Protein sectors: statistical coupling analysis versus conservation.
PLoS Comput Biol
; 11(2): e1004091, 2015 Feb.
Article
en En
| MEDLINE
| ID: mdl-25723535
ABSTRACT
Statistical coupling analysis (SCA) is a method for analyzing multiple sequence alignments that was used to identify groups of coevolving residues termed "sectors". The method applies spectral analysis to a matrix obtained by combining correlation information with sequence conservation. It has been asserted that the protein sectors identified by SCA are functionally significant, with different sectors controlling different biochemical properties of the protein. Here we reconsider the available experimental data and note that it involves almost exclusively proteins with a single sector. We show that in this case sequence conservation is the dominating factor in SCA, and can alone be used to make statistically equivalent functional predictions. Therefore, we suggest shifting the experimental focus to proteins for which SCA identifies several sectors. Correlations in protein alignments, which have been shown to be informative in a number of independent studies, would then be less dominated by sequence conservation.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Proteínas
/
Alineación de Secuencia
/
Biología Computacional
/
Análisis de Secuencia de Proteína
/
Dominios y Motivos de Interacción de Proteínas
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
PLoS Comput Biol
Asunto de la revista:
BIOLOGIA
/
INFORMATICA MEDICA
Año:
2015
Tipo del documento:
Article
País de afiliación:
Estados Unidos