Browsing by Author "Bustamante, Carlos"

Now showing 1 - 4 of 4
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    The heat released during catalytic turnover enhances the diffusion of an enzyme
    (Nature Publishing Group, 2015-01-08) Riedel, Clement; Gabizon, Ronen; Wilson, Christian A. M.; Hamadani, Kambiz; Tsekouras, Konstantinos; Marqusee, Susan; Pressé, Steve; Bustamante, Carlos; Department of Cellular & Integrative Physiology, IU School of Medicine
    Recent studies have shown that the diffusivity of enzymes increases in a substrate-dependent manner during catalysis,. Although this observation has been reported and characterized for several different systems–, the precise origin of this phenomenon is unknown. Calorimetric methods are often used to determine enthalpies from enzyme-catalysed reactions and can therefore provide important insight into their reaction mechanisms,. The ensemble averages involved in traditional bulk calorimetry cannot probe the transient effects that the energy exchanged in a reaction may have on the catalyst. Here we obtain single-molecule fluorescence correlation spectroscopy data and analyse them within the framework of a stochastic theory to demonstrate a mechanistic link between the enhanced diffusion of a single enzyme molecule and the heat released in the reaction. We propose that the heat released during catalysis generates an asymmetric pressure wave that results in a differential stress at the protein–solvent interface that transiently displaces the centre-of-mass of the enzyme (chemoacoustic effect). This novel perspective on how enzymes respond to the energy released during catalysis suggests a possible effect of the heat of reaction on the structural integrity and internal degrees of freedom of the enzyme.
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    Single Molecule Conformational Memory Extraction: P5ab RNA Hairpin
    (American Chemical Society, 2014-06-19) Pressé, Steve; Peterson, Jack; Lee, Julian; Elms, Phillip; MacCallum, Justin L.; Marqusee, Susan; Bustamante, Carlos; Dill, Ken; Department of Physics, School of Science
    Extracting kinetic models from single molecule data is an important route to mechanistic insight in biophysics, chemistry, and biology. Data collected from force spectroscopy can probe discrete hops of a single molecule between different conformational states. Model extraction from such data is a challenging inverse problem because single molecule data are noisy and rich in structure. Standard modeling methods normally assume (i) a prespecified number of discrete states and (ii) that transitions between states are Markovian. The data set is then fit to this predetermined model to find a handful of rates describing the transitions between states. We show that it is unnecessary to assume either (i) or (ii) and focus our analysis on the zipping/unzipping transitions of an RNA hairpin. The key is in starting with a very broad class of non-Markov models in order to let the data guide us toward the best model from this very broad class. Our method suggests that there exists a folding intermediate for the P5ab RNA hairpin whose zipping/unzipping is monitored by force spectroscopy experiments. This intermediate would not have been resolved if a Markov model had been assumed from the onset. We compare the merits of our method with those of others.
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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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    Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis
    (ACS Publications, 2017-06-14) Lee, Antony; Tsekouras, Konstantinos; Calderon, Christopher; Bustamante, Carlos; Pressé, Steve; Chemistry and Chemical Biology, School of Science
    Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light’s diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we’ve termed the interpretation problem.