Development of a novel multiepitope vaccine against Menangle virus (MenV) using in-silico approaches by targeting its transmembrane proteins

Menangle Virus (MenV) is a zoonotic pathogenic virus that can penetrate humans, causing infection in them. It is an essential threat to public health due to the possibility of cross-species transmissions. Traditional vaccine development methods are time-consuming and resource-intensive, highlighting the need for innovative approaches. This study uses computational techniques to develop a multiepitope vaccine for MenV. We employed in silico tools to find potential B-cell, T-cell, and MHC-binding epitopes in the viral proteome. To ensure their safety and efficacy, these epitopes were evaluated for antigenicity, allergenicity, and toxicity. The selected epitopes were assembled into a multiepitope construct optimized for molecular stability and immunogenicity. Studies of molecular docking and MD simulations were considered to assess the interaction of vaccine candidates with human receptors, indicating a strong immune response. The goal of developing this vaccine is to prepare for potential future outbreaks. This in-silico vaccine design proposes a promising, efficient path to developing effective preventative measures against MenV, potentially expediting vaccine production and contributing to world health security.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-04-21
DOI
https://doi.org/10.1038/s41598-025-98151-4
Citations
5
Primary Topic
Virology and Viral Diseases
Type
article
Field-Weighted Citation Impact
6.77

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development of a novel multiepitope vaccine against Menangle virus (MenV) using in-silico approaches by targeting its transmembrane proteins

Abdullah F. Alasmari, Ayaz Ali Khan, Tariq Aziz, Thamer H. Albekairi et al.
5 citations
Scientific Reports
Virology and Viral Diseases
6.77
article

Development of a novel multiepitope vaccine against Menangle virus (MenV) using in-silico approaches by targeting its transmembrane proteins

Abdullah F. Alasmari, Ayaz Ali Khan, Tariq Aziz, Thamer H. Albekairi, Adeeba Ali, Fatima Noor, Metab Alharbi, Furrmein Fatima, Samreen Amjad, Muhammad Naveed
article en
5 citations

Abstract

Menangle Virus (MenV) is a zoonotic pathogenic virus that can penetrate humans, causing infection in them. It is an essential threat to public health due to the possibility of cross-species transmissions. Traditional vaccine development methods are time-consuming and resource-intensive, highlighting the need for innovative approaches. This study uses computational techniques to develop a multiepitope vaccine for MenV. We employed in silico tools to find potential B-cell, T-cell, and MHC-binding epitopes in the viral proteome. To ensure their safety and efficacy, these epitopes were evaluated for antigenicity, allergenicity, and toxicity. The selected epitopes were assembled into a multiepitope construct optimized for molecular stability and immunogenicity. Studies of molecular docking and MD simulations were considered to assess the interaction of vaccine candidates with human receptors, indicating a strong immune response. The goal of developing this vaccine is to prepare for potential future outbreaks. This in-silico vaccine design proposes a promising, efficient path to developing effective preventative measures against MenV, potentially expediting vaccine production and contributing to world health security.

Scientific ReportsVol. 15(1)
University of Central Punjab (PK), King Saud University (SA), University of Malakand (PK)
King Saud University
Zero hunger
Openalex Percentile: Top 3%
Virology and Viral Diseases
6.77
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.