Computational prediction of a multi-epitope Human Metapneumovirus vaccine candidate through integrated reverse vaccinology and pan-genomic approaches
5 affiliations
- 1Analytical Research and Development, The ACME Laboratories Ltd, Dhulivita, Dhamrai, Dhaka, Bangladesh
- 2Microbiology Program, Department of Mathematics and Natural Sciences, BRAC University, Dhaka, Bangladesh
- 3One Health Laboratory, International Centre for Diarrheal Diseases Research, Bangladesh (icddr, b), Dhaka, Bangladesh
- 4Laboratory of Pharmaceutical Biotechnology and Bioinformatics, Jashore University of Science and Technology, Jashore, Bangladesh
- 5Infinity Research and Innovation Institute, Dhaka, Bangladesh
Abstract
Human metapneumovirus (HMPV) is a primary cause of global respiratory infections yet no approved vaccine currently exists. This study computationally predicts a multi-epitope vaccine candidate using a diverse dataset of 65 HMPV sequences spanning five continents. Following the screening of lead proteins for antigenicity and virulence, fifteen highly conserved MHC-I, MHC-II and B-cell epitopes were prioritized. These were integrated with a putative L7/L12 adjuvant using optimized AAY, GPGPG, and KK linkers to design three constructs (HMPV_V1–V3). Structural validation identified HMPV-V2 as the lead candidate that exhibits a Z-score of-5.24 and 87.7% of residues in favored Ramachandran regions indicating excellent stereochemical quality and structural stability. In silico docking indicated a strong predicted binding affinity between HMPV-V2 and the TLR4 receptor (energy: −969.2). Immune simulations predicted a robust adaptive response characterized by high IgG1 titers, memory B-cell maturation, and a Th1-dominant cytokine profile. Furthermore, molecular dynamics simulations suggested exceptional structural integrity for HMPV-V2, maintaining a low RMSD of 8.213 and RMSF of 0.737 throughout the simulation. Optimized in silico cloning into the pET28a (+) vector indicated a high potential for protein expression in E. coli systems. While these findings provide a theoretically grounded blueprint for vaccine development, this study is entirely computational and lacks experimental validation. Further in vitro and in vivo testing is required to confirm the actual safety and immunogenicity of the proposed candidate.
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BibTeX
@article{alam2026computational,
title = {{Computational prediction of a multi-epitope Human Metapneumovirus vaccine candidate through integrated reverse vaccinology and pan-genomic approaches}},
author = {Nazia Fairooz Alam and Moumita Chakrabarty and Tanmoy Debnath and Mohammad Ashik Sheikh and Md Imtiaz and Sadia Jannat Tauhida and Md Nazmul Hasan and Md Mohaimenul Islam Tareq},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0357758},
url = {https://doi.org/10.1371/journal.pone.0357758},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Computational prediction of a multi-epitope Human Metapneumovirus vaccine candidate through integrated reverse vaccinology and pan-genomic approaches
AU - Nazia Fairooz Alam
AU - Moumita Chakrabarty
AU - Tanmoy Debnath
AU - Mohammad Ashik Sheikh
AU - Md Imtiaz
AU - Sadia Jannat Tauhida
AU - Md Nazmul Hasan
AU - Md Mohaimenul Islam Tareq
PY - 2026
DA - 2026/09/15
JO - PLOS One
DO - 10.1371/journal.pone.0357758
UR - https://doi.org/10.1371/journal.pone.0357758
AB - Human metapneumovirus (HMPV) is a primary cause of global respiratory infections yet no approved vaccine currently exists. This study computationally predicts a multi-epitope vaccine candidate using a diverse dataset of 65 HMPV sequences spanning five continents. Following the screening of lead proteins for antigenicity and virulence, fifteen highly conserved MHC-I, MHC-II and B-cell epitopes were prioritized. These were integrated with a putative L7/L12 adjuvant using optimized AAY, GPGPG, and KK linkers to design three constructs (HMPV_V1–V3). Structural validation identified HMPV-V2 as the lead candidate that exhibits a Z-score of-5.24 and 87.7% of residues in favored Ramachandran regions indicating excellent stereochemical quality and structural stability. In silico docking indicated a strong predicted binding affinity between HMPV-V2 and the TLR4 receptor (energy: −969.2). Immune simulations predicted a robust adaptive response characterized by high IgG1 titers, memory B-cell maturation, and a Th1-dominant cytokine profile. Furthermore, molecular dynamics simulations suggested exceptional structural integrity for HMPV-V2, maintaining a low RMSD of 8.213 and RMSF of 0.737 throughout the simulation. Optimized in silico cloning into the pET28a (+) vector indicated a high potential for protein expression in E. coli systems. While these findings provide a theoretically grounded blueprint for vaccine development, this study is entirely computational and lacks experimental validation. Further in vitro and in vivo testing is required to confirm the actual safety and immunogenicity of the proposed candidate.
SN - 1932-6203
ER -