Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response

Autores
McGowan, Ed; Rosenthal, Rachel; Fiore Gartland, Andrew; Macharia, Gladys; Balinda, Sheila; Kapaata, Anne; Umviligihozo, Gisele; Muok, Erick; Dalel, Jama; Streatfield, Claire L.; Coutinho, Helen; Dilernia, Dario; Monaco, Daniela C.; Morrison, David; Yue, Ling; Hunter, Eric; Nielsen, Morten; Gilmour, Jill; Hare, Jonathan
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Predictive models are becoming more and more commonplace as tools for candidate antigen discovery to meet the challenges of enabling epitope mapping of cohorts with diverse HLA properties. Here we build on the concept of using two key parameters, diversity metric of the HLA profile of individuals within a population and consideration of sequence diversity in the context of an individual's CD8 T-cell immune repertoire to assess the HIV proteome for defined regions of immunogenicity. Using this approach, analysis of HLA adaptation and functional immunogenicity data enabled the identification of regions within the proteome that offer significant conservation, HLA recognition within a population, low prevalence of HLA adaptation and demonstrated immunogenicity. We believe this unique and novel approach to vaccine design as a supplement to vitro functional assays, offers a bespoke pipeline for expedited and rational CD8 T-cell vaccine design for HIV and potentially other pathogens with the potential for both global and local coverage.
Fil: McGowan, Ed. Imperial College London; Reino Unido
Fil: Rosenthal, Rachel. Francis Crick Institute; Reino Unido
Fil: Fiore Gartland, Andrew. Fred Hutchinson Cancer Research Cente; Estados Unidos
Fil: Macharia, Gladys. Imperial College London; Reino Unido
Fil: Balinda, Sheila. Uganda Virus Research Institute; Uganda
Fil: Kapaata, Anne. Uganda Virus Research Institute; Uganda
Fil: Umviligihozo, Gisele. Center for Family Health Research; Ruanda
Fil: Muok, Erick. Center for Family Health Research; Ruanda
Fil: Dalel, Jama. Imperial College London; Reino Unido
Fil: Streatfield, Claire L.. Imperial College London; Reino Unido
Fil: Coutinho, Helen. Imperial College London; Reino Unido
Fil: Dilernia, Dario. University of Emory; Estados Unidos
Fil: Monaco, Daniela C.. University of Emory; Estados Unidos
Fil: Morrison, David. South Walsham; Reino Unido
Fil: Yue, Ling. University of Emory; Estados Unidos
Fil: Hunter, Eric. University of Emory; Estados Unidos
Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Gilmour, Jill. Imperial College London; Reino Unido
Fil: Hare, Jonathan. International Aids Vaccine Initiative; Estados Unidos
Materia
CD8 T-CELLS
HIV
MACHINE LEARNING
T-CELL EPITOPES
VACCINES
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/178451
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV ResponseMcGowan, EdRosenthal, RachelFiore Gartland, AndrewMacharia, GladysBalinda, SheilaKapaata, AnneUmviligihozo, GiseleMuok, ErickDalel, JamaStreatfield, Claire L.Coutinho, HelenDilernia, DarioMonaco, Daniela C.Morrison, DavidYue, LingHunter, EricNielsen, MortenGilmour, JillHare, JonathanCD8 T-CELLSHIVMACHINE LEARNINGT-CELL EPITOPESVACCINEShttps://purl.org/becyt/ford/3.3https://purl.org/becyt/ford/3Predictive models are becoming more and more commonplace as tools for candidate antigen discovery to meet the challenges of enabling epitope mapping of cohorts with diverse HLA properties. Here we build on the concept of using two key parameters, diversity metric of the HLA profile of individuals within a population and consideration of sequence diversity in the context of an individual's CD8 T-cell immune repertoire to assess the HIV proteome for defined regions of immunogenicity. Using this approach, analysis of HLA adaptation and functional immunogenicity data enabled the identification of regions within the proteome that offer significant conservation, HLA recognition within a population, low prevalence of HLA adaptation and demonstrated immunogenicity. We believe this unique and novel approach to vaccine design as a supplement to vitro functional assays, offers a bespoke pipeline for expedited and rational CD8 T-cell vaccine design for HIV and potentially other pathogens with the potential for both global and local coverage.Fil: McGowan, Ed. Imperial College London; Reino UnidoFil: Rosenthal, Rachel. Francis Crick Institute; Reino UnidoFil: Fiore Gartland, Andrew. Fred Hutchinson Cancer Research Cente; Estados UnidosFil: Macharia, Gladys. Imperial College London; Reino UnidoFil: Balinda, Sheila. Uganda Virus Research Institute; UgandaFil: Kapaata, Anne. Uganda Virus Research Institute; UgandaFil: Umviligihozo, Gisele. Center for Family Health Research; RuandaFil: Muok, Erick. Center for Family Health Research; RuandaFil: Dalel, Jama. Imperial College London; Reino UnidoFil: Streatfield, Claire L.. Imperial College London; Reino UnidoFil: Coutinho, Helen. Imperial College London; Reino UnidoFil: Dilernia, Dario. University of Emory; Estados UnidosFil: Monaco, Daniela C.. University of Emory; Estados UnidosFil: Morrison, David. South Walsham; Reino UnidoFil: Yue, Ling. University of Emory; Estados UnidosFil: Hunter, Eric. University of Emory; Estados UnidosFil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gilmour, Jill. Imperial College London; Reino UnidoFil: Hare, Jonathan. International Aids Vaccine Initiative; Estados UnidosFrontiers Media2021-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/178451McGowan, Ed; Rosenthal, Rachel; Fiore Gartland, Andrew; Macharia, Gladys; Balinda, Sheila; et al.; Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response; Frontiers Media; Frontiers in Immunology; 12; 609884; 2-2021; 1-91664-3224CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3389/fimmu.2021.609884info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fimmu.2021.609884/fullinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:57:27Zoai:ri.conicet.gov.ar:11336/178451instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:57:27.8CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response
title Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response
spellingShingle Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response
McGowan, Ed
CD8 T-CELLS
HIV
MACHINE LEARNING
T-CELL EPITOPES
VACCINES
title_short Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response
title_full Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response
title_fullStr Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response
title_full_unstemmed Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response
title_sort Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response
dc.creator.none.fl_str_mv McGowan, Ed
Rosenthal, Rachel
Fiore Gartland, Andrew
Macharia, Gladys
Balinda, Sheila
Kapaata, Anne
Umviligihozo, Gisele
Muok, Erick
Dalel, Jama
Streatfield, Claire L.
Coutinho, Helen
Dilernia, Dario
Monaco, Daniela C.
Morrison, David
Yue, Ling
Hunter, Eric
Nielsen, Morten
Gilmour, Jill
Hare, Jonathan
author McGowan, Ed
author_facet McGowan, Ed
Rosenthal, Rachel
Fiore Gartland, Andrew
Macharia, Gladys
Balinda, Sheila
Kapaata, Anne
Umviligihozo, Gisele
Muok, Erick
Dalel, Jama
Streatfield, Claire L.
Coutinho, Helen
Dilernia, Dario
Monaco, Daniela C.
Morrison, David
Yue, Ling
Hunter, Eric
Nielsen, Morten
Gilmour, Jill
Hare, Jonathan
author_role author
author2 Rosenthal, Rachel
Fiore Gartland, Andrew
Macharia, Gladys
Balinda, Sheila
Kapaata, Anne
Umviligihozo, Gisele
Muok, Erick
Dalel, Jama
Streatfield, Claire L.
Coutinho, Helen
Dilernia, Dario
Monaco, Daniela C.
Morrison, David
Yue, Ling
Hunter, Eric
Nielsen, Morten
Gilmour, Jill
Hare, Jonathan
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv CD8 T-CELLS
HIV
MACHINE LEARNING
T-CELL EPITOPES
VACCINES
topic CD8 T-CELLS
HIV
MACHINE LEARNING
T-CELL EPITOPES
VACCINES
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.3
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Predictive models are becoming more and more commonplace as tools for candidate antigen discovery to meet the challenges of enabling epitope mapping of cohorts with diverse HLA properties. Here we build on the concept of using two key parameters, diversity metric of the HLA profile of individuals within a population and consideration of sequence diversity in the context of an individual's CD8 T-cell immune repertoire to assess the HIV proteome for defined regions of immunogenicity. Using this approach, analysis of HLA adaptation and functional immunogenicity data enabled the identification of regions within the proteome that offer significant conservation, HLA recognition within a population, low prevalence of HLA adaptation and demonstrated immunogenicity. We believe this unique and novel approach to vaccine design as a supplement to vitro functional assays, offers a bespoke pipeline for expedited and rational CD8 T-cell vaccine design for HIV and potentially other pathogens with the potential for both global and local coverage.
Fil: McGowan, Ed. Imperial College London; Reino Unido
Fil: Rosenthal, Rachel. Francis Crick Institute; Reino Unido
Fil: Fiore Gartland, Andrew. Fred Hutchinson Cancer Research Cente; Estados Unidos
Fil: Macharia, Gladys. Imperial College London; Reino Unido
Fil: Balinda, Sheila. Uganda Virus Research Institute; Uganda
Fil: Kapaata, Anne. Uganda Virus Research Institute; Uganda
Fil: Umviligihozo, Gisele. Center for Family Health Research; Ruanda
Fil: Muok, Erick. Center for Family Health Research; Ruanda
Fil: Dalel, Jama. Imperial College London; Reino Unido
Fil: Streatfield, Claire L.. Imperial College London; Reino Unido
Fil: Coutinho, Helen. Imperial College London; Reino Unido
Fil: Dilernia, Dario. University of Emory; Estados Unidos
Fil: Monaco, Daniela C.. University of Emory; Estados Unidos
Fil: Morrison, David. South Walsham; Reino Unido
Fil: Yue, Ling. University of Emory; Estados Unidos
Fil: Hunter, Eric. University of Emory; Estados Unidos
Fil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Gilmour, Jill. Imperial College London; Reino Unido
Fil: Hare, Jonathan. International Aids Vaccine Initiative; Estados Unidos
description Predictive models are becoming more and more commonplace as tools for candidate antigen discovery to meet the challenges of enabling epitope mapping of cohorts with diverse HLA properties. Here we build on the concept of using two key parameters, diversity metric of the HLA profile of individuals within a population and consideration of sequence diversity in the context of an individual's CD8 T-cell immune repertoire to assess the HIV proteome for defined regions of immunogenicity. Using this approach, analysis of HLA adaptation and functional immunogenicity data enabled the identification of regions within the proteome that offer significant conservation, HLA recognition within a population, low prevalence of HLA adaptation and demonstrated immunogenicity. We believe this unique and novel approach to vaccine design as a supplement to vitro functional assays, offers a bespoke pipeline for expedited and rational CD8 T-cell vaccine design for HIV and potentially other pathogens with the potential for both global and local coverage.
publishDate 2021
dc.date.none.fl_str_mv 2021-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/178451
McGowan, Ed; Rosenthal, Rachel; Fiore Gartland, Andrew; Macharia, Gladys; Balinda, Sheila; et al.; Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response; Frontiers Media; Frontiers in Immunology; 12; 609884; 2-2021; 1-9
1664-3224
CONICET Digital
CONICET
url http://hdl.handle.net/11336/178451
identifier_str_mv McGowan, Ed; Rosenthal, Rachel; Fiore Gartland, Andrew; Macharia, Gladys; Balinda, Sheila; et al.; Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response; Frontiers Media; Frontiers in Immunology; 12; 609884; 2-2021; 1-9
1664-3224
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3389/fimmu.2021.609884
info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fimmu.2021.609884/full
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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