Context similarity scoring improves protein sequence alignments in the midnight zone.
Bioinformatics
; 31(5): 674-81, 2015 Mar 01.
Article
en En
| MEDLINE
| ID: mdl-25338715
ABSTRACT
MOTIVATION High-quality protein sequence alignments are essential for a number of downstream applications such as template-based protein structure prediction. In addition to the similarity score between sequence profile columns, many current profile-profile alignment tools use extra terms that compare 1D-structural properties such as secondary structure and solvent accessibility, which are predicted from short profile windows around each sequence position. Such scores add non-redundant information by evaluating the conservation of local patterns of hydrophobicity and other amino acid properties and thus exploiting correlations between profile columns. RESULTS:
Here, instead of predicting and comparing known 1D properties, we follow an agnostic approach. We learn in an unsupervised fashion a set of maximally conserved patterns represented by 13-residue sequence profiles, without the need to know the cause of the conservation of these patterns. We use a maximum likelihood approach to train a set of 32 such profiles that can best represent patterns conserved within pairs of remotely homologs, structurally aligned training profiles. We include the new context score into our Hmm-Hmm alignment tool hhsearch and improve especially the quality of difficult alignments significantly.CONCLUSION:
The context similarity score improves the quality of homology models and other methods that depend on accurate pairwise alignments.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Proteínas
/
Alineación de Secuencia
/
Análisis de Secuencia de Proteína
/
Aminoácidos
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2015
Tipo del documento:
Article
País de afiliación:
Alemania