Immunoinformatics design of multi-epitope peptide-based vaccine against Haemophilus influenzae strain using cell division protein
Haemophilus influenzae is a pathogen that causes invasive bacterial infections in humans. The highest prevalence lies in both young children and adults. Generally, there are no vaccines available that target all the strains of Haemophilus influenzae . Hence, the purpose of this research is to employ...
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creator | AlChalabi, Rawaa Al-Rahim, Aya Omer, Dania Suleiman, Ahmed AbdulJabbar |
description | Haemophilus influenzae
is a pathogen that causes invasive bacterial infections in humans. The highest prevalence lies in both young children and adults. Generally, there are no vaccines available that target all the strains of
Haemophilus influenzae
. Hence, the purpose of this research is to employ bioinformatics and immunoinformatics approaches to design a Multi-Epitope Vaccine candidate employing the pathogenic cell division protein
FtsN
that specifically combat all the
Haemophilus influenzae
strains. The current research focuses on developing subunit vaccine in contrast to vaccines generated from the entire pathogen. This will be accomplished by combining multiple bioinformatics and immunoinformatics approaches. As a result, prospective T cells (helper T lymphocyte and cytotoxic T lymphocytes) and B cells epitopes were investigated. The human leukocyte antigen allele having strong associations with the antigenic and overlapping epitopes were chosen, with 70% of the total coverage of the world population. To construct a linked vaccine design, multiple linkers were used. To increase the immunogenic profile, an adjuvant was linked using EAAAK linker. The final vaccine construct with 149 amino acids was obtained after adjuvants and linkers were added. The developed Multi-Epitope Vaccine has a high antigenicity as well as viable physiochemical features. The 3D conformation was modeled and undergoes refinement and validation using bioinformatics methods. Furthermore, protein–protein molecular docking analysis was performed to predict the effective binding poses of Multi-Epitope Vaccine with the
Toll-like receptor 4
protein. Besides, vaccine underwent the codon translational optimization and computational cloning to verify the reliability and proper Multi-Epitope Vaccine expression. In addition, it is necessary to conduct experiments and research in the laboratory to demonstrate that the vaccine that has been developed is immunogenic and protective. |
doi_str_mv | 10.1007/s13721-022-00395-x |
format | Article |
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is a pathogen that causes invasive bacterial infections in humans. The highest prevalence lies in both young children and adults. Generally, there are no vaccines available that target all the strains of
Haemophilus influenzae
. Hence, the purpose of this research is to employ bioinformatics and immunoinformatics approaches to design a Multi-Epitope Vaccine candidate employing the pathogenic cell division protein
FtsN
that specifically combat all the
Haemophilus influenzae
strains. The current research focuses on developing subunit vaccine in contrast to vaccines generated from the entire pathogen. This will be accomplished by combining multiple bioinformatics and immunoinformatics approaches. As a result, prospective T cells (helper T lymphocyte and cytotoxic T lymphocytes) and B cells epitopes were investigated. The human leukocyte antigen allele having strong associations with the antigenic and overlapping epitopes were chosen, with 70% of the total coverage of the world population. To construct a linked vaccine design, multiple linkers were used. To increase the immunogenic profile, an adjuvant was linked using EAAAK linker. The final vaccine construct with 149 amino acids was obtained after adjuvants and linkers were added. The developed Multi-Epitope Vaccine has a high antigenicity as well as viable physiochemical features. The 3D conformation was modeled and undergoes refinement and validation using bioinformatics methods. Furthermore, protein–protein molecular docking analysis was performed to predict the effective binding poses of Multi-Epitope Vaccine with the
Toll-like receptor 4
protein. Besides, vaccine underwent the codon translational optimization and computational cloning to verify the reliability and proper Multi-Epitope Vaccine expression. In addition, it is necessary to conduct experiments and research in the laboratory to demonstrate that the vaccine that has been developed is immunogenic and protective.</description><identifier>ISSN: 2192-6662</identifier><identifier>ISSN: 2192-6670</identifier><identifier>EISSN: 2192-6670</identifier><identifier>DOI: 10.1007/s13721-022-00395-x</identifier><identifier>PMID: 36465492</identifier><language>eng</language><publisher>Vienna: Springer Vienna</publisher><subject>Adjuvants ; Amino acids ; Antigenicity ; Antigens ; Applications of Graph Theory and Complex Networks ; Bacteria ; Bioinformatics ; Cell division ; Cloning ; Computational Biology/Bioinformatics ; Computer Science ; Cytokines ; Cytotoxicity ; Epitopes ; Genomes ; Haemophilus influenzae ; Health Informatics ; Histocompatibility antigen HLA ; Immunogenicity ; Lymphocytes ; Lymphocytes B ; Lymphocytes T ; Molecular docking ; Original ; Original Article ; Pathogenesis ; Pathogens ; Peptides ; Physiochemistry ; Pneumonia ; Proteins ; Strains (organisms) ; Three dimensional models ; TLR4 protein ; Toll-like receptors ; Vaccines</subject><ispartof>Network modeling and analysis in health informatics and bioinformatics, 2022-11, Vol.12 (1), p.1, Article 1</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-dda5b4a011061494aabaecb379c07fa983cc21289f9b39b08808a2052b5c50743</citedby><cites>FETCH-LOGICAL-c474t-dda5b4a011061494aabaecb379c07fa983cc21289f9b39b08808a2052b5c50743</cites><orcidid>0000-0001-7427-4483</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s13721-022-00395-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2920057026?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>230,314,776,780,881,21367,27901,27902,33721,41464,42533,43781,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36465492$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>AlChalabi, Rawaa</creatorcontrib><creatorcontrib>Al-Rahim, Aya</creatorcontrib><creatorcontrib>Omer, Dania</creatorcontrib><creatorcontrib>Suleiman, Ahmed AbdulJabbar</creatorcontrib><title>Immunoinformatics design of multi-epitope peptide-based vaccine against Haemophilus influenzae strain using cell division protein</title><title>Network modeling and analysis in health informatics and bioinformatics</title><addtitle>Netw Model Anal Health Inform Bioinforma</addtitle><addtitle>Netw Model Anal Health Inform Bioinform</addtitle><description>Haemophilus influenzae
is a pathogen that causes invasive bacterial infections in humans. The highest prevalence lies in both young children and adults. Generally, there are no vaccines available that target all the strains of
Haemophilus influenzae
. Hence, the purpose of this research is to employ bioinformatics and immunoinformatics approaches to design a Multi-Epitope Vaccine candidate employing the pathogenic cell division protein
FtsN
that specifically combat all the
Haemophilus influenzae
strains. The current research focuses on developing subunit vaccine in contrast to vaccines generated from the entire pathogen. This will be accomplished by combining multiple bioinformatics and immunoinformatics approaches. As a result, prospective T cells (helper T lymphocyte and cytotoxic T lymphocytes) and B cells epitopes were investigated. The human leukocyte antigen allele having strong associations with the antigenic and overlapping epitopes were chosen, with 70% of the total coverage of the world population. To construct a linked vaccine design, multiple linkers were used. To increase the immunogenic profile, an adjuvant was linked using EAAAK linker. The final vaccine construct with 149 amino acids was obtained after adjuvants and linkers were added. The developed Multi-Epitope Vaccine has a high antigenicity as well as viable physiochemical features. The 3D conformation was modeled and undergoes refinement and validation using bioinformatics methods. Furthermore, protein–protein molecular docking analysis was performed to predict the effective binding poses of Multi-Epitope Vaccine with the
Toll-like receptor 4
protein. Besides, vaccine underwent the codon translational optimization and computational cloning to verify the reliability and proper Multi-Epitope Vaccine expression. In addition, it is necessary to conduct experiments and research in the laboratory to demonstrate that the vaccine that has been developed is immunogenic and protective.</description><subject>Adjuvants</subject><subject>Amino acids</subject><subject>Antigenicity</subject><subject>Antigens</subject><subject>Applications of Graph Theory and Complex Networks</subject><subject>Bacteria</subject><subject>Bioinformatics</subject><subject>Cell division</subject><subject>Cloning</subject><subject>Computational Biology/Bioinformatics</subject><subject>Computer Science</subject><subject>Cytokines</subject><subject>Cytotoxicity</subject><subject>Epitopes</subject><subject>Genomes</subject><subject>Haemophilus influenzae</subject><subject>Health Informatics</subject><subject>Histocompatibility antigen HLA</subject><subject>Immunogenicity</subject><subject>Lymphocytes</subject><subject>Lymphocytes B</subject><subject>Lymphocytes T</subject><subject>Molecular docking</subject><subject>Original</subject><subject>Original Article</subject><subject>Pathogenesis</subject><subject>Pathogens</subject><subject>Peptides</subject><subject>Physiochemistry</subject><subject>Pneumonia</subject><subject>Proteins</subject><subject>Strains (organisms)</subject><subject>Three dimensional models</subject><subject>TLR4 protein</subject><subject>Toll-like receptors</subject><subject>Vaccines</subject><issn>2192-6662</issn><issn>2192-6670</issn><issn>2192-6670</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kU9rFTEUxYMottR-ARcScB29yfzJZCNIsbZQcNOuQyZzZ5oyk4xJ5lG76zc39dWnbgyBBM655x74EfKWwwcOID8mXknBGQjBACrVsPsX5FhwJVjbSnh5-LfiiJymdAfldOXy5jU5qtq6bWoljsnj5bJsPjg_hriY7GyiAyY3eRpGumxzdgxXl8OKdMU1uwFZbxIOdGesdR6pmYzzKdMLg0tYb928JVrS5g39g0Gaciw63ZLzE7U4z3RwO5dc8HSNIaPzb8ir0cwJT5_fE3Jz_uX67IJdfft6efb5itla1pkNg2n62gDn0PJa1cb0Bm1fSWVBjkZ1lbWCi06Nqq9UD10HnRHQiL6xDci6OiGf9rnr1i84WPSl2qzX6BYTf-hgnP5X8e5WT2GnlQTJVVsC3j8HxPB9w5T1XdiiL521UAKgkSCeXGLvsjGkFHE8bOCgn8jpPTldyOlf5PR9GXr3d7fDyG9OxVDtDalIfsL4Z_d_Yn8Cyq6ooQ</recordid><startdate>20221128</startdate><enddate>20221128</enddate><creator>AlChalabi, Rawaa</creator><creator>Al-Rahim, Aya</creator><creator>Omer, Dania</creator><creator>Suleiman, Ahmed AbdulJabbar</creator><general>Springer Vienna</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-7427-4483</orcidid></search><sort><creationdate>20221128</creationdate><title>Immunoinformatics design of multi-epitope peptide-based vaccine against Haemophilus influenzae strain using cell division protein</title><author>AlChalabi, Rawaa ; Al-Rahim, Aya ; Omer, Dania ; Suleiman, Ahmed AbdulJabbar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-dda5b4a011061494aabaecb379c07fa983cc21289f9b39b08808a2052b5c50743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Adjuvants</topic><topic>Amino acids</topic><topic>Antigenicity</topic><topic>Antigens</topic><topic>Applications of Graph Theory and Complex Networks</topic><topic>Bacteria</topic><topic>Bioinformatics</topic><topic>Cell division</topic><topic>Cloning</topic><topic>Computational Biology/Bioinformatics</topic><topic>Computer Science</topic><topic>Cytokines</topic><topic>Cytotoxicity</topic><topic>Epitopes</topic><topic>Genomes</topic><topic>Haemophilus influenzae</topic><topic>Health Informatics</topic><topic>Histocompatibility antigen HLA</topic><topic>Immunogenicity</topic><topic>Lymphocytes</topic><topic>Lymphocytes B</topic><topic>Lymphocytes T</topic><topic>Molecular docking</topic><topic>Original</topic><topic>Original Article</topic><topic>Pathogenesis</topic><topic>Pathogens</topic><topic>Peptides</topic><topic>Physiochemistry</topic><topic>Pneumonia</topic><topic>Proteins</topic><topic>Strains (organisms)</topic><topic>Three dimensional models</topic><topic>TLR4 protein</topic><topic>Toll-like receptors</topic><topic>Vaccines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>AlChalabi, Rawaa</creatorcontrib><creatorcontrib>Al-Rahim, Aya</creatorcontrib><creatorcontrib>Omer, Dania</creatorcontrib><creatorcontrib>Suleiman, Ahmed AbdulJabbar</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Network modeling and analysis in health informatics and bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>AlChalabi, Rawaa</au><au>Al-Rahim, Aya</au><au>Omer, Dania</au><au>Suleiman, Ahmed AbdulJabbar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Immunoinformatics design of multi-epitope peptide-based vaccine against Haemophilus influenzae strain using cell division protein</atitle><jtitle>Network modeling and analysis in health informatics and bioinformatics</jtitle><stitle>Netw Model Anal Health Inform Bioinforma</stitle><addtitle>Netw Model Anal Health Inform Bioinform</addtitle><date>2022-11-28</date><risdate>2022</risdate><volume>12</volume><issue>1</issue><spage>1</spage><pages>1-</pages><artnum>1</artnum><issn>2192-6662</issn><issn>2192-6670</issn><eissn>2192-6670</eissn><abstract>Haemophilus influenzae
is a pathogen that causes invasive bacterial infections in humans. The highest prevalence lies in both young children and adults. Generally, there are no vaccines available that target all the strains of
Haemophilus influenzae
. Hence, the purpose of this research is to employ bioinformatics and immunoinformatics approaches to design a Multi-Epitope Vaccine candidate employing the pathogenic cell division protein
FtsN
that specifically combat all the
Haemophilus influenzae
strains. The current research focuses on developing subunit vaccine in contrast to vaccines generated from the entire pathogen. This will be accomplished by combining multiple bioinformatics and immunoinformatics approaches. As a result, prospective T cells (helper T lymphocyte and cytotoxic T lymphocytes) and B cells epitopes were investigated. The human leukocyte antigen allele having strong associations with the antigenic and overlapping epitopes were chosen, with 70% of the total coverage of the world population. To construct a linked vaccine design, multiple linkers were used. To increase the immunogenic profile, an adjuvant was linked using EAAAK linker. The final vaccine construct with 149 amino acids was obtained after adjuvants and linkers were added. The developed Multi-Epitope Vaccine has a high antigenicity as well as viable physiochemical features. The 3D conformation was modeled and undergoes refinement and validation using bioinformatics methods. Furthermore, protein–protein molecular docking analysis was performed to predict the effective binding poses of Multi-Epitope Vaccine with the
Toll-like receptor 4
protein. Besides, vaccine underwent the codon translational optimization and computational cloning to verify the reliability and proper Multi-Epitope Vaccine expression. In addition, it is necessary to conduct experiments and research in the laboratory to demonstrate that the vaccine that has been developed is immunogenic and protective.</abstract><cop>Vienna</cop><pub>Springer Vienna</pub><pmid>36465492</pmid><doi>10.1007/s13721-022-00395-x</doi><orcidid>https://orcid.org/0000-0001-7427-4483</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adjuvants Amino acids Antigenicity Antigens Applications of Graph Theory and Complex Networks Bacteria Bioinformatics Cell division Cloning Computational Biology/Bioinformatics Computer Science Cytokines Cytotoxicity Epitopes Genomes Haemophilus influenzae Health Informatics Histocompatibility antigen HLA Immunogenicity Lymphocytes Lymphocytes B Lymphocytes T Molecular docking Original Original Article Pathogenesis Pathogens Peptides Physiochemistry Pneumonia Proteins Strains (organisms) Three dimensional models TLR4 protein Toll-like receptors Vaccines |
title | Immunoinformatics design of multi-epitope peptide-based vaccine against Haemophilus influenzae strain using cell division protein |
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