In-silico Protein-Ligand Docking Studies against the NS5 Methyltransferase Protein of Dengue Virus
Journal of Proteomics & Bioinformatics

Journal of Proteomics & Bioinformatics
Open Access

ISSN: 0974-276X

Research Article - (2018) Volume 11, Issue 6

In-silico Protein-Ligand Docking Studies against the NS5 Methyltransferase Protein of Dengue Virus

Nidhi Katiyar1* and Ravindra Nath2
1Dr. APJ Abdul Kalam Technical University, Lucknow, India
2University Institute of Engineering and Technology, CSJM University, Kanpur, India
*Corresponding Author: Nidhi Katiyar, Dr. APJ Abdul Kalam Technical University (AKTU), Lucknow, India, Tel: +0522-2771079

Keywords: Methyltransferase (MTase); NS5 (Nonstructural protein); DENV (Dengue virus); RTP (Ribavirin triphosphate); SAH (S-Adenosyl-L-homocysteine); Protein data bank


The cause of dengue fever is Aedes mosquitoes (Aedes aegypti). It is a viral disease and transmitted between human hosts. Every year more than 20 million cases of dengue fever recorded in whole world. The dengue viruses are members of the genus Flavivirus in the family Flaviviridae. Flavivirus includes the West Nile virus (WNV), Japanese encephalitis virus (JEV), Yellow fever virus (YEV), Murray valley encephalitis virus (MVEV), Usutu virus (USUV), West Nile virus (WNV), St. Louis encephalitis, and Kunjin virus (KUN) are member of the genus. The symptoms of dengue fever result in severe flulike, including fever, headache, and myalgia, but more severe cases can progress into a hemorrhagic fever and shock syndrome with considerable lethality. An Interesting “pandemic of dengue” (likely chikungunya) occurred in the years 1870-73, appearing first on the coast of East Africa, then on the Arabian coast. The human dengue viruses evolved as a parasite of Subhuman primates. Animal infected with ancestral dengue virus must have become separated for prolonged periods permitting the evolution of viruses whose envelop proteins differed sufficiently to escape cross- neutralization. Treatment of dengue fever is nonspecific and only based on the symptomatic with a regimen of analgesics and fluid replacement [1,2].

NS5 methyltransferase is one of the seven nonstructural proteins in the polyprotein encoded by the Flavivirus genome’s single open reading frame. This is non-structural proteins NS5 Methyltransferase (MTase). It is the largest and most conserved protein in a Flavivirus. Methyltransferase is recently established drug target in dengue virus [3,4]. The nonstructural protein (NS5) has separate encoded in separate domains. The two terminal of NS5 protein are present called the C-terminal and N-terminal. The C-terminal contains 600 amino acids of NS5 RNA-dependent RNA-polymerase (RdRp) and the N-terminal contains 300 amino acids represent the NS5 methyltransferase domain. This methylates the cap structure found on the 5’ end of viral RNA. Both RNA polymerase and methyltransferase activity are essential for the viral lifecycle. The methyltransferase NS5 activity includes both 2’-0 methylation and N7 methylation, which is rare since other methyltransferase typically, carry out only one type of methylation reaction [5].

Here present dengue MTase protein with their PDB IDs.

>gi|29726395|pdb|1L9K|AChainA, Dengue Methyltransferase

>gi|55669630|pdb|1R6A|A Chain A, Structure of the Dengue Virus

2'o Methyltransferase in Complex with S-Adenosyl Homocysteine and Ribavirin5'Triphosphate

We performed a computer-aided virtual screen more than 20 available chemical compounds to find potential inhibitors of NS5 MTase. We are using Structure-based virtual screening using the crystal structure of NS5 MTase [PDB: 1L9K]. The structure based compounds screening performed using the LIDAEUS program. These map the active site of the receptor and screen it against many commercially available compounds in the database. These discovered the several compounds that fit into the two binding sites of NS5 MTase with a higher binding affinity than its active ligands. Molegro Virtual Docker software was use to prepare ligands for virtual screening and docking process. The LIDAEUS (Ligand Discovery at Edinburgh University) program was using to search for potential inhibitors based on a map of the NS5 MTase binding site by screening of chemical compounds. EDULISS (EDinburgh University LIgand Selection System) is a chemical compounds database that was use throughout this study to screen for potential inhibitors based on ligand structure similarities with the known natural NS5 MTase substrates RTP and SAH [6-10].

The structure preparation and analysis of NS5 MTase

The three-dimensional structure of NS5 MTase S-adenosyl-L-homocysteine (SAH) retrieved from the Protein data bank [PDB: 1L9K]. Structure of NS5 MTase ribavirin (RTP) is prepared for structure-based virtual screening and molecular docking processes by removing all sulfate ions, hydrogen ions and water molecules. Ligands (SAH and RTP) removed and saved as two separate pdb files for further ligand-based virtual screening and docking DENV Methyltransferase (Figure 1).


Figure 1: Structures of the MTase complexed with S-adenosyl- L-homocysteine (SAH) molecule dock to the SAM site and ribavirin triphosphate (RTP) molecule dock to the RNA site.


In-silico drug discovery process is a important and beneficial way to design novel drugs by increasing the knowledge about the biological processes, biochemical activities of chemical compounds and targets. Bioinformatics tools are majorly facilitating in silico drug discovery.

Selection of target protein

Identifying and selecting the most appropriate drug target or receptor is the initial step in the drug designing procedure. Excellent drug targets can be identified with the help of bioinformatics. Mostly proteins act as good targets for the drugs. In some cases, enzymes can also serve as excellent drug targets [11]. NS5 methyltransferase protein of Dengue virus-2 was selected for the present study. The three dimensional structure of dengue virus NS5 methyltransferase is available in the Protein Data Bank (accession code: 1L9K and 1R6A) and is shown in Figure 2. Once the target has been is identified, the candidate ligand of drugs can be selected either by analyzing previously known drugs having potent effects against DENV or by designing the novel inhibiting compounds.


Figure 2: Crystal Structure of Dengue Virus NS5 Methyltransferase (A) 1L9K (B) 1R6A.

Structure- based virtual screening

To utilized the search for potential inhibitors against NS5 MTase by matching the binding site map of the receptor against chemical compounds in the database. First, the NS5 MTase structure uploaded into LIDAEUS after that SAH and RTP separately. LIDAEUS generated an energy map and site points on the MTase structure based on the positions where SAH and RTP reside in their binding pockets (the SAM binding site and RNA cap-binding site, respectively). We have taken 28 compounds for molecular docking in which three compounds get best for virtual screening. After the virtual screening process, we find the two compounds are the best for drug designing purpose.

Ligand-based virtual screening

Using the web-based EDULISS programs (software), we have done Virtual screening based on the structure of NS5 MTase active ligands (SAH and RTP). To search similar compound we used two-dimensional structure of SAH and RTP in the EDULISS database from common chemical compounds. Take 20 chemical compounds, which have the highest structural similarity to SAH and RTP was use for further molecular docking analysis [10]. Then we get molecule which have highest energy of ligand.

Protein-ligand based molecular docking

Molecular docking performed using the Molegro Virtual Docker program. This protein-ligand software is integrated software for predicting protein-ligand interactions. It is deals with high quality docking based on an optimization technique. Software also has a user interface that will focus on usability and productivity. It also handles maximum aspects of the process, from preparing the molecules to determining the potential binding site of the target protein.

Inhibitors bound to SAM binding site in (1L9K Protein)

Aurin tricarboxylic acid: Influenza viruses cause serious infections that can be prevent or treated using vaccines or antiviral agents, respectively. While vaccines are effective, they have a number of limitations. Influenza strains resistant to currently available antiinfluenza drugs are increasingly isolated. Aurin tricarboxylic acid (ATA) is a polyromantic carboxylic acid derivative that inhibits nucleases and nucleic acid processing enzymes. Aurin tricarboxylic acid (ATA) inhibit the serve acute respiratory syndrome associated coronavirus (SARS-CoV) and vaccinia virus. ATA can substantially inhibit the replication of several strains of cultures with moderate cytotoxicity [12].

Dehydrosinefungin: Dehydrosinefungin is novel inhibitors of NS5 Methyltransferase. It consists of 4, 5- dehydroadenosine and ornithylvaline.

Sinefungin: Sinefungin, a known inhibitor of protein methylation, inhibited the myelin basic protein (arginine) methyltransferase activity in homogenates of cultured cerebral cells from embryonic mice. Fifty percent inhibition achieved with 25-microM sinefungin. Strong competitive inhibitor of methyltransferases which uses S-adenosyl-L-methionine as the methyl group donor to yield methylated products such as 5- methylcytosine or N6- methyl adenosine on DNA and RNA. In addition, sinefungin is involved in a number of physiological processes [13].

Inhibitors bound to RNA cap site in (1R6A)

We performed molecular docking of 20 compounds obtained from the ligand-based EDULISS screen. These 20 compounds ranked based on their binding affinities. The top five with binding affinities for the RNA cap site that are having more negative affinity than that of RTP which are listed in the Table 1. The five compounds that showed the strongest affinities for the RNA cap site of NS5 MTase were SPH1-103-799, SPH1- 101-102, 28SPH1-115-917, 35SPH1-021-288 and 28SPH1-185-015 each with a binding affinity of -7.8 and -7.9 kcal/mol respectively.

NS5 methyltransferase protein 1L9K NS5 methyltransferase protein 1R6A
S.No Ligand Name IC50 References Ligand Name IC50 References
1. 5-aza-2-deoxycytidine   (stock 2005) 28SPH1-021-288 4.9 -to- 7.1 μM (Lim 2011)
2. Dimethylated arginine   J Leiper 2011 28SPH1-024-902    
3. Monomethylated arginine   J Leiper  2011 28SPH1-026-800    
4. Cantharidin   E Lissina 2011 28SPH1-081-432    
5. Procarnamide   (Lim 2011) 28SPH1-115-917    
6. Catechol-o   (Palma P. N., January 2012) SPH1-103-799    
7. Azacitidine   (Lyko 2008) 28SPH1-149-718    
8. Decitabine   (Lyko 2008) 28SPH1-185-015    
9. Chactocin     28SPH1-348-781    
10. Thymidylated     28SPH1-63-033    
11. Tricostatin     SPH1-014-180    
12. Zebularin     SPH1-000-259    
13. Sinefungin   (Podvinec, January 2010) SPH1-013-271    
14. Dehydrosinefungin 2.3 μM (Podvinec, January 2010) SPH1-013-272    
15. Aurin tricarboxylic acid (ATA)   (M. Hashem 2007) SPH1-013-273    
16. Ribavirin triphosphate   (Lim 2011) SPH1-013-274    
17. Prolglhydroxylase     SPH1-027-074    
18. NSC15765 14.3 μM (Podvinec, January 2010) SPH1-047-692    
19. NSC140047 8.78 μM (Podvinec, January 2010) SPH1-101-102    
20. NSC14778 1.52 μM (Podvinec, January 2010)      
21. ZINC03369470 4.80 μM (Podvinec, January 2010)      
22. ZINC02911543 7.56 μM (Podvinec, January 2010)      
23. ZINC01078518 9.28 μM (Podvinec, January 2010)      
24. SPH1-103-799 4.9 and 7.1 μM (Lim 2011)      
25. SPH1-007-088   (Lim 2011)      
26. SPH1-101-102 4.9 and 7.1 μM (Lim 2011)      
27. 28SPH1-115-917 4.9 and 7.1 μM (Lim 2011)      
28. 25SPH1-103-433   (Lim 2011)      

Table 1: List of NS5 MTase inhibitors (1L9K and 1R6A) Protein.

Database used

The three dimensional structure of dengue virus NS5 methyltransferase is available in the Protein Data Bank (accession code: 1L9K and 1R6A). For the in silico design and molecular docking studies a number of inhibitors of NS5 methyltransferase were identified and selected. There were 28 compounds [11-17] which were selected for molecular studies having drug like properties against dengue virus. These drugs were selected through the analysis of previous and current research literature on the dengue virus. Performing the virtual screening process to fetched the analogues from zinc database.

Results and Discussion

Molecular docking

On the basis of molecular docking, a usually smaller molecule which binds with the larger molecule such as enzyme or protein initiates the replication process. On the basis of docking of anti-Dengue drugs with the receptor protein, the 1L9K and 1R6A was searched for its active site. There after we perform the virtual screening of lead compounds to against the dengue virus NS5 MTase. Once all the 28 compounds were docked against the receptor protein 1L9K and the 19 compounds were docked against the receptor protein 1R6A, these docked complexes were visualized in VMD. From these selected compounds screening for further molecule docking experiments using docking software. These structure and results shown in the form of the figures and tables.

Lead identification

Protein binding interactions 1L9K protein with the 28 chemical compounds and 1R6A protein with the 19 chemical compounds have shown in Table 1. 1L9K protein with crystallized conformation of SAH shown in yellow carbons. The best-redocked conformation of SAH has shown in dotted lines carbons. Second 1R6A protein with Crystallized conformation of RTP shown in yellow carbons. The best-redocked pose of RTP shown in green carbons. Residues with hydrophobic contacts with RTP and SAH labeled in red and hydrogen bonds drawn as dashed lines show in above Figure 3. In Table 2 show the molecular docking result, docking validation was performed by redocking co-crystallized into SAH into their respective binding sites. The co-crystallized ligand with binding affinities of -164 (kcal/mol) for SAH respectively. There after performing the molecular docking result and we find the best molecule name as Aurin tricarboxylic acid (ATA) has high Moldock score-187(kcal/mol).


Figure 3: Redocking of RTP and SAH into RNA cap site and SAM binding site with accession code: 1L9k and 1R6A protein.

    SAM Site
S. No Ligand Name MoldockScore (kcal/mol) HBond
  SAH molecule -164.224 -16.9123
1. Aurin tricarboxylic acid (ATA) -187.769 -15.067
2. ZINC03369470 -172.542 -10.2867
3. Dehydrosinefungin -163.99 -6.65296
4. Sinefungin -158.197 -15.8751
5. ZINC02911543 -145.884 -6.44874
6. SPH1-007-088 -144.558 -4.35874
7. NSC140047 -141.083 -9.48148
8. NSC14778 -139.009 -9.29705
9. NSC15765 -137.073 -13.1919
10. SPH1-101-102 -121.837 -2.89083
11. Prolglhydroxylase -118.668 -7.82975
12. Ribavirin triphosphate -114.918 -10.1049
13. Monomethylated arginine -112.163 -15.5086
14. Tricostatin -108.424 -12.1929
15. Azacitidine -105.293 -7.01151
16. Decitabine -104.357 -11.2344
17. Zebularine -104.087 -4.41083
18. Procarnamide -103.684 -1.09283
19. Dimethylated arginine -96.3692 -14.3438
20. 5-aza-2-deoxycytidine -93.6301 -12.7013
21. catechol-o -93.3611 -8.43426
22. thymidylated -66.1526 -3.89358
23. 25SPH1-103-433 872.129 -5.26063
24. 28SPH1-115-917 893.693 -3.83338
25. Cantharidin 929.524 -2.61824
26. SPH1-103-799 4847.89 -12.4371
27. Chactocin 4851.39 -15.4786
28. ZINC01078518 5869.31 -3.8606

Table 2: Docking Result (1L9K Protein).

Lead compounds

After the molecular docking, we found the three best potential lead compounds. This shows the high protein ligand binding energy. Although mostly compounds show good interactions with the target protein, but high binding affinity of compounds is shown in Figure 4A-C. (a) Aurin Tricarboxylic Acid (b) ZINC03369470 and (C) Dehydrosinefungin (Table 3).


Figure 4: Docking views of (A) Aurin Tricarboxylic Acid (B) ZINC03369470 and (C) Dehydrosinefungin.


Figure 5: Docking views of (SPH1-013-271) compound.

S. No Ligand Name Moldock Score kcal/mol HBond Common residues Searched Similar Compounds   (Zinc Id)
1. Aurin tricarboxylic Acid -187.769 -15.067 Thr, Lys, Gly, Trp, ATA (23) analogs
2. ZINC03369470 -172.542 -10.2867 Lys, Gly SF (546)analogs
3. Dehydrosinefungin -163.99 -6.65296 Gly, DSF(20) analogs
4. Sinefungin -158.197 -15.8751   ZINCO3369470(507) analogs
5. ZINC02911543 -145.884 -6.44874    

Table 3: List of best five compounds showing the high Moldock energy.

Analogs searched

After that the molecular docking process we search the thousand analogs of these chemical compounds from Zinc database and thus allowed to the Virtual Screening. On the bases of compound similarity we searched the analogs: Twenty three analogs of Aurintricarboxylic acid, five hundred forty six analogs of Sinefungin, twenty analogs of Dehydrosinefungin and five hundred seven analogs of ZINC03369470 searched from the zinc database. All the analogs were docked within the active site of 1L9K with the same procedure as discussed earlier. After that, the virtual screening of 1L9K we find the several analogs, which have the high energy binding conformation for SAH, respectively. The best confirmation was selected up on the least binding energy of analog in 5 conformations. Binding energy of analogs are shown in Table 4. Each selected analog conformation was saved and then analyzed. On the basis of protein-ligand interactions with the target protein 1L9K, analog 1,2,3 of sinefungin and analog 1,2,3 of ZINC03369470 has showed the maximum binding affinity. Although mostly analogs of lead compounds show good interactions with the target protein, but high binding affinity is shown in above Table 4 with these analogs. Therefore, these analogs showed the good interactions with the target protein 1L9K. Same procedure we follow the target protein 1R6A. To obtained the protein structure from Protein Data Bank and removes the water molecules before the docking experiments. Then for the ligand preparation, input the ligand and Gasteiger charges were merged with non-polar hydrogens of ligand. Performing the molecular docking process we find the active conformation of ligand and the binding modes of 19 selected compounds with the active site of the target protein 1R6A. After the docked compounds then we found only one of the compound SPH1-013-271 which showing the high Moldock Score -177 (kcal/mol) for RTP molecule. Show the above Table 5, we search the thousands of similar compounds of SPH1-013-271 and analogs from zinc database on 60% similarity and thus allowed to the Virtual Screening. In Figure 5 shows the, maximum interactions with the target protein 1R6A to the lead compound (SPH1-013-271). On the bases of virtual screening result to find the maximum binding energy conformation of analogs with the target protein 1R6A (Table 6).

S.No Inhibitor Name Moldoc Score S.No Analogs Id Analogs Moldock Score (kcal/mol) HBond
1. Aurintricarboxylic acid (23) -187.769 1) ZINC04974299 -179.745 -21.3507
      2) ZINC02358769 -170.08 -119.655
      3) ZINC02546473 -168.786 -19.5128
      4) ZINC32113592 -155.442 -6.29071
      5) ZINC04726498 -154.764 -18.1523
2. Dehydrosinefungin (20) -163.99 1) ZINC38555871 -175.236 -21.7835
      2) ZINC3855587_1 -174.574 -19.4225
      3) ZINC38555870 -173.745 -23.1811
      4) ZINC3855587 -168.432 -16.7849
      5) ZINC17111254 -162.2 -7.51562
3. Sinefungin (546) -158.197 1) ZINC03869249 -204.594 -23.3822
      2) ZINC04228234 -198.761 -20.0403
      3) ZINC03869240 -197.221 -26.4742
      4) ZINC03869250 -194.709 -25.721
      5) ZINC13518964 -175.605 -28.8811
      6) ZINC01842158 -167.275 -24.201
      7) ZINC01571045 -165.254 -29.0619
      8) ZINC1358982 -169.024 -25.5744
      9) ZINC1713574 -166.989 -25.0689
      10) ZINC03869815 -162.369 -14.6262
      11) ZINC03870702 -162.247 -19.1449
      12) ZINC01532626 -162.199 -21.5453
      13) ZINC03869814 -160.866 -14.4104
4. ZINC03369470 (507) -172.542 1) ZINC00669666 -205.507 -1.84742
      2) ZINC00668589 -194.447 -4.74314
      3) ZINC0444329 -179.4 -1.303
      4) ZINC15907608 -176.549 -9.08478
      5) ZINC17409522 -170.514 -7.45826
      6) ZINC02091089 -169.594 -8.16742

Table 4: Virtual Screening Results of (1L9K Protein)

    RNA Site    
S.No Ligand Name Moldock Score (kcal/mol) HBond Similarity% Analogs Id
  RTP molecule -153.323 -18.2607    
1. SPH1-013-271 -177.417 -6.90699 (60%) 596 Analogs
2. 28SPH1-081-432 -146.201 -1.74436 (80%) 270 Analogs
3. 28SPH1-63-033 -135.636 -10.9112    
4. SPH1-103-799 -126.554 -9.70314    
5. 28SPH1-026-800 -122.308 0    
6. SPH1-027-074 -122.272 -10.6789    
7. SPH1-047-692 -119.721 -10.4141    
8. 28SPH1-348-781 -115.477 -11.3199    
9. SPH1-014-180 -111.596 -10.9081    
10. SPH1-013-274 -104.832 -15.2752    
11. 28SPH1-115-917 -100.596 -3.34326    
12. 28SPH1-185-015 -94.7596 -17.6787    
13. SPH1-101-102 -91.5418 -17.4868    
14. SPH1-013-272 -83.2589 -19.478    
15. 28SPH1-149-718 -82.2765 -7.1879    
16. 28SPH1-021-288 -81.915 -18.5785    
17. SPH1-000-259 -81.68 -1.33649    
18. 28SPH1-024-902 -75.7888 -648934    
19. SPH1-013-273 -68.7674 -6.57661    

Table 5: Docking Result (1R6A Protein).

Inhibitor Name Moldock Score S.
Analogs Id Analogs Moldock Score (kcal/mol) HBond
1. SPH1-013-271 (596) -177.417 1) ZINC08430514 -170.241 -8.47353
  2) ZINC09557581 -167.623 -11.8234
3) ZINC09557582 -167.432 -13.4492
4) ZINC08430386 -167.107 -8.16354
5) ZINC08430379 -160.892 -10.9542
2. 28SPH1-081-432 (270) -146.201 1) ZINC7164225 -161.35 -10
  2) ZINC71164227 -158.462 -10
3) ZINC2039310 -157.222 -9.88252
4) ZINC71164226 -156.382 -9.9845
5) ZINC71164228 -156.145 -9.91116
6) ZINC71164216 -154.079 -9.91116
7) ZINC13764232 -153.296 -7.19322
8) ZINC0430160 -153.022 -10
3. 28SPH1-63-033 (508) -135.636 1) ZINC03048499 -154.125 -5
  2) ZINC34984187 -142.997 -13.064

Table 6: Virtual Screening Results of (1R6A Protein).

In this study, we have done molecular docking then performing the screening of 1thousand similar compounds which searched from a zinc database and then we find some lead compounds which are the best for the anti-dengue drug.

We discovered several compounds that fit into the two binding sites of NS5 MTase with a higher binding affinity than its active ligands. These compounds can be further tested to confirm their biological activities against the DV. Dengue and DHF remain pharmacologically neglected diseases, especially in developing countries. Because these countries have healthcare facilities is limited. Therefore, research group has taken an initiative to be developed drug for the poor and developed countries [14,15].

Binding affinities used for the selection of potential compounds. Compounds with stronger binding affinities for the RNA site and SAM binding sites than that of SAH and RTP molecule (NS5 MTase active ligands) selected as potential inhibitors. In addition, the selected compounds must interact with residues that are catalytically important for the function of NS5 MTase. In contrast to the RNA cap site, the SAM binding site is rather closed and long. These compounds are thought to specifically bind to dengue NS5 MTase and do not interfere with related human enzymes [18]. It has been suggested that the guanosine moiety is important for promoting the activity of inhibitors that bind to the SAM binding site due to the strong hydrophobic contacts between the guanosine ring and Lys105 and Ile147 [16]. Nevertheless, Podvinec et al. reported that two compounds with no guanosine moiety bound to the SAM binding site (IC50 of 4.4 and 9.5 μM) [19]. In further studies we analyze the all proteins of dengue virus with their resolution power with the help of machine learning approaches to find the best protein sequence [20-30].


In this work, we find the novel drug molecules as anti-dengue compounds using the structure-based drug design technique. 1L9K and 1R6A protein was used as a target for the purpose of finding novel drugs. About 28 drugs (1L9K) and 19 drugs (1R6A) were selected as ligands for this study that showed activity against the dengue virus. Each ligand with the target, the complexes formed was analyzed based on their energy values and their binding affinities with the target. Based on the results of this analysis, a lead compound was identified. It is finding that two lead compounds such as Aurintricarboxylic acid and ZINC03369470 compound get after docking and screening process. It is above show in Table 4 molecule that show the highest energy of Aurintricarboxylic acid and ZINC03369470 compound and its analogs are (ZINC00669666, ZINC00668589, ZINC0444329, and ZINC15907608) to after virtual screening. For the RNA cap site in (1R6A) protein, also show the moldock score of only one compound SPH1-013-271 and its more than five hundred analogs compounds. Therefore, we suggest that these two compounds (Aurintricarboxylic and ZINC03369470) the best out of all compounds or analogs can used for developing an effective dengue drug for future research.


  1. Gubler DJ (1998) Dengue and dengue hemorrhagic fever Clin Microbial Rev 11: 480-496.
  2. Ligon BL (2005) Dengue fever and dengue hemorrhagic fever: a review of the history, transmission, treatment, and prevention. Semin Pediatr Infect Dis 16: 60-65.
  3. Luzhkov VB, Selisko B, Nordqvist A, Peyrane F, Decroly E, et al. (2007) Virtual screening and bioassay study of novel inhibitors for dengue virus mRNA cap (nucleoside-2’O)-methyltransferase. Bioorg Med Chem 15: 7795-7802
  4. Geiss BJ, Thompson AA, Andrews AJ, Sons RL, Gari HH, et al. (2009) Analysis of flavivirus NS5 methyltransferase cap binding. J Mol Biol 385: 1643-1654.
  5. Roberts S, Bhattacharya D (2010) DENGUE NS5- Methyltransferase target. J Med Chem 2010.
  6. Zhou Y, Ray D, Zhao Y, Dong H, Ren S, et al. (2007) Structure and function of flavivirus NS5 methyltransferase. J Virol 81: 3891-3903.
  7. Kasam V, Zimmermann M, Maass A, Schwichtenberg H, Wolf A, et al. (2007) Design of new plasmepsin inhibitors: a virtual high throughput screening approach on the EGEE grid. J Chem Inf Model 47: 1818-1828.
  8. Podvinec M, Lim SP, Schmidt T, Scarsi M, Wen D, et al. (2010) Novel inhibitors of dengue virus methyltransferase: discovery by in vitro-driven virtual screening on a desktop computer grid. J Med Chem 53: 1483-1495.
  9. TaqI M, Schluckebier G (1997) Differential binding of S-adenosylmethionine S adenosylhomocysteine and Sinefungin to the adenine-specific DNA methyltransferase. J Mol Biol 265: 56.
  10. Podvinec M, Schwede T, Peitsch MC (2008) Docking for neglected diseases as community efforts. In Computational Structural Biology: Methods and Applications: World Scientific Publishing Singapore 683-704.
  11. Noureen N, Rashid H, Kalsoom S (2010) Identification of type-specific anticancer histone deacetylase inhibitors: road to success. Cancer Chemotherapy and Pharmacology 66: 625-633.
  12. Fornsworth: Aurin tricarboxylic acid inhibit the serve acute respiratory syndrome associated Coronavirus (SARS-CoV) and vaccinia virus Inhibitors Res. 2007.
  13. Taqi M, Schluckebier G (1997) Sinefungin, a known inhibitor of protein methylation.
  14. Pang T (2003) Vaccines for the prevention of neglected diseases–dengue fever. Curr Opin Biotechnol 14: 332-336.
  15. Halstead SB, Deen J (2002) The future of dengue vaccines. Lancet 360: 1243-1245.
  16. Dong H, Zhang B, Shi PY (2008) Flavivirus methyltransferase: A novel antiviral target Antiviral Res  80: 1-10
  17. Lim SV, Basyaruddin Mohd, Rahman A, Bimo AT (2011) Structure-based and ligand-based virtual screening of novel methyltransferase inhibitors of the dengue virus. BMC Bioinformatics 13: S24
  18. Lim SP, Sonntag LS, Noble C, Nilar SH, Ng RH, et al. (2011) Small molecule inhibitors that selectively block dengue virus methyltransferase. J Biol Chem 286: 6233-6240.
  19. Podvinec M, Lim SP, Schmidt T, Scarsi M, Wen D, et al. (2010) Novel inhibitors of dengue virus methyltransferase: discovery by in vitro-driven virtual screening on a desktop computer grid. J Med Chem 53: 1483-1495.
  20. Subramaniam S, Mehrotra M, Gupta D (2008) Virtual high throughput screening (vHTS)–a perspective. Bioinformation 3: 14-17.
  21. Chang MW, Lindstrom W, Olson AJ, Belew RK (2007) Analysis of HIV wild-type and mutant structures via in silico docking against diverse ligand libraries. J Chem Inf Model 47: 1258-1262.
  22. Katiyar N, Nath R (2016) Methods for Protein Structure Prediction and Its Application in Drug Design Using Hidden Markov Model. Inter J Emerging Trends in Sci Tech.  ISSN2348-9480.
  23. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31: 455-461.
  24. Han LY, Lin HH, Li ZR, Zheng CJ, Cao ZW (2006) PEARLS: program for energetic analysis of receptor-ligand system. J Chem Inf Model 46: 445-450.
  25. Wallace AC, Laskowski RA, Thornton JM (1995) LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng 8: 127-134.
  26. Shave SR (2009) The development of high performances structure and ligand based virtual screening techniques. PhD Thesis Edinburgh: University of Edinburgh.
  27. Cleaves GR, Dubin DT (1979) Methylation status of intracellular dengue type 2 40 S RNA. Virology 96: 159-165.
  28. Ray D, Shah A, Tilgner M, Guo Y, Zhao Y (2006) West Nile virus 5’-cap structure is formed by sequential guanine N-7 and ribose 2’-O methylations by nonstructural protein 5. J Virol 80: 8362-8370.
  29. Guo JT, Hayashi J, Seeger C (2005) West Nile virus inhibits the signal transduction pathway of alpha interferon. J Virol 79: 1343-1350.
  30. Padmanabhan R, Mueller N, Reichert E, Yon C, Teramoto T, et al. (2006) Multiple enzyme activities of flavivirus proteins. Novartis Found Symp 277: 74-84.
Citation: Katiyar N, Nath R (2018) In-silico Protein-Ligand Docking Studies against the NS5 Methyltransferase Protein of Dengue Virus. J Proteomics Bioinform 11:127-134.

Copyright: © 2018 Katiyar N, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.