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Ensemble-Based Interpretations of NMR Structural Data to Describe Protein Internal Dynamics

Faculty of Information Technology, Pázmány Péter Catholic University, Budapest 1083, Práter street 50/A, Hungary
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Molecules 2013, 18(9), 10548-10567; https://doi.org/10.3390/molecules180910548
Received: 4 July 2013 / Revised: 9 August 2013 / Accepted: 22 August 2013 / Published: 30 August 2013
(This article belongs to the Special Issue NMR of Proteins and Small Biomolecules)
NMR spectroscopy is the leading technique to characterize protein internal dynamics at the atomic level and on multiple time scales. However, the structural interpretation of the observables obtained by various measurements is not always straightforward and in many cases dynamics-related parameters are only used to “decorate” static structural models without offering explicit description of conformational heterogeneity. To overcome such limitations, several computational techniques have been developed to generate ensemble-based representations of protein structure and dynamics with the use of NMR-derived data. An important common aspect of the methods is that NMR observables and derived parameters are interpreted as properties of the ensemble instead of individual conformers. The resulting ensembles reflect the experimentally determined internal mobility of proteins at a given time scale and can be used to understand the role of internal motions in biological processes at atomic detail. In this review we provide an overview of the calculation methods currently available and examples of biological insights obtained by the ensemble-based models of the proteins investigated. View Full-Text
Keywords: dynamic structural ensembles; protein NMR spectroscopy; protein mobility; molecular dynamics simulation; ensemble generation dynamic structural ensembles; protein NMR spectroscopy; protein mobility; molecular dynamics simulation; ensemble generation
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F. Ángyán, A.; Gáspári, Z. Ensemble-Based Interpretations of NMR Structural Data to Describe Protein Internal Dynamics. Molecules 2013, 18, 10548-10567.

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