Distance Measurement on the Nanometer Scale with FRET and Molecular Dynamics Simulations

Single molecule fluorescence resonance energy transfer (smFRET) experiments exploit the distance dependency of the measurable transfer efficiency between two dyes to determine distances on the nm scale. The transfer efficiency in addition depends on the instantaneous mutual orientation Κ2 inaccesible to experiments. In molecular dynamics simulations, the mutual orientation of the dyes is accessible. Therefore, combining simulations with experiments results in more accurate distances as estimated from experiments alone.

Financial Support: SFB 755 - Nanoscale Photonic Imaging, German Israeli Foundation grant (Agreement No. 1000.89.9/2008)

Collaborations: Claus Seidel (University of Düsseldorf), Ben Schuler (University of Zurich, Switzerland)

Figure 1: Illustration of the smFRET principle

Single molecule fluorescence resonance energy transfer (smFRET) experiments measure the non-radiative transfer efficiency from a donor to an acceptor dye. The transfer efficiency depends on the inter-dye distance and is measured by monitoring the relative fluorescence intensities of donor and acceptor. Measurements of the FRET efficiency on individual molecules with dyes attached probe the distance in or between biomolecules and biomolecular subunits. The optical read-out of fluorescence allows application of the technique at ambient conditions and in vivo. The recent development of time-resolved (tr) experiments enables the extraction of information on single molecule dynamics. These advantages resulted in the emergence of smFRET as a powerful tool in biophysics providing a sensitive nanometer distance reporter to investigate biological structures and their function. Ligand-receptor interactions, conformational changes, protein synthesis and folding kinetics as well as translocation of genes are only some of the multitude of issues that can be addressed by tr-smFRET.

In analogy to television broadcasting, where the received signal strength depends on the antenna orientation, the transfer efficiency not only depends on the inter-dye distance, but also the mutual dye orientation. Therefore, the extraction of distance information from smFRET experiments is not straightforward and involves approximations. Typical approximations are are isotropic and uncorrelated dye transition dipole orientations and that the coupling potential is sufficiently described by dipole-dipole coupling of the transition densities. With these and further approximations, Theodor Förster expressed [Förster, 1948] the transfer efficiency as

\( E(R) = \frac{1}{1+\frac{R^6}{R_0}} \)                    (Eq. 1)

where R is the inter-dye distance and R0 the distance with 50% excitation transfer. The orientation dependency of dipole-dipole coupling is given by

\( \kappa = \text{cos}\,\theta_{DA} - 3 \cdot \text{cos}\,\theta_D \cdot \text{cos}\,\theta_A \)

with  θDA as angle between the two transition dipoles of the dyes.  θD and  θA are the angle between transition dipole of donor or acceptor and the dye separation vector R, respectively. The Förster radius R06 is ∝κ2 and <κ2> = 2/3 for isotropic orientation distributions.

Dyes attached to biomolecules are sterically restricted and interact with the dye's surface. In fact, strong deviations of the isotropicity and thus also of <Κ2> from 2/3 were found [van Beek et al., 2007]. Thus, accurate distance measurements with FRET are hindered by the uncertainty of the dye orientation. In contrast to experiments, the orientation of dyes is accessible at each instant in molecular dynamics simulations (MD).

Dye Dynamics and FRET Efficiency Distributions from MD simulations

Aiming at improving the accuracy of distance reconstruction, we developed a combination of dye orientation statistics and dynamics from MD simulations with experimentally measured efficiency distributions [Hoefling et al., 2011]. As test system, we studied poly-proline with two dyes attached at the ends. Poly-prolines form a stable type II helix in water and were extensively studied by FRET experiments in the past [Stryer et al., 1976; Schuler et al., 2005]. To mimic the kinetic pathways of smFRET experiments, we developed a Markov Chain Monte Carlo method with time dependent FRET rates calculated from MD trajectories.

Figure 2: Left Efficiency histograms of all-trans and cis-isomers. Central cis-bonds reduce the distance and lead to increased FRET efficiency. Right Efficiency histogram from fast and slow dye motions, corresponding to closed and open conformations of the dyes in comparison with the experiment.

Using the developed Markov Chain Monte Carlo for photon generation [Pool et al., 2012], photons bursts were generated as measured by the experiment. In analogy to the experiment, the donor and acceptor photon counts yield burst efficiencies and efficiency distributions can be compared with experimentally obtained ones. Simulations demonstrate, that the origin of the experimentally observed heterogeneity is twofold. First, poly-proline has a non-negligible isomerization probability and thus part of the species in the ensemble are cis-isomers with smaller end-to-end distance. Second, the dyes interact with the poly-proline backbone leading to open and closed conformations with different dynamics and lifetimes [Hoefling et al., 2011].

Distance Reconstruction

Figure 3: Exemplary distance reconstruction.

Next, we developed a method for distance reconstruction based on transfer functions as distance distribution. The transfer function formalism describes a convolution of the distance distribution with an arbitrary convolution kernel. In the simplest case, the Förster equation (1) is the convolution kernel. Proceeding from this, we altered the convolution kernel by reducing the approximations in Eq. (1) and replacing them by orientaion information from our simulations [Hoefling et al., 2011]. In Figure 3, an exemplary reconstruction is shown. The Experimental efficiency distribution is transformed by the transfer matrix (rainbow color). The resulting distance distributions for different levels of approximation are shown on the bottom. As seen by comparison to the reference distance distribution, each removal of an approximation systematically improves the accuracy of reconstructed distances.

Video

<h4>Exemplary pathways in Markov Chain Monte Carlo</h4>
First, the donor is excited emitting a photon after a while. In the second case, the excitation is transfered to the acceptor dye via FRET and released as photon after a while as well. more

The Computational Toolkit md2fret

To assess the impact of dye orientation approximations, we present the algorithms and implementation of a computational toolkit for the simulation of smFRET on the basis of molecular dynamics (MD) trajectory ensembles. In this study [Hoefling et al., 2013], the dye orientation dynamics, which are used to determine dynamic FRET efficiencies, are extracted from MD simulations. In a subsequent step, photons and bursts are generated using a Monte Carlo algorithm.

AMBER-DYES - A Modular Fluorescent Label Force Field

Recent advances in smFRET methodology and theory allow a direct comparison and improved interpretation of experiment and simulation. To this end, force fields for a larger number of dyes are required which are compatible with and can be integrated into existing biomolecular force fields. Here (Graen et al., 2014), we developed, characterized, and implemented AMBER-DYES, a modular fluorescent label force field, for a set of 22 fluorescent dyes and their linkers from the Alexa, Atto, and Cy families, which are in common use for single molecule spectroscopy experiments.

Publications

Graen, T.; Höfling, M.; Grubmüller, H.: AMBER-DYES: Characterization of charge fluctuations and force field parameterization of fluorescent dyes for molecular dynamics simulations. Journal of Chemical Theory and Computation 10 (12), pp. 5505 - 5512 (2014)
Hoefling, M.; Grubmüller, H.: In silico FRET from simulated dye dynamics. Computer physics communications 184 (3), pp. 841 - 852 (2013)
Pool, R.; Heringa, J.; Höfling, M.; Schulz, R.; Smith, J. C.; Feenstra, K. A.: Enabling grand-canonical Monte Carlo: Extending the flexibility of GROMACS through the GromPy python interface module. Journal of Computational Chemistry 33 (12), pp. 1207 - 1214 (2012)
Höfling, M.; Lima, N.; Haenni, D.; Seidel, C. A. M.; Schuler, B.; Grubmüller, H.: Structural heterogeneity and quantitative FRET efficiency distributions of polyprolines through a hybrid atomistic simulation and Monte Carlo approach. PLoS One 6 (5), e19791 (2011)

References

Förster, T.
Zwischenmolekulare Energiewanderung und Fluoreszenz.
Annalen der Physik 2 (1-2), 55-75, (1948)
vanBeek, D. B.; Zwier, M. C.; Shorb, J. M.; Krueger, B. P.
Fretting about FRET: Correlation between kappa and R
Biophysical Journal 92, 4168-4178, (2007)
Stryer, L.; Haugland, R. P.
Energy transfer: a spectroscopic ruler
Proceedings of the National Academy of Sciences of the United States of America 58, 719-126, (1976)
Schuler, B.;  Lipman, E. A.;  Steinbach, P. J.; Kumke, M.; Eaton, W. A.
Polyproline and the "spectroscopic ruler" revisited with single-molecule fluorescence
Proceedings of the National Academy of Sciences of the United States 102, 2754-2759, (2005)
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