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Default An ensemble dynamics approach to decipher solid-state NMR observables of membrane proteins

An ensemble dynamics approach to decipher solid-state NMR observables of membrane proteins

February 2012
Publication year: 2012
Source:Biochimica et Biophysica Acta (BBA) - Biomembranes, Volume 1818, Issue 2



Solid-state NMR (SSNMR) is an invaluable tool for determining orientations of membrane proteins and peptides in lipid bilayers. Such orientational descriptions provide essential information about membrane protein functions. However, when a semi-static single conformer model is used to interpret various SSNMR observables, important dynamics information can be missing, and, sometimes, even orientational information can be misinterpreted. In addition, over the last decade, molecular dynamics (MD) simulation and semi-static SSNMR interpretation have shown certain levels of discrepancies in terms of transmembrane helix orientation and dynamics. Dynamic fitting models have recently been proposed to resolve these discrepancies by taking into account transmembrane helix whole body motions using additional parameters. As an alternative approach, we have developed SSNMR ensemble dynamics (SSNMR-ED) using multiple conformer models, which generates an ensemble of structures that satisfies the experimental observables without any fitting parameters. In this review, various computational methods for determining transmembrane helix orientations are discussed, and the distributions of VpuTM (from HIV-1) and WALP23 (a synthetic peptide) orientations from SSNMR-ED simulations are compared with those from MD simulations and semi-static/dynamic fitting models. Such comparisons illustrate that SSNMR-ED can be used as a general means to extract both membrane protein structure and dynamics from the SSNMR measurements. This article is part of a Special Issue entitled: Membrane protein structure and function.
Graphical abstract

Highlights

?A review of solid-state NMR (SSNMR) structure calculation methods. ?Illustration of TM helix dynamics' effects in interpretation of SSNMR observables. ?An introduction of SSNMR ensemble dynamics (SSNMR-ED). ?Comparison of SSNMR-ED with MD and semi-static/dynamic fitting methods.





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