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In silico Vaccine Design against Dengue Virus Type 2 Envelope Glycoprotein

HAYATI Journal of Biosciences · Vol. 27 No. 3 (2020)

DOI
10.4308/hjb.27.3.228
ISSN
2086-4094, 1978-3019
Terbit
2020-07-01
Penerbit
Bogor Agricultural University, Indonesia
Bahasa
eng

Abstrak

Dengue fever is caused by the mosquito-borne virus termed (DENV). However, DENV-2 has been identified as the most prevalent amongst the Indonesian pediatric urban population, in contrast with the other four serotypes. Therefore, it is important to reduce severe infection risk by adopting preventive measures, including through vaccine development. The aim of this study, therefore is to use various in silico tools in the design of epitope-based peptide vaccines (T-cell and B-cell types), based on the DENV-2 envelope glycoprotein sequences available. Therefore, in silico methods were adopted in the analysis of the retrieved protein sequences. This technique was required to determine the most immunogenic protein, and is achieved through conservancy analysis, epitope identification, molecular simulation, and allergenicity assessment. Furthermore, B4XPM1, and KAWLVHRQW were identified from positions 204-212, while the 77 to 85 peptide region was considered the most potent T-cell and B-cell epitopes. The interaction between KAWLVHRQW and HLA-C*12:03 occurs with maximum population coverage, alongside high conservancy (96.98%) and binding affinity. These results indicated a potential for the designed epitopes to demonstrate high immunity against DENV-2.

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  • Universitas Gadjah Mada
  • Universitas Islam Negeri Ar-Raniry
  • National Institute of Technology and Evaluation
  • Justus-Liebig-Universität Gießen
  • Sunan Kalijaga State Islamic University Yogyakarta
  • Jenderal Soedirman University
  • State University of Semarang
  • Organisasi lain
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Aris Tri HaryantoUniversitas Gadjah Mada7
Tri WibawaUniversitas Gadjah Mada4
Michael KannJustus-Liebig-Universität Gießen3
Jaka WidadaUniversitas Gadjah Mada2
Marwiyati MarwiyatiUniversitas Islam Negeri Ar-Raniry2
Hevi WihadmadyatamiUniversitas Gadjah Mada2
Rarastoeti PratiwiUniversitas Gadjah Mada2
Tri Rini NuringtyasUniversitas Gadjah Mada2
Rizki Maulana Syafe'iAfiliasi tidak diketahui1
Muhammad AdnanSunan Kalijaga State Islamic University Yogyakarta1
Ikhsan Fatkhul AzisUniversitas Gadjah Mada1
Diani MentariJenderal Soedirman University1
Slamet HaryonoState University of Semarang1
Agus CahyonoAfiliasi tidak diketahui1
Rahmi RahmiUniversitas Islam Negeri Ar-Raniry1
Fithriady FithriadyUniversitas Islam Negeri Ar-Raniry1
Rinawati RinawatiUniversitas Teuku Umar1
E. SuharyantoUniversitas Gadjah Mada1
Zia ThahiraUniversitas Syiah Kuala1
Zurriyani ZurriyaniUniversitas Abulyatama1
Hera NirwatiUniversitas Gadjah Mada1
Sp.A SutaryoUniversitas Gadjah Mada1
Nenny Sri MulyaniAfiliasi tidak diketahui1
Titis Budi WidowatiAfiliasi tidak diketahui1
Purnomo HadiAfiliasi tidak diketahui1
Abdul Rahman SiregarUniversitas Gadjah Mada1
Shinta HartantoUniversitas Gadjah Mada1
Sofia MubarikaUniversitas Gadjah Mada1
Camelia HerdiniUniversitas Gadjah Mada1
Bambang HariwiyantoUniversitas Gadjah Mada1
Akira HosoyamaNational Institute of Technology and Evaluation1
Atsushi YamazoeNational Institute of Technology and Evaluation1
Hideaki NojiriThe University of Tokyo1
M. MurwantokoAfiliasi tidak diketahui1
T. TrijokoUniversitas Gadjah Mada1
Aushia Tanzih Al HaqUniversitas Gadjah Mada1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://journal.ipb.ac.id/index.php/hayati/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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	<dc:title xml:lang="en">In silico Vaccine Design against Dengue Virus Type 2 Envelope Glycoprotein</dc:title>
	<dc:creator xml:lang="en">Adnan, Muhammad </dc:creator>
	<dc:creator xml:lang="en">Nuhamunada, Matin </dc:creator>
	<dc:creator xml:lang="en">Hidayati, Lisna </dc:creator>
	<dc:creator xml:lang="en">Wijayanti, Nastiti </dc:creator>
	<dc:description xml:lang="en">Dengue fever is caused by the mosquito-borne virus termed (DENV). However, DENV-2 has been identified as the most prevalent amongst the Indonesian pediatric urban population, in contrast with the other four serotypes. Therefore, it is important to reduce severe infection risk by adopting preventive measures, including through vaccine development. The aim of this study, therefore is to use various in silico tools in the design of epitope-based peptide vaccines (T-cell and B-cell types), based on the DENV-2 envelope glycoprotein sequences available. Therefore, in silico methods were adopted in the analysis of the retrieved protein sequences. This technique was required to determine the most immunogenic protein, and is achieved through conservancy analysis, epitope identification, molecular simulation, and allergenicity assessment. Furthermore, B4XPM1, and KAWLVHRQW were identified from positions 204-212, while the 77 to 85 peptide region was considered the most potent T-cell and B-cell epitopes. The interaction between KAWLVHRQW and HLA-C*12:03 occurs with maximum population coverage, alongside high conservancy (96.98%) and binding affinity. These results indicated a potential for the designed epitopes to demonstrate high immunity against DENV-2.</dc:description>
	<dc:publisher xml:lang="en">Bogor Agricultural University, Indonesia</dc:publisher>
	<dc:date>2020-07-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>http://journal.ipb.ac.id/hayati/article/view/33469</dc:identifier>
	<dc:identifier>10.4308/hjb.27.3.228</dc:identifier>
	<dc:source xml:lang="en">HAYATI Journal of Biosciences; Vol. 27 No. 3 (2020): July 2020; 228</dc:source>
	<dc:source>2086-4094</dc:source>
	<dc:source>1978-3019</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>http://journal.ipb.ac.id/hayati/article/view/33469/20724</dc:relation>
</oai_dc:dc>