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Modeling Excluded Volume Effects for the Faithful Description of the Background Signal in Double Electron–Electron Resonance
journal contribution
posted on 2013-12-27, 00:00 authored by Daniel
R. Kattnig, Jörg Reichenwallner, Dariush HinderbergerWe discuss excluded volume effects
on the background signal of
double electron–electron resonance (DEER) experiments. Assuming
spherically symmetric pervaded volumes, an analytical expression of
the background signal is derived based on the shell-factorization
approach. The effects of crowding and off-center label positions are
discussed. Crowding is taken into account using the Percus–Yevick
approximation for the radial distribution function of the particle
centers. In addition, a versatile approach relating the pair-correlation
function of the particle centers with those of off-center labels is
introduced. Limiting expressions applying to short and long dipolar
evolution times are derived. Furthermore, we show under which conditions
the background with significant excluded volume effects resembles
that originating from a fractal dimensionality ranging from 3 to 6.
DEER time domain data of spin-probed samples of human serum albumin
(HSA) are shown to be strongly affected by excluded-volume effects.
The excluded volume is determined from the simultaneous analysis of
spectra recorded at various protein concentrations but a constant
probe-to-protein ratio. The spin-probes 5-DOXYL-stearic acid (5-DSA)
and 16-DOXYL-stearic acid (16-DSA) are used, which, when taken up
by HSA, give rise to broad and well-defined distance distributions,
respectively. We compare different, model-free approaches of analyzing
these data. The most promising results are obtained by the concurrent
Tikhonov regularization of all spectra when a common background model
is simultaneously adjusted such that the a posteriori probability
is maximized. For the samples of 16-DSA in HSA, this is the only approach
that allows suppressing a background artifact. We suggest that the
delineated simultaneous analysis procedure can be generally applied
to reduce ambiguities related to the ill-posed extraction of distance
distributions from DEER spectra. This approach is particularly valuable
for dipolar signals resulting from broad distance distributions, which
as a consequence, are devoid of explicit dipolar oscillations.