DESAIN MULTIEPITOP DAN ANALISIS DOCKING UNTUK DIAGNOSIS ANTIBODI SPESIFIK VIRUS ZIKA

• Main Author: Yaumil Fitri, Sakina
• Format: Final Project
• Language: Indonesia
• Subjects: Ilmu hayati ; Biologi
• Online Access: https://digilib.itb.ac.id/gdl/view/64081
• Institution: Institut Teknologi Bandung

The diagnosis of Zika virus (ZIKV) is very important because ZIKV is associated with other diseases such as Guillain-Barre syndrome and microcephaly. The similarity of the ZIKV genome to DENV causes the diagnosis of ZIKV to be quite susceptible to cross-reactive antibodies. Protein E are known to be antigenic markers and play a role in the pathogenicity of ZIKV infection. The use of multiepitope in serological diagnosis can increase the potential for the recognition of ZIKV-specific antibodies. In this study, multiepitope design and docking analysis were carried out for specific ZIKV, specific DENV, and cross-reactive antibodies through in-silico study. There are several stages of the method carried out: construction of phylogenetic tree, retrieval of ZIKV E protein sequences, epitope selection, multiepitope design with linkers, and multiepitope docking analysis with ZIKV-specific, DENV-specific antibodies, and cross-reactive antibodies using ClusPro. The ZIKV E protein sequences used were sequences from the ZIKV strain JMB-185. Three epitopes E1, E2, and E3 were selected, respectively, from the EDII and EDIII protein regions. Four multiepitope models M1, M2, M3, M4 were obtained which are a combination of 2-3 epitopes with linkers. The results of docking analysis with ClusPro showed that the M1 and M2 multiepitope models interacted very well with the ZIKV specific antibody, namely ZV48 with the lowest binding energy values of -560.3 and -589.8 kcal/mol, respectively. Models M3 and M4 interacted very well with ZIKV specific antibody, namely ZV64 with binding energy values of -561.5 and -698.8 kcal/mol, respectively. The antibodies ZV48 and ZV64 recognized the epitope of the EDIII region whereas the M2, M3, and M4 models contained the epitope of the EDIII region while the M1 model did not. Based on the results of visualization with PyMOL, the M1 and M2 models recognized the ZV48 antibody through the interaction of the E2 and E1 epitope of the EDII region which was suspected to be a false positive result. The M3 and M4 models recognize the ZV64 antibody through the interaction of the E3 epitope from the EDIII region which is known to be part of the ZV64 footprint so it is recommended.