Cargando…
Vaccinomics-based next-generation multi-epitope chimeric vaccine models prediction against Leishmania tropica - a hierarchical subtractive proteomics and immunoinformatics approach
Leishmania tropica is a vector-borne parasitic protozoa that is the leading cause of leishmaniasis throughout the global tropics and subtropics. L. tropica is a multidrug-resistant parasite with a diverse set of serological, biochemical, and genomic features. There are currently no particular vaccin...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10540849/ https://www.ncbi.nlm.nih.gov/pubmed/37781384 http://dx.doi.org/10.3389/fimmu.2023.1259612 |
_version_ | 1785113795125837824 |
---|---|
author | Aiman, Sara Ahmad, Abbas Khan, Azmat Ali Alanazi, Amer M. Samad, Abdus Ali, Syed Luqman Li, Chunhua Ren, Zhiguang Khan, Asifullah Khattak, Saadullah |
author_facet | Aiman, Sara Ahmad, Abbas Khan, Azmat Ali Alanazi, Amer M. Samad, Abdus Ali, Syed Luqman Li, Chunhua Ren, Zhiguang Khan, Asifullah Khattak, Saadullah |
author_sort | Aiman, Sara |
collection | PubMed |
description | Leishmania tropica is a vector-borne parasitic protozoa that is the leading cause of leishmaniasis throughout the global tropics and subtropics. L. tropica is a multidrug-resistant parasite with a diverse set of serological, biochemical, and genomic features. There are currently no particular vaccines available to combat leishmaniasis. The present study prioritized potential vaccine candidate proteins of L. tropica using subtractive proteomics and vaccinomics approaches. These vaccine candidate proteins were downstream analyzed to predict B- and T-cell epitopes based on high antigenicity, non-allergenic, and non-toxic characteristics. The top-ranked overlapping MHC-I, MHC-II, and linear B-cell epitopes were prioritized for model vaccine designing. The lead epitopes were linked together by suitable linker sequences to design multi-epitope constructs. Immunogenic adjuvant sequences were incorporated at the N-terminus of the model vaccine constructs to enhance their immunological potential. Among different combinations of constructs, four vaccine designs were selected based on their physicochemical and immunological features. The tertiary structure models of the designed vaccine constructs were predicted and verified. The molecular docking and molecular dynamic (MD) simulation analyses indicated that the vaccine design V1 demonstrated robust and stable molecular interactions with toll-like receptor 4 (TLR4). The top-ranked vaccine construct model-IV demonstrated significant expressive capability in the E. coli expression system during in-silico restriction cloning analysis. The results of the present study are intriguing; nevertheless, experimental bioassays are required to validate the efficacy of the predicted model chimeric vaccine. |
format | Online Article Text |
id | pubmed-10540849 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105408492023-09-30 Vaccinomics-based next-generation multi-epitope chimeric vaccine models prediction against Leishmania tropica - a hierarchical subtractive proteomics and immunoinformatics approach Aiman, Sara Ahmad, Abbas Khan, Azmat Ali Alanazi, Amer M. Samad, Abdus Ali, Syed Luqman Li, Chunhua Ren, Zhiguang Khan, Asifullah Khattak, Saadullah Front Immunol Immunology Leishmania tropica is a vector-borne parasitic protozoa that is the leading cause of leishmaniasis throughout the global tropics and subtropics. L. tropica is a multidrug-resistant parasite with a diverse set of serological, biochemical, and genomic features. There are currently no particular vaccines available to combat leishmaniasis. The present study prioritized potential vaccine candidate proteins of L. tropica using subtractive proteomics and vaccinomics approaches. These vaccine candidate proteins were downstream analyzed to predict B- and T-cell epitopes based on high antigenicity, non-allergenic, and non-toxic characteristics. The top-ranked overlapping MHC-I, MHC-II, and linear B-cell epitopes were prioritized for model vaccine designing. The lead epitopes were linked together by suitable linker sequences to design multi-epitope constructs. Immunogenic adjuvant sequences were incorporated at the N-terminus of the model vaccine constructs to enhance their immunological potential. Among different combinations of constructs, four vaccine designs were selected based on their physicochemical and immunological features. The tertiary structure models of the designed vaccine constructs were predicted and verified. The molecular docking and molecular dynamic (MD) simulation analyses indicated that the vaccine design V1 demonstrated robust and stable molecular interactions with toll-like receptor 4 (TLR4). The top-ranked vaccine construct model-IV demonstrated significant expressive capability in the E. coli expression system during in-silico restriction cloning analysis. The results of the present study are intriguing; nevertheless, experimental bioassays are required to validate the efficacy of the predicted model chimeric vaccine. Frontiers Media S.A. 2023-09-15 /pmc/articles/PMC10540849/ /pubmed/37781384 http://dx.doi.org/10.3389/fimmu.2023.1259612 Text en Copyright © 2023 Aiman, Ahmad, Khan, Alanazi, Samad, Ali, Li, Ren, Khan and Khattak https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Aiman, Sara Ahmad, Abbas Khan, Azmat Ali Alanazi, Amer M. Samad, Abdus Ali, Syed Luqman Li, Chunhua Ren, Zhiguang Khan, Asifullah Khattak, Saadullah Vaccinomics-based next-generation multi-epitope chimeric vaccine models prediction against Leishmania tropica - a hierarchical subtractive proteomics and immunoinformatics approach |
title | Vaccinomics-based next-generation multi-epitope chimeric vaccine models prediction against Leishmania tropica - a hierarchical subtractive proteomics and immunoinformatics approach |
title_full | Vaccinomics-based next-generation multi-epitope chimeric vaccine models prediction against Leishmania tropica - a hierarchical subtractive proteomics and immunoinformatics approach |
title_fullStr | Vaccinomics-based next-generation multi-epitope chimeric vaccine models prediction against Leishmania tropica - a hierarchical subtractive proteomics and immunoinformatics approach |
title_full_unstemmed | Vaccinomics-based next-generation multi-epitope chimeric vaccine models prediction against Leishmania tropica - a hierarchical subtractive proteomics and immunoinformatics approach |
title_short | Vaccinomics-based next-generation multi-epitope chimeric vaccine models prediction against Leishmania tropica - a hierarchical subtractive proteomics and immunoinformatics approach |
title_sort | vaccinomics-based next-generation multi-epitope chimeric vaccine models prediction against leishmania tropica - a hierarchical subtractive proteomics and immunoinformatics approach |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10540849/ https://www.ncbi.nlm.nih.gov/pubmed/37781384 http://dx.doi.org/10.3389/fimmu.2023.1259612 |
work_keys_str_mv | AT aimansara vaccinomicsbasednextgenerationmultiepitopechimericvaccinemodelspredictionagainstleishmaniatropicaahierarchicalsubtractiveproteomicsandimmunoinformaticsapproach AT ahmadabbas vaccinomicsbasednextgenerationmultiepitopechimericvaccinemodelspredictionagainstleishmaniatropicaahierarchicalsubtractiveproteomicsandimmunoinformaticsapproach AT khanazmatali vaccinomicsbasednextgenerationmultiepitopechimericvaccinemodelspredictionagainstleishmaniatropicaahierarchicalsubtractiveproteomicsandimmunoinformaticsapproach AT alanaziamerm vaccinomicsbasednextgenerationmultiepitopechimericvaccinemodelspredictionagainstleishmaniatropicaahierarchicalsubtractiveproteomicsandimmunoinformaticsapproach AT samadabdus vaccinomicsbasednextgenerationmultiepitopechimericvaccinemodelspredictionagainstleishmaniatropicaahierarchicalsubtractiveproteomicsandimmunoinformaticsapproach AT alisyedluqman vaccinomicsbasednextgenerationmultiepitopechimericvaccinemodelspredictionagainstleishmaniatropicaahierarchicalsubtractiveproteomicsandimmunoinformaticsapproach AT lichunhua vaccinomicsbasednextgenerationmultiepitopechimericvaccinemodelspredictionagainstleishmaniatropicaahierarchicalsubtractiveproteomicsandimmunoinformaticsapproach AT renzhiguang vaccinomicsbasednextgenerationmultiepitopechimericvaccinemodelspredictionagainstleishmaniatropicaahierarchicalsubtractiveproteomicsandimmunoinformaticsapproach AT khanasifullah vaccinomicsbasednextgenerationmultiepitopechimericvaccinemodelspredictionagainstleishmaniatropicaahierarchicalsubtractiveproteomicsandimmunoinformaticsapproach AT khattaksaadullah vaccinomicsbasednextgenerationmultiepitopechimericvaccinemodelspredictionagainstleishmaniatropicaahierarchicalsubtractiveproteomicsandimmunoinformaticsapproach |