 2016). Due to the viral infection, Toll-like Receptor (TLR) 3 is also activated (Hamel et al., 2015). Through TRIF and TRAM proteins, TLR3 leads to the activation of IRF3 transcription factor (Borden et al., 2007). IRF3 later binds to specific DNA sequences inside the
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nucleus to jump-start IFN transcription. SIAH (Seven in Absentia Homolog) 2 [Uniprot ID: O43255 (human origin)] is an E3 ubiquitin
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ligase that mediates ubiquitination and subsequent proteasomal degradation of target proteins,
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such as those involved in the regulation of transcription (Hu et al., 2008). Interaction of flavivirus NS3 and NS5 proteins with SIAH2 has already been reported, later confirmed by
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Mairiang et al (Le Breton et al., 2011; Mairiang et al., 2013). In the present study, a detailed
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computational analysis was carried out to find the interaction pattern between NS5, SIAH2 and STAT2 proteins. The study was helpful to gather an in-depth understanding of the interface
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residues of each protein involved in interaction as well as to predict the underlying mechanism of
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NS5 mediated IFN antagonism and the subsequent propagation and development of ZIKV
et al., 2015).
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pathogenesis. Similar in silico work had been conducted in case of DENV by Aslam et al (Aslam
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The crystal structure of ZIKV NS5 protein has been resolved. Therefore, it was used to predict the PPIs (Protein-Protein Interactions) and to analyze the unique features of the interacting residues. According to our results, NS5 may first interact with the host SIAH2, which can later proceed to bind STAT2. These findings demonstrate that ZIKV hijacks the host proteins to destroy specific host cellular proteins described in IFN-stimulated signaling pathways, which are crucial for antiviral activity of IFN.
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2 Material and methods 2.1 Protein evaluation The crystal structure of ZIKV NS5 protein was extracted from PDB ID 5TFR and checked for
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different parameters (Longenecker et al., 2016). Firstly, the structure was assessed by using
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ERRAT score, which provides information about the residues that can be rejected above a
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certain threshold, i.e. having error scale greater than 95% (Colovos and Yeates, 1993). The Ramachandran plots of the structures were produced using RAMPAGE (Lovell et al., 2003).
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RAMPAGE also provides additional information such as the percentage of amino acids residing
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in favorable region, allowed region and outlier regions. The structures having amino acids predominantly in the favorable (and allowed region) were identified. Finally, Qmean score of the
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structure was determined (Benkert et al., 2008).
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Secondary structure prediction was performed on renumbered ZIKV NS5 structure and topology diagram was generated by using PDBsum (Laskowski, 2001). The result was visualized
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accordingly.
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2.2 Energy minimization
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For the energy minimization of the crystal structure of NS5, GROMACS (GROningen MAchine for Chemical Simulations) was used (Abraham et al., 2015). The protein was subjected to molecular simulations to mimic the biological environment faced by the protein. This exercise was required for the stability of protein. GROMACS, a command based Molecular Dynamics (MD) simulation program, was used to stabilize the structure of NS5. The PDB file was converted to gro file in order to maintain the default force field compliant topology. OPLS-AA (Optimized potential for Liquid simulation)
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force field was selected (Kaminski et al., 2001). The protein was restrained in a rhombic dodecahedron cubic box as it can accommodate the solvent molecule. The protein was placed in the center of the cube having a 1 nm distance from the edge of the cube so that the periodic image of the protein was 2 nm apart. The solvent used to simulate protein was water, spc216.gro,
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it is position restrained having a force constant (kpr) of 1000 kJ mol-1 nm-2. The charge on the
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protein was predicted to be +12. To remove the charge on protein a tool present in GROMACS
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was used, named as genion. Ions.mdp file was generated to remove the charge on the protein and the cut-off scheme used was Verlet and electrostatic forces were applied. 12 chloride ions were
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added at the place of solvent molecule. Energy minimization was carried out and at 1600 step the
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final energy minimized structure was obtained. The graph of the stages of energy minimization was obtained and analyzed using Xmgrace (Turner, 2005). NVT isothermal-isochoric ensemble
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equilibration was used at 100ps to stabilize the temperature of protein up to a certain value.
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During NVT equilibration, velocity was generated so that a number of simulation can run at a variety of initial speeds. V-rescale was used, Temperature was then analyzed from temperature
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graph. Pressure and densities of stabilized NS5 were then determined using NPT ensemble
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containing almost the same parameters, the process constituted 50000 steps. The equilibrated stabilized NS5 was then MD simulated at 1ns. RMSD of backbone of energy minimized and
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RMSD of initial structure were predicted and results were generated in form of graphs. To predict the compactness of structure, radius of gyration was also predicted.
2.3 Determination of protein-protein interactions The structures were fed into guru level interface HADDOCK (High Ambiguity Driven proteinprotein DOCKing) server using default settings (van Zundert et al., 2016; Wassenaar et al., 2012). Guru level interface is an advanced HADDOCK interface that permits the identification
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of flexible regions from the simulation perspective, as opposed to the easy level interface. The top cluster was refined for better orientation, leading to improved HADDOCK scores. PDBsum was used for intermolecular structural analysis (Laskowski, 2001). CPORT (Consensus Prediction Of Interface Residues in Transient complexes) was used for the
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prediction of active and passive residues involved in interaction (de Vries and Bonvin, 2011). CPORT uses a combination of six methods to accomplish the task (Chen and Zhou, 2005; de
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Vries et al., 2006; Kufareva et al., 2007; Liang et al., 2006; Neuvirth et al., 2004; Porollo and
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Meller, 2006). In case of SIAH2, the structure was directly submitted to CPORT to predict active residues and passive residues. HADDOCK requires comma spaced residues for its work. In case
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of STAT2, the structure was prepared in Chimera and then submitted at CPORT for the
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prediction of residues (Pettersen et al., 2004). In case of ZIKV NS5, the energy minimized
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structure was renumbered and then submitted at CPORT.
2.4 Phylogenetic analysis
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PSI-BLAST was performed for the sequence of ZIKV NS5 structure under default conditions
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(Altschul and Koonin, 1998). The sequences having greater than 60% identity were retrieved and multiple aligned. Phylogenetic tree was constructed using MEGA7 to infer the evolutionary
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relationships between the sequences (Kumar et al., 2016). VMD (Visual Molecular Dynamics) was used for the visualization of the NS5 protein and a stable frame was picked (Humphrey et al., 1996). Pymol and chimera were used to visualize the figures complexes generated by HADDOCK (DeLano, 2002; Pettersen et al., 2004). PDBsum was used for the analysis of intramolecular and intermolecular attractions.
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3 Results 3.1 Crystal structure evaluation The crystal structure was evaluated using different tools. ERRAT score was evaluated and the
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overall quality score was found to be quite significant i.e. 94.96. Similarly, the Ramachandran
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plot obtained through RAMPAGE server predicted that 854 residues were present in the favored
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region, followed by 24 in the allowed region and only 2 in the outlier region. Hence, the orientation of the protein was found to be satisfactory. Moreover, the QMean score was found to
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be significant i.e. 0.72. Hence, after checking the different parameters, the structure was
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subjected to molecular simulations later.
The secondary structure prediction was performed as visualized in Fig 2. According to analysis,
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the protein having 882 amino acids contains 42 helices, 7 beta sheets, 60 beta turns and 9 gamma
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turns. The topology of the protein was generated and analyzed as shown in Fig 3.
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Fig 2. Secondary structure prediction of ZIKV NS5.
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Fig 3. Topology diagram of ZIKV NS5.
3.2 Protein structure preparation
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The mass of the protein calculated after applying OPLS-AA was 101010.794 amu. The volume of the box enclosing protein was 1934.90 nm3 while the initial density was 999.639 g/l. Initially, 58664 water molecules were added into the system. The start terminus had Gly1-NH3+ charge and the end terminus contain Leu-903 COO- charge. The total charge on protein was +12 so 12 chloride negative ions were added in the place of solvent molecules. After this refinement, the number of water molecules left were 58652. The final potential energy of ZIKV NS5 protein was
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found to be -3.3394580e+6 KJ/mol. According to potential energy and other graphs (shown in S1 Fig), the protein was stabilized and energy of the protein was lowered by a significant value and its average value was -3.24231e+6 KJ/mol with a total drift of -331697 KJ/mol. The temperature of the stabilized structure was then analyzed from graph and predicted to be
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299.737K. The average pressure after 50000 steps of NPT was -2.64314 bar while the average
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density of the protein was 1023.19 kg/m3.
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3.3 Analysis of interactions between ZIKV NS5 and SIAH2
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Modeled structure of SIAH2 was obtained from Aslam et al. with permission and was docked with crystal structure of ZIKV NS5 using Guru level interface of HADDOCK (Aslam et al.,
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2015).
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Guru level requires active and passive residues of the proteins, so we used CPORT to predict the
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active and passive residues of SIAH2 and NS5. CPORT results are shown in S2 Fig. Default conditions were used in Guru level interface to dock ZIKV NS5 with SIAH2. 33.5 % water
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refined 67 structures were generated that were clustered in 8 clusters. The top most cluster was
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used having HADDOCK score 15.8 +/- 14.1, the Z-score, Vender’s Wall energy and other scores were observed. The HADDOCK score was refined and statistical analysis was conducted, as
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shown in S3 Fig. The chains were visualized and interacting residues were focused (Fig 4). Fig 4. (A) Figure showing interacting chains between NS5 and SIAH2. (B) Another diagram from different orientation displaying all the interacting residues between both chains. The refined structure was then analyzed for PPI in PDBsum as shown in Fig 5. According to the results, collectively 30 interface residues of ZIKV NS5 were shown to interact with 30 residues
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of SIAH2. The interface area (Å2) of NS5 was found to be 1606, while that of SIAH2 was found to be 1614. Fig 5. (A) Figure showing all interacting residues between NS5 and SIAH2. The number of H-bond lines between any two residues indicates the number of potential hydrogen bonds
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between them. For non-bonded contacts, which can be plentiful, the width of the striped line is proportional to the number of atomic contacts. Here, blue color is used to label hydrogen bonds.
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Yellow color indicates disulphide bonds and red color shows salt bridges. While inter-spaced
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lines indicate non-bonded contacts. (B) Interacting residues between NS5 and SIAH2, focused view.
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3.3.1 Hydrogen bonds
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Structural analysis revealed the presence of 11 hydrogen bonds between ZIKV NS5 and SIAH2.
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Residues Tyr2541, Lys2544 (part of helix 2) and Lys2545 of NS5 were predicted to form a hydrogen bond with Asp146, with distance being 2.65 Å, 2.74 and 2.67 Å, respectively. Arg2557
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(part of helix 3) of ZIKV NS5 was predicted to interact with Thr45, distance being 2.82 Å.
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Similarly, Lys2561 was predicted to interact with two amino acids i.e. Asp46 and Asp54, with distance 2.66 Å each. Asp2562 was found to form hydrogen bond with amino acid Lys30,
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distance being 2.67 Å. Arg2843 (part of helix 12) was found to form two hydrogen bonds with amino acid Gly63, distance being 2.81 Å and 2.95 Å. Asp2850 was predicted to form hydrogen bonds with two amino acids Cys61 and Phe62, with distance 2.91 and 2.79 Å respectively. Surprisingly, none of the residues from 2851 onwards were predicted to be involved in hydrogen bonding.
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3.3.2 Salt bridges Six salt bridges have been observed between ZIKV NS5 and SIAH2. Lys2544 (part of helix 2) and Lys2545 was shown to be linked to Asp146 with distance 2.74 Å and 2.64 Å. Similarly, Lys2561 was predicted to form salt bridges with Asp46 and Asp54 at 2.66 Å distance each.
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Asp2562 was observed to form salt bridge with Lys30 at 2.67 Å distance while Lys2847 (part of
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helix 113) was observed to form salt bridge with Glu96 at 3.60 Å distance.
3.3.3 Non-bonded contacts
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Additionally, the total number of non-bonded contacts between ZIKV NS5 and SIAH2 was
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found to be 149. However, no Disulphide bonds were predicted.
3.4 Analysis of interactions between ZIKV NS5 and STAT2
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Modeled structure of STAT2 was obtained from Aslam et al. with permission and was docked
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with prepared structure of ZIKV NS5 using Guru level interface of HADDOCK (Aslam et al., 2015). Guru level requires active and passive residues of the proteins, so we used CPORT to
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predict the active and passive residues of STAT2 and NS5. Default conditions were used in Guru
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level interface to dock ZIKV NS5 with STAT2. 16.0% water refined 32 structures were generated in 4 cluster. The top most cluster was used for further analysis. The cluster had
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HADDOCK score 65.9 +/- 5.7, the Z-score, Vender’s Wall energy and other parameters were observed. The HADDOCK score was refined and analysis was conducted, as shown in S4 Fig. The chains were visualized and interacting residues were noted (Fig 6). Fig 6. (A) Figure showing interacting chains between NS5 and STAT2. (B) Another figure from different orientation displaying all the interacting residues between both chains.
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The refined docked structure was then analyzed for PPI in PDB sum as shown in Fig 7. According to the results collectively 29 interface residues of ZIKV NS5 were shown to interact with 24 interface residues of STAT2. The Interface area (Å2) of NS5 was found to be 1383 while that of STAT2 was found to be 1394.
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Fig 7. (A) Figure visualizing all interacting residues between NS5 and STAT2. The number of H-bond lines between any two residues indicates the number of potential hydrogen bonds
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between them. For non-bonded contacts, the width of the striped line is proportional to the
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number of atomic contacts. Here, blue color is used to label hydrogen bonds. Yellow color indicates disulphide bonds and red color shows salt bridges. While inter-spaced lines indicate
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non-bonded contacts. (B) Interacting residues between NS5 and STAT2, focused view.
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3.4.1 Hydrogen bonds
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15 hydrogen bonds were predicted between NS5 and STAT2. Residues Lys2544 (part of helix 2) and Arg2729 were predicted to form a hydrogen bond with Asp52 at a distance of 2.65 Å and
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3.02 Å respectively. Gly2625 was observed to form hydrogen bond with Gln42, with distance
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being 2.93 Å. Glu2665 formed a hydrogen bond with Lys55 at a distance of 2.69 Å. Two residues Asn2839 and Gly2840 (part of helix 12) were found to make hydrogen bond with
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Asp77 at a distance of 2.76 Å. Moreover, Ser3259 (part of beta sheet D) was also predicted to form hydrogen bond with Asp77 at a distance of 2.68 Å. Arg2843 (part of helix 12) was found to form two hydrogen bonds with Gln33 at 2.77 Å and 2.90 Å. Lys2847 (part of helix 13) was predicted to form hydrogen bond with Glu5 and Gln8 at 2.69 Å and 3.08 Å respectively. Likewise, Gly3263 formed hydrogen bond with Gln76 at 2.78 Å while Ile3266 (part of helix 36) was predicted to make hydrogen bond with Glu79, with distance being 2.75 Å. Arg3372 (part of helix 41) formed two hydrogen bonds with Gln126 at a distance of 2.98 Å and 3.08 Å.
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3.4.2 Salt bridge Similar to SIAH2, six salt bridges were predicted between ZIKV NS5 and STAT2. Lys2544 (part of helix 2) was predicted to form a salt bridge with Asp52 at a distance of 2.65 Å. Similarly, Glu2627 and Glu2665 were predicted to form salt bridge with His62 and Lys55 at
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distance of 3.34 and 2.69 Å respectively. Likewise, Arg2729 formed salt bridge with Asp52,
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with distance being 3.02 Å. Lys2847 (part of helix 13) was predicted to form salt bridge with
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Glu5 with distance 2.69 Å while Arg3372 (part of helix 41) was predicted to form it with Glu131
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at a distance of 3.43 Å.
3.4.3 Non-bonded contacts
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Additionally, 161 total number of non-bonded contacts were observed between NS5 and STAT2.
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However, no disulphide bonds were predicted similar to SIAH2 in our in-silico analysis.
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Phylogenetic analysis
Multiple sequence alignment was generated using the MEGA7 (Kumar et al., 2016) built-in
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MUSCLE program (Edgar, 2004) to analyze the conservation of the various interface residues of
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the ZIKV NS5 involved in interaction with SIAH2 and/or STAT2 proteins. Sequence alignment in combination with evolutionary phylogenetic analysis (Fig 8 and Fig 9) confirmed that the
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predicted residues were mostly conserved across the different flaviviral sequences as shown. Our own ZIKV NS5 residue-level conservation analysis suggested that these interface residues were conserved significantly within ZIKV, with change in a residue often maintaining the physicochemical property of the amino acids. Fig 8. Multiple sequence alignment of flavivirus NS5 sequences. Multiple sequence alignment was generated to analyze the conservation of interface residues across related flaviviral NS5
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sequences. The NS5 sequences were multiple-aligned using MEGA7 built-in MUSCLE tool (Edgar, 2004; Kumar et al., 2016). The residues were renumbered as per the genomic position of the query sequence. Here, only the interface residues are shown as observed in the alignment. Jalview was used to show the alignment in a better way (Waterhouse et al., 2009).
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Fig 9. Evolutionary relationships of NS5 sequences. The evolutionary history was inferred using the Neighbor-Joining method (Saitou and Nei, 1987). The optimal tree with the sum of
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branch length = 6.17891434 is shown. The tree is drawn to scale, with branch lengths in the same
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units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Poisson correction method and are in the units of the number
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of amino acid substitutions per site (Zuckerkandl and Pauling, 1965). The analysis involved 64
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amino acid sequences. All positions containing gaps and missing data were eliminated. There were a total of 862 positions in the final dataset. Evolutionary analyses were conducted in
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MEGA7 (Kumar et al., 2016). The query sequence is highlighted in red while the most related
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sequences are highlighted in pink. The other sequences showing comparatively more variation in
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their interacting residues are shown in black.
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4 Discussion
Flaviviruses are similar viruses reported to have similar features that can be exploited. ZIKV is a member of the flavivirus most closely related to Spondweni virus. However, unlike Spondweni virus NS5 and like DENV, ZIKV NS5 expression led to the decrease in STAT2 levels in human cell lines, thus implying that it binds and subsequently degrades STAT2 protein (Grant et al., 2016). Moreover, both ZIKV and DENV-mediated IFN antagonism behavior was
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observed in human but not in mice, suggesting a host-specific infection pattern of both viruses. However, unlike DENV, ZIKV does not appear to require E3 ubiquitin ligase URB4 to degrade STAT2. Moreover, experimental evidence shows that proteolytic processing of the N terminal of DENV NS5 is required for DENV-mediated STAT2 degradation (Ashour et al., 2009). In
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contrast, ZIKV NS5 does not require an authentic N terminus to degrade STAT2. It is believed
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that UBR4 functions specifically in the degradation of proteins containing destabilizing N
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termini or N-degrons, hence it is necessary for DENV-mediated IFN-inhibition (Tasaki et al.,
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2005).
Experiments have shown that the RdRP domain of DENV NS5 is capable of inhibiting IFN-α
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signaling alone, suggesting that the residues responsible for the inhibitory activity are present
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within this domain (Mazzon et al., 2009). However, less inhibition was observed in cells expressing the RdRp domain alone compared with full-length DENV NS5. Nevertheless,
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according to our in-silico analysis, only a few interacting residues were found to be in the MTase
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domain; Hence, majority of the residues of the protein were present in the RdRP domain, thus
protein.
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suggesting that the RdRP domain of ZIKV NS5 is involved majorly in interaction with STAT2
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The crystal structure was analyzed to check and evaluate different parameters. ERRAT score helped determine the quality of the overall structures. ERRAT server provided an overview of the different residues of NS5 that can be rejected above 95% error scale. The Ramachandran plots assessed by RAMPAGE provided further information about the bond angles and orientation of protein molecules within the permitted constraints due to peptide bond. Moreover, it provided crucial information such as the percentage of amino acid residues in the favorable, allowed and outlier regions. QMean score of structure is based on multiple parameters, including Carbon beta
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interaction energy, All-atom pairwise energy, Solvation energy, Torsion angle energy, Secondary structure agreement and Solvent accessibility agreement. Structural analysis of the crystal structure revealed that it had bond angles, dihedrals, lengths and interaction energies within the required threshold range; Moreover, the structure was stable as atomic collisions were not
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present. Simulations were carried out to prepare the NS5 protein before docking. During energy minimization in GROMACS an important parameter emtol = 1000 was used in
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the mdp file so that the final structure obtained is stable and its force is not greater than 1000 kJ
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mol-1 nm-1. To equilibrate the ions and solvent molecules around the protein, the protein was simulated at the required temperature in order to properly orient the protein in the solvent
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molecule.
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Guru level interface in HADDOCK was used to get more refined results in terms of flexibility
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and stability of protein complexes. Default setting of histidine residues, radius of gyration and distance of atoms were selected to generate the final clusters.
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HADDOCK system provides meaningful results based on the integration of the biochemical
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and/or biophysical interaction data (Liang et al., 2006) in addition to ab initio PPIs. HADDOCK uses the docking protocol, which incorporates useful features such as flexibility of specified
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protein regions, solvated docking, and use of modified amino acids. Refinements were carried out to further characterize the PPIs. Notably, the buried surface area 3241.7 +/- 269.9 was further increased to 3290.5 +/- 37.8 in case of NS5-SIAH2 docking while RMSD from the overall lowest-energy structure declined from 15.3 +/- 0.1 to 0.2 +/- 0.1. Overall, HADDOCK score improved from 15.8 +/- 14.1 to -195.8 +/- 1.9, thus implying a significant interaction between NS5 and SIAH2 proteins.
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Comparatively, Buried Surface Area decreased from 3065.6 +/- 119.1 to 2861.4 +/- 40.3 while the RMSD from the overall lowest-energy structure declined from 37.6 +/- 0.2 to 0.3 +/- 0.2 after refinements in case of NS5-STAT2 interactions. Overall, HADDOCK score improved significantly i.e. from 65.9 +/- 5.7 to -138.8 +/- 3.9. Based on the docking results, a logical
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conclusion was drawn that ZIKV NS5 interacted strongly with both SIAH2 and STAT2 proteins,
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with more significant interaction found in case of former.
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On the basis of HADDOCK score it is proposed that ZIKV NS5 first recruits SIAH2 and then
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uses the protein to degrade STAT2 thus antagonizing the responses of IFN. Similar study had been conducted for DENV NS5, the only other flavivirus implicated in STAT2 degradation
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(Aslam et al., 2015). Although Spondweni virus is more closely related to ZIKV yet it only binds
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weakly with STAT2 (Grant et al., 2016). Spondweni virus NS5 has been proposed to inhibit the
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other protein involved in JAK/STAT pathway.
Identification of amino acid residues of ZIKV NS5 involved in IFN antagonism can permit the
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design of mutant ZIKVs that are attenuated due to their failure to prevent IFN-stimulated
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signaling. It is possible to grow these modified viruses in IFN-deficient cell lines and use them as live attenuated vaccines provided that their safety and immunogenicity is confirmed (Grant et al.,
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2016). Moreover, identification of crucial amino acid residues of ZIKV NS5 would permit the design of therapeutic interventions to control the epidemic infection. One of the objectives of this study was to ascertain whether ZIKV NS5 facilitated the interaction of SIAH2 E3 ubiquitin ligase with STAT2, possibly leading to its degradation. Identification and characterization of potential PPIs between NS5, SIAH2 and STAT2 are required for understanding ZIKV mediated STAT2 degradation and may set the guidelines for therapies directed specifically to target NS5-SIAH2 and/or NS5-STAT2 interaction. Structural evaluation
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as well as PPIs were performed to get a comprehensive overview and physico-chemical features of interactions were determined. Our study provides a starting point for the development of ZIKV-specific therapeutic interventions. However, in-vivo and animal model studies needs to be carried out to further
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confirm these useful results.
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5 Acknowledgements We want to thank Dr Babar Aslam for providing us with models of SIAH2 and STAT2 proteins. We want to acknowledge Afreenish Hassan for help with GROMACS simulations. We also want to acknowledge the help of Taha Nemat from the Institute of Geographical Information System,
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NUST, in operating Linux system. This work was performed in supervision of Dr Amjad Ali and
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Dr Rehan Zafar and the administrative support of Atta-ur-Rahman School of Applied Biosciences. This research did not receive any specific grant from funding agencies in the public,
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commercial, or not-for-profit sectors.
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6 References
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Abraham, M.J., Murtola, T., Schulz, R., Páll, S., Smith, J.C., Hess, B., Lindahl, E., 2015. GROMACS: High performance molecular simulations through multi-level parallelism from
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laptops to supercomputers. SoftwareX 1, 19–25. doi:10.1016/j.softx.2015.06.001
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Altschul, S.F., Koonin, E. V, 1998. Iterated profile searches with PSI-BLAST--a tool for
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discovery in protein databases. Trends Biochem. Sci. 23, 444–7. Ashour, J., Laurent-Rolle, M., Shi, P.-Y., García-Sastre, A., 2009. NS5 of dengue virus mediates
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STAT2 binding and degradation. J. Virol. 83, 5408–18. doi:10.1128/JVI.02188-08 Aslam, B., Ahmad, J., Ali, A., Paracha, R., Tareen, S., Khusro, S., Ahmad, T., Muhammad, S., Niazi, U., Azevedo, V., 2015. Structural modeling and analysis of dengue-mediated inhibition of interferon signaling pathway. funpecrp.com.br Genet. Mol. Res. Mol. Res 14, 4215–4237. doi:10.4238/2015.April.28.4 Benkert, P., Tosatto, S.C.E., Schomburg, D., 2008. QMEAN: A comprehensive scoring function
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for model quality assessment. Proteins Struct. Funct. Bioinforma. 71, 261–277. doi:10.1002/prot.21715 Borden, E.C., Sen, G.C., Uze, G., Silverman, R.H., Ransohoff, R.M., Foster, G.R., Stark, G.R., 2007. Interferons at age 50: past, current and future impact on biomedicine. Nat. Rev. Drug
IP
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Discov. 6, 975–990. doi:10.1038/nrd2422
CR
Cao-Lormeau, V.-M., Blake, A., Mons, S., Lastère, S., Roche, C., Vanhomwegen, J., Dub, T., Baudouin, L., Teissier, A., Larre, P., Vial, A.-L., Decam, C., Choumet, V., Halstead, S.K.,
US
Willison, H.J., Musset, L., Manuguerra, J.-C., Despres, P., Fournier, E., Mallet, H.-P., Musso, D., Fontanet, A., Neil, J., Ghawché, F., 2016. Guillain-Barré Syndrome outbreak
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associated with Zika virus infection in French Polynesia: a case-control study. Lancet 387,
M
1531–1539. doi:10.1016/S0140-6736(16)00562-6
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Chen, H., Zhou, H.-X., 2005. Prediction of interface residues in protein-protein complexes by a consensus neural network method: Test against NMR data. Proteins Struct. Funct.
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Bioinforma. 61, 21–35. doi:10.1002/prot.20514
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Colovos, C., Yeates, T., 1993. ERRAT: an empirical atom-based method for validating protein
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structures. Protein Sci. 2, 1511--1519. de Vries, S.J., Bonvin, A.M.J.J., 2011. CPORT: A Consensus Interface Predictor and Its Performance in Prediction-Driven Docking with HADDOCK. PLoS One 6, e17695. doi:10.1371/journal.pone.0017695 de Vries, S.J., van Dijk, A.D.J., Bonvin, A.M.J.J., 2006. WHISCY: What information does surface conservation yield? Application to data-driven docking. Proteins Struct. Funct. Bioinforma. 63, 479–489. doi:10.1002/prot.20842
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DeLano, W.L., 2002. The PyMOL molecular graphics system. Diamond, M.S., 2009. Mechanisms of Evasion of the Type I Interferon Antiviral Response by Flaviviruses. J. Interf. Cytokine Res. 29, 521–530. doi:10.1089/jir.2009.0069
T
Duffy, M.R., Chen, T.-H., Hancock, W.T., Powers, A.M., Kool, J.L., Lanciotti, R.S., Pretrick,
IP
M., Marfel, M., Holzbauer, S., Dubray, C., Guillaumot, L., Griggs, A., Bel, M., Lambert,
CR
A.J., Laven, J., Kosoy, O., Panella, A., Biggerstaff, B.J., Fischer, M., Hayes, E.B., 2009. Zika Virus Outbreak on Yap Island, Federated States of Micronesia. N. Engl. J. Med. 360,
US
2536–2543. doi:10.1056/NEJMoa0805715
AN
Edgar, R.C., 2004. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–7. doi:10.1093/nar/gkh340
M
Enfissi, A., Codrington, J., Roosblad, J., Kazanji, M., Rousset, D., Lanciotti, R., Kosoy, O.,
ED
Laven, J., al., et, Cao-Lormeau, V., Musso, D., Oehler, E., Watrin, L., Larre, P., al., et,
PT
Zanluca, C., Melo, V. de, Mosimann, A., Santos, G. Dos, Santos, C. Dos, Luz, K., WHO, ECDC, 2016. Zika virus genome from the Americas. Lancet (London, England) 387, 227–8.
CE
doi:10.1016/S0140-6736(16)00003-9
AC
European Centre for Disease Prevention and Control, 2016. Zika Virus Infection [WWW Document]. URL http://ecdc.europa.eu (accessed 11.24.16). Grant, A., Ponia, S.S., Tripathi, S., Balasubramaniam, V., Miorin, L., Sourisseau, M., Schwarz, M.C., Sánchez-Seco, M.P., Evans, M.J., Best, S.M., García-Sastre, A., 2016. Zika Virus Targets Human STAT2 to Inhibit Type I Interferon Signaling, Cell Host & Microbe. doi:10.1016/j.chom.2016.05.009
ACCEPTED MANUSCRIPT 23
Hamel, R., Dejarnac, O., Wichit, S., Ekchariyawat, P., Neyret, A., Luplertlop, N., Perera-Lecoin, M., Surasombatpattana, P., Talignani, L., Thomas, F., Cao-Lormeau, V.-M., Choumet, V., Briant, L., Desprès, P., Amara, A., Yssel, H., Missé, D., 2015. Biology of Zika Virus Infection in Human Skin Cells. J. Virol. 89, 8880–96. doi:10.1128/JVI.00354-15
IP
T
Horner, S.M., Gale, M., 2009. Intracellular Innate Immune Cascades and Interferon Defenses That Control Hepatitis C Virus. J. Interf. Cytokine Res. 29, 489–498.
CR
doi:10.1089/jir.2009.0063
US
Hu, X., Chakravarty, S.D., Ivashkiv, L.B., 2008. Regulation of interferon and Toll-like receptor signaling during macrophage activation by opposing feedforward and feedback inhibition
AN
mechanisms. Immunol. Rev. 226, 41–56. doi:10.1111/j.1600-065X.2008.00707.x
M
Humphrey, W., Dalke, A., Schulten, K., 1996. VMD: Visual molecular dynamics. J. Mol. Graph.
ED
14, 33–38. doi:10.1016/0263-7855(96)00018-5
PT
Kaminski, G.A., Friesner*, R.A., Tirado-Rives, J., Jorgensen, W.L., 2001. Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate
CE
Quantum Chemical Calculations on Peptides†. doi:10.1021/JP003919D
AC
Kufareva, I., Budagyan, L., Raush, E., Totrov, M., Abagyan, R., 2007. PIER: Protein interface recognition for structural proteomics. Proteins Struct. Funct. Bioinforma. 67, 400–417. doi:10.1002/prot.21233 Kumar, S., Stecher, G., Tamura, K., 2016. MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets. Mol. Biol. Evol. 33, 1870–4. doi:10.1093/molbev/msw054 Kuno, G., Chang, G.J., Tsuchiya, K.R., Karabatsos, N., Cropp, C.B., 1998. Phylogeny of the
ACCEPTED MANUSCRIPT 24
genus Flavivirus. J. Virol. 72, 73–83. Kuno, G., Chang, G.-J.J., 2007. Full-length sequencing and genomic characterization of Bagaza, Kedougou, and Zika viruses. Arch. Virol. 152, 687–696. doi:10.1007/s00705-006-0903-z
T
Laskowski, R.A., 2001. PDBsum: summaries and analyses of PDB structures. Nucleic Acids
IP
Res. 29, 221–2. doi:10.1093/NAR/29.1.221
CR
Le Breton, M., Meyniel-Schicklin, L., Deloire, A., Coutard, B., Canard, B., de Lamballerie, X., Andre, P., Rabourdin-Combe, C., Lotteau, V., Davoust, N., 2011. Flavivirus NS3 and NS5
AN
11, 234. doi:10.1186/1471-2180-11-234
US
proteins interaction network: a high-throughput yeast two-hybrid screen. BMC Microbiol.
Liang, S., Zhang, C., Liu, S., Zhou, Y., 2006. Protein binding site prediction using an empirical
M
scoring function. Nucleic Acids Res. 34, 3698–707. doi:10.1093/nar/gkl454
ED
Longenecker, K.., Upadhyay, A.., Cyr, M., 2016. Crystal structure of Zika Virus NS5 protein. To
PT
Be Publ. doi:10.2210/PDB5TFR/PDB
CE
Lovell, S.C., Davis, I.W., Arendall, W.B., de Bakker, P.I.W., Word, J.M., Prisant, M.G., Richardson, J.S., Richardson, D.C., 2003. Structure validation by Cα geometry: ϕ,ψ and Cβ
AC
deviation. Proteins Struct. Funct. Bioinforma. 50, 437–450. doi:10.1002/prot.10286 Mairiang, D., Zhang, H., Sodja, A., Murali, T., Suriyaphol, P., Malasit, P., Limjindaporn, T., Finley, R.L., 2013. Identification of New Protein Interactions between Dengue Fever Virus and Its Hosts, Human and Mosquito. PLoS One 8, e53535. doi:10.1371/journal.pone.0053535 Mazzon, M., Jones, M., Davidson, A., Chain, B., Jacobs, M., 2009. Dengue virus NS5 inhibits
ACCEPTED MANUSCRIPT 25
interferon-alpha signaling by blocking signal transducer and activator of transcription 2 phosphorylation. J. Infect. Dis. 200, 1261–70. doi:10.1086/605847 Mlakar, J., Korva, M., Tul, N., Popović, M., Poljšak-Prijatelj, M., Mraz, J., Kolenc, M., Resman Rus, K., Vesnaver Vipotnik, T., Fabjan Vodušek, V., Vizjak, A., Pižem, J., Petrovec, M.,
IP
T
Avšič Županc, T., 2016. Zika Virus Associated with Microcephaly. N. Engl. J. Med. 374,
CR
951–958. doi:10.1056/NEJMoa1600651
Musso, D., Cao-Lormeau, V.M., Gubler, D.J., Organization, P.A.H., Duffy, M., Chen, T.,
US
Hancock, al., et, Cao-Lormeau, V., Musso, D., Musso, D., Nilles, E., Cao-Lormeau, V., Weaver, S., Lecuit, M., Nhan, T., Musso, D., Musso, D., Nilles, E., Cao-Lormeau, V., 2015.
AN
Zika virus: following the path of dengue and chikungunya? Lancet (London, England) 386,
M
243–4. doi:10.1016/S0140-6736(15)61273-9
ED
Neuvirth, H., Raz, R., Schreiber, G., 2004. ProMate: A Structure Based Prediction Program to Identify the Location of Protein–Protein Binding Sites. J. Mol. Biol. 338, 181–199.
PT
doi:10.1016/j.jmb.2004.02.040
CE
Pettersen, E.F., Goddard, T.D., Huang, C.C., Couch, G.S., Greenblatt, D.M., Meng, E.C., Ferrin,
AC
T.E., 2004. UCSF Chimera?A visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612. doi:10.1002/jcc.20084 Porollo, A., Meller, J., 2006. Prediction-based fingerprints of protein-protein interactions. Proteins Struct. Funct. Bioinforma. 66, 630–645. doi:10.1002/prot.21248 Saitou, N., Nei, M., 1987. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425. doi:10.1093/OXFORDJOURNALS.MOLBEV.A040454
ACCEPTED MANUSCRIPT 26
Tasaki, T., Mulder, L.C.F., Iwamatsu, A., Lee, M.J., Davydov, I. V, Varshavsky, A., Muesing, M., Kwon, Y.T., 2005. A family of mammalian E3 ubiquitin ligases that contain the UBR box motif and recognize N-degrons. Mol. Cell. Biol. 25, 7120–36. doi:10.1128/MCB.25.16.7120-7136.2005
IP
T
Turner, P., 2005. XMGRACE, Version 5.1. 19. Cent. Coast. Land-Margin Res. Oregon Grad.
CR
Inst. Sci. Technol. Beaverton, OR.
van Zundert, G.C.P., Rodrigues, J.P.G.L.M., Trellet, M., Schmitz, C., Kastritis, P.L., Karaca, E.,
US
Melquiond, A.S.J., van Dijk, M., de Vries, S.J., Bonvin, A.M.J.J., 2016. The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular
AN
Complexes, Journal of Molecular Biology. doi:10.1016/j.jmb.2015.09.014
M
Wassenaar, T.A., van Dijk, M., Loureiro-Ferreira, N., van der Schot, G., de Vries, S.J., Schmitz,
ED
C., van der Zwan, J., Boelens, R., Giachetti, A., Ferella, L., Rosato, A., Bertini, I., Herrmann, T., Jonker, H.R.A., Bagaria, A., Jaravine, V., Güntert, P., Schwalbe, H.,
PT
Vranken, W.F., Doreleijers, J.F., Vriend, G., Vuister, G.W., Franke, D., Kikhney, A.,
CE
Svergun, D.I., Fogh, R.H., Ionides, J., Laue, E.D., Spronk, C., Jurkša, S., Verlato, M., Badoer, S., Dal Pra, S., Mazzucato, M., Frizziero, E., Bonvin, A.M.J.J., 2012. WeNMR:
AC
Structural Biology on the Grid. J. Grid Comput. 10, 743–767. doi:10.1007/s10723-0129246-z
Waterhouse, A.M., Procter, J.B., Martin, D.M.A., Clamp, M., Barton, G.J., 2009. Jalview Version 2--a multiple sequence alignment editor and analysis workbench. Bioinformatics 25, 1189–91. doi:10.1093/bioinformatics/btp033 WHO | Zika virus [WWW Document], 2016. . WHO. URL
ACCEPTED MANUSCRIPT 27
http://www.who.int/mediacentre/factsheets/zika/en/ Zhu, Z., Chan, J.F.-W., Tee, K.-M., Choi, G.K.-Y., Lau, S.K.-P., Woo, P.C.-Y., Tse, H., Yuen, K.-Y., 2016. Comparative genomic analysis of pre-epidemic and epidemic Zika virus strains for virological factors potentially associated with the rapidly expanding epidemic.
IP
T
Emerg. Microbes Infect. 5, e22. doi:10.1038/emi.2016.48
CR
Zika Virus Response Updates from FDA, 2016.
Zuckerkandl, E., Pauling, L., 1965. Evolutionary divergence and convergence in proteins. Evol.
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PT
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genes proteins 97, 97–166.
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A detailed computational study was carried out to understand the dynamics of Zika virus NS5-mediated IFN antagonism. Our docking results suggest that NS5 protein of Zika virus recruits SIAH2 ligase for the proteasomal degradation of STAT2 protein that is crucial for Type I interferon production. Similar mechanism has been proposed previously for Dengue virus NS5-mediated STAT2 degradation. The residues of Zika virus NS5 involved in interaction with SIAH2 and STAT2 proteins were found to be conserved considerably among flaviviruses.
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