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Deriving correlated motions in proteins from X-ray structure refinement by using TLS parameters.
Liu, Yen-Yi; Shih, Chien-Hua; Hwang, Jenn-Kang; Chen, Chih-Chieh.
Afiliação
  • Liu YY; Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 30068, Taiwan, ROC. yyl.bi97g@nctu.edu.tw
Gene ; 518(1): 52-8, 2013 Apr 10.
Article em En | MEDLINE | ID: mdl-23270923
ABSTRACT
Dynamic information in proteins may provide valuable information for understanding allosteric regulation of protein complexes or long-range effects of the mutations on enzyme activity. Experimental data such as X-ray B-factors or NMR order parameters provide a convenient estimate of atomic fluctuations (or atomic auto-correlated motions) in proteins. However, it is not as straightforward to obtain atomic cross-correlated motions in proteins - one usually resorts to more sophisticated computational methods such as Molecular Dynamics, normal mode analysis or atomic network models. In this report, we show that atomic cross-correlations can be reliably obtained directly from protein structure using X-ray refinement data. We have derived an analytic form of atomic correlated motions in terms of the original TLS parameters used to refine the B-factors of X-ray structures. The correlated maps computed using this equation are well correlated with those of the method based on a mechanical model (the correlation coefficient is 0.75) for a non-homologous dataset comprising 100 structures. We have developed an approach to compute atomic cross-correlations directly from X-ray protein structure. Being in analytic form, it is fast and provides a feasible way to compute correlated motions in proteins in a high throughput way. In addition, avoiding sophisticated computational operations; it provides a quick, reliable way, especially for non-computational biologists, to obtain dynamics information directly from protein structure relevant to its function.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteínas / Modelos Moleculares Tipo de estudo: Prognostic_studies Idioma: En Revista: Gene Ano de publicação: 2013 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteínas / Modelos Moleculares Tipo de estudo: Prognostic_studies Idioma: En Revista: Gene Ano de publicação: 2013 Tipo de documento: Article
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