Cholesterol-recognizing amino acid consensus motifs in transmembrane proteins: Comparative analysis of in silico studies and structural data

Autores
Azzaz, Fodil; Chahinian, Henri; Yahi, Nouara; Di Scala, Coralie; Baier, Carlos J.; Barrantes, Francisco José
Año de publicación
2022
Idioma
inglés
Tipo de recurso
parte de libro
Estado
versión publicada
Descripción
Fil: Azzaz, Fodil. Institut national de la santé et de la recherche médicale; Francia
Fil: Azzaz, Fodil. Aix-Marseille Université; Francia
Fil: Chahinian, Henri. Institut national de la santé et de la recherche médicale; Francia
Fil: Chahinian, Henri. Aix-Marseille Université; Francia
Fil: Yahi, Nouara. Institut national de la santé et de la recherche médicale; Francia
Fil: Yahi, Nouara. Aix-Marseille Université; Francia.
Fil: Di Scala, Coralie. University of Helsinki. Neuroscience Center; Finlandia
Fil: Baier, Carlos J. Universidad Nacional del Sur. Instituto de Ciencias Biológicas y Biomédicas del Sur. Laboratorio de Toxicología; Argentina
Fil: Baier, Carlos J. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Barrantes, Francisco José. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas. Laboratorio de Neurobiología Molecular; Argentina
Fil: Barrantes, Francisco José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Abstract: Cholesterol binding to proteins is a dynamic process that involves a combination of geometric, biochemical, and biophysical principles. These properties can be viewed as basic rules which govern any kind of molecular interactions. Nevertheless, cholesterol displays unique features that have made cholesterol recognition motifs in proteins remarkably convergent upon biological evolution. Consequently, simple algorithms based on consensus amino acid sequences (e.g., CARC and CRAC) have been developed to predict the presence of such cholesterol-binding motifs in proteins. The intrinsic weakness of this approach is that CARC and CRAC are both based on a linear (1D) sequence motif, whereas cholesterol-binding sites have a three-dimensional (3D) structure. This issue is discussed in detail in this chapter. We then analyze the performance of these algorithms in the light of structural data obtained by X-ray diffraction and cryoelectron microscopy of membrane proteins, and structure-function studies based on site-directed mutagenesis. Our study not only confirms the overall reliability of CARC and CRAC algorithms but also reveals new clues that could bring forth new ideas on cholesterol recognition motifs in the 3D structure of transmembrane proteins.
Fuente
Bukiya, A.N., Dopico, A.M. (eds.). Cholesterol. From Chemistry and Biophysics to the Clinic. Londres: Academic Press, 2022
Materia
COLESTEROL
AMINOACIDOS
MEMBRANAS CELULARES
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
Repositorio Institucional (UCA)
Institución
Pontificia Universidad Católica Argentina
OAI Identificador
oai:ucacris:123456789/14433
Acceder
id RIUCA_586e731b476e1d2311a94e535e3baceb
oai_identifier_str oai:ucacris:123456789/14433
network_acronym_str RIUCA
repository_id_str 2585
network_name_str Repositorio Institucional (UCA)
spelling Cholesterol-recognizing amino acid consensus motifs in transmembrane proteins: Comparative analysis of in silico studies and structural dataAzzaz, FodilChahinian, HenriYahi, NouaraDi Scala, CoralieBaier, Carlos J.Barrantes, Francisco JoséCOLESTEROLAMINOACIDOSMEMBRANAS CELULARESFil: Azzaz, Fodil. Institut national de la santé et de la recherche médicale; FranciaFil: Azzaz, Fodil. Aix-Marseille Université; FranciaFil: Chahinian, Henri. Institut national de la santé et de la recherche médicale; FranciaFil: Chahinian, Henri. Aix-Marseille Université; FranciaFil: Yahi, Nouara. Institut national de la santé et de la recherche médicale; FranciaFil: Yahi, Nouara. Aix-Marseille Université; Francia.Fil: Di Scala, Coralie. University of Helsinki. Neuroscience Center; FinlandiaFil: Baier, Carlos J. Universidad Nacional del Sur. Instituto de Ciencias Biológicas y Biomédicas del Sur. Laboratorio de Toxicología; ArgentinaFil: Baier, Carlos J. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Barrantes, Francisco José. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas. Laboratorio de Neurobiología Molecular; ArgentinaFil: Barrantes, Francisco José. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaAbstract: Cholesterol binding to proteins is a dynamic process that involves a combination of geometric, biochemical, and biophysical principles. These properties can be viewed as basic rules which govern any kind of molecular interactions. Nevertheless, cholesterol displays unique features that have made cholesterol recognition motifs in proteins remarkably convergent upon biological evolution. Consequently, simple algorithms based on consensus amino acid sequences (e.g., CARC and CRAC) have been developed to predict the presence of such cholesterol-binding motifs in proteins. The intrinsic weakness of this approach is that CARC and CRAC are both based on a linear (1D) sequence motif, whereas cholesterol-binding sites have a three-dimensional (3D) structure. This issue is discussed in detail in this chapter. We then analyze the performance of these algorithms in the light of structural data obtained by X-ray diffraction and cryoelectron microscopy of membrane proteins, and structure-function studies based on site-directed mutagenesis. Our study not only confirms the overall reliability of CARC and CRAC algorithms but also reveals new clues that could bring forth new ideas on cholesterol recognition motifs in the 3D structure of transmembrane proteins.‎ Academic Press2022info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_3248info:ar-repo/semantics/parteDeLibroapplication/pdfhttps://repositorio.uca.edu.ar/handle/123456789/14433978-0-323-85857-110.1016/B978-0-323-85857-1.00004-3Azzaz, F., et al. Cholesterol-recognizing amino acid consensus motifs in transmembrane proteins: Comparative analysis of in silico studies and structural data [en línea]. En: Bukiya, A.N., Dopico, A.M. (eds.). Cholesterol. From Chemistry and Biophysics to the Clinic. Londres: Academic Press, 2022 doi:10.1016/B978-0-323-85857-1.00004-3 Disponible en: https://repositorio.uca.edu.ar/handle/123456789/14433Bukiya, A.N., Dopico, A.M. (eds.). Cholesterol. From Chemistry and Biophysics to the Clinic. Londres: Academic Press, 2022reponame:Repositorio Institucional (UCA)instname:Pontificia Universidad Católica Argentinaenginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/2025-07-03T10:58:41Zoai:ucacris:123456789/14433instacron:UCAInstitucionalhttps://repositorio.uca.edu.ar/Universidad privadaNo correspondehttps://repositorio.uca.edu.ar/oaiclaudia_fernandez@uca.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:25852025-07-03 10:58:41.779Repositorio Institucional (UCA) - Pontificia Universidad Católica Argentinafalse
dc.title.none.fl_str_mv Cholesterol-recognizing amino acid consensus motifs in transmembrane proteins: Comparative analysis of in silico studies and structural data
title Cholesterol-recognizing amino acid consensus motifs in transmembrane proteins: Comparative analysis of in silico studies and structural data
spellingShingle Cholesterol-recognizing amino acid consensus motifs in transmembrane proteins: Comparative analysis of in silico studies and structural data
Azzaz, Fodil
COLESTEROL
AMINOACIDOS
MEMBRANAS CELULARES
title_short Cholesterol-recognizing amino acid consensus motifs in transmembrane proteins: Comparative analysis of in silico studies and structural data
title_full Cholesterol-recognizing amino acid consensus motifs in transmembrane proteins: Comparative analysis of in silico studies and structural data
title_fullStr Cholesterol-recognizing amino acid consensus motifs in transmembrane proteins: Comparative analysis of in silico studies and structural data
title_full_unstemmed Cholesterol-recognizing amino acid consensus motifs in transmembrane proteins: Comparative analysis of in silico studies and structural data
title_sort Cholesterol-recognizing amino acid consensus motifs in transmembrane proteins: Comparative analysis of in silico studies and structural data
dc.creator.none.fl_str_mv Azzaz, Fodil
Chahinian, Henri
Yahi, Nouara
Di Scala, Coralie
Baier, Carlos J.
Barrantes, Francisco José
author Azzaz, Fodil
author_facet Azzaz, Fodil
Chahinian, Henri
Yahi, Nouara
Di Scala, Coralie
Baier, Carlos J.
Barrantes, Francisco José
author_role author
author2 Chahinian, Henri
Yahi, Nouara
Di Scala, Coralie
Baier, Carlos J.
Barrantes, Francisco José
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv COLESTEROL
AMINOACIDOS
MEMBRANAS CELULARES
topic COLESTEROL
AMINOACIDOS
MEMBRANAS CELULARES
dc.description.none.fl_txt_mv Fil: Azzaz, Fodil. Institut national de la santé et de la recherche médicale; Francia
Fil: Azzaz, Fodil. Aix-Marseille Université; Francia
Fil: Chahinian, Henri. Institut national de la santé et de la recherche médicale; Francia
Fil: Chahinian, Henri. Aix-Marseille Université; Francia
Fil: Yahi, Nouara. Institut national de la santé et de la recherche médicale; Francia
Fil: Yahi, Nouara. Aix-Marseille Université; Francia.
Fil: Di Scala, Coralie. University of Helsinki. Neuroscience Center; Finlandia
Fil: Baier, Carlos J. Universidad Nacional del Sur. Instituto de Ciencias Biológicas y Biomédicas del Sur. Laboratorio de Toxicología; Argentina
Fil: Baier, Carlos J. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Barrantes, Francisco José. Pontificia Universidad Católica Argentina. Facultad de Ciencias Médicas. Instituto de Investigaciones Biomédicas. Laboratorio de Neurobiología Molecular; Argentina
Fil: Barrantes, Francisco José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Abstract: Cholesterol binding to proteins is a dynamic process that involves a combination of geometric, biochemical, and biophysical principles. These properties can be viewed as basic rules which govern any kind of molecular interactions. Nevertheless, cholesterol displays unique features that have made cholesterol recognition motifs in proteins remarkably convergent upon biological evolution. Consequently, simple algorithms based on consensus amino acid sequences (e.g., CARC and CRAC) have been developed to predict the presence of such cholesterol-binding motifs in proteins. The intrinsic weakness of this approach is that CARC and CRAC are both based on a linear (1D) sequence motif, whereas cholesterol-binding sites have a three-dimensional (3D) structure. This issue is discussed in detail in this chapter. We then analyze the performance of these algorithms in the light of structural data obtained by X-ray diffraction and cryoelectron microscopy of membrane proteins, and structure-function studies based on site-directed mutagenesis. Our study not only confirms the overall reliability of CARC and CRAC algorithms but also reveals new clues that could bring forth new ideas on cholesterol recognition motifs in the 3D structure of transmembrane proteins.
description Fil: Azzaz, Fodil. Institut national de la santé et de la recherche médicale; Francia
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_3248
info:ar-repo/semantics/parteDeLibro
format bookPart
status_str publishedVersion
dc.identifier.none.fl_str_mv https://repositorio.uca.edu.ar/handle/123456789/14433
978-0-323-85857-1
10.1016/B978-0-323-85857-1.00004-3
Azzaz, F., et al. Cholesterol-recognizing amino acid consensus motifs in transmembrane proteins: Comparative analysis of in silico studies and structural data [en línea]. En: Bukiya, A.N., Dopico, A.M. (eds.). Cholesterol. From Chemistry and Biophysics to the Clinic. Londres: Academic Press, 2022 doi:10.1016/B978-0-323-85857-1.00004-3 Disponible en: https://repositorio.uca.edu.ar/handle/123456789/14433
url https://repositorio.uca.edu.ar/handle/123456789/14433
identifier_str_mv 978-0-323-85857-1
10.1016/B978-0-323-85857-1.00004-3
Azzaz, F., et al. Cholesterol-recognizing amino acid consensus motifs in transmembrane proteins: Comparative analysis of in silico studies and structural data [en línea]. En: Bukiya, A.N., Dopico, A.M. (eds.). Cholesterol. From Chemistry and Biophysics to the Clinic. Londres: Academic Press, 2022 doi:10.1016/B978-0-323-85857-1.00004-3 Disponible en: https://repositorio.uca.edu.ar/handle/123456789/14433
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv ‎ Academic Press
publisher.none.fl_str_mv ‎ Academic Press
dc.source.none.fl_str_mv Bukiya, A.N., Dopico, A.M. (eds.). Cholesterol. From Chemistry and Biophysics to the Clinic. Londres: Academic Press, 2022
reponame:Repositorio Institucional (UCA)
instname:Pontificia Universidad Católica Argentina
reponame_str Repositorio Institucional (UCA)
collection Repositorio Institucional (UCA)
instname_str Pontificia Universidad Católica Argentina
repository.name.fl_str_mv Repositorio Institucional (UCA) - Pontificia Universidad Católica Argentina
repository.mail.fl_str_mv claudia_fernandez@uca.edu.ar
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score 13.13397

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