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<p>Emerging groups/subgroups of hMPV, a respiratory pathogen, pose a public health concern. Despite its global prevalence and recent outbreaks, no vaccines or targeted therapies exist. Using an immunoinformatics approach, chimeric messenger RNA (mRNA) vaccines targeting the major structural proteins of hMPV, including the glycoprotein (G), fusion protein (F), small hydrophobic protein (SH), and matrix protein (M), was aimed to be developed. The target protein sequences, formatted in FASTA, were sourced from the NCBI Virus Resource, followed by redundancy removal using CD-Hit. Epitope prediction for B-cells, Cytotoxic T-cells (CTLs), and the Helper T-cells (HTLs) was conducted using ABCpred, IEDB’s ANNs 4.0, and artificial neural network-based alignment tool (NN-align 2.3)/ML-based tool (NetMHCII 2.3). Using immunoinformatics platforms, the construct’s physiochemical characteristics, structural modeling (secondary and tertiary), molecular docking (MD), normal mode analysis (NMA), and molecular dynamic simulation (MDS) with TLRs and MHCs were accomplished. The hMPVbeta1 vaccine construct was identified as the most promising candidate, demonstrating a nonallergenic profile and non-toxic properties, with a predicted antigenicity score (PAS) = 0.746 having 383 residues, a molecular weight of 39,633.71 Da, pI of 9.92, and favorable stability parameters (AI: 68.75, GRAVY: -0.260, I-i: 31.24). It exhibited high solubility (score: 0.784). The ProSA Z-score of − 8.38 confirmed the structural stability, reliability, and precision of the hMPVbeta1 3D model, comparable to experimental structures. Furthermore, 97.7% of all the residues located within favored or allowed regions in a crucial Ramachandran plot confirmed the model’s exceptional structural integrity and quality. TLR4-hMPV beta1 interaction involved <i>n</i> = 36 and <i>n</i> = 30 residues, respectively, establishing <i>n</i> = 08 salt bridges, <i>n</i> = 16 hydrogen bonds, and <i>n</i> = 214 nonbonded contacts across a 1610–1717 Ų interface, signifying robust, rigid docking stability. Docking evaluation of hMPVbeta1 with TLR2, MHC class I, and MHC class II exhibited robust polar and non-polar interactions, signifying strong binding stability. NMA and MDS of the docked complexes suggest their ability to enhance immune receptor activation under physiological conditions. Among the analyzed complexes, hMPVbeta1 was predicted to trigger a robust immune response with broad global population coverage. Based on the evaluated parameters, the vaccine constructs designed in this study exhibited significant potential as effective candidates against hMPV. This study lays the groundwork for developing an efficient hMPV vaccine, with further experimental validation needed to confirm the computational findings.</p>

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Rational design and in silico characterization of a multiepitope mRNA vaccine candidate against human metapneumovirus (hMPV) using reverse vaccinology and immunoinformatics approaches

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月24日

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  • Mohammad Asrar Izhari,
  • Ahmed R. A. Gosady,
  • Fahad Alghamdi,
  • Wael A. Alghamdi,
  • Mansour A. A. Hadadi,
  • Ahmad H. A. Almontasheri,
  • Daifallah M. M. Dardari,
  • Ahmad A. Salem,
  • Foton E. Alotaibi

摘要

Emerging groups/subgroups of hMPV, a respiratory pathogen, pose a public health concern. Despite its global prevalence and recent outbreaks, no vaccines or targeted therapies exist. Using an immunoinformatics approach, chimeric messenger RNA (mRNA) vaccines targeting the major structural proteins of hMPV, including the glycoprotein (G), fusion protein (F), small hydrophobic protein (SH), and matrix protein (M), was aimed to be developed. The target protein sequences, formatted in FASTA, were sourced from the NCBI Virus Resource, followed by redundancy removal using CD-Hit. Epitope prediction for B-cells, Cytotoxic T-cells (CTLs), and the Helper T-cells (HTLs) was conducted using ABCpred, IEDB’s ANNs 4.0, and artificial neural network-based alignment tool (NN-align 2.3)/ML-based tool (NetMHCII 2.3). Using immunoinformatics platforms, the construct’s physiochemical characteristics, structural modeling (secondary and tertiary), molecular docking (MD), normal mode analysis (NMA), and molecular dynamic simulation (MDS) with TLRs and MHCs were accomplished. The hMPVbeta1 vaccine construct was identified as the most promising candidate, demonstrating a nonallergenic profile and non-toxic properties, with a predicted antigenicity score (PAS) = 0.746 having 383 residues, a molecular weight of 39,633.71 Da, pI of 9.92, and favorable stability parameters (AI: 68.75, GRAVY: -0.260, I-i: 31.24). It exhibited high solubility (score: 0.784). The ProSA Z-score of − 8.38 confirmed the structural stability, reliability, and precision of the hMPVbeta1 3D model, comparable to experimental structures. Furthermore, 97.7% of all the residues located within favored or allowed regions in a crucial Ramachandran plot confirmed the model’s exceptional structural integrity and quality. TLR4-hMPV beta1 interaction involved n = 36 and n = 30 residues, respectively, establishing n = 08 salt bridges, n = 16 hydrogen bonds, and n = 214 nonbonded contacts across a 1610–1717 Ų interface, signifying robust, rigid docking stability. Docking evaluation of hMPVbeta1 with TLR2, MHC class I, and MHC class II exhibited robust polar and non-polar interactions, signifying strong binding stability. NMA and MDS of the docked complexes suggest their ability to enhance immune receptor activation under physiological conditions. Among the analyzed complexes, hMPVbeta1 was predicted to trigger a robust immune response with broad global population coverage. Based on the evaluated parameters, the vaccine constructs designed in this study exhibited significant potential as effective candidates against hMPV. This study lays the groundwork for developing an efficient hMPV vaccine, with further experimental validation needed to confirm the computational findings.