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PubMed · 15807505

Spin label EPR-based characterization of biosystem complexity.

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

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AlgorithmsCell MembraneComputer SimulationElectron Spin Resonance SpectroscopyMembrane ProteinsSpin LabelsJournal ArticleResearch Support, Non-U.S. Gov't

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Janez Strancar, Tilen Koklic, Zoran Arsov, Bogdan Filipic, David Stopar, Marcus A Hemminga. Spin label EPR-based characterization of biosystem complexity.. https://doi.org/10.1021/ci049748h

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