An introduction to NMR-based approaches for measuring protein dynamics
Proteins are inherently flexible at ambient temperature. At equilibrium, they are characterized by a set of conformations that undergo continuous exchange within a hierarchy of spatial and temporal scales ranging from nanometers to micrometers and femtoseconds to hours. Dynamic properties of protein...
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Published in | Biochimica et biophysica acta Vol. 1814; no. 8; pp. 942 - 968 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Netherlands
Elsevier B.V
01.08.2011
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Subjects | |
Online Access | Get full text |
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Summary: | Proteins are inherently flexible at ambient temperature. At equilibrium, they are characterized by a set of conformations that undergo continuous exchange within a hierarchy of spatial and temporal scales ranging from nanometers to micrometers and femtoseconds to hours. Dynamic properties of proteins are essential for describing the structural bases of their biological functions including catalysis, binding, regulation and cellular structure. Nuclear magnetic resonance (NMR) spectroscopy represents a powerful technique for measuring these essential features of proteins. Here we provide an introduction to NMR-based approaches for studying protein dynamics, highlighting eight distinct methods with recent examples, contextualized within a common experimental and analytical framework. The selected methods are (1) Real-time NMR, (2) Exchange spectroscopy, (3) Lineshape analysis, (4) CPMG relaxation dispersion, (5) Rotating frame relaxation dispersion, (6) Nuclear spin relaxation, (7) Residual dipolar coupling, (8) Paramagnetic relaxation enhancement. This article is part of a Special Issue entitled: Protein Dynamics: Experimental and Computational Approaches.
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► Internal dynamics of proteins are essential for proper function. ► Protein dynamics can be rigorously studied using NMR spectroscopy. ► Eight distinct NMR based approaches are discussed with recent examples. ► The process of analyzing complex experimental data is generalized into a common model-based framework. |
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Bibliography: | http://dx.doi.org/10.1016/j.bbapap.2010.10.012 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 ObjectType-Feature-3 ObjectType-Review-1 |
ISSN: | 1570-9639 0006-3002 1878-1454 |
DOI: | 10.1016/j.bbapap.2010.10.012 |