A probabilistic approach to protein backbone tracing in electron density maps.
Bioinformatics
; 22(14): e81-9, 2006 Jul 15.
Article
em En
| MEDLINE
| ID: mdl-16873525
ABSTRACT
One particularly time-consuming step in protein crystallography is interpreting the electron density map; that is, fitting a complete molecular model of the protein into a 3D image of the protein produced by the crystallographic process. In poor-quality electron density maps, the interpretation may require a significant amount of a crystallographer's time. Our work investigates automating the time-consuming initial backbone trace in poor-quality density maps. We describe ACMI (Automatic Crystallographic Map Interpreter), which uses a probabilistic model known as a Markov field to represent the protein. Residues of the protein are modeled as nodes in a graph, while edges model pairwise structural interactions. Modeling the protein in this manner allows the model to be flexible, considering an almost infinite number of possible conformations, while rejecting any that are physically impossible. Using an efficient algorithm for approximate inference--belief propagation--allows the most probable trace of the protein's backbone through the density map to be determined. We test ACMI on a set of ten protein density maps (at 2.5 to 4.0 A resolution), and compare our results to alternative approaches. At these resolutions, ACMI offers a more accurate backbone trace than current approaches.
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Bases de dados:
MEDLINE
Assunto principal:
Proteínas
/
Modelos Moleculares
/
Cristalografia por Raios X
/
Análise de Sequência de Proteína
/
Microanálise por Sonda Eletrônica
/
Modelos Químicos
Tipo de estudo:
Risk_factors_studies
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2006
Tipo de documento:
Article
País de afiliação:
Estados Unidos