Browsing by Subject "Protein complexes"

Now showing 1 - 3 of 3
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    A novel method to accurately locate and count large numbers of steps by photobleaching
    (The American Society for Cell Biology, 2016-11-07) Tsekouras, Konstantinos; Custer, Thomas C.; Jashnsaz, Hossein; Walter, Nils G.; Pressé, Steve; Department of Physics, School of Science
    Photobleaching event counting is a single-molecule fluorescence technique that is increasingly being used to determine the stoichiometry of protein and RNA complexes composed of many subunits in vivo as well as in vitro. By tagging protein or RNA subunits with fluorophores, activating them, and subsequently observing as the fluorophores photobleach, one obtains information on the number of subunits in a complex. The noise properties in a photobleaching time trace depend on the number of active fluorescent subunits. Thus, as fluorophores stochastically photobleach, noise properties of the time trace change stochastically, and these varying noise properties have created a challenge in identifying photobleaching steps in a time trace. Although photobleaching steps are often detected by eye, this method only works for high individual fluorophore emission signal-to-noise ratios and small numbers of fluorophores. With filtering methods or currently available algorithms, it is possible to reliably identify photobleaching steps for up to 20-30 fluorophores and signal-to-noise ratios down to ∼1. Here we present a new Bayesian method of counting steps in photobleaching time traces that takes into account stochastic noise variation in addition to complications such as overlapping photobleaching events that may arise from fluorophore interactions, as well as on-off blinking. Our method is capable of detecting ≥50 photobleaching steps even for signal-to-noise ratios as low as 0.1, can find up to ≥500 steps for more favorable noise profiles, and is computationally inexpensive.
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    Stochastic approach to the molecular counting problem in superresolution microscopy.
    (PNAS, 2015-01-13) Rollins, Geoffrey C.; Yen Shin, Jae; Bustamante, Carlos; Pressé, Steve; Department of Physics, School of Science
    Superresolution imaging methods--now widely used to characterize biological structures below the diffraction limit--are poised to reveal in quantitative detail the stoichiometry of protein complexes in living cells. In practice, the photophysical properties of the fluorophores used as tags in superresolution methods have posed a severe theoretical challenge toward achieving this goal. Here we develop a stochastic approach to enumerate fluorophores in a diffraction-limited area measured by superresolution microscopy. The method is a generalization of aggregated Markov methods developed in the ion channel literature for studying gating dynamics. We show that the method accurately and precisely enumerates fluorophores in simulated data while simultaneously determining the kinetic rates that govern the stochastic photophysics of the fluorophores to improve the prediction's accuracy. This stochastic method overcomes several critical limitations of temporal thresholding methods.
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    Stochastic machines as a colocalization mechanism for scaffold protein function
    (Wiley, 2013) Xue, Bin; Romero, Pedro R.; Noutsou, Maria; Maurice, Madelon M.; Rüdiger, Stefan G. D.; William, Albert M., Jr.; Mizianty, Marcin J.; Kurgan, Lukasz; Uversky, Vladimir N.; Dunker, A. Keith; Biochemistry and Molecular Biology, School of Medicine
    The axis inhibition (Axin) scaffold protein colocalizes β-catenin, casein kinase Iα, and glycogen synthetase kinase 3β by their binding to Axin's long intrinsically disordered region, thereby yielding structured domains with flexible linkers. This complex leads to the phosphorylation of β-catenin, marking it for destruction. Fusing proteins with flexible linkers vastly accelerates chemical interactions between them by their colocalization. Here we propose that the complex works by random movements of a "stochastic machine," not by coordinated conformational changes. This non-covalent, modular assembly process allows the various molecular machine components to be used in multiple processes.