Deriving correlated motions in proteins from X-ray structure refinement by using TLS parameters

Chih Chieh Chen, Jenn-Kang Hwang, Chien Hua Shih, Yen Yi Liu

Research output: Contribution to journalArticle

1 Scopus citations

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 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.
Original languageEnglish
Pages (from-to)52-58
Number of pages7
JournalGene
Volume518
Issue number1
DOIs
StatePublished - 10 Apr 2013

Keywords

  • TLS parameter; TLS model; Correlated motion; Atomic cross-correlation

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