In silico design of a multiepitope vaccine against antibiotic drug-resistant Acinetobacter baumannii

Acinetobacter baumannii, a member of the ESKAPE pathogens, causes approximately 722,000 infections annually and is notorious for rapidly developing resistance to multiple antibiotics, resulting in poor clinical outcomes. Due to the rapid emergence of drug-resistant strains and limited effective treatments, the World Health Organization has listed A. baumannii as a top-priority pathogen for vaccine development, highlighting the urgent need for novel therapeutic strategies. In this study, we employed an in silico approach to design a multiepitope vaccine (MEV) targeting multidrug-resistant strains by analyzing RNA-seq data from meropenem-resistant isolates. The vaccine non-toxicity, probably antigenic, and non-allergenic nature was predicated via ToxinPred, VaxiJen v2.0, and AllerTOP v2.0 tools. CTL and HTL epitope prediction was carried out using NetMHCpan and NetMHCIIpan respectively. PEP-FOLD3.5 server predicted T-cell epitopes 3D structure. Molecular docking of the modeled epitopes into the MHC binding grooves was performed using the HADDOCK 2.4 web server for strong binding interactions. MD simulations were carried out using GROMACS version 2021.4 for the MEV stability and PCA was performed to identify dominant conformational states during the simulation. C-ImmSim server was used for the simulation of immune response. RNA-seq analysis from meropenem resistant A. baumannii strains, we identified 1,240 significantly upregulated genes linked to resistance mechanisms. From 156 non-homologous proteins, 12 highly conserved, non-allergenic antigenic proteins were selected to predict 12 CTL and 7 HTL epitopes with strong HLA-binding affinity and immunogenic potential. Molecular docking and dynamics simulations confirmed stable epitope-MHC interactions and structural stability of the 335-amino-acid MEV construct, while immune simulations indicated robust activation of innate and adaptive responses. This in silico study lays a promising foundation for vaccine development against A. baumannii and provides a critical step toward future in vitro and in vivo validation.

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Journal
Scientific Reports
Published
2026-04-03
DOI
https://doi.org/10.1038/s41598-025-30795-8
Primary Topic
vaccines and immunoinformatics approaches
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article
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article

In silico design of a multiepitope vaccine against antibiotic drug-resistant Acinetobacter baumannii

Muhammad Ajmal Khan, Sudeep Khadka, Noor Zada Khan, Shahid Iqbal et al.
Scientific Reports
vaccines and immunoinformatics approaches
article

In silico design of a multiepitope vaccine against antibiotic drug-resistant Acinetobacter baumannii

Muhammad Ajmal Khan, Sudeep Khadka, Noor Zada Khan, Shahid Iqbal, Aishma khattak, Amin Ullah
article en

Abstract

Acinetobacter baumannii, a member of the ESKAPE pathogens, causes approximately 722,000 infections annually and is notorious for rapidly developing resistance to multiple antibiotics, resulting in poor clinical outcomes. Due to the rapid emergence of drug-resistant strains and limited effective treatments, the World Health Organization has listed A. baumannii as a top-priority pathogen for vaccine development, highlighting the urgent need for novel therapeutic strategies. In this study, we employed an in silico approach to design a multiepitope vaccine (MEV) targeting multidrug-resistant strains by analyzing RNA-seq data from meropenem-resistant isolates. The vaccine non-toxicity, probably antigenic, and non-allergenic nature was predicated via ToxinPred, VaxiJen v2.0, and AllerTOP v2.0 tools. CTL and HTL epitope prediction was carried out using NetMHCpan and NetMHCIIpan respectively. PEP-FOLD3.5 server predicted T-cell epitopes 3D structure. Molecular docking of the modeled epitopes into the MHC binding grooves was performed using the HADDOCK 2.4 web server for strong binding interactions. MD simulations were carried out using GROMACS version 2021.4 for the MEV stability and PCA was performed to identify dominant conformational states during the simulation. C-ImmSim server was used for the simulation of immune response. RNA-seq analysis from meropenem resistant A. baumannii strains, we identified 1,240 significantly upregulated genes linked to resistance mechanisms. From 156 non-homologous proteins, 12 highly conserved, non-allergenic antigenic proteins were selected to predict 12 CTL and 7 HTL epitopes with strong HLA-binding affinity and immunogenic potential. Molecular docking and dynamics simulations confirmed stable epitope-MHC interactions and structural stability of the 335-amino-acid MEV construct, while immune simulations indicated robust activation of innate and adaptive responses. This in silico study lays a promising foundation for vaccine development against A. baumannii and provides a critical step toward future in vitro and in vivo validation.

Scientific ReportsVol. 16(1)
Shaheed Benazir Bhutto Women University Peshawar (PK), University of Maryland, Baltimore (US), The University of Agriculture, Peshawar (PK), King Saud University (SA), Iqra National University (PK), University of Peshawar (PK), Sidney Kimmel Comprehensive Cancer Center (US)
Good health and well-being
Openalex Percentile: Top 35%
vaccines and immunoinformatics approaches
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