標題: Deriving correlated motions in proteins from X-ray structure refinement by using TLS parameters
作者: Liu, Yen-Yi
Shih, Chien-Hua
Hwang, Jenn-Kang
Chen, Chih-Chieh
生物資訊及系統生物研究所
Institude of Bioinformatics and Systems Biology
關鍵字: TLS parameter;TLS model;Correlated motion;Atomic cross-correlation
公開日期: 10-四月-2013
摘要: 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 MS 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. (c) 2012 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.gene.2012.11.086
http://hdl.handle.net/11536/21323
ISSN: 0378-1119
DOI: 10.1016/j.gene.2012.11.086
期刊: GENE
Volume: 518
Issue: 1
起始頁: 52
結束頁: 58
顯示於類別:會議論文


文件中的檔案:

  1. 000316424100008.pdf