• Users Online: 249
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 

Table of Contents
Year : 2021  |  Volume : 58  |  Issue : 2  |  Page : 126-134

A structure-based virtual screening and molecular docking by using potent inhibitors against nucleoprotein of Crimean-Congo hemorrhagic fever virus

1 Institute of Basic Medical Sciences, Khyber Medical University, Peshawar, Pakistan
2 Department of Molecular Biology and Biotechnology, University of Sheffield, UK
3 Faculty Allied Health Sciences, Iqra National University Swat, KP-, Pakistan

Date of Submission21-Dec-2019
Date of Acceptance28-Dec-2020
Date of Web Publication13-Jan-2022

Correspondence Address:
Tayyab Ur Rehman
Faculty Allied Health Sciences, Iqra National University Swat, KP
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0972-9062.321757

Rights and Permissions

Background & objectives: Crimean-Congo Hemorrhagic Fever Virus (CCHFV) is a vector borne pathogen, well-known for causing endemic hemorrhagic fever in Asia, Europe and Africa. There is no specific drug or vaccine available against CCHFV. The recent upsurge of Crimean-Congo Hemorrhagic Fever around the globe has made it a major health issue and this demands investigation for specific inhibitors to viral proteins. The objective of this study was to assess inhibitors that may have the potential to dock CCHFV nucleoprotein which plays an important role in viral assembly.
Methods: We performed structure-based virtual screening and molecular docking by using potent inhibitors against nucleoprotein of CCHFV. Screening was performed by a webserver, MtiOpenScreen which gave 1000 drug-like molecules from PubChem. PyRx Autodock vina was utilized to dock the protein. The docking poses were observed for interaction analysis by LigPlot+. This study provided ten potential candidates capable of binding to the active site of NP of CCHFV. The selected hits were then subjected to toxicity prediction by ProTox-II.
Results: Four hits were identified that specifically dock nucleoprotein at the presumed binding site. Furthermore, these compounds have less binding energy i.e., 9.7 kcal/mol, 9.8 kcal/mol and 10.4 kcal/mol and with equal toxicity measures when compared to an FDA approved drug.
Interpretation & conclusion: This study illustrates that virtual screening is an efficient in silico approach to identify target-specific inhibitors. Researchers in this area who investigate drugs or synthesize agents against CCHFV with better efficacy could utilize reported inhibitors rather than trying random compounds ambivalently.

Keywords: Drug designing; Docking; Crimean-Congo Hemorrhagic Fever virus; Nucleoprotein

How to cite this article:
Nayab H, Ali R, Sarwar T, Khan M A, Ul Hassan M, Ur Rehman T. A structure-based virtual screening and molecular docking by using potent inhibitors against nucleoprotein of Crimean-Congo hemorrhagic fever virus. J Vector Borne Dis 2021;58:126-34

How to cite this URL:
Nayab H, Ali R, Sarwar T, Khan M A, Ul Hassan M, Ur Rehman T. A structure-based virtual screening and molecular docking by using potent inhibitors against nucleoprotein of Crimean-Congo hemorrhagic fever virus. J Vector Borne Dis [serial online] 2021 [cited 2022 Jun 25];58:126-34. Available from: https://www.jvbd.org/text.asp?2021/58/2/126/321757

  Introduction Top

Crimean-Congo Hemorrhagic Fever (CCHF) is an ancient lethal zoonosis, caused by a tick-borne virus i.e. Crimean-Congo Hemorrhagic Fever Virus (CCHFV). The infection causes severe hemorrhagic shock in human characterized by bleeding from gums, skin, nose and gastrointestinal track. The virus is contagious with a fatality rate of up to 30%[1].

Currently, CCHF infection is controlled by avoiding direct or indirect contact with infected animals or humans. The World Health Organization (WHO) announced CCHFV as a potential bioweapon, as there is no specific antiviral drug or vaccine available against the virus. However, ribavirin is prescribed by many physicians, but its efficacy is yet to be established. Lack of effective antivirals and successively increasing outbreaks of CCFHV has intensified interest to find out effective target specific drugs against the virus[2],[3].

In vitro testing of a list of available drugs or discovering of a new agent is laborious, time consuming and very expensive process, as the virus needs a biosafety level 4 containment facilities and trained personnel[4]. However, computational biology has been an effective resource for research in this regard. Structure-based virtual screening plays a vital role in finding site-specific drug molecules against proteins by screening the whole drug database in less time. This screening process is based on the structural geometry of a protein that relatively speeds up the ligand identification process which is indeed cumbersome without knowing the type of interaction at target site the ligand could make with other molecules. This approach could also be helpful in finding the biological activity of many discovered potential drug-like compounds recorded in databases with unknown activity.

CCHFV belongs to family Nairoviridae. Its negative sense genome consists of three segments of single stranded RNA, named according to their sizes as large (L), medium (M) and small (S). The S segment encodes protein known as nucleoprotein that encapsulates the negative sense RNA to form ribonucleoproteins (RNPs)[5]. In this study we utilized nucleoprotein as a target protein for stability as drug target for many reasons. It is the only protein of CCHFV with crystalized three-dimensional structure and plays a major role in viral replication and assembly. Secondly, the amino acid residues involved in the active site are conserved among the related viral nucleoprotein sequences. Targeting nucleoprotein could possibly be the key to halt the viral pathogenesis[6]. All these facts have made nucleoprotein a valid target to inhibit viral replication. Changes in structure of nucleoprotein might disturb the binding of viral RNA to produce ribonucleoprotein. Disturbance in the oligomerization in turn would produce bare RNA molecules that could be easily disrupted by host RNases.

Ribavirin is a synthetic RNA analogue, used in treating many antiviral infections including CCHFV. It targets the enzyme RNA dependent RNA polymerase and inhibits the viral replication[7]. Ribavirin is well known for causing point mutations readily in hepatitis viruses[8]. A recent study reported that it also prompts mutation i.e. transversion and transition in CCHFV genome which in turn decreases viral load in patient’s plasma. But this was a single-case study, and therefore has limited impact[9]. Researchers have been determining ribavirin’s efficacy against CCHFV for a decade. Many scientists recommended ribavirin for treatment of CCHFV as it was found effective in the early stage of infection as well as prophylactic agent in few cases[10]. However, other researchers are not convinced yet about its efficacy- as per their findings ribavirin was found to have no effect on mortality rate. The mortality rate depends more on the severity of the disease rather than the ribavirin treatment. Also, all the studies conducted in this regard were insufficient to deduce the application of drug vividly. The role of ribavirin in CCHFV treatment is still unclear, randomized controlled trials are needed[2],[11],[12],[13]. The persistent conflicting result after prolonged in vivo and in vitro testing demands target-based alternative potent inhibitors. Furthermore, for targeting CCHFV nucleoprotein, ribavirin is inappropriate, as it is an RNA analogue and more often reduces the viral load instead of directly blocking the protein[14]. Herein, we applied computational tools to virtually screen a library of drug-like compounds to identify potential inhibitors to dock the nucleoprotein. However, application of the drugs in vitro and in vivo would suggest its role in treatment. Further studies and experimental analysis are needed to ensure the effects of these drugs.

  Material & Methods Top

Structure preparation of proteins

Three-dimensional structure of nucleoprotein was obtained from Protein Data Bank (PDB) under the ID 4AQG[6]. A ligand SO4 was present in the globular region and the whole protein was surrounded by water molecules. Both the ligand and water molecules were removed from the structure for further processing.

Potential binding site prediction

The functional site (pocket) of protein has been very useful for drug designing purpose. Ligand binding site (LBS) prediction tools analyze these pockets and suggest binding residues on target that could actively participate in ligand binding. In this study, protein in the PDB format, was provided to different binding site prediction software. Zhang laboratory suite COACH that concludes prediction given by TM-SITE, S-SITE, FINDSITE, COFACTOR, and ConCavity and generate final proposed binding sites. These software uses ligands from related protein structures and cluster them on query to make binding sites prediction[15].

Structure-based virtual screening

Structure-based virtual screening utilizes target properties and screens the best ligand thus relatively increases the ligand identification which indeed is a cumbersome process in vitro[16]. A new web server MtiOpenScreen, was utilized for structure-based virtual screening[17]. Protein molecules were uploaded in the PDB format and grid radius was provided as X:35.74, Y:31.52, Z:34.33 Å. The criteria for the selection of compounds was according to the Lipinski rule of five such as molecular weight less than 500g/mol, Xlog p (-0.4 to +5.6); rotatable bonds (>10); polar surface area (<140Å); hydrogen donor (<5) and hydrogen acceptor (<10)[18]. Screening was performed to identify drug-like compounds that are assembled in a prepared compound library called the Diverse-lib containing 99,288 compounds extracted from PubChem.

Molecular docking

The compounds provided by MtiOpenScreen were docked through Autodock vina of PyRx 0.8v, one of the widely used docking approach. The docking was performed to eliminate false positive ones. The 3D conformers of lead-like compounds downloaded from PubChem. For docking, the PDB files of 3D structures of nucleoprotein plus the downloaded ligands were uploaded to PyRx v0.8 Open Babel. Both the macromolecule and ligands were then converted to the PDBQT format. The coordinates of the grid box were set as for the MTIOpenScreen input. After docking, binding energies and docking poses provided by PyRx were downloaded as .csv and PDBQT files respectively[19]. Docking poses were visualized via PyMol and CHIMERA v1.13.1[20],[21].

Interaction analysis

LigPlot+was used to investigate the residues and atoms involved in the interactions between the ligand and protein complex. The program automatically generates two dimensional representations of protein-ligand complexes from Protein Data Bank. The output is a color PostScript file that shows a simple and informative representation of the intermolecular interactions including hydrophobic interactions, hydrogen bonds and accessible atoms[22].

Pharmacokinetic analysis

The oral toxicity of the top hits was checked by the ProTox-II web server. The software also provides prediction for compounds’ organ toxicity (hepatotoxicity), toxicity end points (carcinogenicity, mutagenicity, immunotoxicity) and its interaction with toxicity targets[23].

  Results Top

Binding site prediction

The Zhang laboratory servers ranks pockets according to the confidence score ranging from 0-1. High score shows a reliable ligand binding site has been predicted[15]. The pocket residues provided by COACH having the highest confidence score are Asp60, Arg177, Ser295, Arg298, Gln300, Ser301, Gln303, Ile304, Thr341, Phe363, Arg372, Tyr374, Thr381, Ala382, Gly383, Arg384, Glu387, Lys411, Val449, His453, His456, Gln457, Ala469, Ala479 [Figure 1]. The pocket was predicted by taking 3t5q as a template which is the PDB code for crystallized structure of Lassa virus nucleoprotein with ssRNA attached.
Figure 1: Image of predicted binding site and single stranded RNA molecule orientation provided by COACH visualized through CHIMERA v1.13.1. Nucleoprotein is colored dark khaki whereas nucleic acid is colored fuchsia. Predicted binding site residues are provided by three letter code with their specifier.

Click here to view

The x-ray analysis of nucleoprotein revealed that it carried a ligand SO4 in the globular region. The ligand was bonded to residues such as Ile304, Arg384, Lys411, His453 and Gln457 therefore this region was suggested as the putative active site of protein[6] [Figure 2]a. LigPlot+ of protein shows that O2 and O4 of ligand formed hydrogen bonds with Gln457 and O1 forms hydrogen bond with residue Ile 304 [Figure 2]b. His453 only shows hydrophobic interaction with the ligand. LigPlot+ allowed residues to be visible that have bond length less than 4, the reason being absence of remaining residues Arg384 and Lys411[6],[22]. Residues Ile304, Arg384, Lys411, His453 and Gln457 to which the ligand SO4 was attached are also considered as binding site by binding site prediction webserver COACH.
Figure 2: Illustration of the residues in active site interacting with SO4 ligand (a). ligand SO4 and active site residue from COTH (wwwn.ebi.ac.uk/pdbe/entry/pdb/4aqg) and (b) the interaction shown by ligplot+.

Click here to view

Virtual screening

MtiOpenScreen virtually screened drug-like molecules from diverse library of PubChem against the nucleoprotein. A list of thousand compounds with their molecular weight, hydrogen bond acceptor, hydrogen bond donor, logP value and binding energies are given as shown in [Table 1]. MtiOpenScreen utilizes FAFDrug3 to analyze the oral toxicity and then assembles all the lead-like molecules in the respective library. These compounds were categorized as accepted and intermediate according to the absence or presence of toxicophore in the respective structure.
Table 1: Drug-like compound given by virtual screening with their properties.

Click here to view

Docking and interaction analysis

Docking of NP was performed by PyRx Autodock vina. Recently, nucleoprotein of CCHFV was docked using virtual screening against Food and Drug Administration (FDA) approved drugs and against doxycycline- an antibacterial agent- have been identified as potential drug to dock NP[24]. Now we compared an FDA approved drug doxycycline with our virtual screened ligands. The cut-off value of binding energy was set as that of the doxycycline i.e. -9.1 kcal/mol. Out of thousand, only ninety-four drugs were retained considering the favorable docking poses and cut-off energy value. Top ten hits with their respective properties are given in [Table 1]. The interaction analysis of top hits with lowest binding energy would be discussed here. Those ligands that binds to or near to the active site or predicted binding site of nucleoprotein are expected to be the lead compounds that might act as a potential inhibitor for the targeted protein. The length of hydrogen bond and the residues involved in the interactions are shown in [Figure 3] and [Table 2].
Figure 3: Residues of nucleoprotein involved in hydrogen and hydrophobic interactions with selected ligands given by ligplot+.

Click here to view
Table 2: PubChem ID, IUPAC names, docking energies by PyRx, and interacting residues of the nucleoprotein with top ligands

Click here to view

Pharmacokinetics analysis

Although FAFDrug3 software already categorized lead molecules to accepted and intermediate annotation. To know more about toxicity of the chemicals, webserver ProTox-II was used. Generally, the oral toxicity is measured by LD50 value. LD50 below 300 is highly toxic, LD50 above 300 to 1000 is moderate and above 1000 to 5000 is considered as slightly toxic[23]. Among ten ligands only five are predicted with more accuracy and less toxicity. All the ligands show no binding with the toxicity targets specified by the webserver. Although some of them showed 0.5 or above probability of carcinogenicity and mutagenicity etc. as shown in [Table 3].
Table 3: Predicted LD50 and toxicity targets of top ten ligands.

Click here to view

  Discussion Top

Crimean-Congo Hemorrhagic Fever Virus (CCHFV) is one of the most contagious hemorrhagic fever viruses. Lack of a specific antiviral agent and reported epidemiology has driven scientists to think seriously about this issue. Virtual screening is a basic but significant approach for identifying target specific drugs from a pool of drug/ drug like compounds in drug databases. Target-based virtual screening requires ligand binding site for the protein to dock. The analysis of nucleoprotein has revealed that it carried a ligand SO4 in the globular region that was bonded to residues such as Ile304, Arg384, Lys411, His453 and Gln457, therefore, this region was suggested as the putative active site of protein[6] [Figure 2]a. LigPlot+ of protein also showed hydrogen bonds formation with two of the presumed residues Gln457 and Ile 304 [Figure 2]b. His453 only shows hydrophobic interaction with the ligand. LigPlot+ allowed residues to be visible that have bond length less than 4, this could be the reason for absence of remaining residues in 2D representation. Rather, the figure describes that Arg384, Lys411 were also involved in binding[6],[22]. The pocket was predicted by taking 3t5q as a template. 3t5q is the PDB code for structure of Lassa virus nucleoprotein with bonded ssRNA. Phylogenetic analysis revealed that CCHFV nucleoprotein shared structural homology with the Lassa virus N protein[25]. Therefore, it is plausible to suggest this pocket as a binding site for ssRNA to bind to CCHFV nucleoprotein.

Ligands screened were characterized as accepted annotation where the ligand has no chance for toxicity, and as intermediate annotation indicating that ligand has low risk of toxicity[17]. This implies that although the intermediate compounds could be considered as drug-like but as there is a risk of low toxicity, these ligands need modification before testing. All the ligands have multiple aromatic rings in their structure thereby many hydrophobic interactions have been predicted, hydrogen bonding will make these interactions more stable as it is a strongest affinity amongst intermolecular forces[26]. Residues Gln300, Lys411 and His456 are important for nucleoprotein functionality because mutation of these residues aborted or significantly reduced its RNA-binding activity. Another study suggested that Lys411 has a pivotal role in DN-ase activity[6],[27]. Compounds having IDs 17387802 and 24830852 could potentially dock nucleoprotein as they form hydrogen bond with Lys411 in addition to other active site residues. It is evident that residue Tyr374 mutation slightly affect endonuclease activity, which is presumed to be important for pathogenesis; three ligands bonded with the protein at this site[27]. Ligand ID 74374029 form hydrogen bond with Tyr374 while 4251527 and 24372737 were involved in hydrophobic interaction.

Surface of nucleoprotein consists of positively charged platform in head adjacent to the stalk region. This region proposed for the negative sense genomic RNA to bind. Oligomerizations of nucleoprotein generates a double superhelix viral nucleocapsid that protect the RNA genome. The amino acids where the sulfate ion was attached with positive charged residue may serve as an RNA binding site for the settlement of RNA[26]. Besides the top rank, hit 1 i.e. 49680415 (-10.4kcal/mol) terminal structure also shows similarity with the ligand SO4 bonded to nucleo-protein, presumably makes it a valid inhibitor against the protein. LigPlot+ of the protein shows a hydrogen bond between ligand SO4 and Ile304, though researchers have been unable to describe the amino acid’s role. Whereas, binding site prediction software recognizes it as an active site residue for ligand to bind. Ligand ID 17387802 also forms hydrogen bond with Ile304 in addition to its interac tion with the other pocket residues.

All the drug-like compounds discussed in our study bind to the active site residues with lowest binding energies as compared to doxycycline. Previously, it has been reported that doxycycline forms two hydrogen bonds with residue Lys411 and Leu387 at energy value -9.1 kcal/ mol[24]. In this study ligands ID 17387802 and 24830852 interacted with the same residue i.e. Lys411 at energy, -9.8 kcal/mol and -9.7 kcal/mol respectively, which is comparatively less than that of doxycycline.

According to the sequence alignment of nucleoprotein, the positive RNA binding residues and putative active site are conserved among all the family member of Nairoviridae[6],[27]. The RNA-binding groove of nucleoprotein could be a valid target for antiviral drugs countervailing the RNA-binding activity of the protein. Hydrogen bonding of ligands to the active site of globular domain of nucleoprotein justify them as potent inhibitors for nucleoprotein of CCHFV and other related viruses, therefore suggesting them as the best options to be used in further in vitro testing and animal trials.

For Ligand 49680415 predicted LD50 dose is 1600mg/kg and oral toxicity is ranked 4 (from 1 highest toxicity to 6 lowest toxicity). The probability of carcinogenicity and hepatotoxicity is above 0.5 which shows that there is slightly a chance of toxicity. The ligand 17387802 was less toxic and more accurate among the other hits. The compound lethal dose, accuracy values were somewhat near to doxycycline. Also, it has less chance to induce mutagenesis. Compound 24819415 and 24830852 could also be considered as a potent inhibitor because it has LD50 dose 1000mg/kg, plus it has less toxicity probability as shown in [Table 3]. As these compounds have been an intermediate hit so it would need some modulation that makes it more acceptable for the community.

  Conclusion Top

The present study focused on target-based identification of potent inhibitors against nucleoprotein (NP) of CCHFV with its role in replication and assembly of the viral progeny. The sequence analysis of the protein revealed that it was conserved among the Nairoviridae, thus, is considered a perfect candidate to dock. Computational tools were utilized to study structure geometry and enquire target specific inhibitor for the protein. All the ligands showed fewer binding energies than doxycycline. The pharmacokinetic studies show four compounds with PubChem IDs 49680415, 17387802, 24819415, 24328366 have got less chance of toxicity and are predicted to be the potential inhibitors for the target proteins and could further be assessed for their role in interrupting viral life.

  References Top

Hewson R, Gmyl A, Gmyl L, Smirnova SE, Karganova G, Jamil B, et al. Evidence of segment reassortment in Crimean-Congo haemorrhagic fever virus. Journal of General Virology 2004; 85(10): 3059–70.  Back to cited text no. 1
Leblebicioglu H. Crimean–Congo haemorrhagic fever in Eurasia. International journal of antimicrobial agents 2010; 36(Suppl 1): S43–S6.  Back to cited text no. 2
Al-Abri SS, Al Abaidani I, Fazlalipour M, Mostafavi E, Leble-bicioglu H, Pshenichnaya N, et al. Current status of Crimean- Congo haemorrhagic fever in the World Health Organization Eastern Mediterranean Region: issues, challenges, and future directions. International journal of infectious diseases 2017; 58: 82–9.  Back to cited text no. 3
Whitehouse CA. Crimean–Congo hemorrhagic fever. Antiviral research 2004; 64(3): 145–60.  Back to cited text no. 4
Shayan S, Bokaean M, Shahrivar MR, Chinikar S. Crimean- Congo hemorrhagic fever. Laboratory medicine 2015; 46(3): 180–9.  Back to cited text no. 5
Wang Y, Dutta S, Karlberg H, Devignot S, Weber F, Hao Q, et al. Structure of Crimean-Congo hemorrhagic fever virus nucleoprotein: superhelical homo-oligomers and the role of cas-pase-3 cleavage. Journal of virology 2012; 86(22): 12294–303.  Back to cited text no. 6
Saleem M, Tanvir M, Akhtar MF, Saleem A. Crimean-Congo hemorrhagic fever: etiology, diagnosis, management and potential alternative therapy. Asian Pacific Journal of Tropical Medicine 2020; 13(4): 143.  Back to cited text no. 7
Lutchman G, Danehower S, Song BC, Liang TJ, Hoofnagle JH, Thomson M, et al. Mutation rate of the hepatitis C virus NS5B in patients undergoing treatment with ribavirin monotherapy. Gastroenterology 2007; 132(5): 175–66.  Back to cited text no. 8
Espy N, Pérez-Sautu U, Ramírez de Arellano E, Negredo A, Wiley MR, Bavari S, et al. Ribavirin had demonstrable effects on the Crimean-Congo hemorrhagic fever virus (CCHFV) population and load in a patient with CCHF infection. The Journal of Infectious Diseases 2018; 217(12): 1952–6.  Back to cited text no. 9
Ceylan B, Turhan V. The efficacy of ribavirin in Crimean-Congo hemorrhagic fever-randomized trials are urgently needed. Int J Infect Dis 2014; 29: 297–8.  Back to cited text no. 10
Soares-Weiser K, Thomas S, Thomson G, Garner P. Ribavirin for Crimean-Congo hemorrhagic fever: systematic review and meta-analysis. BMC infectious diseases 2010; 10(1): 207.  Back to cited text no. 11
Duygu F, Kaya T, Baysan P. Re-evaluation of 400 Crimean- Congo hemorrhagic fever cases in an endemic area: is ribavirin treatment suitable? Vector-borne and zoonotic Diseases 2012; 12(9): 812–6.  Back to cited text no. 12
Dokuzoguz B, Celikbas AK, Gök ŞE, Baykam N, Eroglu MN, Ergönül Ö. Severity scoring index for Crimean-Congo hemorrhagic fever and the impact of ribavirin and corticosteroids on fatality. Clinical infectious diseases 2013; 57(9): 1270–4.  Back to cited text no. 13
Safronetz D, Haddock E, Feldmann F, Ebihara H, Feldmann H. In vitro and in vivo activity of ribavirin against Andes virus infection. PLoS One 2011; 6(8): e23560.  Back to cited text no. 14
Yang J, Roy A, Zhang Y. Protein–ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics 2013; 29(20): 2588–95.  Back to cited text no. 15
Vyas V, Jain A, Jain A, Gupta A. Virtual screening: a fast tool for drug design. Scientia Pharmaceutica 2008; 76(3): 333–60.  Back to cited text no. 16
Labbé CM, Rey J, Lagorce D, Vavruša M, Becot J, Sperandio O, et al. MTiOpenScreen: a web server for structure-based virtual screening. Nucleic acids research 2015; 43(W1): W448–W54.  Back to cited text no. 17
Lipinski CA. Lead-and drug-like compounds: the rule-of-five revolution. Drug Discovery Today: Technologies 2004; 1(4): 337–41.  Back to cited text no. 18
Dallakyan S, Olson AJ. Small-molecule library screening by docking with PyRx. Chemical biology: Springer 2014: 243–50.  Back to cited text no. 19
Lill MA, Danielson ML. Computer-aided drug design platform using PyMOL. Journal of computer-aided molecular design 2011; 25(1): 13–9.  Back to cited text no. 20
Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF Chimera—a visualization system for exploratory research and analysis. Journal of computational chemistry 2004; 25(13): 1605–12.  Back to cited text no. 21
Laskowski RA, Swindells MB. LigPlot+: multiple ligand–protein interaction diagrams for drug discovery. ACS Publications; 2011.  Back to cited text no. 22
Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic acids research 2018; 46(W1): W257–W63.  Back to cited text no. 23
Sharifi A, Amanlou A, Moosavi-Movahedi F, Golestanian S, Amanlou M. Tetracyclines as a potential antiviral therapy against Crimean Congo hemorrhagic fever virus: Docking and molecular dynamic studies. Computational Biology and Chemistry. 2017; 70: 1–6.  Back to cited text no. 24
Carter SD, Surtees R, Walter CT, Ariza A, Bergeron É, Nichol ST, et al. Structure, function, and evolution of the Crimean- Congo hemorrhagic fever virus nucleocapsid protein. Journal of virology 2012; 86(20): 10914–23.  Back to cited text no. 25
Abazari D, Moghtadaei M, Behvarmanesh A, Ghannadi B, Aghaei M, Behruznia M, et al. Molecular docking based screening of predicted potential inhibitors for VP40 from Ebola virus. Bioinformation 2015; 11(5): 243.  Back to cited text no. 26
Guo Y, Wang W, Ji W, Deng M, Sun Y, Zhou H, et al. Crimean–Congo hemorrhagic fever virus nucleoprotein reveals endo-nuclease activity in bunyaviruses. Proceedings of the National Academy of Sciences 2012; 109(13): 5046–51.  Back to cited text no. 27


  [Figure 1], [Figure 2], [Figure 3]

  [Table 1], [Table 2], [Table 3]


    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

  Material & M...
  In this article
Article Figures
Article Tables

 Article Access Statistics
    PDF Downloaded139    
    Comments [Add]    

Recommend this journal