مقالات پذیرفته شده در هشتمین کنگره بین المللی زیست پزشکی
In silico identification of B and T cell epitopes for the development of a vaccine against Human T-lymphotropic virus-1 (HTLV-1) Glycoprotein 62
In silico identification of B and T cell epitopes for the development of a vaccine against Human T-lymphotropic virus-1 (HTLV-1) Glycoprotein 62
Nayereh ShariatGonabadi,1Seyed Masoud HOSSEINI,2,*
1. Department of Cell and Molecular Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran 2. Department of Microbiology and Microbial Biotechnology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran
Introduction: Human T-cell leukemia virus type 1 (HTLV-1) is a human retrovirus that has been linked to cancer. It has been found in humans and has infected many people in the world. Over the decades, a significant effort has been to study the biological and disease-causing characteristics of HTLV-1. As a result, several experimental vaccination and treatment approaches have been developed to combat HTLV-1 infection. Although current therapies exhibit several benefits, they frequently encounter constraints, such as potentially reduced effectiveness caused by the genetic variability of HTLV-1 strains. Consequently, Additional research is needed to address these challenges and improve the effectiveness of epitope-based vaccines for HTLV-1. This immunoinformatic study examined the prediction of conformational linear B-cell and T-cell epitopes of the HTLV-1 glycoprotein 62 (gp62) viral protein, which plays a crucial role in virus entry, to assess their potential as vaccine candidates.
Methods: In this study, UniProt was used to retrieve all target protein amino acid sequences in FASTA format with the UniProt ID Q85611. The physicochemical properties of the target glycoprotein were analyzed using Expasy ProtParam. The antigenicity of each protein was evaluated utilizing the VaxiJen 2.0 server with a prediction threshold of 0.4. The target glycoprotein's 3D structure was modeled using Phyre2, Swissmodel, and I Tasser, popular homology modeling tools. Galaxy refine server reduced structure distortions after homology modeling to refine models. The models were then Ramachandran plotted using the RAMPAGE server to determine their quality and reliability.The models with the best results after all these analyses with high levels of coverage were extracted. The NetCTL.1.2 server predicted the 9-mer T cell epitopes recognized by the most common human HLA Class I supertypes: A1, A2, A3, A24, A26, B7, B8, B27, B39, B44, B58, and B62. NetCTL.1.2 server thresholds for Transporter Associated with Antigen Processing transport efficiency, proteasomal C-terminal cleavage, and epitope identification were 0.05, 0.15, and 0.75. Immune epitope database-consensus (IEDB) was also used to identify epitopes recognized by HLA Class I alleles A-02:01, B-35:01, B-51:01, and B-58:01. Net MHC II pan 3.2 server and IEDB NetMHCIIpan 4.1 EL (recommended epitope predictor-2023.09) predicted the 15-mer epitopes recognized by HLA Class II DRB1 alleles: 01:01, 03:01, 04:01, 07:01, 08:03, 10:01, 11:01, 12:01, 13:02, 14:01, and 15:01.The study's HLA Class I, II epitopes should cover over 70% of the global population. Then, overlapping epitopes with integral sequences of CTL and HTL epitopes can activate cytotoxic and helper T cells. The predicted promiscuous epitopes' antigenicity was assessed using VaxiJen server 2.0. We kept the antigenicity prediction threshold at 0.4. BCpred 2.0 server was employed for the identification of linear/continuous B cell epitopes. The predicted B cell epitopes were also experimented for antigenicity. Finally, the Ellipro server predicted conformational/discontinuous B cell epitopes. To predict using the Ellipro server, the minimum score and maximum distance (Angstrom) were set to 0.5 and 6 Å, respectively.
Results: Based on the computational analysis, two B cell epitopes were identified: DYSPSCCTLTIGVSSYHSKPCNP (spanning from 21–43) and MGKFLATLILFFQFCPLILGDY (spanning from 1–22). Additionally, two T-cell epitopes were discovered: LLFGYPVYV (ranging from 28-37) and ITWPLLPHV (ranging from 27-36). These epitopes were determined to be highly antigenic and immunogenic which could be highly potential to be utilized in vaccine structure.
Conclusion: In sum, this study highlights the capability of employing a bioinformatic method to discover new epitopes from gp62 for the design of epitope-based vaccines. This finding presents a promising lead for the development of novel HTLV-1 vaccines although further research is necessary to confirm the effectiveness and safety of these epitopes in preclinical models and clinical trials.