In silico design and in vivo evaluation of two multi-epitope vaccines containing build-in adjuvant with chitosan nanoparticles against uropathogenic Escherichia coli

Rezaei, Maryam; Esmaeili, Fariba; Karam, Mohammad Reza Asadi; Ehsani, Parastoo; Farsangi, Zeinab Abbasnezhad; Bouzari, Saeid

doi:10.1016/j.intimp.2023.109999

Cited by 7 publications

(9 citation statements)

References 41 publications

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“…Due to its salt bridge coupled to lysine and glutamic acid, the EAAAK linker creates a stable helix structure and prevents protein domains from convergence 81 .CTB has been investigated as a conventional mucosal adjuvant that can increase the immunogenicity of vaccines 68,82 . Employing the adjuvant used by Mariam et al 83 and Tamalika et al 84 helped us achieve the desired stability while not affecting the antigenicity. The structure of the developed vaccine solution was predicted to allow easy access to the host.…”

Section: Discussionmentioning

confidence: 99%

Proteomics-based vaccine targets annotation and design of multi-epitope vaccine against antibiotic-resistant Streptococcus gallolyticus

Chao,

Zhang,

Zhang

et al. 2024

Sci Rep

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Streptococcus gallolyticus is a non-motile, gram-positive bacterium that causes infective endocarditis. S. gallolyticus has developed resistance to existing antibiotics, and no vaccine is currently available. Therefore, it is essential to develop an effective S. gallolyticus vaccine. Core proteomics was used in this study together with subtractive proteomics and reverse vaccinology approach to find antigenic proteins that could be utilized for the design of the S. gallolyticus multi-epitope vaccine. The pipeline identified two antigenic proteins as potential vaccine targets: penicillin-binding protein and the ATP synthase subunit. T and B cell epitopes from the specific proteins were forecasted employing several immunoinformatics and bioinformatics resources. A vaccine (360 amino acids) was created using a combination of seven cytotoxic T cell lymphocyte (CTL), three helper T cell lymphocyte (HTL), and five linear B cell lymphocyte (LBL) epitopes. To increase immune responses, the vaccine was paired with a cholera enterotoxin subunit B (CTB) adjuvant. The developed vaccine was highly antigenic, non-allergenic, and stable for human use. The vaccine's binding affinity and molecular interactions with the human immunological receptor TLR4 were studied using molecular mechanics/generalized Born surface area (MMGBSA), molecular docking, and molecular dynamic (MD) simulation analyses. Escherichia coli (strain K12) plasmid vector pET-28a ( +) was used to examine the ability of the vaccine to be expressed. According to the outcomes of these computer experiments, the vaccine is quite promising in terms of developing a protective immunity against diseases. However, in vitro and animal research are required to validate our findings.

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Section: Discussionmentioning

confidence: 99%

Proteomics-based vaccine targets annotation and design of multi-epitope vaccine against antibiotic-resistant Streptococcus gallolyticus

Chao,

Zhang,

Zhang

et al. 2024

Sci Rep

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“…We tested AutoEpiCollect's pan-cancer vaccine design on common cancers containing prevalent PIK3CA mutations. Previous cancer vaccines targeting the PIK3CA gene primarily target small sets of missense mutations, thus reducing the scope and efficiency of the vaccine [1][2][3][4][5]. Therefore, no clinical trials have been conducted with pan-cancer vaccines targeting the PIK3CA gene.…”

Section: Discussionmentioning

confidence: 99%

“…The development of these vaccine designs has been steadily increasing due to the ability of vaccines to safely boost the immune system's innate defense mechanisms against a broad number of diseases and pathogens. Some of the latest findings involving in silico vaccine designs include potential methods of protection against different viruses, bacteria, and some single-celled organisms [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA Neoantigens

Samudrala,

Dhaveji,

Savsani

et al. 2024

Bioengineering

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Previous epitope-based cancer vaccines have focused on analyzing a limited number of mutated epitopes and clinical variables preliminarily to experimental trials. As a result, relatively few positive clinical outcomes have been observed in epitope-based cancer vaccines. Further efforts are required to diversify the selection of mutated epitopes tailored to cancers with different genetic signatures. To address this, we developed the first version of AutoEpiCollect, a user-friendly GUI software, capable of generating safe and immunogenic epitopes from missense mutations in any oncogene of interest. This software incorporates a novel, machine learning-driven epitope ranking method, leveraging a probabilistic logistic regression model that is trained on experimental T-cell assay data. Users can freely download AutoEpiCollectGUI with its user guide for installing and running the software on GitHub. We used AutoEpiCollect to design a pan-cancer vaccine targeting missense mutations found in the proto-oncogene PIK3CA, which encodes the p110ɑ catalytic subunit of the PI3K kinase protein. We selected PIK3CA as our gene target due to its widespread prevalence as an oncokinase across various cancer types and its lack of presence as a gene target in clinical trials. After entering 49 distinct point mutations into AutoEpiCollect, we acquired 361 MHC Class I epitope/HLA pairs and 219 MHC Class II epitope/HLA pairs. From the 49 input point mutations, we identified MHC Class I epitopes targeting 34 of these mutations and MHC Class II epitopes targeting 11 mutations. Furthermore, to assess the potential impact of our pan-cancer vaccine, we employed PCOptim and PCOptim-CD to streamline our epitope list and attain optimized vaccine population coverage. We achieved a world population coverage of 98.09% for MHC Class I data and 81.81% for MHC Class II data. We used three of our predicted immunogenic epitopes to further construct 3D models of peptide-HLA and peptide-HLA-TCR complexes to analyze the epitope binding potential and TCR interactions. Future studies could aim to validate AutoEpiCollect’s vaccine design in murine models affected by PIK3CA-mutated or other mutated tumor cells located in various tissue types. AutoEpiCollect streamlines the preclinical vaccine development process, saving time for thorough testing of vaccinations in experimental trials.

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“…A recent study demonstrated better immunogenicity of nanoparticle-encapsulated subunit vaccines against UPEC in a mouse model. 147 Here, the researchers developed 2 vaccines, each containing B- or T-cell epitopes of previously explored antigens FdeC (adherence factor), Hma (adhesion autotransporter), and UpaB (iron receptor) with the cholera toxin subunit B adjuvant, encapsulated in chitosan nanoparticles. 147 When intranasally delivered to mice, the B cell construct generated high levels of IgG1 and IL-4 characteristic of T H 2 response, while the T cell construct elicited high levels of IgG2a and IFNγ characteristic of T H 1 response.…”

Section: New Vaccine Technologies For Expecmentioning

confidence: 99%

“… 147 Here, the researchers developed 2 vaccines, each containing B- or T-cell epitopes of previously explored antigens FdeC (adherence factor), Hma (adhesion autotransporter), and UpaB (iron receptor) with the cholera toxin subunit B adjuvant, encapsulated in chitosan nanoparticles. 147 When intranasally delivered to mice, the B cell construct generated high levels of IgG1 and IL-4 characteristic of T H 2 response, while the T cell construct elicited high levels of IgG2a and IFNγ characteristic of T H 1 response. 147 Although mice immunized with either nanoparticle coated or non-coated vaccines were protected from UPEC colonization in bladder, the coated vaccines produced significantly stronger antibody- and cell-mediated immune responses.…”

Section: New Vaccine Technologies For Expecmentioning

confidence: 99%

Vaccines against extraintestinal pathogenic Escherichia coli (ExPEC): progress and challenges

Qiu,

Chirman,

Clark

et al. 2024

Gut Microbes

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In silico design and in vivo evaluation of two multi-epitope vaccines containing build-in adjuvant with chitosan nanoparticles against uropathogenic Escherichia coli

Cited by 7 publications

References 41 publications

Proteomics-based vaccine targets annotation and design of multi-epitope vaccine against antibiotic-resistant Streptococcus gallolyticus

Proteomics-based vaccine targets annotation and design of multi-epitope vaccine against antibiotic-resistant Streptococcus gallolyticus

AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA Neoantigens

Vaccines against extraintestinal pathogenic Escherichia coli (ExPEC): progress and challenges

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