Hierarchical design of hyaluronic acid-peptide constructs for glioblastoma targeting: Combining insights from NMR and molecular dynamics simulations
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
DOI: | 10.1016/j.molliq.2020.113774 |
Texto Completo: | http://hdl.handle.net/10316/90813 https://doi.org/10.1016/j.molliq.2020.113774 |
Resumo: | The main bottleneck of glioblastoma still relies on the existence of the blood brain-blood brain tumor dual barrier, along with the lack of therapy specificity. The present work deals with the question of whether (and how) different targeting hyaluronic acid (HA)-peptide [c(RGDfK) and/or H7K(R2)2] moieties hierarchically interact with each other, to ensure a unique entity with specificity to glioblastoma. A dual experimental-computational approach, encompassing nuclear magnetic resonance and molecular dynamics simulations is enclosed. Relevant contact patterns based on the identification of the stabilizing/destabilizing noncovalent interactions within the constructs are detailed. The synthesis pathway requires the HA-c(RGDfK)-H7k(R2)2 association hierarchy, stemming from the size and amino acid residue rearrangement, in the 1:1 M ratio, to obtain a stable conjugate ultimately able to interact with the tumor cell membrane. To our knowledge, the structural and mechanistic rationale for the formation of hybrid polymer-peptide constructs, including HA-c(RGDfK)-H7k(R2)2, for glioblastoma has not been addressed so far. |
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Hierarchical design of hyaluronic acid-peptide constructs for glioblastoma targeting: Combining insights from NMR and molecular dynamics simulationsHyaluronic acidc(RGDfK)H7k(R2)2Polymer-peptide conjugatesGlioblastomaThe main bottleneck of glioblastoma still relies on the existence of the blood brain-blood brain tumor dual barrier, along with the lack of therapy specificity. The present work deals with the question of whether (and how) different targeting hyaluronic acid (HA)-peptide [c(RGDfK) and/or H7K(R2)2] moieties hierarchically interact with each other, to ensure a unique entity with specificity to glioblastoma. A dual experimental-computational approach, encompassing nuclear magnetic resonance and molecular dynamics simulations is enclosed. Relevant contact patterns based on the identification of the stabilizing/destabilizing noncovalent interactions within the constructs are detailed. The synthesis pathway requires the HA-c(RGDfK)-H7k(R2)2 association hierarchy, stemming from the size and amino acid residue rearrangement, in the 1:1 M ratio, to obtain a stable conjugate ultimately able to interact with the tumor cell membrane. To our knowledge, the structural and mechanistic rationale for the formation of hybrid polymer-peptide constructs, including HA-c(RGDfK)-H7k(R2)2, for glioblastoma has not been addressed so far.Elsevier2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/90813http://hdl.handle.net/10316/90813https://doi.org/10.1016/j.molliq.2020.113774eng01677322https://www.sciencedirect.com/science/article/pii/S016773222032331XMendes, MariaCova, TâniaBasso, JoãoRamos, M. LuísaVitorino, RuiSousa, JoãoPais, AlbertoVitorino, Carlainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-10-20T11:33:47Zoai:estudogeral.uc.pt:10316/90813Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:10:50.333561Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Hierarchical design of hyaluronic acid-peptide constructs for glioblastoma targeting: Combining insights from NMR and molecular dynamics simulations |
title |
Hierarchical design of hyaluronic acid-peptide constructs for glioblastoma targeting: Combining insights from NMR and molecular dynamics simulations |
spellingShingle |
Hierarchical design of hyaluronic acid-peptide constructs for glioblastoma targeting: Combining insights from NMR and molecular dynamics simulations Hierarchical design of hyaluronic acid-peptide constructs for glioblastoma targeting: Combining insights from NMR and molecular dynamics simulations Mendes, Maria Hyaluronic acid c(RGDfK) H7k(R2)2 Polymer-peptide conjugates Glioblastoma Mendes, Maria Hyaluronic acid c(RGDfK) H7k(R2)2 Polymer-peptide conjugates Glioblastoma |
title_short |
Hierarchical design of hyaluronic acid-peptide constructs for glioblastoma targeting: Combining insights from NMR and molecular dynamics simulations |
title_full |
Hierarchical design of hyaluronic acid-peptide constructs for glioblastoma targeting: Combining insights from NMR and molecular dynamics simulations |
title_fullStr |
Hierarchical design of hyaluronic acid-peptide constructs for glioblastoma targeting: Combining insights from NMR and molecular dynamics simulations Hierarchical design of hyaluronic acid-peptide constructs for glioblastoma targeting: Combining insights from NMR and molecular dynamics simulations |
title_full_unstemmed |
Hierarchical design of hyaluronic acid-peptide constructs for glioblastoma targeting: Combining insights from NMR and molecular dynamics simulations Hierarchical design of hyaluronic acid-peptide constructs for glioblastoma targeting: Combining insights from NMR and molecular dynamics simulations |
title_sort |
Hierarchical design of hyaluronic acid-peptide constructs for glioblastoma targeting: Combining insights from NMR and molecular dynamics simulations |
author |
Mendes, Maria |
author_facet |
Mendes, Maria Mendes, Maria Cova, Tânia Basso, João Ramos, M. Luísa Vitorino, Rui Sousa, João Pais, Alberto Vitorino, Carla Cova, Tânia Basso, João Ramos, M. Luísa Vitorino, Rui Sousa, João Pais, Alberto Vitorino, Carla |
author_role |
author |
author2 |
Cova, Tânia Basso, João Ramos, M. Luísa Vitorino, Rui Sousa, João Pais, Alberto Vitorino, Carla |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Mendes, Maria Cova, Tânia Basso, João Ramos, M. Luísa Vitorino, Rui Sousa, João Pais, Alberto Vitorino, Carla |
dc.subject.por.fl_str_mv |
Hyaluronic acid c(RGDfK) H7k(R2)2 Polymer-peptide conjugates Glioblastoma |
topic |
Hyaluronic acid c(RGDfK) H7k(R2)2 Polymer-peptide conjugates Glioblastoma |
description |
The main bottleneck of glioblastoma still relies on the existence of the blood brain-blood brain tumor dual barrier, along with the lack of therapy specificity. The present work deals with the question of whether (and how) different targeting hyaluronic acid (HA)-peptide [c(RGDfK) and/or H7K(R2)2] moieties hierarchically interact with each other, to ensure a unique entity with specificity to glioblastoma. A dual experimental-computational approach, encompassing nuclear magnetic resonance and molecular dynamics simulations is enclosed. Relevant contact patterns based on the identification of the stabilizing/destabilizing noncovalent interactions within the constructs are detailed. The synthesis pathway requires the HA-c(RGDfK)-H7k(R2)2 association hierarchy, stemming from the size and amino acid residue rearrangement, in the 1:1 M ratio, to obtain a stable conjugate ultimately able to interact with the tumor cell membrane. To our knowledge, the structural and mechanistic rationale for the formation of hybrid polymer-peptide constructs, including HA-c(RGDfK)-H7k(R2)2, for glioblastoma has not been addressed so far. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/90813 http://hdl.handle.net/10316/90813 https://doi.org/10.1016/j.molliq.2020.113774 |
url |
http://hdl.handle.net/10316/90813 https://doi.org/10.1016/j.molliq.2020.113774 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
01677322 https://www.sciencedirect.com/science/article/pii/S016773222032331X |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1822226281730670592 |
dc.identifier.doi.none.fl_str_mv |
10.1016/j.molliq.2020.113774 |