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Conformational Ensembles from Experimental Data
and Computer Simulations
Poster Abstracts
58
25-POS
Board 25
Conformational Dynamics of Nucleic Acids by Orientation Selective PELDOR
Nicole Erlenbach
1
, Lukas S. Stelzl
2
, Gerhard Hummer
2,3
, Thomas F. Prisner
1
.
1
Geothe University, Frankfurt am Main, Germany,
2
Max Planck Institute of Biophysics,
Frankfurt am Main, Germany,
3
Goethe University, Frankfurt am Main, Germany.
PELDOR (Pulsed Electron Electron Double Resonance)
1
experiments on nucleic acids with rigid
spin-labels provide highly accurate distance and orientation information, which can be used to
study their structure and dynamics.
2
PELDOR experiments on dsDNA, with the rigid spin label
Ҫ, a cytidine analogue, have already revealed a twist-stretch motion.
3
Molecular dynamics (MD) simulations can act as a 'computational microscope' to resolve the
dynamics of individual atoms. However, for a long time the application of MD to nucleic acids
was hindered by the lack of accurate force fields. Experimental PELDOR signals can be used as
sensitive benchmark data for the evaluation of MD simulations on nucleic acid molecules. In
contrast to older force fields, signals calculated from MD simulations with the new parmbsc1
4
and OL15
5
force fields closely match the experimental PELDOR traces, which confirms that
these force fields are significant improvements in the computational description of DNA. This
evaluation can be combined with answering the fundamental question as to the nature of the
conformational dynamics of DNA. MD simulations show that dsDNA undergoes bending and
twist-stretch motions in solution.
For dsRNA, the comparison of initial PELDOR experiments with MD simulations shows larger
deviations between experiments and theory, suggesting that further optimization of the force
fields is required for accurate description.
1
A. D. Milov et al., Sov. Phys. Solid State, 1981, 23, 975–982.
2
T. F. Prisner et al. J. Magn. Reson., 2015, 252, 187–198.
3
A. Marko et al. J. Am. Chem. Soc., 2011, 133, 13375.
4
I. Ivani et al. Nat. Methods, 2015, 13, 55–58.
5
M. Zgarbová et al. J. Chem. Theory Comput., 2015, 11, 5723–5736.