Evidence map›Paper›PMID 36459272›Full record

ArticleImmunologic research2023

Immunoinformatic-guided designing of multi-epitope vaccine construct against Brucella Suis 1300.

Khurshid Jalal, Kanwal Khan, Reaz Uddin

Open access · bronzeAbstract read
In one paragraph

Article in Immunologic research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
1.6field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 21 citations in OpenAlex.

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  15. Artificial intelligence-driven reverse vaccinology forFrontiers in molecular biosciences · 2024
    Article
  16. Frontiers in cellular and infection microbiology · 2024
    Review
  17. Article
  18. Computational and structural biotechnology journal · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Khurshid JalalHEJ Research Institute of Chemistry International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan.
Kanwal KhanLab 103 PCMD Ext. Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan.
Reaz UddinLab 103 PCMD Ext. Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan. mriazuddin@iccs.edu.ORCID 0000-0002-9928-4482
University of Karachi · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brucella suis mediates the transmission of brucellosis in humans and animals and a significant facultative zoonotic pathogen found in livestock. It has the capacity to survive and multiply in a phagocytic environment and to acquire resistance under hostile conditions thus becoming a threat globally. Antibiotic resistance is posing a substantial public health threat, hence there is an unmet and urgent clinical need for immune-based non-antibiotic methods to treat brucellosis. Hence, we aimed to explore the whole proteome of Brucella suis to predict antigenic proteins as a vaccine target and designed a novel chimeric vaccine (multi-epitope vaccine) through subtractive genomics-based reverse vaccinology approaches. The applied subsequent hierarchical shortlisting resulted in the identification of Multidrug efflux Resistance-nodulation-division (RND) transporter outer membrane subunit (gene BepC) that may act as a potential vaccine target. T-cell and B-cell epitopes have been predicted from target proteins using a number of immunoinformatic methods. Six MHC I, ten MHC II, and four B-cell epitopes were used to create a 324-amino-acid MEV construct, which was coupled with appropriate linkers and adjuvant. To boost the immunological response to the vaccine, the vaccine was combined with the TLR4 agonist HBHA protein. The MEV structure predicted was found to be highly antigenic, non-toxic, non-allergenic, flexible, stable, and soluble. To confirm the interactions with the receptors, a molecular docking simulation of the MEV was done using the human TLR4 (toll-like receptor 4) and HLAs. The stability and binding of the MEV-docked complexes with TLR4 were assessed using molecular dynamics (MD) simulation. Finally, MEV was reverse translated, its cDNA structure was evaluated, and then, in silico cloning into an E. coli expression host was conducted to promote maximum vaccine protein production with appropriate post-translational modifications. These comprehensive computer calculations backed up the efficacy of the suggested MEV in protecting against B. suis infections. However, more experimental validations are needed to adequately assess the vaccine candidate's potential. HIGHLIGHTS: • Subtractive genomic analysis and reverse vaccinology for the prioritization of novel vaccine target • Examination of chimeric vaccine in terms of allergenicity, antigenicity, MHC I, II binding efficacy, and structural-based studies • Molecular docking simulation method to rank based vaccine candidate and understand their binding modes.

Indexed as

Brucella suisBrucella VaccineBrucellosisAnimalsBacterial ProteinsComputational BiologyDrug DesignDrug Resistance, BacterialEpitopesEpitopes, B-LymphocyteEpitopes, T-LymphocyteEscherichia coliHumansMolecular Docking SimulationProteomeToll-Like Receptor 4Bacterial ProteinsBrucella VaccineEpitopesEpitopes, B-LymphocyteEpitopes, T-LymphocyteProteomeToll-Like Receptor 4Vaccines, SubunitBrucella suisChimeric vaccineEpitope predictionReverse vaccinologyVaccine target

Identifiers

PMID36459272
PMCPMC9716126
OpenAlexW4310595371

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.