Research Article
Received: 2023-02-05 | Revised:2023-02-22 | Accepted: 2023-02-26 | Published: 2023-02-26
Pages: 12-20
DOI: https://doi.org/10.56717/jpp.2023.v02i01.013
Abstract
Dengue is
causing significant morbidity and mortality worldwide. In poor and
underdeveloped countries, the disease is spreading at an alarming rate due to a
rise in population density and a decline in environmental cleanliness. Due to
the mutation and variety of distinct dengue virus species, the disease is
difficult to cure with standard techniques. In addition, there is still a need
for effective vaccination against this fatal virus. Designing a vaccine needs a
full explanation of the structural characteristics of the NS3 protease, the
primary antigenic component of the virus. Several bioinformatics methods were
utilized in this study to characterize the NS3 protease of the dengue virus
utilizing data from various public databases. Different physio-chemical
properties were determined using the ProtParam tool. Secondary structure and
motifs were predicted using the SOPMA server and MEME suit. Finally, homology
modeling of the selected protein was conducted using the PHYRE2 server. Quality
assessment of the predicted structures was performed by employing Ramachandran
plot, ERRAT, RAMPAGE, verify 3D, and RMSD scores to establish and suggest one
best model for further experimentation. A satisfactory validation score in all
those quality assessments implies the proposed model to be a good fit for the
future experiment on this protein. Such homology modeling of the viral protein
paves the way to a successful protein model and consequently leads to efficient
vaccine design.
Abstract Keywords
Dengue
4 NS3 protease, Motif analysis, homology modeling
References
1.
Samal, R.R.; Gupta, S.; Kumar, S. An overview of factors
affecting dengue transmission in asian region and its predictive models. J. Appl. Nat. Sci. 2020,
12, 460470, doi:10.31018/jans.v12i3.2360.
2.
Ramos-Castañeda, J.; Barreto dos Santos, F.; Martínez-Vega,
R.; Galvão de Araujo, J.M.; Joint, G.; Sarti, E. Dengue in Latin America:
Systematic Review of Molecular Epidemiological Trends. PLoS Negl. Trop. Dis.
2017, 11, e0005224,
doi:10.1371/journal.pntd.0005224.
3.
Ferreira-de-Lima, V.H.; Lima-Camara, T.N. Natural vertical
transmission of dengue virus in Aedes aegypti and Aedes albopictus: a
systematic review. Parasit. Vectors. 2018, 11, 77, doi:10.1186/s13071-018-2643-9.
4.
Fink, A.L.; Williams, K.L.; Harris, E.; Alvine, T.D.;
Henderson, T.; Schiltz, J.; Nilles, M.L.; Bradley, D.S. Dengue virus specific
IgY provides protection following lethal dengue virus challenge and is
neutralizing in the absence of inducing antibody dependent enhancement. PLoS
Negl. Trop. Dis. 2017, 11,
e0005721, doi:10.1371/journal.pntd.0005721.
5.
Silva, N.M.; Santos, N.C.; Martins, I.C. Dengue and Zika
Viruses: Epidemiological History, Potential Therapies, and Promising Vaccines. Trop.
Med. Infect. Dis. 2020, 5,
150, doi:10.3390/tropicalmed5040150.
6.
Uno, N.; Ross, T.M. Dengue virus and the host innate immune
response. Emerg. Microbes Infect. 2018, 7, 1–11, doi:10.1038/s41426-018-0168-0.
7.
Nasar, S.; Rashid, N.; Iftikhar, S. Dengue proteins with
their role in pathogenesis, and strategies for developing an effective
anti‐dengue treatment: A review. J. Med. Virol. 2020, 92, 941–955, doi:10.1002/jmv.25646.
8.
Xu, T.; Sampath, A.; Chao, A.; Wen, D.; Nanao, M.; Chene, P.;
Vasudevan, S.G.; Lescar, J. Structure of the Dengue virus helicase/nucleoside
triphosphatase catalytic domain at a resolution of 2.4 A. J. Virol. 2005, 79, 10278–10288.
9.
Norazharuddin, H.; Lai, N.S. Roles and Prospects of Dengue
Virus Nonstructural Proteins as Antiviral Targets: An Easy Digest. Malays.
J. Med. Sci. 2018, 25,
6–15, doi:10.21315/mjms2018.25.5.2.
10.
Luo, D.; Vasudevan, S.G.; Lescar, J. The flavivirus NS2B–NS3
protease–helicase as a target for antiviral drug development. Antivir. Res. 2015, 118, 148–158,
doi:10.1016/j.antiviral.2015.03.014.
11.
Chowdhury, H.U.; Adnan, M.; Oh, K.K.; Cho, D.H. Decrypting
molecular mechanism insight of Phyllanthus emblica L. fruit in the treatment of
type 2 diabetes mellitus by network pharmacology. Phytomedicine Plus 2021, 100144,
doi:10.1016/J.PHYPLU.2021.100144.
12.
Adnan, M.; Jeon, B.B.; Chowdhury, M.H.U.; Oh, K.K.; Das, T.;
Chy, M.N.U.; Cho, D.H. Network Pharmacology Study to Reveal the Potentiality of
a Methanol Extract of Caesalpinia sappan L. Wood against Type-2 Diabetes
Mellitus. Life. 2022, 12,
277, doi:10.3390/LIFE12020277.
13.
Shovo, M.A.R.B.; Tona, M.R.; Mouah, J.; Islam, F.; Chowdhury,
M.H.U.; Das, T.; Paul, A.; Ağagündüz, D.; Rahman, M.M.; Emran, T. Bin; et al.
Computational and Pharmacological Studies on the Antioxidant, Thrombolytic,
Anti-Inflammatory, and Analgesic Activity of Molineria capitulata. Curr.
Issues Mol. Biol. 2021, 43,
434–456, doi:10.3390/CIMB43020035.
14.
Enam, T.; Agarwala, R.; Chowdhury, M.H.U.; Chy, M.N.U.;
Adnan, M. An in silico ADME/T and molecular docking studies of phytochemicals
derived from Holigarna caustica (Dennst.) for the management of pain. J.
Phytomol. Pharmacol. 2022, 1,
37–46, doi:10.56717/JPP.2022.V01I01.005.
15.
Xie, X.; Zou, J.; Wang, Q.-Y.; Shi, P.-Y. Targeting dengue
virus NS4B protein for drug discovery. Antivir. Res. 2015, 118, 39–45,
doi:10.1016/j.antiviral.2015.03.007.
16.
Gasteiger E., Hoogland C., Gattiker A., Duvaud S., Wilkins
M.R., Appel R.D., B.A. Protein Identification and Analysis Tools on the ExPASy
Server. In The Proteomics Protocols Handbook; Humana Press, 2005,
571–607.
17.
Geourjon, C.; Deléage, G. Sopma: Significant improvements in
protein secondary structure prediction by consensus prediction from multiple
alignments. Bioinformatics 1995,
11, 681–684, doi:10.1093/bioinformatics/11.6.681.
18.
Bailey, T.L.; Boden, M.; Buske, F.A.; Frith, M.; Grant, C.E.;
Clementi, L.; Ren, J.; Li, W.W.; Noble, W.S. MEME SUITE: tools for motif
discovery and searching. Nucleic acids research 2009, 37, W202-8, doi:10.1093/nar/gkp335.
19.
Kelley, L.A.; Mezulis, S.; Yates, C.M.;
Wass, M.N.; Sternberg, M.J.E. The Phyre2 web portal for protein modeling,
prediction and analysis. Nat. Protoc. 2015, 10, 845–858,
doi:10.1038/nprot.2015.053.
20.
Zhao, F.; Peng, J.; Debartolo, J.;
Freed, K.F.; Sosnick, T.R.; Xu, J. A probabilistic and continuous model of
protein conformational space for template-free modeling. J. Comput. Biol. 2010,
17, 783–798, doi:10.1089/cmb.2009.0235.
21.
Sippl, M.J. Recognition of errors in
three‐dimensional structures of proteins. Proteins: Structure, Function, and
Bioinformatics 1993, 17, 355–362, doi:10.1002/prot.340170404.
22.
Laskowski, R.A.; MacArthur, M.W.; Moss,
D.S.; Thornton, J.M. PROCHECK: a program to check the stereochemical quality of
protein structures. J. Appl. Crystallogr. 1993, 26, 283–291,
doi:10.1107/s0021889892009944.
23.
Eisenberg, D.; Lüthy, R.; Bowie, J.U.
VERIFY3D: Assessment of protein models with three-dimensional profiles. Meth.
Enzymol. 1997, 277, 396–404, doi:10.1016/S0076-6879(97)77022-8.
24.
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. Structure validation by Cα geometry: φ,ψ and Cβ deviation.
Proteins: Structure, Function and Genetics. 2003, 50, 437–450,
doi:10.1002/prot.10286.
25.
Colovos, C.; Yeates, T.O. Verification
of protein structures: Patterns of nonbonded atomic interactions. Protein Sci.
1993, 2, 1511–1519, doi:10.1002/pro.5560020916.
26.
Sivakumar, K.; Balaji, S.;
Gangaradhakrishnan In silico characterization of antifreeze proteins using
computational tools and servers. In Proceedings of the Journal of Chemical
Sciences; Springer, 2007, 119, 571–579.
27.
Guruprasad, K.; Reddy, B.V.B.; Pandit,
M.W. Correlation between stability of a protein and its dipeptide composition:
a novel approach for predicting in vivo stability of a protein from its primary
sequence. Protein Eng. Des. Sel. 1990, 4, 155–161, doi:10.1093/protein/4.2.155.
28.
Pradeep, N. V; Vidyashree, K.G.;
Lakshmi, P. In silico Characterization of Industrial Important Cellulases using
Computational Tools. Adv. Life Sci. Technol. 2012, 4, 8–15.
29.
Rogers, S.; Wells, R.; Rechsteiner, M.
Amino acid sequences common to rapidly degraded proteins: The PEST hypothesis.
Science 1986, 234, 364–368, doi:10.1126/science.2876518.
This work is licensed under the
Creative Commons Attribution
4.0
License (CC BY-NC 4.0).
Abstract
Dengue is
causing significant morbidity and mortality worldwide. In poor and
underdeveloped countries, the disease is spreading at an alarming rate due to a
rise in population density and a decline in environmental cleanliness. Due to
the mutation and variety of distinct dengue virus species, the disease is
difficult to cure with standard techniques. In addition, there is still a need
for effective vaccination against this fatal virus. Designing a vaccine needs a
full explanation of the structural characteristics of the NS3 protease, the
primary antigenic component of the virus. Several bioinformatics methods were
utilized in this study to characterize the NS3 protease of the dengue virus
utilizing data from various public databases. Different physio-chemical
properties were determined using the ProtParam tool. Secondary structure and
motifs were predicted using the SOPMA server and MEME suit. Finally, homology
modeling of the selected protein was conducted using the PHYRE2 server. Quality
assessment of the predicted structures was performed by employing Ramachandran
plot, ERRAT, RAMPAGE, verify 3D, and RMSD scores to establish and suggest one
best model for further experimentation. A satisfactory validation score in all
those quality assessments implies the proposed model to be a good fit for the
future experiment on this protein. Such homology modeling of the viral protein
paves the way to a successful protein model and consequently leads to efficient
vaccine design.
Abstract Keywords
Dengue
4 NS3 protease, Motif analysis, homology modeling
This work is licensed under the
Creative Commons Attribution
4.0
License (CC BY-NC 4.0).
Editor-in-Chief
This work is licensed under the
Creative Commons Attribution 4.0
License.(CC BY-NC 4.0).