Arnold Abakah

Ghana

Immunoinformatics approach for developing a multi-epitope vaccine against Pseudomonas aeruginosa infection

Arnold Abakah1, Prince Manu2, Paa Kwesi Anfu1, Kweku Foh Gyasi1, Helena Okyere2, Abdul Latif Koney Shardow3, Priscilla Osei-Poku1,3, Alexander Kwarteng1,3

1Department of Biochemistry and Biotechnology, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
2Department of Chemistry, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
3Kumasi Centre for Collaborative Research in Tropical Medicine (KCCR), Kumasi, Ghana

Abstract

Background

The rising threat of multidrug-resistant Pseudomonas aeruginosa has intensified the need for alternative therapeutic strategies, particularly vaccines that target virulence rather than viability. Quorum sensing (QS) regulators LasR and PqsR drive the pathogen’s ability to coordinate infection, evade immune responses, and persist within host environments, yet remain underexploited as vaccine targets. This study presents a computationally designed multi-epitope subunit vaccine aimed at disarming P. aeruginosa through immune recognition of its QS machinery.

Methods

Using immunoinformatics, machine learning-based epitope prediction, and reverse vaccinology approaches, immunodominant B-cell, MHC-I, and MHC-II epitopes were identified from the regulatory proteins. Six vaccine constructs were assembled with varied epitope arrangements, linked to a human β-defensin-3 adjuvant and TAT peptide for enhanced delivery and immunogenicity. Structural modeling, molecular docking with bacterial QS proteins, as well as Toll-like receptors (TLR 2/4), and immune simulations, were used to assess vaccine’s performance.

Results

The top construct exhibited favorable physicochemical properties, strong structural integrity, and potent in silico immunogenicity, including predicted induction of IFN-γ, memory T and B cells, and high-affinity interactions with both target proteins and immune receptors.

Conclusions

The study presents a promising computational vaccine candidate with novel potential to interfere with P. aeruginosa’s virulence strategies and support future translational development, although subsequent in vitro and in vivo validation is warranted.