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

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 hav...

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Detalles Bibliográficos
Autores principales: Azzaz, Fodil, Chahinian, Henri, Yahi, Nouara, Di Scala, Coralie, Baier, Carlos J., Barrantes, Francisco José
Formato: Parte de libro
Lenguaje:Inglés
Publicado: ‎ Academic Press 2022
Materias:
COLESTEROL
AMINOACIDOS
MEMBRANAS CELULARES
Acceso en línea:https://repositorio.uca.edu.ar/handle/123456789/14433
Aporte de:
Repositorio Institucional de la Universidad Católica Argentina (UCA) de Universidad Católica Argentina
id I33-R139-123456789-14433
record_format dspace
institution Universidad Católica Argentina
institution_str I-33
repository_str R-139
collection Repositorio Institucional de la Universidad Católica Argentina (UCA)
language Inglés
topic COLESTEROL
AMINOACIDOS
MEMBRANAS CELULARES
spellingShingle COLESTEROL
AMINOACIDOS
MEMBRANAS CELULARES
Azzaz, Fodil
Chahinian, Henri
Yahi, Nouara
Di Scala, Coralie
Baier, Carlos J.
Barrantes, Francisco José
Cholesterol-recognizing amino acid consensus motifs in transmembrane proteins: Comparative analysis of in silico studies and structural data
topic_facet COLESTEROL
AMINOACIDOS
MEMBRANAS CELULARES
description 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.
format Parte de libro
author Azzaz, Fodil
Chahinian, Henri
Yahi, Nouara
Di Scala, Coralie
Baier, Carlos J.
Barrantes, Francisco José
author_facet Azzaz, Fodil
Chahinian, Henri
Yahi, Nouara
Di Scala, Coralie
Baier, Carlos J.
Barrantes, Francisco José
author_sort Azzaz, Fodil
title Cholesterol-recognizing amino acid consensus motifs in transmembrane proteins: Comparative analysis of in silico studies and structural data
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
publisher ‎ Academic Press
publishDate 2022
url https://repositorio.uca.edu.ar/handle/123456789/14433
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