Design of a Multi-Epitope Vaccine Against Pseudomonas aeruginosa Using an Immunoinformatics Approach

Pseudomonas aeruginosa is a high-priority pathogen identified by the WHO. With the slow development of new antibiotics, alternative approaches are essential. This study aims to design a multi-epitope vaccine to prevent P. aeruginosa infections using reverse vaccinology and immunoinformatics. We screened the entire proteome of P. aeruginosa (5,563 proteins) for non-homology with the human proteome, stability, non-allergenicity, adhesion, and immunogenicity. The signal peptide-containing proteins like Probable Coat Protein A of bacteriophage Pf1, Alkaline Phosphatase L, and a Secreted Protein were identified as suitable vaccine targets. These targets underwent physicochemical analysis followed by prediction of B-cell, CTL, and HTL epitopes. Only antigenic, non-allergenic, and non-toxic epitopes were selected for molecular docking with human TLR2 and TLR4 receptors. We designed six vaccine candidates (DV1 - DV6) incorporating various adjuvants. The designed constructs were evaluated for physicochemical properties, and 3D structures were validated with a Ramachandran plot. Further molecular dynamics simulations indicated that DV5, containing HBHA conserved peptide sequence as an adjuvant, exhibited promising stable binding affinities with TLR2 and TLR4. The population coverage of DV5 was found to be 98.28% worldwide. Finally, the In-silico cloning was carried out to check the expression feasibility of the vaccine construct. In conclusion, this multi-epitope vaccine could be a potential candidate against P. aeruginosa infections, necessitating further research to confirm its safety and efficacy.

Authors

Institutions

Publication Details

Journal
In Silico Research in Biomedicine
Published
2026-09-01
DOI
https://doi.org/10.1016/j.insi.2026.100578
Primary Topic
vaccines and immunoinformatics approaches
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Design of a Multi-Epitope Vaccine Against Pseudomonas aeruginosa Using an Immunoinformatics Approach

Vasundhra Bhandari, Divya Vemula
In Silico Research in Biomedicine
vaccines and immunoinformatics approaches
article

Design of a Multi-Epitope Vaccine Against Pseudomonas aeruginosa Using an Immunoinformatics Approach

Vasundhra Bhandari, Divya Vemula
article en

Abstract

Pseudomonas aeruginosa is a high-priority pathogen identified by the WHO. With the slow development of new antibiotics, alternative approaches are essential. This study aims to design a multi-epitope vaccine to prevent P. aeruginosa infections using reverse vaccinology and immunoinformatics. We screened the entire proteome of P. aeruginosa (5,563 proteins) for non-homology with the human proteome, stability, non-allergenicity, adhesion, and immunogenicity. The signal peptide-containing proteins like Probable Coat Protein A of bacteriophage Pf1, Alkaline Phosphatase L, and a Secreted Protein were identified as suitable vaccine targets. These targets underwent physicochemical analysis followed by prediction of B-cell, CTL, and HTL epitopes. Only antigenic, non-allergenic, and non-toxic epitopes were selected for molecular docking with human TLR2 and TLR4 receptors. We designed six vaccine candidates (DV1 - DV6) incorporating various adjuvants. The designed constructs were evaluated for physicochemical properties, and 3D structures were validated with a Ramachandran plot. Further molecular dynamics simulations indicated that DV5, containing HBHA conserved peptide sequence as an adjuvant, exhibited promising stable binding affinities with TLR2 and TLR4. The population coverage of DV5 was found to be 98.28% worldwide. Finally, the In-silico cloning was carried out to check the expression feasibility of the vaccine construct. In conclusion, this multi-epitope vaccine could be a potential candidate against P. aeruginosa infections, necessitating further research to confirm its safety and efficacy.

In Silico Research in Biomedicine
National Institute of Pharmaceutical Education and Research - Ahmedabad (IN), National Institute of Pharmaceutical Education and Research (IN)
Good health and well-being
Openalex Percentile: Top 17%
vaccines and immunoinformatics approaches
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.