Interpreting Protein Structural
Dynamics from NMR
Chemical Shifts
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Abstract
In this investigation, semiempirical NMR chemical shift
prediction
methods are used to evaluate the dynamically averaged values of backbone
chemical shifts obtained from unbiased molecular dynamics (MD) simulations
of proteins. MD-averaged chemical shift predictions generally improve
agreement with experimental values when compared to predictions made
from static X-ray structures. Improved chemical shift predictions
result from population-weighted sampling of multiple conformational
states and from sampling smaller fluctuations within conformational
basins. Improved chemical shift predictions also result from discrete
changes to conformations observed in X-ray structures, which may result
from crystal contacts, and are not always reflective of conformational
dynamics in solution. Chemical shifts are sensitive reporters of fluctuations
in backbone and side chain torsional angles, and averaged <sup>1</sup>H chemical shifts are particularly sensitive reporters of fluctuations
in aromatic ring positions and geometries of hydrogen bonds. In addition,
poor predictions of MD-averaged chemical shifts can identify spurious
conformations and motions observed in MD simulations that may result
from force field deficiencies or insufficient sampling and can also
suggest subsets of conformational space that are more consistent with
experimental data. These results suggest that the analysis of dynamically
averaged NMR chemical shifts from MD simulations can serve as a powerful
approach for characterizing protein motions in atomistic detail