Förster Resonance Energy Transfer (FRET) combined with single-molecule spectroscopy probes macromolecular structure and dynamics and identifies coexisting conformational states. We review recent methodological developments in integrative structural modeling by satisfying spatial restraints on networks of FRET pairs (hybrid-FRET). We discuss procedures to incorporate prior structural knowledge and to obtain optimal distance networks. Finally, a workflow for hybrid-FRET is presented that automates integrative structural modeling and experiment planning to put hybrid-FRET on rails. To test this workflow, we simulate realistic single-molecule experiments and resolve three protein conformers, exchanging at 30μs and 10ms, with accuracies of 1-3Å RMSD versus the target structure. Incorporation of data from other spectroscopies and imaging is also discussed.

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Single particle tracking (SPT) is a powerful class of techniques for exploring the dynamics of single molecules moving inside living cells. In order to extract information about the biophysical processes under study, one desires both the trajectories of the tracked particles as well as the parameters of their motions, such as diffusion coefficients, confinement lengths, and similar values. Typically this information is determined in two steps, first through trajectory estimation from the image data and then parameter estimation from the trajectories In prior work, we have introduced a general algorithm known as Sequential Monte Carlo-Expectation Maximization (SMC-EM) that leverages nonlinear system identification tools using particle filters and particle smoothers to handle nonlinear models of motion as well as nonlinear models of observation, allowing us to incorporate camera models, varying point spread functions, and other experimental realities. These scheme does not separate the estimation of trajectory and motion parameters. SMC-EM is in fact a family of algorithms described by the choice of filter and smoother. In this work, we undertake a systematic study of the effect the choice of these different sub-algorithms, focusing on diffusion models as well as the simplest setting that is biologically relevant. The algorithms are compared with respect to a variety of measures, including speed of convergence, computational complexity, trajectory estimation accuracy, parameter estimation accuracy, and robustness to noise. We present a Bayesian approach to fluorescence correlation spectroscopy (FCS) parameter estimation. Traditionally, FCS data are analyzed by leastsquare fitting the autocorrelation function of the fluorescent intensity signal. We show by simulation that a least-square analysis of FCS autocorrelation functions is problematic both in the sense that the analysis results in an order-of-magnitude overestimation of confidence in the fitting parameters, but more importantly in systematic shifts of parameters away from the true value. This effect is more pronounced the shorter the data set is. Motivated by this result, we developed a Bayesian framework for the analysis of FCS data that takes single photon arrival times as input. So far, we have solved the time-independent probability distribution of photon arrival times for a 3d Gaussian beam shape and we are in the process of comparing our method to traditional photon counting histograms in FCS. In the future, we will incorporate the dynamics of fluorescent molecule diffusion into the model. 685-PosExperimental Dissection of Excluded Volume Effects from Quinary Interactions in Macromolecular Crowding . Current models of diffusion are, at best, approximations of what happens in vivo. Fick's Law and Stokes-Einstein approximate dilute solution behavior, but they do not account for in vivo environmental factors that modulate particle motion such as electrostatic forces, hydrophobic interactions, and excluded volume. Such factors represent the contributions f...

cosolute distributions in vapor pressure osmometry and Donnan equilibrium measurements. In this work we describe preferential interactions between charged and neutral cosolute species (Kþ/Cl-and urea) and their interactions with an RNA sequence that forms a hyperstable GCAA tetraloop using isothermal (NVT) MD simulations. The RNA-KCl preferential interactions (Donnan coefficients) are determined at cellular (~100 mM) and high (1-3 M) salt concentrations, and show an unexpected linear concentration dependence. At 3 M KCl, the Donnan coefficient is zero. In high (2-6 M) concentrations of urea, KCl preferentially excludes urea, while RNA preferentially attracts urea. Interestingly, despite the depletion of urea around KCl, the RNA-KCl preferential interaction increases. These results offer new insight into the interactions between RNA and many of the cosolute species that affect RNA folding in vivo.

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