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Advanced in silico design of an optimized multi-epitope peptide vaccine employing immunoinformatics and reverse vaccinology strategies on the model of Listeria monocytogenes

Authors:
Mainak Bhattacharjee at Heritage Institute of Technology
Mainak Bhattacharjee
Monojit Banerjee at TDB college
Monojit Banerjee
  • TDB college
Arun Mukherjee
Arun Mukherjee
Arun Mukherjee
  • This person is not on ResearchGate, or hasn't claimed this research yet.
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Abstract and Figures

Listeria monocytogenes is a food-borne pathogen responsible for causing listeriosis with severe consequences in expectant women and immunodeficient individuals due to age, viral infections, or transplants. Despite its alarming mortality rate of 21–50%, there is currently no appropriate medication or protective measure available to prevent the infection in humans. In this context, our current research intends to devise a proficient anti-listeriosis vaccine through thoughtful exploration of reverse vaccinology tools. We examined 368 protein sequences of L. monocytogenes strain CLIP80459 and culled 29 of them as the most potent immunogens. We then followed a stringent subtractive selection strategy to identify 11 cytotoxic T-cell, 9 helper T-cell, and 8 linear B-cell epitopes from the preselected antigens, based on multiple relevant structural, chemical, and immunological features and population coverage. We merged these epitopes using appropriate linkers and included an adjuvant to create the fused peptide vaccine. The physico-chemical and immunological properties of the chimeric peptide were modelled and analyzed, revealing it to be stable, non-toxic, non-allergenic, and highly soluble. Additional investigations involving molecular docking studies followed by molecular dynamics simulation and immune simulation revealed that the designed vaccine is adequately immunogenic and capable of stable, extensive interactions with HLA and TLR2, leading to activation of humoral and cell-mediated immunity. The peptide’s suitability for recombinant expression and simple purification using an E. coli host was demonstrated through in silico cloning studies. Thus, our study led to the development of a preventive yet safe vaccine against listeriosis that awaits wet-lab validation.
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Vol.:(0123456789)
Journal of Proteins and Proteomics (2025) 16:213–236
https://doi.org/10.1007/s42485-025-00185-9
RESEARCH
Advanced insilico design ofanoptimized multi‑epitope peptide
vaccine employing immunoinformatics andreverse vaccinology
strategies onthemodel ofListeria monocytogenes
MainakBhattacharjee1· MonojitBanerjee2· ArunMukherjee2
Received: 10 December 2024 / Revised: 21 March 2025 / Accepted: 29 March 2025 / Published online: 13 April 2025
© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025
Abstract
Listeria monocytogenes is a food-borne pathogen responsible for causing listeriosis with severe consequences in expectant
women and immunodeficient individuals due to age, viral infections, or transplants. Despite its alarming mortality rate of
21–50%, there is currently no appropriate medication or protective measure available to prevent the infection in humans.
In this context, our current research intends to devise a proficient anti-listeriosis vaccine through thoughtful exploration of
reverse vaccinology tools. We examined 368 protein sequences of L. monocytogenesstrain CLIP80459 and culled 29 of them
as the most potent immunogens. We then followed a stringent subtractive selection strategy to identify 11 cytotoxic T-cell,
9 helper T-cell, and 8 linear B-cell epitopes from the preselected antigens, based on multiple relevant structural, chemical,
and immunological features and population coverage. We merged these epitopes using appropriate linkers and included an
adjuvant to create the fused peptide vaccine. The physico-chemical and immunological properties of the chimeric peptide
were modelled and analyzed, revealing it to be stable, non-toxic, non-allergenic, and highly soluble. Additional investigations
involving molecular docking studies followed by molecular dynamics simulation and immune simulation revealed that the
designed vaccine is adequately immunogenic and capable of stable, extensive interactions with HLA and TLR2, leading to
activation of humoral and cell-mediated immunity. The peptide’s suitability for recombinant expression and simple purifi-
cation using an E. coli host was demonstrated through in silico cloning studies. Thus, our study led to the development of a
preventive yet safe vaccine against listeriosis that awaits wet-lab validation.
Keywords Listeriosis· Reverse vaccinology· Epitopes· Immunogenic· Food poisoning· Multi-epitope vaccine
Introduction
Leveraging the enormous potential of computer-aided vac-
cine or drug design has now become an indispensable tool
for biological researchers. The prime advantages like cost-
effectiveness and getting predicted outcomes at much less
time in comparison with traditional vaccine development
become a popular choice before delving into experimen-
tal research. Immunoinformatics is another popular area
of bioinformatics. It uses large amounts of genomic or
proteomic data along with well-known algorithm-based
tools to guess antigen properties or even the results of
immunological reactions that happen inside the body.
Unquestionably, this is an invaluable tool to screen the key
pathogenic components for vaccine development, espe-
cially for those pathogens that are difficult to culture or
have variable genetic diversity (Mushebenge etal. 2023;
Shaker etal. 2021). The development of a vaccine against
Neisseria meningitides serogroup B (MenB) has surpassed
the traditional vaccine development and brought a revo-
lution to introduce the concept of reverse vaccinology
(Masignani etal. 2019). Despite the significant potential
of immunoinformatics in vaccine construction, we must
conduct experimental validation to confirm the accuracy
of algorithm-based predictions. The point of this article
is to stress how powerful immunoinformatics can be in
creating multiple epitope-based vaccines using Listeria
Mainak Bhattacharjee and Monojit Banerjee Contributed equally.
* Arun Mukherjee
arunmukherjee16@gmail.com
1 Department ofBiotechnology, Heritage Institute
ofTechnology, 994, Madurdaha, Kolkata700107, India
2 Department ofZoology, Triveni Devi Bhalotia College,
Raniganj713347, India
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