Using fluorescence correlation spectroscopy (FCS) to distinguish between different types of diffusion processes is often a perilous undertaking because the analysis of the resulting autocorrelation data is model dependant. Two recently introduced strategies, however, can help move toward a model-independent interpretation of FCS experiments: 1) the obtention of correlation data at different length scales and 2) their inversion to retrieve the mean-squared displacement associated with the process under study. We use computer simulations to examine the signature of several biologically relevant diffusion processes (simple diffusion, continuous-time random walk, caged diffusion, obstructed diffusion, two-state diffusion, and diffusing diffusivity) in variable-length-scale FCS. We show that, when used in concert, length-scale variation and data inversion permit us to identify non-Gaussian processes and, regardless of Gaussianity, to retrieve their mean-squared displacement over several orders of magnitude in time. This makes unbiased discrimination between different classes of diffusion models possible.
The resolution of stimulated emission depletion (STED) microscopes is ultimately limited by the quality of the doughnut-shaped illumination profile of the STED erase beam. We show here that in the focal plane this illumination profile is well approximated by an analytical expression - a difference of Gaussian functions, which tends towards a first order Laguerre-Gaussian profile in the case of a well aligned beam with a true zero-intensity central minimum. We further show that along the optical axis the maximum intensity profile is reasonably approximated by a Gaussian decay away from the focal plane. The result is a fully Gaussian analytical approximation of the three-dimensional point-spread function of STED erase beams. This allows the derivation of an analytical form for the autocorrelation function of the fluorescence generated by fluorophore diffusion through the STED depletion volume. We verified this form to be correct by performing fluorescence correlation spectroscopy (FCS) experiments in solutions of the dye Alexa Fluor 532. Since the quality of the illumination profile is reflected in the shape of the autocorrelation function, we propose that fluctuation analysis can be used as a tool to assess the quality of STED erase beams.
Using fluorescence correlation spectroscopy (FCS) to distinguish between different types of diffusion processes is often a perilous undertaking, as the analysis of the resulting autocorrelation data is model-dependant. Two recently introduced strategies, however, can help move towards a model-independent interpretation of FCS experiments: 1) the obtention of correlation data at different length-scales and 2) its inversion to retrieve the mean-squared displacement associated with the process under study. We use computer simulations to examine the signature of several biologically relevant diffusion processes (simple diffusion, continuous-time random walk, caged diffusion, obstructed diffusion, two-state diffusion and diffusing diffusivity) in variable-lengthscale FCS. We show that, when used in concert, lengthscale variation and data inversion permit to identify non-Gaussian processes and, regardless of Gaussianity, to retrieve their mean-squared displacement over several orders of magnitude in time. This makes unbiased discrimination between different classes of diffusion models possible.Received for publication xxx and in final form xxx.
Models of Titan predict that there is a subsurface ocean of water and ammonia under a layer of ice. This model could prove important in the search for extraterrestrial life due to its prediction of a potentially habitable environment. In this study, we used molecular dynamics simulations to study proteins in Earth and Titan-like conditions, focusing on the most common secondary structure types: alpha helix and beta sheet. The Earth environment was simulated using a temperature of 300 K, a pressure of 1 bar, and water. The Titan environment was simulated using a temperature of 300 K, a pressure of 1000 bar, and a eutectic mixture of water and ammonia. We analyzed protein compactness, flexibility, and backbone dihedral distributions to identify differences between the two environments. Proteins in the Titan environment were more compact and less flexible, and have small differences in backbone dihedral preferences (e.g., in one instance a stable p-helix formed). These differences could impact affinities between these proteins and other biomolecules.
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