In silico development of a novel anti-mutation, multi-epitope mRNA vaccine against MPXV variants of emerging lineage and sub-lineages by using immunoinformatics approaches

View Dataset
Tan, Caixia;Zhou, Jingxiang;Wu, Anhua;Li, Chunhui

Description

Over the past year, an unexpected surge in human monkeypox (hMPX) cases has been observed. This outbreak differs from previous ones, displaying distinct epidemiological characteristics and transmission patterns, believed to be influenced by a newly emerging monkeypox virus (MPXV) lineage. Notably, this emerging MPXV lineage has exhibited several non-synonymous mutations, some of which are linked to immunomodulatory activities and antigenic characteristics that aid in host detection. However, specific treatments or vaccines for human monkeypox are currently lacking. Hence, we aim to develop a multi-epitope mRNA vaccine by using immunoinformatics approaches against the MPXV, particularly its emerging variants. Six proteins (A29L, A35R, B6R, M1R, H3L, and E8L) were chosen for epitope and mutation site identification. Seventeen top-performing epitopes and eight epitopes containing mutation sites were selected and combined with adjuvants, the PADRE sequence, and linkers for vaccine development. The molecular and physical properties of the designed vaccine (WLmpx) were favorable. Immunological characteristics of WLmpx were assessed through molecular docking, molecular dynamics (MD) simulations, and immune simulations. Finally, the vaccine sequence was utilized to formulate an mRNA-based vaccine. The informatics-based predicted results indicated that the designed vaccine exhibits significant potential in eliciting high-level humoral and cellular immune responses, but further validation through in vivo and vitro studies is warranted.

Citations (0)

Mentions (0)

Metrics

Dataset Index

0.1

FAIR Score

85%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Taylor & Francis

Assigned Domain

Subfield

Molecular Biology

Field

Biochemistry, Genetics and Molecular Biology

Domain

Life Sciences

Confidence Score

59%

Source

Scholar Data Model

Keywords

BiochemistryMedicineMicrobiologyFOS: Biological sciencesGeneticsBiotechnologyEnvironmental Sciences not elsewhere classifiedImmunologyFOS: Clinical medicineBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedCancerInfectious DiseasesFOS: Health sciencesVirology

Normalization Factors

FT

30.77

CTw

1.00

MTw

1.00