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Designing a Novel Peptide-Based Multi-Epitope Vaccine to Evoke a Robust Immune Response against Pathogenic
Muhammad Naveed1, Mohsin Sheraz1, Aatif Amin2
1Department of Biotechnology, Faculty of Life Sciences, University of Central Punjab, Lahore 54590, Pakistan.
Abstract:
Providencia heimbachae, a Gram -ve, rod-shaped, and opportunistic bacteria isolated from the urine, feces, and skin of humans engage in a wide range of infectious diseases such as urinary tract infection (UTI), gastroenteritis, and bacteremia. This bacterium belongs to the Enterobacteriaceae family and can resist antibiotics known as multidrug-resistant (MDR), and as such can be life-threatening to humans. After retrieving the whole proteomic sequence of P. heimbachae ATCC 35613, a total of 6 non-homologous and pathogenic proteins were separated. These shortlisted proteins were further analyzed for epitope prediction and found to be highly non-toxic, non-allergenic, and antigenic. From these sequences, T-cell and B-cell (major histocompatibility complex class 1 and 2) epitopes were extracted that provided vaccine constructs, which were then analyzed for population coverage to find its reliability worldwide. The population coverage for MHC-1 and MHC-2 was 98.29% and 81.81%, respectively. Structural prediction was confirmed by validation through physiochemical molecular and immunological characteristics to design a stable and effective vaccine that could give positive results when injected into the body of the organism. Due to this approach, computational vaccines could be an effective alternative against pathogenic microbe since they cover a large population with positive results. In the end, the given findings may help the experimental vaccinologists to develop a very potent and effective peptide-based vaccine.
Insights
This study developed a computational vaccine against the opportunistic pathogen *Providencia heimbachae*. The peptide-based vaccine targets key proteins, offering high population coverage and a promising alternative to traditional vaccines.
Area of Science:
- Microbiology
- Immunology
- Computational Biology
Background:
- *Providencia heimbachae* is an opportunistic Gram-negative bacterium causing various infections like UTIs and bacteremia.
- This pathogen exhibits multidrug resistance (MDR), posing a significant threat to human health.
- Current treatment options are limited due to antibiotic resistance.
Purpose of the Study:
- To computationally design a novel peptide-based vaccine against *Providencia heimbachae*.
- To identify and analyze immunogenic, non-toxic, and non-allergenic pathogenic proteins for vaccine development.
- To predict and evaluate the population coverage of potential vaccine epitopes.
Main Methods:
- Whole proteomic sequencing of *P. heimbachae* ATCC 35613.
- Identification of six non-homologous pathogenic proteins.
- Epitope prediction for T-cell and B-cell targets (MHC class I and II).
- Structural prediction and validation using physiochemical and immunological characteristics.
Main Results:
- Six pathogenic proteins were identified and analyzed for epitope potential.
- Predicted epitopes were non-toxic, non-allergenic, and highly antigenic.
- High population coverage was predicted for MHC-1 (98.29%) and MHC-2 (81.81%) epitopes.
- A stable and effective vaccine construct was computationally designed.
Conclusions:
- Computational vaccine design offers a viable strategy against multidrug-resistant bacteria like *P. heimbachae*.
- The developed vaccine construct demonstrates high potential for broad population coverage.
- This approach can guide experimental vaccinologists in creating effective peptide-based vaccines.
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