Exploring computational approaches to design mRNA Vaccine against vaccinia and Mpox viruses

IF 3.1 4区医学 Q3 IMMUNOLOGY

Immunity, Inflammation and Disease Pub Date : 2024-08-16 DOI:10.1002/iid3.1360

Elijah K. Oladipo, Olanrewaju D. Oyelakin, Abdulsamad O. Aiyelabegan, Elizabeth O. Olajide, Victoria O. Olatayo, Kaothar P. Owolabi, Yewande B. Shittu, Rhoda O. Olugbodi, Hezekiah A. Ajala, Raji A. Rukayat, Deborah O. Olayiwola, Boluwatife A. Irewolede, Esther M. Jimah, Julius K. Oloke, Taiwo O. Ojo, Olumide F. Ajani, Bamidele A. Iwalokun, Olatunji M. Kolawole, Olumuyiwa E. Ariyo, Daniel A. Adediran, Seun E. Olufemi, Helen Onyeaka

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引用次数: 0

Abstract

Background

Messenger RNA (mRNA) vaccines emerged as a powerful tool in the fight against infections. Unlike traditional vaccines, this unique type of vaccine elicits robust and persistent innate and humoral immune response with a unique host cell-mediated pathogen gene expression and antigen presentation.

Methods

This offers a novel approach to combat poxviridae infections. From the genome of vaccinia and Mpox viruses, three key genes (E8L, E7R, and H3L) responsible for virus attachment and virulence were selected and employed for designing the candidate mRNA vaccine against vaccinia and Mpox viral infection. Various bioinformatics tools were employed to generate (B cell, CTL, and HTL) epitopes, of which 28 antigenic and immunogenic epitopes were selected and are linked to form the mRNA vaccine construct. Additional components, including a 5′ cap, 5′ UTR, adjuvant, 3′ UTR, and poly(A) tail, were incorporated to enhance stability and effectiveness. Safety measures such as testing for human homology and in silico immune simulations were implemented to avoid autoimmunity and to mimics the immune response of human host to the designed mRNA vaccine, respectively. The mRNA vaccine's binding affinity was evaluated by docking it with TLR-2, TLR-3, TLR-4, and TLR-9 receptors which are subsequently followed by molecular dynamics simulations for the highest binding one to predict the stability of the binding complex.

Results

With a 73% population coverage, the mRNA vaccine looks promising, boasting a molecular weight of 198 kDa and a molecular formula of C₈₉₀₁H₁₃₆₀₉N₂₄₃₁O₂₆₁₁S₄₈ and it is said to be antigenic, nontoxic and nonallergic, making it safe and effective in preventing infections with Mpox and vaccinia viruses, in comparison with other insilico-designed vaccine for vaccinia and Mpox viruses.

Conclusions

However, further validation through in vivo and in vitro techniques is underway to fully assess its potential.

Abstract Image

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探索设计针对疫苗和 Mpox 病毒的 mRNA 疫苗的计算方法。

背景：信使核糖核酸（mRNA）疫苗是抗感染的有力工具。与传统疫苗不同，这种独特类型的疫苗通过独特的宿主细胞介导的病原体基因表达和抗原递呈，引起强大而持久的先天和体液免疫反应：方法：这为抗击痘病毒感染提供了一种新方法。方法：这提供了一种对抗痘病毒感染的新方法。从疫苗和 Mpox 病毒的基因组中筛选出了负责病毒附着和毒力的三个关键基因（E8L、E7R 和 H3L），并将其用于设计对抗疫苗和 Mpox 病毒感染的候选 mRNA 疫苗。利用各种生物信息学工具生成（B 细胞、CTL 和 HTL）表位，从中筛选出 28 个抗原性和免疫原性表位，并将其连接成 mRNA 疫苗构建体。此外还加入了其他成分，包括 5' cap、5' UTR、佐剂、3' UTR 和 poly(A) 尾部，以提高稳定性和有效性。为了避免自身免疫和模拟人类宿主对所设计的 mRNA 疫苗的免疫反应，还采取了一些安全措施，如人类同源性测试和硅学免疫模拟。通过与 TLR-2、TLR-3、TLR-4 和 TLR-9 受体对接，评估了 mRNA 疫苗的结合亲和力：该 mRNA 疫苗的群体覆盖率为 73%，分子量为 198 kDa，分子式为 C8901H13609N2431O2611S48，据说具有抗原性、无毒性和无过敏性，与其他针对疫苗和 Mpox 病毒的实验室设计疫苗相比，它在预防 Mpox 和疫苗病毒感染方面安全有效：然而，要全面评估其潜力，还需通过体内和体外技术进行进一步验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Immunity, Inflammation and Disease Medicine-Immunology and Allergy

CiteScore

3.60

自引率

0.00%

发文量

146

审稿时长

8 weeks

期刊介绍： Immunity, Inflammation and Disease is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research across the broad field of immunology. Immunity, Inflammation and Disease gives rapid consideration to papers in all areas of clinical and basic research. The journal is indexed in Medline and the Science Citation Index Expanded (part of Web of Science), among others. It welcomes original work that enhances the understanding of immunology in areas including: • cellular and molecular immunology • clinical immunology • allergy • immunochemistry • immunogenetics • immune signalling • immune development • imaging • mathematical modelling • autoimmunity • transplantation immunology • cancer immunology