M. Nizam Zulfi Zakaria - Department of Biology, Faculty of Mathematics and Life Sciences, Universitas Brawijaya, Malang, Indonesia
Ahmad Fariduddin Aththar - Department of Biology, Faculty of Mathematics and Life Sciences, Universitas Brawijaya, Malang, Indonesia
Michelle Fai - Department of Biology, Faculty of Mathematics and Life Sciences, Universitas Brawijaya, Malang, Indonesia
Syeftyan Muhammad Ali Hamami - Department of Biology, Faculty of Mathematics and Life Sciences, Universitas Brawijaya, Malang, Indonesia
Viol Dhea Kharisma - Generasi Biologi Indonesia Foundation, Gresik, Indonesia
Ahmad Affan Ali Murtadlo - Generasi Biologi Indonesia Foundation, Gresik, Indonesia
Arif Nur Muhammad Ansori - Generasi Biologi Indonesia Foundation, Gresik, Indonesia
Teguh Hari Sucipto - Dengue Study Group, Institute of Tropical Disease, Universitas Airlangga, Surabaya, Indonesia
Rahadian Zainul - Department of Chemistry, Faculty of Mathematics and Life Sciences, Universitas Negeri Padang, Padang, Indonesia

The Chikungunya virus (CHIKV), a zoonotic virus transmitted through mosquito bites, can cause dengue-like fever in humans. Despite the lack of specific clinical treatments and vaccines, there has been limited attention given to CHIKV in recent decades. This study utilized an immunoinformatics approach to design a potential multi-epitope vaccine for CHIKV. The CHIKV multi-epitope vaccine (CHIKV-MEV) was created by combining predicted linear B-cell lymphocyte (LBL), cytotoxic T-lymphocyte (CTL), and helper T-lymphocyte (HTL) epitopes targeting the viral envelope glycoprotein E۱/E۲ of CHIKV. The selection of each epitope was based on parameters such as antigenicity, immunogenicity, toxicity, and allergenicity. These selected epitopes were incorporated to generate refined and validated ۳D models of CHIKV-MEV. Molecular docking simulations were performed to assess the interaction between the generated ۳D model of CHIKV-MEV and TLR-۱/۲. Immune response simulations and population coverage analysis were conducted to evaluate the potential effectiveness of the vaccine. The proposed CHIKV-MEV consists of ۴۳۹ amino acids, encompassing ۱۸ epitopes, and exhibits predicted properties of being antigenic, immunogenic, non-allergenic, and non-toxic. The binding energy of -۱۰۷۹.۰ kcal/mol indicated that CHIKV-MEV can interact with TLR-۱/۲, leading to immune responses. Immune response simulations of CHIKV-MEV demonstrated an increase in immunoglobulin levels, as well as population of LBL, CTL, and HTL, and cytokine levels associated with the defence against viral infections. Furthermore, based on compatibility with human leukocyte antigen (HLA), CHIKV-MEV potentially covers ۹۶.۲۵% of the global population. This research contributes to the development of a globally applicable multi-epitope peptide-based vaccine against CHIKV, supported by comprehensive in vitro and in vivo studies.

Bioinformatics, Chikungunya virus, Envelope glycoprotein, Multiepitope, Vaccine