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基于自旋标记电子顺磁共振的生物系统复杂性表征

Spin label EPR-based characterization of biosystem complexity.

作者信息

Strancar Janez, Koklic Tilen, Arsov Zoran, Filipic Bogdan, Stopar David, Hemminga Marcus A

机构信息

Laboratory of Biophysics, JoZef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia.

出版信息

J Chem Inf Model. 2005 Mar-Apr;45(2):394-406. doi: 10.1021/ci049748h.

Abstract

Following the widely spread EPR spin-label applications for biosystem characterization, a novel approach is proposed for EPR-based characterization of biosystem complexity. Hereto a computational method based on a hybrid evolutionary optimization (HEO) is introduced. The enormous volume of information obtained from multiple HEO runs is reduced with a novel so-called GHOST condensation method for automatic detection of the degree of system complexity through the construction of two-dimensional solution distributions. The GHOST method shows the ability of automatic quantitative characterization of groups of solutions, e.g. the determination of average spectral parameters and group contributions. The application of the GHOST condensation algorithm is demonstrated on four synthetic examples of different complexity and applied to two physiologically relevant examples--the determination of domains in biomembranes (lateral heterogeneity) and the study of the low-resolution structure of membrane proteins.

摘要

随着电子顺磁共振(EPR)自旋标记在生物系统表征中的广泛应用,提出了一种基于EPR的生物系统复杂性表征新方法。为此,引入了一种基于混合进化优化(HEO)的计算方法。通过一种新颖的所谓GHOST凝聚方法,从多次HEO运行中获得的大量信息得以减少,该方法通过构建二维解分布来自动检测系统复杂程度。GHOST方法显示了对解组进行自动定量表征的能力,例如确定平均光谱参数和组贡献。在四个不同复杂程度的合成示例上展示了GHOST凝聚算法的应用,并将其应用于两个生理相关示例——生物膜中结构域的确定(横向异质性)以及膜蛋白低分辨率结构的研究。

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