1
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Hamerla C, Mondal P, Hegger R, Burghardt I. Controlled destabilization of caged circularized DNA oligonucleotides predicted by replica exchange molecular dynamics simulations. Phys Chem Chem Phys 2023; 25:26132-26144. [PMID: 37740309 DOI: 10.1039/d3cp02961a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/24/2023]
Abstract
Spatiotemporal control is a critical issue in the design of strategies for the photoregulation of oligonucleotide activity. Efficient uncaging, i.e., activation by removal of photolabile protecting groups (PPGs), often necessitates multiple PPGs. An alternative approach is based on circularization strategies, exemplified by intrasequential circularization, also denoted photo-tethering, as introduced in [Seyfried et al., Angew. Chem., Int. Ed., 2017, 56, 359]. Here, we develop a computational protocol, relying on replica exchange molecular dynamics (REMD), in order to characterize the destabilization of a series of circularized, caged DNA oligonucleotides addressed in the aforementioned study. For these medium-sized (32 nt) oligonucleotides, melting temperatures are computed, whose trend is in good agreement with experiment, exhibiting a large destabilization and, hence, reduction of the melting temperature of the order of ΔTm ∼ 30 K as compared with the native species. The analysis of free energy landscapes confirms the destabilization pattern experienced by the circularized oligonucleotides. The present study underscores that computational protocols that capture controlled destabilization and uncaging of oligonucleotides are promising as predictive tools in the tailored photocontrol of nucleic acids.
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Affiliation(s)
- Carsten Hamerla
- Institute of Physical and Theoretical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 7, 60438 Frankfurt am Main, Germany.
| | - Padmabati Mondal
- Department of Chemistry and Center for Atomic, Molecular, and Optical Sciences and Technologies (CAMOST), Indian Institute of Science Education and Research (IISER) Tirupati, Panguru (G.P), Yerpedu Mandal, 517619 - Tirupati Dist., Andhra Pradesh, India
| | - Rainer Hegger
- Institute of Physical and Theoretical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 7, 60438 Frankfurt am Main, Germany.
| | - Irene Burghardt
- Institute of Physical and Theoretical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 7, 60438 Frankfurt am Main, Germany.
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2
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Chen T, Gao F, Tan YW. Transition Time Determination of Single-Molecule FRET Trajectories via Wasserstein Distance Analysis in Steady-State Variations in smFRET (WAVE). J Phys Chem B 2023; 127:7819-7828. [PMID: 37672727 DOI: 10.1021/acs.jpcb.3c02498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Many biological molecules respond to external stimuli that can cause their conformational states to shift from one steady state to another. Single-molecule FRET (Fluorescence Resonance Energy Transfer) is of particular interest to not only define the steady-state conformational ensemble usually averaged out in the ensemble of molecules but also characterize the dynamics of biomolecules. To study steady-state transitions, i.e., non-equilibrium transitions, a data analysis methodology is necessary to analyze single-molecule FRET photon trajectories, which contain mixtures of contributions from two steady-state statuses and include non-equilibrium transitions. In this study, we introduce a novel methodology called WAVE (Wasserstein distance Analysis in steady-state Variations in smFRET) to detect and locate non-equilibrium transition positions in FRET trajectories. Our method first utilizes a combined STaSI-HMM (Stepwise Transitions with State Inference Hidden Markov Model) algorithm to convert the original FRET trajectories into discretized trajectories. We then apply Maximum Wasserstein Distance analysis to differentiate the FRET state compositions of the fitting trajectories before and after the non-equilibrium transition. Forward and backward algorithms, based on the Minimum Description Length (MDL) principle, are used to find the refined positions of the non-equilibrium transitions. This methodology allows us to observe changes in experimental conditions in chromophore-tagged biomolecules or vice versa.
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Affiliation(s)
- Ting Chen
- State Key Laboratory of Surface Physics, Multiscale Research Institute of Complex Systems, Department of Physics, Fudan University, Shanghai 200433, China
| | - Fengnan Gao
- School of Mathematics and Statistics, University College Dublin, Belfield, Dublin 4, Ireland
- School of Data Science, Fudan University, Shanghai 200433, China
| | - Yan-Wen Tan
- State Key Laboratory of Surface Physics, Multiscale Research Institute of Complex Systems, Department of Physics, Fudan University, Shanghai 200433, China
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3
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Dominic AJ, Cao S, Montoya-Castillo A, Huang X. Memory Unlocks the Future of Biomolecular Dynamics: Transformative Tools to Uncover Physical Insights Accurately and Efficiently. J Am Chem Soc 2023; 145:9916-9927. [PMID: 37104720 DOI: 10.1021/jacs.3c01095] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Conformational changes underpin function and encode complex biomolecular mechanisms. Gaining atomic-level detail of how such changes occur has the potential to reveal these mechanisms and is of critical importance in identifying drug targets, facilitating rational drug design, and enabling bioengineering applications. While the past two decades have brought Markov state model techniques to the point where practitioners can regularly use them to glimpse the long-time dynamics of slow conformations in complex systems, many systems are still beyond their reach. In this Perspective, we discuss how including memory (i.e., non-Markovian effects) can reduce the computational cost to predict the long-time dynamics in these complex systems by orders of magnitude and with greater accuracy and resolution than state-of-the-art Markov state models. We illustrate how memory lies at the heart of successful and promising techniques, ranging from the Fokker-Planck and generalized Langevin equations to deep-learning recurrent neural networks and generalized master equations. We delineate how these techniques work, identify insights that they can offer in biomolecular systems, and discuss their advantages and disadvantages in practical settings. We show how generalized master equations can enable the investigation of, for example, the gate-opening process in RNA polymerase II and demonstrate how our recent advances tame the deleterious influence of statistical underconvergence of the molecular dynamics simulations used to parameterize these techniques. This represents a significant leap forward that will enable our memory-based techniques to interrogate systems that are currently beyond the reach of even the best Markov state models. We conclude by discussing some current challenges and future prospects for how exploiting memory will open the door to many exciting opportunities.
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Affiliation(s)
- Anthony J Dominic
- Department of Chemistry, University of Colorado Boulder, Boulder, Colorado 80309, USA
| | - Siqin Cao
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | | | - Xuhui Huang
- Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
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4
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Saurabh A, Fazel M, Safar M, Sgouralis I, Pressé S. Single-photon smFRET. I: Theory and conceptual basis. BIOPHYSICAL REPORTS 2022; 3:100089. [PMID: 36582655 PMCID: PMC9793182 DOI: 10.1016/j.bpr.2022.100089] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022]
Abstract
We present a unified conceptual framework and the associated software package for single-molecule Förster resonance energy transfer (smFRET) analysis from single-photon arrivals leveraging Bayesian nonparametrics, BNP-FRET. This unified framework addresses the following key physical complexities of a single-photon smFRET experiment, including: 1) fluorophore photophysics; 2) continuous time kinetics of the labeled system with large timescale separations between photophysical phenomena such as excited photophysical state lifetimes and events such as transition between system states; 3) unavoidable detector artefacts; 4) background emissions; 5) unknown number of system states; and 6) both continuous and pulsed illumination. These physical features necessarily demand a novel framework that extends beyond existing tools. In particular, the theory naturally brings us to a hidden Markov model with a second-order structure and Bayesian nonparametrics on account of items 1, 2, and 5 on the list. In the second and third companion articles, we discuss the direct effects of these key complexities on the inference of parameters for continuous and pulsed illumination, respectively.
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Affiliation(s)
- Ayush Saurabh
- Center for Biological Physics, Arizona State University, Tempe, Arizona,Department of Physics, Arizona State University, Tempe, Arizona
| | - Mohamadreza Fazel
- Center for Biological Physics, Arizona State University, Tempe, Arizona,Department of Physics, Arizona State University, Tempe, Arizona
| | - Matthew Safar
- Center for Biological Physics, Arizona State University, Tempe, Arizona,Department of Mathematics and Statistical Science, Arizona State University, Tempe, Arizona
| | - Ioannis Sgouralis
- Department of Mathematics, University of Tennessee Knoxville, Knoxville, Tennesse
| | - Steve Pressé
- Center for Biological Physics, Arizona State University, Tempe, Arizona,Department of Physics, Arizona State University, Tempe, Arizona,School of Molecular Sciences, Arizona State University, Tempe, Arizona,Corresponding author
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5
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Götz M, Barth A, Bohr SSR, Börner R, Chen J, Cordes T, Erie DA, Gebhardt C, Hadzic MCAS, Hamilton GL, Hatzakis NS, Hugel T, Kisley L, Lamb DC, de Lannoy C, Mahn C, Dunukara D, de Ridder D, Sanabria H, Schimpf J, Seidel CAM, Sigel RKO, Sletfjerding MB, Thomsen J, Vollmar L, Wanninger S, Weninger KR, Xu P, Schmid S. A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories. Nat Commun 2022. [PMID: 36104339 DOI: 10.1101/2021.11.23.469671v2.article-info] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2023] Open
Abstract
Single-molecule FRET (smFRET) is a versatile technique to study the dynamics and function of biomolecules since it makes nanoscale movements detectable as fluorescence signals. The powerful ability to infer quantitative kinetic information from smFRET data is, however, complicated by experimental limitations. Diverse analysis tools have been developed to overcome these hurdles but a systematic comparison is lacking. Here, we report the results of a blind benchmark study assessing eleven analysis tools used to infer kinetic rate constants from smFRET trajectories. We test them against simulated and experimental data containing the most prominent difficulties encountered in analyzing smFRET experiments: different noise levels, varied model complexity, non-equilibrium dynamics, and kinetic heterogeneity. Our results highlight the current strengths and limitations in inferring kinetic information from smFRET trajectories. In addition, we formulate concrete recommendations and identify key targets for future developments, aimed to advance our understanding of biomolecular dynamics through quantitative experiment-derived models.
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Affiliation(s)
- Markus Götz
- Centre de Biologie Structurale, CNRS UMR 5048, INSERM U1054, Univ Montpellier, 60 rue de Navacelles, 34090, Montpellier, France.
- PicoQuant GmbH, Rudower Chaussee 29, 12489, Berlin, Germany.
| | - Anders Barth
- Institut für Physikalische Chemie, Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-Universität, Universitätsstr. 1, 40225, Düsseldorf, Germany
- Department of Bionanoscience, Kavli Institute of Nanoscience Delft, Delft University of Technology, Van der Maasweg 9, 2629, HZ Delft, The Netherlands
| | - Søren S-R Bohr
- Department of Chemistry & Nano-science Center, University of Copenhagen, 2100, Copenhagen, Denmark
- Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Richard Börner
- Department of Chemistry, University of Zurich, 8057, Zurich, Switzerland
- Laserinstitut Hochschule Mittweida, University of Applied Sciences Mittweida, 09648, Mittweida, Germany
| | - Jixin Chen
- Department of Chemistry and Biochemistry, Ohio University, Athens, OH, USA
| | - Thorben Cordes
- Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Großhadernerstr. 2-4, 82152, Planegg-Martinsried, Germany
| | - Dorothy A Erie
- Department of Chemistry, University of North Carolina, Chapel Hill, NC, 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Christian Gebhardt
- Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Großhadernerstr. 2-4, 82152, Planegg-Martinsried, Germany
| | | | - George L Hamilton
- Department of Physics and Astronomy, Clemson University, Clemson, SC, 29634, USA
- Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY, 10016, USA
| | - Nikos S Hatzakis
- Department of Chemistry & Nano-science Center, University of Copenhagen, 2100, Copenhagen, Denmark
- Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Thorsten Hugel
- Institute of Physical Chemistry, University of Freiburg, Freiburg, Germany
- Signalling Research Centers BIOSS and CIBSS, University of Freiburg, Freiburg, Germany
| | - Lydia Kisley
- Department of Physics, Case Western Reserve University, Cleveland, OH, USA
- Department of Chemistry, Case Western Reserve University, Cleveland, OH, USA
| | - Don C Lamb
- Department of Chemistry and Center for Nano Science (CeNS), Ludwig Maximilians-Universität München, Butenandtstraße 5-13, 81377, München, Germany
| | - Carlos de Lannoy
- Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB, Wageningen, The Netherlands
| | - Chelsea Mahn
- Department of Physics, North Carolina State University, Raleigh, NC, 27695, USA
| | - Dushani Dunukara
- Department of Physics, Case Western Reserve University, Cleveland, OH, USA
| | - Dick de Ridder
- Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB, Wageningen, The Netherlands
| | - Hugo Sanabria
- Department of Physics and Astronomy, Clemson University, Clemson, SC, 29634, USA
| | - Julia Schimpf
- Institute of Physical Chemistry, University of Freiburg, Freiburg, Germany
- Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg, Germany
| | - Claus A M Seidel
- Institut für Physikalische Chemie, Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-Universität, Universitätsstr. 1, 40225, Düsseldorf, Germany
| | - Roland K O Sigel
- Department of Chemistry, University of Zurich, 8057, Zurich, Switzerland
| | - Magnus Berg Sletfjerding
- Department of Chemistry & Nano-science Center, University of Copenhagen, 2100, Copenhagen, Denmark
- Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Johannes Thomsen
- Department of Chemistry & Nano-science Center, University of Copenhagen, 2100, Copenhagen, Denmark
- Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Leonie Vollmar
- Institute of Physical Chemistry, University of Freiburg, Freiburg, Germany
- Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg, Germany
| | - Simon Wanninger
- Department of Chemistry and Center for Nano Science (CeNS), Ludwig Maximilians-Universität München, Butenandtstraße 5-13, 81377, München, Germany
| | - Keith R Weninger
- Department of Physics, North Carolina State University, Raleigh, NC, 27695, USA
| | - Pengning Xu
- Department of Physics, North Carolina State University, Raleigh, NC, 27695, USA
| | - Sonja Schmid
- NanoDynamicsLab, Laboratory of Biophysics, Wageningen University, Stippeneng 4, 6708WE, Wageningen, The Netherlands.
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6
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Götz M, Barth A, Bohr SSR, Börner R, Chen J, Cordes T, Erie DA, Gebhardt C, Hadzic MCAS, Hamilton GL, Hatzakis NS, Hugel T, Kisley L, Lamb DC, de Lannoy C, Mahn C, Dunukara D, de Ridder D, Sanabria H, Schimpf J, Seidel CAM, Sigel RKO, Sletfjerding MB, Thomsen J, Vollmar L, Wanninger S, Weninger KR, Xu P, Schmid S. A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories. Nat Commun 2022; 13:5402. [PMID: 36104339 PMCID: PMC9474500 DOI: 10.1038/s41467-022-33023-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 08/30/2022] [Indexed: 01/04/2023] Open
Abstract
Single-molecule FRET (smFRET) is a versatile technique to study the dynamics and function of biomolecules since it makes nanoscale movements detectable as fluorescence signals. The powerful ability to infer quantitative kinetic information from smFRET data is, however, complicated by experimental limitations. Diverse analysis tools have been developed to overcome these hurdles but a systematic comparison is lacking. Here, we report the results of a blind benchmark study assessing eleven analysis tools used to infer kinetic rate constants from smFRET trajectories. We test them against simulated and experimental data containing the most prominent difficulties encountered in analyzing smFRET experiments: different noise levels, varied model complexity, non-equilibrium dynamics, and kinetic heterogeneity. Our results highlight the current strengths and limitations in inferring kinetic information from smFRET trajectories. In addition, we formulate concrete recommendations and identify key targets for future developments, aimed to advance our understanding of biomolecular dynamics through quantitative experiment-derived models.
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Affiliation(s)
- Markus Götz
- Centre de Biologie Structurale, CNRS UMR 5048, INSERM U1054, Univ Montpellier, 60 rue de Navacelles, 34090, Montpellier, France.
- PicoQuant GmbH, Rudower Chaussee 29, 12489, Berlin, Germany.
| | - Anders Barth
- Institut für Physikalische Chemie, Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-Universität, Universitätsstr. 1, 40225, Düsseldorf, Germany
- Department of Bionanoscience, Kavli Institute of Nanoscience Delft, Delft University of Technology, Van der Maasweg 9, 2629, HZ Delft, The Netherlands
| | - Søren S-R Bohr
- Department of Chemistry & Nano-science Center, University of Copenhagen, 2100, Copenhagen, Denmark
- Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Richard Börner
- Department of Chemistry, University of Zurich, 8057, Zurich, Switzerland
- Laserinstitut Hochschule Mittweida, University of Applied Sciences Mittweida, 09648, Mittweida, Germany
| | - Jixin Chen
- Department of Chemistry and Biochemistry, Ohio University, Athens, OH, USA
| | - Thorben Cordes
- Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Großhadernerstr. 2-4, 82152, Planegg-Martinsried, Germany
| | - Dorothy A Erie
- Department of Chemistry, University of North Carolina, Chapel Hill, NC, 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Christian Gebhardt
- Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Großhadernerstr. 2-4, 82152, Planegg-Martinsried, Germany
| | | | - George L Hamilton
- Department of Physics and Astronomy, Clemson University, Clemson, SC, 29634, USA
- Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY, 10016, USA
| | - Nikos S Hatzakis
- Department of Chemistry & Nano-science Center, University of Copenhagen, 2100, Copenhagen, Denmark
- Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Thorsten Hugel
- Institute of Physical Chemistry, University of Freiburg, Freiburg, Germany
- Signalling Research Centers BIOSS and CIBSS, University of Freiburg, Freiburg, Germany
| | - Lydia Kisley
- Department of Physics, Case Western Reserve University, Cleveland, OH, USA
- Department of Chemistry, Case Western Reserve University, Cleveland, OH, USA
| | - Don C Lamb
- Department of Chemistry and Center for Nano Science (CeNS), Ludwig Maximilians-Universität München, Butenandtstraße 5-13, 81377, München, Germany
| | - Carlos de Lannoy
- Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB, Wageningen, The Netherlands
| | - Chelsea Mahn
- Department of Physics, North Carolina State University, Raleigh, NC, 27695, USA
| | - Dushani Dunukara
- Department of Physics, Case Western Reserve University, Cleveland, OH, USA
| | - Dick de Ridder
- Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB, Wageningen, The Netherlands
| | - Hugo Sanabria
- Department of Physics and Astronomy, Clemson University, Clemson, SC, 29634, USA
| | - Julia Schimpf
- Institute of Physical Chemistry, University of Freiburg, Freiburg, Germany
- Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg, Germany
| | - Claus A M Seidel
- Institut für Physikalische Chemie, Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-Universität, Universitätsstr. 1, 40225, Düsseldorf, Germany
| | - Roland K O Sigel
- Department of Chemistry, University of Zurich, 8057, Zurich, Switzerland
| | - Magnus Berg Sletfjerding
- Department of Chemistry & Nano-science Center, University of Copenhagen, 2100, Copenhagen, Denmark
- Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Johannes Thomsen
- Department of Chemistry & Nano-science Center, University of Copenhagen, 2100, Copenhagen, Denmark
- Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Leonie Vollmar
- Institute of Physical Chemistry, University of Freiburg, Freiburg, Germany
- Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg, Germany
| | - Simon Wanninger
- Department of Chemistry and Center for Nano Science (CeNS), Ludwig Maximilians-Universität München, Butenandtstraße 5-13, 81377, München, Germany
| | - Keith R Weninger
- Department of Physics, North Carolina State University, Raleigh, NC, 27695, USA
| | - Pengning Xu
- Department of Physics, North Carolina State University, Raleigh, NC, 27695, USA
| | - Sonja Schmid
- NanoDynamicsLab, Laboratory of Biophysics, Wageningen University, Stippeneng 4, 6708WE, Wageningen, The Netherlands.
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7
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Barth A, Opanasyuk O, Peulen TO, Felekyan S, Kalinin S, Sanabria H, Seidel CAM. Unraveling multi-state molecular dynamics in single-molecule FRET experiments. I. Theory of FRET-lines. J Chem Phys 2022; 156:141501. [PMID: 35428384 PMCID: PMC9014241 DOI: 10.1063/5.0089134] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
Conformational dynamics of biomolecules are of fundamental importance for their function. Single-molecule studies of Förster Resonance Energy Transfer (smFRET) between a tethered donor and acceptor dye pair are a powerful tool to investigate the structure and dynamics of labeled molecules. However, capturing and quantifying conformational dynamics in intensity-based smFRET experiments remains challenging when the dynamics occur on the sub-millisecond timescale. The method of multiparameter fluorescence detection addresses this challenge by simultaneously registering fluorescence intensities and lifetimes of the donor and acceptor. Together, two FRET observables, the donor fluorescence lifetime τD and the intensity-based FRET efficiency E, inform on the width of the FRET efficiency distribution as a characteristic fingerprint for conformational dynamics. We present a general framework for analyzing dynamics that relates average fluorescence lifetimes and intensities in two-dimensional burst frequency histograms. We present parametric relations of these observables for interpreting the location of FRET populations in E–τD diagrams, called FRET-lines. To facilitate the analysis of complex exchange equilibria, FRET-lines serve as reference curves for a graphical interpretation of experimental data to (i) identify conformational states, (ii) resolve their dynamic connectivity, (iii) compare different kinetic models, and (iv) infer polymer properties of unfolded or intrinsically disordered proteins. For a simplified graphical analysis of complex kinetic networks, we derive a moment-based representation of the experimental data that decouples the motion of the fluorescence labels from the conformational dynamics of the biomolecule. Importantly, FRET-lines facilitate exploring complex dynamic models via easily computed experimental observables. We provide extensive computational tools to facilitate applying FRET-lines.
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Affiliation(s)
- Anders Barth
- Institut für Physikalische Chemie, Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-Universität, Düsseldorf, Germany
| | - Oleg Opanasyuk
- Institut für Physikalische Chemie, Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-Universität, Düsseldorf, Germany
| | - Thomas-Otavio Peulen
- Institut für Physikalische Chemie, Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-Universität, Düsseldorf, Germany
| | - Suren Felekyan
- Institut für Physikalische Chemie, Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-Universität, Düsseldorf, Germany
| | - Stanislav Kalinin
- Institut für Physikalische Chemie, Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-Universität, Düsseldorf, Germany
| | - Hugo Sanabria
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29631, USA
| | - Claus A. M. Seidel
- Institut für Physikalische Chemie, Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-Universität, Düsseldorf, Germany
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8
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Abstract
Single molecule Förster resonance energy transfer (smFRET) is a unique biophysical approach for studying conformational dynamics in biomacromolecules. Photon-by-photon hidden Markov modeling (H2MM) is an analysis tool that can quantify FRET dynamics of single biomolecules, even if they occur on the sub-millisecond timescale. However, dye photophysical transitions intertwined with FRET dynamics may cause artifacts. Here, we introduce multi-parameter H2MM (mpH2MM), which assists in identifying FRET dynamics based on simultaneous observation of multiple experimentally-derived parameters. We show the importance of using mpH2MM to decouple FRET dynamics caused by conformational changes from photophysical transitions in confocal-based smFRET measurements of a DNA hairpin, the maltose binding protein, MalE, and the type-III secretion system effector, YopO, from Yersinia species, all exhibiting conformational dynamics ranging from the sub-second to microsecond timescales. Overall, we show that using mpH2MM facilitates the identification and quantification of biomolecular sub-populations and their origin. In this work, the authors demonstrate the application of multi-parameter photon-by-photon hidden Markov modeling (mpH2MM) on alternating laser excitation (ALEX)-based smFRET measurements. The utility of mpH2MM in identifying and quantifying dynamic biomolecular sub-populations is demonstrated in three different systems.
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9
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Gopich IV, Chung HS. Theory and Analysis of Single-Molecule FRET Experiments. Methods Mol Biol 2022; 2376:247-282. [PMID: 34845614 DOI: 10.1007/978-1-0716-1716-8_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Inter-dye distances and conformational dynamics can be studied using single-molecule FRET measurements. We consider two approaches to analyze sequences of photons with recorded photon colors and arrival times. The first approach is based on FRET efficiency histograms obtained from binned photon sequences. The experimental histograms are compared with the theoretical histograms obtained using the joint distribution of acceptor and donor photons or the Gaussian approximation. In the second approach, a photon sequence is analyzed without binning. The parameters of a model describing conformational dynamics are found by maximizing the appropriate likelihood function. The first approach is simpler, while the second one is more accurate, especially when the population of species is small and transition rates are fast. The likelihood-based analysis as well as the recoloring method has the advantage that diffusion of molecules through the laser focus can be rigorously handled.
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Affiliation(s)
- Irina V Gopich
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA.
| | - Hoi Sung Chung
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
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10
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Zhuo B, Ou X, Li J. Structure and Mechanical Stabilities of the Three-Way Junction Motifs in Prohead RNA. J Phys Chem B 2021; 125:12125-12134. [PMID: 34719230 DOI: 10.1021/acs.jpcb.1c04681] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
The core structure of phi29 prohead RNA (pRNA) is composed of three major helices organized into three-way junction pRNA (3WJ-pRNA) and has stout structural rigidity along the coaxial helices. Prohead RNAs of the other Bacillus subtilis bacteriophages such as GA1 and SF5 share similar secondary structure and function with phi29; whether these pRNAs have similar mechanical rigidity remains to be elucidated. In this study, we constructed the tertiary structures of GA1 and SF5 3WJ-pRNAs by comparative modeling. Both GA1 and SF5 3WJ-pRNAs adopt a similar structure, in which three helices are organized as the three-way junction and two of the three helices are stacked coaxially. Moreover, detailed structural features of GA1 and SF5 3WJ-pRNAs are also similar to those of phi29 3WJ-pRNA: all of the bases of the coaxial helices are paired, and all of the adenines in the junction region are paired, which eliminates the interference of A-minor tertiary interactions. Hence, the coaxial helices tightly join to each other, and the major groove between them is very narrow. Two Mg2+ ions can thus fit into this major groove and form double Mg clamps. A steered molecular dynamics simulation was used to study the mechanical properties of these 3WJ-pRNAs. Both GA1 and SF5 3WJ-pRNAs show strong resistance to applied force in the direction of their coaxial helices. Such mechanical stability can be attributed to the Mg clamps.
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Affiliation(s)
- Boyang Zhuo
- Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Xinwen Ou
- Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Jingyuan Li
- Department of Physics, Zhejiang University, Hangzhou 310027, China
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11
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Manz C, Kobitski AY, Samanta A, Nienhaus K, Jäschke A, Nienhaus GU. Exploring the energy landscape of a SAM-I riboswitch. J Biol Phys 2021; 47:371-386. [PMID: 34698957 PMCID: PMC8603990 DOI: 10.1007/s10867-021-09584-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 08/25/2021] [Indexed: 11/30/2022] Open
Abstract
SAM-I riboswitches regulate gene expression through transcription termination upon binding a S-adenosyl-L-methionine (SAM) ligand. In previous work, we characterized the conformational energy landscape of the full-length Bacillus subtilis yitJ SAM-I riboswitch as a function of Mg2+ and SAM ligand concentrations. Here, we have extended this work with measurements on a structurally similar ligand, S-adenosyl-l-homocysteine (SAH), which has, however, a much lower binding affinity. Using single-molecule Förster resonance energy transfer (smFRET) microscopy and hidden Markov modeling (HMM) analysis, we identified major conformations and determined their fractional populations and dynamics. At high Mg2+ concentration, FRET analysis yielded four distinct conformations, which we assigned to two terminator and two antiterminator states. In the same solvent, but with SAM added at saturating concentrations, four states persisted, although their populations, lifetimes and interconversion dynamics changed. In the presence of SAH instead of SAM, HMM revealed again four well-populated states and, in addition, a weakly populated ‘hub’ state that appears to mediate conformational transitions between three of the other states. Our data show pronounced and specific effects of the SAM and SAH ligands on the RNA conformational energy landscape. Interestingly, both SAM and SAH shifted the fractional populations toward terminator folds, but only gradually, so the effect cannot explain the switching action. Instead, we propose that the noticeably accelerated dynamics of interconversion between terminator and antiterminator states upon SAM binding may be essential for control of transcription.
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Affiliation(s)
- Christoph Manz
- Institute of Applied Physics, Karlsruhe Institute of Technology, Wolfgang-Gaede-Str. 1, 76131, Karlsruhe, Germany
| | - Andrei Yu Kobitski
- Institute of Applied Physics, Karlsruhe Institute of Technology, Wolfgang-Gaede-Str. 1, 76131, Karlsruhe, Germany
| | - Ayan Samanta
- Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, Im Neuenheimer Feld 364, 69120, Heidelberg, Germany.,Department of Chemistry, Uppsala University, Box 538, 751 21, Uppsala, Sweden
| | - Karin Nienhaus
- Institute of Applied Physics, Karlsruhe Institute of Technology, Wolfgang-Gaede-Str. 1, 76131, Karlsruhe, Germany
| | - Andres Jäschke
- Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, Im Neuenheimer Feld 364, 69120, Heidelberg, Germany
| | - Gerd Ulrich Nienhaus
- Institute of Applied Physics, Karlsruhe Institute of Technology, Wolfgang-Gaede-Str. 1, 76131, Karlsruhe, Germany. .,Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany. .,Institute of Biological and Chemical Systems, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany. .,Department of Physics, University of Illinois at Urbana-Champaign, 1110 W. Green St, Urbana, IL, 61801, USA.
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12
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Kilic Z, Sgouralis I, Heo W, Ishii K, Tahara T, Pressé S. Extraction of rapid kinetics from smFRET measurements using integrative detectors. CELL REPORTS. PHYSICAL SCIENCE 2021; 2:100409. [PMID: 34142102 PMCID: PMC8208598 DOI: 10.1016/j.xcrp.2021.100409] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Hidden Markov models (HMMs) are used to learn single-molecule kinetics across a range of experimental techniques. By their construction, HMMs assume that single-molecule events occur on slower timescales than those of data acquisition. To move beyond that HMM limitation and allow for single-molecule events to occur on any timescale, we must treat single-molecule events in continuous time as they occur in nature. We propose a method to learn kinetic rates from single-molecule Förster resonance energy transfer (smFRET) data collected by integrative detectors, even if those rates exceed data acquisition rates. To achieve that, we exploit our recently proposed "hidden Markov jump process" (HMJP), with which we learn transition kinetics from parallel measurements in donor and acceptor channels. HMJPs generalize the HMM paradigm in two critical ways: (1) they deal with physical smFRET systems as they switch between conformational states in continuous time, and (2) they estimate transition rates between conformational states directly without having recourse to transition probabilities or assuming slow dynamics. Our continuous-time treatment learns the transition kinetics and photon emission rates for dynamic regimes that are inaccessible to HMMs, which treat system kinetics in discrete time. We validate our framework's robustness on simulated data and demonstrate its performance on experimental data from FRET-labeled Holliday junctions.
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Affiliation(s)
- Zeliha Kilic
- Center for Biological Physics, Department of Physics, Arizona State University, Tempe, AZ 85287, USA
| | - Ioannis Sgouralis
- Department of Mathematics, University of Tennessee, Knoxville, TN 37996, USA
| | - Wooseok Heo
- Molecular Spectroscopy Laboratory, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Kunihiko Ishii
- Molecular Spectroscopy Laboratory, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- Ultrafast Spectroscopy Research Team, RIKEN Center for Advanced Photonics (RAP), 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Tahei Tahara
- Molecular Spectroscopy Laboratory, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- Ultrafast Spectroscopy Research Team, RIKEN Center for Advanced Photonics (RAP), 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Steve Pressé
- Center for Biological Physics, Department of Physics, Arizona State University, Tempe, AZ 85287, USA
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
- Lead contact
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Sanders JC, Holmstrom ED. Integrating single-molecule FRET and biomolecular simulations to study diverse interactions between nucleic acids and proteins. Essays Biochem 2021; 65:37-49. [PMID: 33600559 PMCID: PMC8052285 DOI: 10.1042/ebc20200022] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 01/17/2021] [Accepted: 01/26/2021] [Indexed: 12/12/2022]
Abstract
The conformations of biological macromolecules are intimately related to their cellular functions. Conveniently, the well-characterized dipole-dipole distance-dependence of Förster resonance energy transfer (FRET) makes it possible to measure and monitor the nanoscale spatial dimensions of these conformations using fluorescence spectroscopy. For this reason, FRET is often used in conjunction with single-molecule detection to study a wide range of conformationally dynamic biochemical processes. Written for those not yet familiar with the subject, this review aims to introduce biochemists to the methodology associated with single-molecule FRET, with a particular emphasis on how it can be combined with biomolecular simulations to study diverse interactions between nucleic acids and proteins. In the first section, we highlight several conceptual and practical considerations related to this integrative approach. In the second section, we review a few recent research efforts wherein various combinations of single-molecule FRET and biomolecular simulations were used to study the structural and dynamic properties of biochemical systems involving different types of nucleic acids (e.g., DNA and RNA) and proteins (e.g., folded and disordered).
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Affiliation(s)
- Joshua C Sanders
- Department of Chemistry, University of Kansas, Lawrence, KS, U.S.A
| | - Erik D Holmstrom
- Department of Chemistry, University of Kansas, Lawrence, KS, U.S.A
- Department of Molecular Biosciences, University of Kansas, Lawrence, KS, U.S.A
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14
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Lerner E, Barth A, Hendrix J, Ambrose B, Birkedal V, Blanchard SC, Börner R, Sung Chung H, Cordes T, Craggs TD, Deniz AA, Diao J, Fei J, Gonzalez RL, Gopich IV, Ha T, Hanke CA, Haran G, Hatzakis NS, Hohng S, Hong SC, Hugel T, Ingargiola A, Joo C, Kapanidis AN, Kim HD, Laurence T, Lee NK, Lee TH, Lemke EA, Margeat E, Michaelis J, Michalet X, Myong S, Nettels D, Peulen TO, Ploetz E, Razvag Y, Robb NC, Schuler B, Soleimaninejad H, Tang C, Vafabakhsh R, Lamb DC, Seidel CAM, Weiss S. FRET-based dynamic structural biology: Challenges, perspectives and an appeal for open-science practices. eLife 2021; 10:e60416. [PMID: 33779550 PMCID: PMC8007216 DOI: 10.7554/elife.60416] [Citation(s) in RCA: 119] [Impact Index Per Article: 39.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 02/09/2021] [Indexed: 12/18/2022] Open
Abstract
Single-molecule FRET (smFRET) has become a mainstream technique for studying biomolecular structural dynamics. The rapid and wide adoption of smFRET experiments by an ever-increasing number of groups has generated significant progress in sample preparation, measurement procedures, data analysis, algorithms and documentation. Several labs that employ smFRET approaches have joined forces to inform the smFRET community about streamlining how to perform experiments and analyze results for obtaining quantitative information on biomolecular structure and dynamics. The recent efforts include blind tests to assess the accuracy and the precision of smFRET experiments among different labs using various procedures. These multi-lab studies have led to the development of smFRET procedures and documentation, which are important when submitting entries into the archiving system for integrative structure models, PDB-Dev. This position paper describes the current 'state of the art' from different perspectives, points to unresolved methodological issues for quantitative structural studies, provides a set of 'soft recommendations' about which an emerging consensus exists, and lists openly available resources for newcomers and seasoned practitioners. To make further progress, we strongly encourage 'open science' practices.
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Affiliation(s)
- Eitan Lerner
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, and The Center for Nanoscience and Nanotechnology, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of JerusalemJerusalemIsrael
| | - Anders Barth
- Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-UniversitätDüsseldorfGermany
| | - Jelle Hendrix
- Dynamic Bioimaging Lab, Advanced Optical Microscopy Centre and Biomedical Research Institute (BIOMED), Hasselt UniversityDiepenbeekBelgium
| | - Benjamin Ambrose
- Department of Chemistry, University of SheffieldSheffieldUnited Kingdom
| | - Victoria Birkedal
- Department of Chemistry and iNANO center, Aarhus UniversityAarhusDenmark
| | - Scott C Blanchard
- Department of Structural Biology, St. Jude Children's Research HospitalMemphisUnited States
| | - Richard Börner
- Laserinstitut HS Mittweida, University of Applied Science MittweidaMittweidaGermany
| | - Hoi Sung Chung
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of HealthBethesdaUnited States
| | - Thorben Cordes
- Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität MünchenPlanegg-MartinsriedGermany
| | - Timothy D Craggs
- Department of Chemistry, University of SheffieldSheffieldUnited Kingdom
| | - Ashok A Deniz
- Department of Integrative Structural and Computational Biology, The Scripps Research InstituteLa JollaUnited States
| | - Jiajie Diao
- Department of Cancer Biology, University of Cincinnati School of MedicineCincinnatiUnited States
| | - Jingyi Fei
- Department of Biochemistry and Molecular Biology and The Institute for Biophysical Dynamics, University of ChicagoChicagoUnited States
| | - Ruben L Gonzalez
- Department of Chemistry, Columbia UniversityNew YorkUnited States
| | - Irina V Gopich
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of HealthBethesdaUnited States
| | - Taekjip Ha
- Department of Biophysics and Biophysical Chemistry, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Howard Hughes Medical InstituteBaltimoreUnited States
| | - Christian A Hanke
- Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-UniversitätDüsseldorfGermany
| | - Gilad Haran
- Department of Chemical and Biological Physics, Weizmann Institute of ScienceRehovotIsrael
| | - Nikos S Hatzakis
- Department of Chemistry & Nanoscience Centre, University of CopenhagenCopenhagenDenmark
- Denmark Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagenDenmark
| | - Sungchul Hohng
- Department of Physics and Astronomy, and Institute of Applied Physics, Seoul National UniversitySeoulRepublic of Korea
| | - Seok-Cheol Hong
- Center for Molecular Spectroscopy and Dynamics, Institute for Basic Science and Department of Physics, Korea UniversitySeoulRepublic of Korea
| | - Thorsten Hugel
- Institute of Physical Chemistry and Signalling Research Centres BIOSS and CIBSS, University of FreiburgFreiburgGermany
| | - Antonino Ingargiola
- Department of Chemistry and Biochemistry, and Department of Physiology, University of California, Los AngelesLos AngelesUnited States
| | - Chirlmin Joo
- Department of BioNanoScience, Kavli Institute of Nanoscience, Delft University of TechnologyDelftNetherlands
| | - Achillefs N Kapanidis
- Biological Physics Research Group, Clarendon Laboratory, Department of Physics, University of OxfordOxfordUnited Kingdom
| | - Harold D Kim
- School of Physics, Georgia Institute of TechnologyAtlantaUnited States
| | - Ted Laurence
- Physical and Life Sciences Directorate, Lawrence Livermore National LaboratoryLivermoreUnited States
| | - Nam Ki Lee
- School of Chemistry, Seoul National UniversitySeoulRepublic of Korea
| | - Tae-Hee Lee
- Department of Chemistry, Pennsylvania State UniversityUniversity ParkUnited States
| | - Edward A Lemke
- Departments of Biology and Chemistry, Johannes Gutenberg UniversityMainzGermany
- Institute of Molecular Biology (IMB)MainzGermany
| | - Emmanuel Margeat
- Centre de Biologie Structurale (CBS), CNRS, INSERM, Universitié de MontpellierMontpellierFrance
| | | | - Xavier Michalet
- Department of Chemistry and Biochemistry, and Department of Physiology, University of California, Los AngelesLos AngelesUnited States
| | - Sua Myong
- Department of Biophysics, Johns Hopkins UniversityBaltimoreUnited States
| | - Daniel Nettels
- Department of Biochemistry and Department of Physics, University of ZurichZurichSwitzerland
| | - Thomas-Otavio Peulen
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
| | - Evelyn Ploetz
- Physical Chemistry, Department of Chemistry, Center for Nanoscience (CeNS), Center for Integrated Protein Science Munich (CIPSM) and Nanosystems Initiative Munich (NIM), Ludwig-Maximilians-UniversitätMünchenGermany
| | - Yair Razvag
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, and The Center for Nanoscience and Nanotechnology, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of JerusalemJerusalemIsrael
| | - Nicole C Robb
- Warwick Medical School, University of WarwickCoventryUnited Kingdom
| | - Benjamin Schuler
- Department of Biochemistry and Department of Physics, University of ZurichZurichSwitzerland
| | - Hamid Soleimaninejad
- Biological Optical Microscopy Platform (BOMP), University of MelbourneParkvilleAustralia
| | - Chun Tang
- College of Chemistry and Molecular Engineering, PKU-Tsinghua Center for Life Sciences, Beijing National Laboratory for Molecular Sciences, Peking UniversityBeijingChina
| | - Reza Vafabakhsh
- Department of Molecular Biosciences, Northwestern UniversityEvanstonUnited States
| | - Don C Lamb
- Physical Chemistry, Department of Chemistry, Center for Nanoscience (CeNS), Center for Integrated Protein Science Munich (CIPSM) and Nanosystems Initiative Munich (NIM), Ludwig-Maximilians-UniversitätMünchenGermany
| | - Claus AM Seidel
- Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-UniversitätDüsseldorfGermany
| | - Shimon Weiss
- Department of Chemistry and Biochemistry, and Department of Physiology, University of California, Los AngelesLos AngelesUnited States
- Department of Physiology, CaliforniaNanoSystems Institute, University of California, Los AngelesLos AngelesUnited States
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15
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Kilic Z, Sgouralis I, Pressé S. Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes. Biophys J 2021; 120:409-423. [PMID: 33421415 PMCID: PMC7896036 DOI: 10.1016/j.bpj.2020.12.022] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 12/25/2020] [Accepted: 12/30/2020] [Indexed: 12/18/2022] Open
Abstract
The hidden Markov model (HMM) is a framework for time series analysis widely applied to single-molecule experiments. Although initially developed for applications outside the natural sciences, the HMM has traditionally been used to interpret signals generated by physical systems, such as single molecules, evolving in a discrete state space observed at discrete time levels dictated by the data acquisition rate. Within the HMM framework, transitions between states are modeled as occurring at the end of each data acquisition period and are described using transition probabilities. Yet, whereas measurements are often performed at discrete time levels in the natural sciences, physical systems evolve in continuous time according to transition rates. It then follows that the modeling assumptions underlying the HMM are justified if the transition rates of a physical process from state to state are small as compared to the data acquisition rate. In other words, HMMs apply to slow kinetics. The problem is, because the transition rates are unknown in principle, it is unclear, a priori, whether the HMM applies to a particular system. For this reason, we must generalize HMMs for physical systems, such as single molecules, because these switch between discrete states in "continuous time". We do so by exploiting recent mathematical tools developed in the context of inferring Markov jump processes and propose the hidden Markov jump process. We explicitly show in what limit the hidden Markov jump process reduces to the HMM. Resolving the discrete time discrepancy of the HMM has clear implications: we no longer need to assume that processes, such as molecular events, must occur on timescales slower than data acquisition and can learn transition rates even if these are on the same timescale or otherwise exceed data acquisition rates.
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Affiliation(s)
- Zeliha Kilic
- Center for Biological Physics, Department of Physics, Arizona State University, Tempe, Arizona
| | - Ioannis Sgouralis
- Department of Mathematics, University of Tennessee, Knoxville, Tennessee
| | - Steve Pressé
- Center for Biological Physics, Department of Physics, Arizona State University, Tempe, Arizona; School of Molecular Sciences, Arizona State University, Tempe, Arizona.
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16
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Pan M, Zhang Y, Yan G, Chen TY. Dissection of Interaction Kinetics through Single-Molecule Interaction Simulation. Anal Chem 2020; 92:11582-11589. [PMID: 32786469 DOI: 10.1021/acs.analchem.0c01014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The ability to extract kinetic interaction parameters from single-molecule fluorescence resonance energy transfer trajectories without the need for solving complex single-molecule differential equations has the potential to address some of the critical biophysical questions. Here, we provide a noise-free single-molecule interaction simulation (SMIS) tool to give the expected dwell-time distributions and relative populations of each FRET level based on the assigned kinetic model and to dissect kinetic interaction parameters from single-molecule FRET trajectories. The method provides the expected dwell-time distributions, average transition rates, and relative populations of each FRET level based on the assigned kinetic model. By comparing with ground truth data and experimental data, we demonstrated that SMIS is useful to quantify the interaction kinetic rate constants without using the traditional single-molecule analytical solution approach.
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Affiliation(s)
- Manhua Pan
- Department of Chemistry, University of Houston, Houston, Texas 77204, United States
| | - Yuteng Zhang
- Department of Chemistry, University of Houston, Houston, Texas 77204, United States
| | - Guangjie Yan
- Department of Chemistry, University of Houston, Houston, Texas 77204, United States
| | - Tai-Yen Chen
- Department of Chemistry, University of Houston, Houston, Texas 77204, United States
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17
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Ligand-bound glutamine binding protein assumes multiple metastable binding sites with different binding affinities. Commun Biol 2020; 3:419. [PMID: 32747735 PMCID: PMC7400645 DOI: 10.1038/s42003-020-01149-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 07/14/2020] [Indexed: 11/08/2022] Open
Abstract
Protein dynamics plays key roles in ligand binding. However, the microscopic description of conformational dynamics-coupled ligand binding remains a challenge. In this study, we integrate molecular dynamics simulations, Markov state model (MSM) analysis and experimental methods to characterize the conformational dynamics of ligand-bound glutamine binding protein (GlnBP). We show that ligand-bound GlnBP has high conformational flexibility and additional metastable binding sites, presenting a more complex energy landscape than the scenario in the absence of ligand. The diverse conformations of GlnBP demonstrate different binding affinities and entail complex transition kinetics, implicating a concerted ligand binding mechanism. Single molecule fluorescence resonance energy transfer measurements and mutagenesis experiments are performed to validate our MSM-derived structure ensemble as well as the binding mechanism. Collectively, our study provides deeper insights into the protein dynamics-coupled ligand binding, revealing an intricate regulatory network underlying the apparent binding affinity. Zhang, Wu, Feng et al. show that ligand-bound glutamine binding protein assumes multiple metastable binding sites, presenting a more dynamic energy landscape than its ligand-free form. This study provides insights into the ligand-binding mechanisms coupled with protein dynamics that underly the apparent binding affinity.
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18
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Okazaki KI, Nakamura A, Iino R. Chemical-State-Dependent Free Energy Profile from Single-Molecule Trajectories of Biomolecular Motors: Application to Processive Chitinase. J Phys Chem B 2020; 124:6475-6487. [DOI: 10.1021/acs.jpcb.0c02698] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Kei-ichi Okazaki
- Department of Theoretical and Computational Molecular Science, Institute for Molecular Science, National Institutes of Natural Sciences, Okazaki, 444-8585, Japan
| | - Akihiko Nakamura
- Department of Life and Coordination-Complex Molecular Science, Institute for Molecular Science, National Institutes of Natural Sciences, Okazaki, 444-8787, Japan
- Department of Applied Life Sciences, Faculty of Agriculture, Shizuoka University, Shizuoka, 422-8529, Japan
| | - Ryota Iino
- Department of Life and Coordination-Complex Molecular Science, Institute for Molecular Science, National Institutes of Natural Sciences, Okazaki, 444-8787, Japan
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19
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Dreßler C, Kabbe G, Brehm M, Sebastiani D. Exploring non-equilibrium molecular dynamics of mobile protons in the solid acid CsH2PO4 at the micrometer and microsecond scale. J Chem Phys 2020; 152:164110. [DOI: 10.1063/5.0002167] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Affiliation(s)
- Christian Dreßler
- Institute of Chemistry, Martin Luther University Halle-Wittenberg, Von-Danckelmann-Platz 4, 06120 Halle (Saale), Germany
| | - Gabriel Kabbe
- Institute of Chemistry, Martin Luther University Halle-Wittenberg, Von-Danckelmann-Platz 4, 06120 Halle (Saale), Germany
| | - Martin Brehm
- Institute of Chemistry, Martin Luther University Halle-Wittenberg, Von-Danckelmann-Platz 4, 06120 Halle (Saale), Germany
| | - Daniel Sebastiani
- Institute of Chemistry, Martin Luther University Halle-Wittenberg, Von-Danckelmann-Platz 4, 06120 Halle (Saale), Germany
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20
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Matsunaga Y, Sugita Y. Use of single-molecule time-series data for refining conformational dynamics in molecular simulations. Curr Opin Struct Biol 2020; 61:153-159. [DOI: 10.1016/j.sbi.2019.12.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 12/24/2019] [Accepted: 12/27/2019] [Indexed: 12/18/2022]
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21
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Dreßler C, Kabbe G, Brehm M, Sebastiani D. Dynamical matrix propagator scheme for large-scale proton dynamics simulations. J Chem Phys 2020; 152:114114. [PMID: 32199428 DOI: 10.1063/1.5140635] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
We derive a matrix formalism for the simulation of long range proton dynamics for extended systems and timescales. On the basis of an ab initio molecular dynamics simulation, we construct a Markov chain, which allows us to store the entire proton dynamics in an M × M transition matrix (where M is the number of oxygen atoms). In this article, we start from common topology features of the hydrogen bond network of good proton conductors and utilize them as constituent constraints of our dynamic model. We present a thorough mathematical derivation of our approach and verify its uniqueness and correct asymptotic behavior. We propagate the proton distribution by means of transition matrices, which contain kinetic data from both ultra-short (sub-ps) and intermediate (ps) timescales. This concept allows us to keep the most relevant features from the microscopic level while effectively reaching larger time and length scales. We demonstrate the applicability of the transition matrices for the description of proton conduction trends in proton exchange membrane materials.
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Affiliation(s)
- Christian Dreßler
- Institute of Chemistry, Martin Luther University Halle-Wittenberg, Von-Danckelmann-Platz 4, 06120 Halle (Saale), Germany
| | - Gabriel Kabbe
- Institute of Chemistry, Martin Luther University Halle-Wittenberg, Von-Danckelmann-Platz 4, 06120 Halle (Saale), Germany
| | - Martin Brehm
- Institute of Chemistry, Martin Luther University Halle-Wittenberg, Von-Danckelmann-Platz 4, 06120 Halle (Saale), Germany
| | - Daniel Sebastiani
- Institute of Chemistry, Martin Luther University Halle-Wittenberg, Von-Danckelmann-Platz 4, 06120 Halle (Saale), Germany
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23
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Affiliation(s)
- Frank Noé
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
- Department of Physics, Freie Universität Berlin, Berlin, Germany
| | - Edina Rosta
- Department of Chemistry, Kings College London, London, England
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Abstract
Most current molecular dynamics simulation and analysis methods rely on the idea that the molecular system can be represented by a single global state (e.g., a Markov state in a Markov state model [MSM]). In this approach, molecules can be extensively sampled and analyzed when they only possess a few metastable states, such as small- to medium-sized proteins. However, this approach breaks down in frustrated systems and in large protein assemblies, where the number of global metastable states may grow exponentially with the system size. To address this problem, we here introduce dynamic graphical models (DGMs) that describe molecules as assemblies of coupled subsystems, akin to how spins interact in the Ising model. The change of each subsystem state is only governed by the states of itself and its neighbors. DGMs require fewer parameters than MSMs or other global state models; in particular, we do not need to observe all global system configurations to characterize them. Therefore, DGMs can predict previously unobserved molecular configurations. As a proof of concept, we demonstrate that DGMs can faithfully describe molecular thermodynamics and kinetics and predict previously unobserved metastable states for Ising models and protein simulations.
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Affiliation(s)
- Simon Olsson
- Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany;
| | - Frank Noé
- Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany;
- Department of Physics, Freie Universität Berlin, 14195 Berlin, Germany
- Department of Chemistry, Rice University, Houston, TX 77005
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25
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Sengupta U, Carballo-Pacheco M, Strodel B. Automated Markov state models for molecular dynamics simulations of aggregation and self-assembly. J Chem Phys 2019; 150:115101. [DOI: 10.1063/1.5083915] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Affiliation(s)
- Ushnish Sengupta
- Institute of Complex Systems: Structural Biochemistry (ICS-6), Forschungszentrum Jülich, 52425 Jülich, Germany
- Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
| | - Martín Carballo-Pacheco
- Institute of Complex Systems: Structural Biochemistry (ICS-6), Forschungszentrum Jülich, 52425 Jülich, Germany
- AICES Graduate School, RWTH Aachen University, Schinkelstraße 2, 52062 Aachen, Germany
- School of Physics and Astronomy, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, United Kingdom
| | - Birgit Strodel
- Institute of Complex Systems: Structural Biochemistry (ICS-6), Forschungszentrum Jülich, 52425 Jülich, Germany
- Institute of Theoretical and Computational Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
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26
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Sgouralis I, Madaan S, Djutanta F, Kha R, Hariadi RF, Pressé S. A Bayesian Nonparametric Approach to Single Molecule Förster Resonance Energy Transfer. J Phys Chem B 2019; 123:675-688. [PMID: 30571128 DOI: 10.1021/acs.jpcb.8b09752] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We develop a Bayesian nonparametric framework to analyze single molecule FRET (smFRET) data. This framework, a variation on infinite hidden Markov models, goes beyond traditional hidden Markov analysis, which already treats photon shot noise, in three critical ways: (1) it learns the number of molecular states present in a smFRET time trace (a hallmark of nonparametric approaches), (2) it accounts, simultaneously and self-consistently, for photophysical features of donor and acceptor fluorophores (blinking kinetics, spectral cross-talk, detector quantum efficiency), and (3) it treats background photons. Point 2 is essential in reducing the tendency of nonparametric approaches to overinterpret noisy single molecule time traces and so to estimate states and transition kinetics robust to photophysical artifacts. As a result, with the proposed framework, we obtain accurate estimates of single molecule properties even when the supplied traces are excessively noisy, subject to photoartifacts, and of short duration. We validate our method using synthetic data sets and demonstrate its applicability to real data sets from single molecule experiments on Holliday junctions labeled with conventional fluorescent dyes.
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Affiliation(s)
- Ioannis Sgouralis
- Center for Biological Physics, Department of Physics , Arizona State University , Tempe , Arizona 85287 , United States
| | - Shreya Madaan
- School of Computing, Informatics, and Decision Systems Engineering , Arizona State University , Tempe , Arizona 85287 , United States
| | - Franky Djutanta
- Biodesign Center for Molecular Design and Biomimetics, Biodesign Institute , Arizona State University , Tempe , Arizona 85287 , United States
| | - Rachael Kha
- School for Engineering of Matter, Transport and Energy , Arizona State University , Tempe , Arizona 85287 , United States
| | - Rizal F Hariadi
- Center for Biological Physics, Department of Physics , Arizona State University , Tempe , Arizona 85287 , United States.,Biodesign Center for Molecular Design and Biomimetics, Biodesign Institute , Arizona State University , Tempe , Arizona 85287 , United States
| | - Steve Pressé
- Center for Biological Physics, Department of Physics , Arizona State University , Tempe , Arizona 85287 , United States.,School of Molecular Sciences , Arizona State University , Tempe , Arizona 85287 , United States
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27
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Welty R, Pabit SA, Katz AM, Calvey GD, Pollack L, Hall KB. Divalent ions tune the kinetics of a bacterial GTPase center rRNA folding transition from secondary to tertiary structure. RNA (NEW YORK, N.Y.) 2018; 24:1828-1838. [PMID: 30254137 PMCID: PMC6239185 DOI: 10.1261/rna.068361.118] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 09/20/2018] [Indexed: 05/22/2023]
Abstract
Folding of an RNA from secondary to tertiary structure often depends on divalent ions for efficient electrostatic charge screening (nonspecific association) or binding (specific association). To measure how different divalent cations modify folding kinetics of the 60 nucleotide Ecoli rRNA GTPase center, we combined stopped-flow fluorescence in the presence of Mg2+, Ca2+, or Sr2+ together with time-resolved small angle X-ray scattering (SAXS) in the presence of Mg2+ to observe the folding process. Immediately upon addition of each divalent ion, the RNA undergoes a transition from an extended state with secondary structure to a more compact structure. Subsequently, specific divalent ions modulate populations of intermediates in conformational ensembles along the folding pathway with transition times longer than 10 msec. Rate constants for the five folding transitions act on timescales from submillisecond to tens of seconds. The sensitivity of RNA tertiary structure to divalent cation identity affects all but the fastest events in RNA folding, and allowed us to identify those states that prefer Mg2+ The GTPase center RNA appears to have optimized its folding trajectory to specifically utilize this most abundant intracellular divalent ion.
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Affiliation(s)
- Robb Welty
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Suzette A Pabit
- School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, USA
| | - Andrea M Katz
- School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, USA
| | - George D Calvey
- School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, USA
| | - Lois Pollack
- School of Applied and Engineering Physics, Cornell University, Ithaca, New York 14853, USA
| | - Kathleen B Hall
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
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28
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Macdonald PJ, Ruan Q, Tetin SY. Direct single-molecule counting for immunoassay applications. Anal Biochem 2018; 566:139-145. [PMID: 30496720 DOI: 10.1016/j.ab.2018.11.019] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 11/19/2018] [Accepted: 11/20/2018] [Indexed: 12/14/2022]
Abstract
Single-molecule methods offer specificity in studying complex systems and dynamics, but they also offer high sensitivity for basic enumeration. We apply single-molecule TIRF to immunoassays by counting the number of target molecules captured on a streptavidin surface. We demonstrate the utility of using single-molecule counting on eluted detection conjugate, following the capture and sandwich formation portions of the assay having been completed on microparticles. This approach is simple and effective, and creates the opportunity for a universal detection platform that can be used to perform a variety of diagnostic and blood screening assays. We take advantage of the low volume requirements of single-molecule detection and apply a sample reloading approach to concentrate sample onto the detection surface. Due to the high affinity of the streptavidin-biotin reaction, concentration through reloading is both quick and robust. These findings are demonstrated on a model system and in an HIV p24 antigen assay. Single-molecule detection techniques do not need to be complex to exhibit power and flexibility, and so can become valuable in the field of immunoassay diagnostics.
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Affiliation(s)
- Patrick J Macdonald
- Applied Research and Technology, Abbott Diagnostics Division, Abbott Laboratories, Abbott Park, IL, 60064, USA
| | - Qiaoqiao Ruan
- Applied Research and Technology, Abbott Diagnostics Division, Abbott Laboratories, Abbott Park, IL, 60064, USA
| | - Sergey Y Tetin
- Applied Research and Technology, Abbott Diagnostics Division, Abbott Laboratories, Abbott Park, IL, 60064, USA.
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29
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Lammert H, Wang A, Mohanty U, Onuchic JN. RNA as a Complex Polymer with Coupled Dynamics of Ions and Water in the Outer Solvation Sphere. J Phys Chem B 2018; 122:11218-11227. [PMID: 30102033 DOI: 10.1021/acs.jpcb.8b06874] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
We unravel the internal and collective modes of a widely studied 58-nucleotide rRNA fragment in solvent using atomically detailed molecular dynamics simulations. The variation of lifetimes for water hydrogen bonds with nucleotide groups indicates heterogeneity of water dynamics on the RNA surface. The time scales of interactions of the discrete water molecules with RNA nucleotides extend from several hundred picoseconds to a few nanoseconds. We determine all of the association sites and the spatial distribution of residence times for Mg2+, K+, and water molecules in those sites. We provide insights into the population of Mg2+ and K+ ions and water molecules in the outer sphere and how their fluctuations are intricately linked with the kinetics of the 58-mer. We find that many of the long-lived Mg2+ sites identified from the simulations agree with the locations of ions in the X-ray structure. We determine the excess ion atmosphere around the rRNA and compare it with experimental data. We investigate the collective behavior of RNA, ions, and water, by performing a joint principle component analysis for the Cartesian coordinates of the RNA phosphorus atoms and for the occupation counts of the association sites. Our results indicate that the 58-mer system is a complex polymer, composed of RNA that is encased by a fluctuating network of associated counterions, co-ions, and water.
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Affiliation(s)
| | - Ailun Wang
- Department of Chemistry , Boston College , Chestnut Hill , Massachusetts 02467 , United States
| | - Udayan Mohanty
- Department of Chemistry , Boston College , Chestnut Hill , Massachusetts 02467 , United States
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30
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Chen J, Chen J, Pinamonti G, Clementi C. Learning Effective Molecular Models from Experimental Observables. J Chem Theory Comput 2018; 14:3849-3858. [DOI: 10.1021/acs.jctc.8b00187] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Justin Chen
- Department of Physics and Astronomy, Rice University, Houston, Texas 77005, United States
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
| | - Jiming Chen
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, United States
| | - Giovanni Pinamonti
- Department of Mathematics and Computer Science, Freie Universität, Berlin, Germany
| | - Cecilia Clementi
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, United States
- Department of Mathematics and Computer Science, Freie Universität, Berlin, Germany
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
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31
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Mondal P, Granucci G, Rastädter D, Persico M, Burghardt I. Azobenzene as a photoregulator covalently attached to RNA: a quantum mechanics/molecular mechanics-surface hopping dynamics study. Chem Sci 2018; 9:4671-4681. [PMID: 29899961 PMCID: PMC5969502 DOI: 10.1039/c8sc00072g] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2018] [Accepted: 04/25/2018] [Indexed: 12/21/2022] Open
Abstract
Azobenzene covalently attached to RNA undergoes trans-to-cis photo-switching on a time scale of ∼15 picoseconds – 30 times slower than in vacuo.
The photoregulation of nucleic acids by azobenzene photoswitches has recently attracted considerable interest in the context of emerging biotechnological applications. To understand the mechanism of photoinduced isomerisation and conformational control in these complex biological environments, we employ a Quantum Mechanics/Molecular Mechanics (QM/MM) approach in conjunction with nonadiabatic Surface Hopping (SH) dynamics. Two representative RNA–azobenzene complexes are investigated, both of which contain the azobenzene chromophore covalently attached to an RNA double strand via a β-deoxyribose linker. Due to the pronounced constraints of the local RNA environment, it is found that trans-to-cis isomerization is slowed down to a time scale of ∼10–15 picoseconds, in contrast to 500 femtoseconds in vacuo, with a quantum yield reduced by a factor of two. By contrast, cis-to-trans isomerization remains in a sub-picosecond regime. A volume-conserving isomerization mechanism is found, similarly to the pedal-like mechanism previously identified for azobenzene in solution phase. Strikingly, the chiral RNA environment induces opposite right-handed and left-handed helicities of the ground-state cis-azobenzene chromophore in the two RNA–azobenzene complexes, along with an almost completely chirality conserving photochemical pathway for these helical enantiomers.
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Affiliation(s)
- Padmabati Mondal
- Institute of Physical and Theoretical Chemistry , Goethe University Frankfurt , Max-von-Laue-Str. 7 , 60438 Frankfurt , Germany . ;
| | - Giovanni Granucci
- Dipartimento di Chimica e Chimica Industriale , Università di Pisa , v. Moruzzi 13 , I-56124 Pisa , Italy .
| | - Dominique Rastädter
- Institute of Physical and Theoretical Chemistry , Goethe University Frankfurt , Max-von-Laue-Str. 7 , 60438 Frankfurt , Germany . ;
| | - Maurizio Persico
- Dipartimento di Chimica e Chimica Industriale , Università di Pisa , v. Moruzzi 13 , I-56124 Pisa , Italy .
| | - Irene Burghardt
- Institute of Physical and Theoretical Chemistry , Goethe University Frankfurt , Max-von-Laue-Str. 7 , 60438 Frankfurt , Germany . ;
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32
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Hu J, Wu M, Jiang L, Zhong Z, Zhou Z, Rujiralai T, Ma J. Combining gold nanoparticle antennas with single-molecule fluorescence resonance energy transfer (smFRET) to study DNA hairpin dynamics. NANOSCALE 2018; 10:6611-6619. [PMID: 29578224 DOI: 10.1039/c7nr08397a] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The association of a plasmonic nano-antenna with single-molecule FRET technique presents new prospects to investigate the dynamics of biological molecules. However, the presence of a plasmonic nano-antenna significantly modifies the FRET rate and efficiency; this makes its applicability to the prevalent single-molecule FRET experiments unclear. Herein, using gold nanoparticle antennas of different sizes and DNA hairpins labelled with FRET pairs (Cy3 and Cy5) as the model system, we performed experiments to study the folding dynamics of single DNA hairpins at various salt concentrations. Our results indicate that gold nanoparticle antennas can enhance single-molecule fluorescence of Cy3 and Cy5 up to 3-5 folds, substantially reduce the FRET efficiency, and alter the obtained FRET efficiency histograms. However, the folding dynamics of DNA hairpins remains unaffected, and the correct kinetic and dynamic information can still be extracted from the seriously modified FRET efficiencies. Therefore, our experiments demonstrate the feasibility and compatibility for applying plasmonic nano-antennas to the mostly used single-molecule FRET assays, which provide a broad range of possibilities for the future applications of these nano-antennas in studying various essential biological processes.
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Affiliation(s)
- Jinyong Hu
- School of Physics, Sun Yat-sen University, Guangzhou 510275, People's Republic of China.
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33
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Manz C, Kobitski AY, Samanta A, Jäschke A, Nienhaus GU. The multi-state energy landscape of the SAM-I riboswitch: A single-molecule Förster resonance energy transfer spectroscopy study. J Chem Phys 2018; 148:123324. [DOI: 10.1063/1.5003783] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
- Christoph Manz
- Institute of Applied Physics, Karlsruhe Institute of Technology, Wolfgang-Gaede-Str. 1, 76131 Karlsruhe, Germany
- HEiKA–Heidelberg Karlsruhe Research Partnership, Karlsruhe Institute of Technology, Hermann-von-Helmholtz Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Andrei Yu. Kobitski
- Institute of Applied Physics, Karlsruhe Institute of Technology, Wolfgang-Gaede-Str. 1, 76131 Karlsruhe, Germany
| | - Ayan Samanta
- Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, Im Neuenheimer Feld 364, 69120 Heidelberg, Germany
| | - Andres Jäschke
- HEiKA–Heidelberg Karlsruhe Research Partnership, Karlsruhe Institute of Technology, Hermann-von-Helmholtz Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
- Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, Im Neuenheimer Feld 364, 69120 Heidelberg, Germany
| | - G. Ulrich Nienhaus
- Institute of Applied Physics, Karlsruhe Institute of Technology, Wolfgang-Gaede-Str. 1, 76131 Karlsruhe, Germany
- HEiKA–Heidelberg Karlsruhe Research Partnership, Karlsruhe Institute of Technology, Hermann-von-Helmholtz Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
- Institute of Nanotechnology and Institute of Toxicology and Genetics, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
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34
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Affiliation(s)
- Brooke E. Husic
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
| | - Vijay S. Pande
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
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35
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Zhang X, Zhang D, Zhao C, Tian K, Shi R, Du X, Burcke AJ, Wang J, Chen SJ, Gu LQ. Nanopore electric snapshots of an RNA tertiary folding pathway. Nat Commun 2017; 8:1458. [PMID: 29133841 PMCID: PMC5684407 DOI: 10.1038/s41467-017-01588-z] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Accepted: 09/29/2017] [Indexed: 12/22/2022] Open
Abstract
The chemical properties and biological mechanisms of RNAs are determined by their tertiary structures. Exploring the tertiary structure folding processes of RNA enables us to understand and control its biological functions. Here, we report a nanopore snapshot approach combined with coarse-grained molecular dynamics simulation and master equation analysis to elucidate the folding of an RNA pseudoknot structure. In this approach, single RNA molecules captured by the nanopore can freely fold from the unstructured state without constraint and can be programmed to terminate their folding process at different intermediates. By identifying the nanopore signatures and measuring their time-dependent populations, we can “visualize” a series of kinetically important intermediates, track the kinetics of their inter-conversions, and derive the RNA pseudoknot folding pathway. This approach can potentially be developed into a single-molecule toolbox to investigate the biophysical mechanisms of RNA folding and unfolding, its interactions with ligands, and its functions. While RNA folding is critical for its function, study of this process is challenging. Here, the authors combine nanopore single-molecule manipulation with theoretical analysis to follow the folding of an RNA pseudoknot, monitoring the intermediate states and the kinetics of their interconversion.
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Affiliation(s)
- Xinyue Zhang
- Department of Bioengineering, University of Missouri, Columbia, MO, 65211, USA.,Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO, 65211, USA
| | - Dong Zhang
- Department of Physics, University of Missouri, Columbia, MO, 65211, USA
| | - Chenhan Zhao
- Department of Physics, University of Missouri, Columbia, MO, 65211, USA
| | - Kai Tian
- Department of Bioengineering, University of Missouri, Columbia, MO, 65211, USA.,Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO, 65211, USA
| | - Ruicheng Shi
- Department of Bioengineering, University of Missouri, Columbia, MO, 65211, USA
| | - Xiao Du
- Department of Bioengineering, University of Missouri, Columbia, MO, 65211, USA
| | - Andrew J Burcke
- Department of Bioengineering, University of Missouri, Columbia, MO, 65211, USA
| | - Jing Wang
- Department of Bioengineering, University of Missouri, Columbia, MO, 65211, USA
| | - Shi-Jie Chen
- Department of Physics, University of Missouri, Columbia, MO, 65211, USA. .,Department of Biochemistry, University of Missouri, Columbia, MO, 65211, USA. .,Informatics Institute, University of Missouri, Columbia, MO, 65211, USA.
| | - Li-Qun Gu
- Department of Bioengineering, University of Missouri, Columbia, MO, 65211, USA. .,Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO, 65211, USA.
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36
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Tavakoli M, Taylor JN, Li CB, Komatsuzaki T, Pressé S. Single Molecule Data Analysis: An Introduction. ADVANCES IN CHEMICAL PHYSICS 2017. [DOI: 10.1002/9781119324560.ch4] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Meysam Tavakoli
- Physics Department; Indiana University-Purdue University Indianapolis; Indianapolis IN 46202 USA
| | - J. Nicholas Taylor
- Research Institute for Electronic Science; Hokkaido University; Kita 20 Nishi 10 Kita-Ku Sapporo 001-0020 Japan
| | - Chun-Biu Li
- Research Institute for Electronic Science; Hokkaido University; Kita 20 Nishi 10 Kita-Ku Sapporo 001-0020 Japan
- Department of Mathematics; Stockholm University; 106 91 Stockholm Sweden
| | - Tamiki Komatsuzaki
- Research Institute for Electronic Science; Hokkaido University; Kita 20 Nishi 10 Kita-Ku Sapporo 001-0020 Japan
| | - Steve Pressé
- Physics Department; Indiana University-Purdue University Indianapolis; Indianapolis IN 46202 USA
- Department of Chemistry and Chemical Biology; Indiana University-Purdue University Indianapolis; Indianapolis IN 46202 USA
- Department of Cell and Integrative Physiology; Indiana University School of Medicine; Indianapolis IN 46202 USA
- Department of Physics and School of Molecular Sciences; Arizona State University; Tempe AZ 85287 USA
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37
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Manz C, Kobitski AY, Samanta A, Keller BG, Jäschke A, Nienhaus GU. Single-molecule FRET reveals the energy landscape of the full-length SAM-I riboswitch. Nat Chem Biol 2017; 13:1172-1178. [DOI: 10.1038/nchembio.2476] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Accepted: 08/03/2017] [Indexed: 11/09/2022]
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38
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Q Nguyen KK, Gomez YK, Bakhom M, Radcliffe A, La P, Rochelle D, Lee JW, Sorin EJ. Ensemble simulations: folding, unfolding and misfolding of a high-efficiency frameshifting RNA pseudoknot. Nucleic Acids Res 2017; 45:4893-4904. [PMID: 28115636 PMCID: PMC5416846 DOI: 10.1093/nar/gkx012] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Accepted: 01/11/2017] [Indexed: 12/11/2022] Open
Abstract
Massive all-atom molecular dynamics simulations were conducted across a distributed computing network to study the folding, unfolding, misfolding and conformational plasticity of the high-efficiency frameshifting double mutant of the 26 nt potato leaf roll virus RNA pseudoknot. Our robust sampling, which included over 40 starting structures spanning the spectrum from the extended unfolded state to the native fold, yielded nearly 120 μs of cumulative sampling time. Conformational microstate transitions on the 1.0 ns to 10.0 μs timescales were observed, with post-equilibration sampling providing detailed representations of the conformational free energy landscape and the complex folding mechanism inherent to the pseudoknot motif. Herein, we identify and characterize two alternative native structures, three intermediate states, and numerous misfolded states, the latter of which have not previously been characterized via atomistic simulation techniques. While in line with previous thermodynamics-based models of a general RNA folding mechanism, our observations indicate that stem-strand-sequence-separation may serve as an alternative predictor of the order of stem formation during pseudoknot folding. Our results contradict a model of frameshifting based on structural rigidity and resistance to mechanical unfolding, and instead strongly support more recent studies in which conformational plasticity is identified as a determining factor in frameshifting efficiency.
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Affiliation(s)
- Khai K Q Nguyen
- Department of Chemistry & Biochemistry, California State University Long Beach, Long Beach, CA 90840, USA.,Department of Computer Engineering & Computer Science, California State University Long Beach, Long Beach, CA 90840, USA
| | - Yessica K Gomez
- Department of Chemistry & Biochemistry, California State University Long Beach, Long Beach, CA 90840, USA.,Department of Physics & Astronomy, California State University Long Beach, Long Beach, CA 90840, USA
| | - Mona Bakhom
- Department of Chemistry & Biochemistry, California State University Long Beach, Long Beach, CA 90840, USA
| | - Amethyst Radcliffe
- Department of Physics & Astronomy, California State University Long Beach, Long Beach, CA 90840, USA
| | - Phuc La
- Department of Chemistry & Biochemistry, California State University Long Beach, Long Beach, CA 90840, USA
| | - Dakota Rochelle
- Department of Chemistry & Biochemistry, California State University Long Beach, Long Beach, CA 90840, USA
| | - Ji Won Lee
- Department of Chemistry & Biochemistry, California State University Long Beach, Long Beach, CA 90840, USA
| | - Eric J Sorin
- Department of Chemistry & Biochemistry, California State University Long Beach, Long Beach, CA 90840, USA
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39
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Single-Molecule Analysis beyond Dwell Times: Demonstration and Assessment in and out of Equilibrium. Biophys J 2017; 111:1375-1384. [PMID: 27705761 DOI: 10.1016/j.bpj.2016.08.023] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 08/01/2016] [Accepted: 08/09/2016] [Indexed: 11/21/2022] Open
Abstract
We present a simple and robust technique for extracting kinetic rate models and thermodynamic quantities from single-molecule time traces. Single-molecule analysis of complex kinetic sequences (SMACKS) is a maximum-likelihood approach that resolves all statistically relevant rates and also their uncertainties. This is achieved by optimizing one global kinetic model based on the complete data set while allowing for experimental variations between individual trajectories. In contrast to dwell-time analysis, which is the current standard method, SMACKS includes every experimental data point, not only dwell times. As a result, it works as well for long trajectories as for an equivalent set of short ones. In addition, the previous systematic overestimation of fast over slow rates is solved. We demonstrate the power of SMACKS on the kinetics of the multidomain protein Hsp90 measured by single-molecule Förster resonance energy transfer. Experiments in and out of equilibrium are analyzed and compared to simulations, shedding new light on the role of Hsp90's ATPase function. SMACKS resolves accurate rate models even if states cause indistinguishable signals. Thereby, it pushes the boundaries of single-molecule kinetics beyond those of current methods.
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40
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Lee A, Tsekouras K, Calderon C, Bustamante C, Pressé S. Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis. Chem Rev 2017; 117:7276-7330. [PMID: 28414216 PMCID: PMC5487374 DOI: 10.1021/acs.chemrev.6b00729] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
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.
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Affiliation(s)
- Antony Lee
- Department of Physics, University of California at Berkeley, Berkeley, California 94720, United States
- Jason L. Choy Laboratory of Single-Molecule Biophysics, University of California at Berkeley, Berkeley, California 94720, United States
| | - Konstantinos Tsekouras
- Department of Physics, University of California at Berkeley, Berkeley, California 94720, United States
- Department of Physics, Arizona State University, Tempe, Arizona 85287, United States
| | | | - Carlos Bustamante
- Jason L. Choy Laboratory of Single-Molecule Biophysics, University of California at Berkeley, Berkeley, California 94720, United States
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, California 94720, United States
- Institute for Quantitative Biosciences-QB3, University of California at Berkeley, Berkeley, California 94720, United States
- Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, California 94720, United States
- Department of Chemistry, University of California at Berkeley, Berkeley, California 94720, United States
- Howard Hughes Medical Institute, University of California at Berkeley, Berkeley, California 94720, United States
- Kavli Energy Nanosciences Institute, University of California at Berkeley, Berkeley, California 94720, United States
| | - Steve Pressé
- Department of Physics, University of California at Berkeley, Berkeley, California 94720, United States
- Department of Chemistry and Chemical Biology, Indiana University–Purdue University Indianapolis, Indianapolis, Indiana 46202, United States
- Department of Cell and Integrative Physiology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
- Department of Physics, Arizona State University, Tempe, Arizona 85287, United States
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41
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Warfield BM, Anderson PC. Molecular simulations and Markov state modeling reveal the structural diversity and dynamics of a theophylline-binding RNA aptamer in its unbound state. PLoS One 2017; 12:e0176229. [PMID: 28437473 PMCID: PMC5402969 DOI: 10.1371/journal.pone.0176229] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 04/08/2017] [Indexed: 11/18/2022] Open
Abstract
RNA aptamers are oligonucleotides that bind with high specificity and affinity to target ligands. In the absence of bound ligand, secondary structures of RNA aptamers are generally stable, but single-stranded and loop regions, including ligand binding sites, lack defined structures and exist as ensembles of conformations. For example, the well-characterized theophylline-binding aptamer forms a highly stable binding site when bound to theophylline, but the binding site is unstable and disordered when theophylline is absent. Experimental methods have not revealed at atomic resolution the conformations that the theophylline aptamer explores in its unbound state. Consequently, in the present study we applied 21 microseconds of molecular dynamics simulations to structurally characterize the ensemble of conformations that the aptamer adopts in the absence of theophylline. Moreover, we apply Markov state modeling to predict the kinetics of transitions between unbound conformational states. Our simulation results agree with experimental observations that the theophylline binding site is found in many distinct binding-incompetent states and show that these states lack a binding pocket that can accommodate theophylline. The binding-incompetent states interconvert with binding-competent states through structural rearrangement of the binding site on the nanosecond to microsecond timescale. Moreover, we have simulated the complete theophylline binding pathway. Our binding simulations supplement prior experimental observations of slow theophylline binding kinetics by showing that the binding site must undergo a large conformational rearrangement after the aptamer and theophylline form an initial complex, most notably, a major rearrangement of the C27 base from a buried to solvent-exposed orientation. Theophylline appears to bind by a combination of conformational selection and induced fit mechanisms. Finally, our modeling indicates that when Mg2+ ions are present the population of binding-competent aptamer states increases more than twofold. This population change, rather than direct interactions between Mg2+ and theophylline, accounts for altered theophylline binding kinetics.
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Affiliation(s)
- Becka M. Warfield
- Department of Physical Sciences, University of Washington, Bothell, Washington, United States of America
| | - Peter C. Anderson
- Department of Physical Sciences, University of Washington, Bothell, Washington, United States of America
- * E-mail:
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Shukla S, Shamsi Z, Moffett AS, Selvam B, Shukla D. Application of Hidden Markov Models in Biomolecular Simulations. Methods Mol Biol 2017; 1552:29-41. [PMID: 28224489 DOI: 10.1007/978-1-4939-6753-7_3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Hidden Markov models (HMMs) provide a framework to analyze large trajectories of biomolecular simulation datasets. HMMs decompose the conformational space of a biological molecule into finite number of states that interconvert among each other with certain rates. HMMs simplify long timescale trajectories for human comprehension, and allow comparison of simulations with experimental data. In this chapter, we provide an overview of building HMMs for analyzing bimolecular simulation datasets. We demonstrate the procedure for building a Hidden Markov model for Met-enkephalin peptide simulation dataset and compare the timescales of the process.
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Affiliation(s)
- Saurabh Shukla
- Department of Chemical & Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Zahra Shamsi
- Department of Chemical & Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Alexander S Moffett
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Balaji Selvam
- Department of Chemical & Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Diwakar Shukla
- Department of Chemical & Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
- Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
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Šponer J, Bussi G, Stadlbauer P, Kührová P, Banáš P, Islam B, Haider S, Neidle S, Otyepka M. Folding of guanine quadruplex molecules-funnel-like mechanism or kinetic partitioning? An overview from MD simulation studies. Biochim Biophys Acta Gen Subj 2016; 1861:1246-1263. [PMID: 27979677 DOI: 10.1016/j.bbagen.2016.12.008] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 12/04/2016] [Accepted: 12/11/2016] [Indexed: 01/18/2023]
Abstract
BACKGROUND Guanine quadruplexes (GQs) play vital roles in many cellular processes and are of much interest as drug targets. In contrast to the availability of many structural studies, there is still limited knowledge on GQ folding. SCOPE OF REVIEW We review recent molecular dynamics (MD) simulation studies of the folding of GQs, with an emphasis paid to the human telomeric DNA GQ. We explain the basic principles and limitations of all types of MD methods used to study unfolding and folding in a way accessible to non-specialists. We discuss the potential role of G-hairpin, G-triplex and alternative GQ intermediates in the folding process. We argue that, in general, folding of GQs is fundamentally different from funneled folding of small fast-folding proteins, and can be best described by a kinetic partitioning (KP) mechanism. KP is a competition between at least two (but often many) well-separated and structurally different conformational ensembles. MAJOR CONCLUSIONS The KP mechanism is the only plausible way to explain experiments reporting long time-scales of GQ folding and the existence of long-lived sub-states. A significant part of the natural partitioning of the free energy landscape of GQs comes from the ability of the GQ-forming sequences to populate a large number of syn-anti patterns in their G-tracts. The extreme complexity of the KP of GQs typically prevents an appropriate description of the folding landscape using just a few order parameters or collective variables. GENERAL SIGNIFICANCE We reconcile available computational and experimental studies of GQ folding and formulate basic principles characterizing GQ folding landscapes. This article is part of a Special Issue entitled "G-quadruplex" Guest Editor: Dr. Concetta Giancola and Dr. Daniela Montesarchio.
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Affiliation(s)
- Jiří Šponer
- Institute of Biophysics, Academy of Sciences of the Czech Republic, Kralovopolska 135, 612 65 Brno, Czech Republic; Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science, Palacky University Olomouc, 17. listopadu 12, 771 46 Olomouc, Czech Republic.
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, Via Bonomea 265, 34136 Trieste, Italy
| | - Petr Stadlbauer
- Institute of Biophysics, Academy of Sciences of the Czech Republic, Kralovopolska 135, 612 65 Brno, Czech Republic; Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science, Palacky University Olomouc, 17. listopadu 12, 771 46 Olomouc, Czech Republic
| | - Petra Kührová
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science, Palacky University Olomouc, 17. listopadu 12, 771 46 Olomouc, Czech Republic
| | - Pavel Banáš
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science, Palacky University Olomouc, 17. listopadu 12, 771 46 Olomouc, Czech Republic
| | - Barira Islam
- Institute of Biophysics, Academy of Sciences of the Czech Republic, Kralovopolska 135, 612 65 Brno, Czech Republic
| | - Shozeb Haider
- UCL School of Pharmacy, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Stephen Neidle
- UCL School of Pharmacy, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Michal Otyepka
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science, Palacky University Olomouc, 17. listopadu 12, 771 46 Olomouc, Czech Republic
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Dimura M, Peulen TO, Hanke CA, Prakash A, Gohlke H, Seidel CA. Quantitative FRET studies and integrative modeling unravel the structure and dynamics of biomolecular systems. Curr Opin Struct Biol 2016; 40:163-185. [PMID: 27939973 DOI: 10.1016/j.sbi.2016.11.012] [Citation(s) in RCA: 113] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Revised: 11/11/2016] [Accepted: 11/11/2016] [Indexed: 01/11/2023]
Abstract
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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Affiliation(s)
- Mykola Dimura
- Chair for Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany; Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Thomas O Peulen
- Chair for Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Christian A Hanke
- Chair for Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Aiswaria Prakash
- Chair for Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Holger Gohlke
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Claus Am Seidel
- Chair for Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany.
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45
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Pérez-Hernández G, Noé F. Hierarchical Time-Lagged Independent Component Analysis: Computing Slow Modes and Reaction Coordinates for Large Molecular Systems. J Chem Theory Comput 2016; 12:6118-6129. [PMID: 27792332 DOI: 10.1021/acs.jctc.6b00738] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Analysis of molecular dynamics, for example using Markov models, often requires the identification of order parameters that are good indicators of the rare events, i.e. good reaction coordinates. Recently, it has been shown that the time-lagged independent component analysis (TICA) finds the linear combinations of input coordinates that optimally represent the slow kinetic modes and may serve in order to define reaction coordinates between the metastable states of the molecular system. A limitation of the method is that both computing time and memory requirements scale with the square of the number of input features. For large protein systems, this exacerbates the use of extensive feature sets such as the distances between all pairs of residues or even heavy atoms. Here we derive a hierarchical TICA (hTICA) method that approximates the full TICA solution by a hierarchical, divide-and-conquer calculation. By using hTICA on distances between heavy atoms we identify previously unknown relaxation processes in the bovine pancreatic trypsin inhibitor.
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Affiliation(s)
- Guillermo Pérez-Hernández
- Department of Mathematics and Computer Science, Freie Universitat Berlin , Arnimallee 6, Berlin, Germany 14195
| | - Frank Noé
- Department of Mathematics and Computer Science, Freie Universitat Berlin , Arnimallee 6, Berlin, Germany 14195
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46
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Chen Y, Shen K, Shan SO, Kou SC. Analyzing Single-Molecule Protein Transportation Experiments via Hierarchical Hidden Markov Models. J Am Stat Assoc 2016; 111:951-966. [PMID: 28943680 PMCID: PMC5606165 DOI: 10.1080/01621459.2016.1140050] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Revised: 12/01/2015] [Indexed: 01/10/2023]
Abstract
To maintain proper cellular functions, over 50% of proteins encoded in the genome need to be transported to cellular membranes. The molecular mechanism behind such a process, often referred to as protein targeting, is not well understood. Single-molecule experiments are designed to unveil the detailed mechanisms and reveal the functions of different molecular machineries involved in the process. The experimental data consist of hundreds of stochastic time traces from the fluorescence recordings of the experimental system. We introduce a Bayesian hierarchical model on top of hidden Markov models (HMMs) to analyze these data and use the statistical results to answer the biological questions. In addition to resolving the biological puzzles and delineating the regulating roles of different molecular complexes, our statistical results enable us to propose a more detailed mechanism for the late stages of the protein targeting process.
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Affiliation(s)
- Yang Chen
- Ph.D. candidate, Department of Statistics, Harvard University, Cambridge, MA 02138
| | - Kuang Shen
- Pfizer fellow of the Life Sciences Research Foundation, Whitehead Institute for Biomedical Research, Cambridge, MA 02142
| | - Shu-Ou Shan
- Professor, Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125
| | - S C Kou
- Professor, Department of Statistics, Harvard University, Cambridge, MA 02138
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47
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Rohlman CE, Blanco MR, Walter NG. Putting Humpty-Dumpty Together: Clustering the Functional Dynamics of Single Biomolecular Machines Such as the Spliceosome. Methods Enzymol 2016; 581:257-283. [PMID: 27793282 DOI: 10.1016/bs.mie.2016.08.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The spliceosome is a biomolecular machine that, in all eukaryotes, accomplishes site-specific splicing of introns from precursor messenger RNAs (pre-mRNAs) with high fidelity. Operating at the nanometer scale, where inertia and friction have lost the dominant role they play in the macroscopic realm, the spliceosome is highly dynamic and assembles its active site around each pre-mRNA anew. To understand the structural dynamics underlying the molecular motors, clocks, and ratchets that achieve functional accuracy in the yeast spliceosome (a long-standing model system), we have developed single-molecule fluorescence resonance energy transfer (smFRET) approaches that report changes in intra- and intermolecular interactions in real time. Building on our work using hidden Markov models (HMMs) to extract kinetic and conformational state information from smFRET time trajectories, we recognized that HMM analysis of individual state transitions as independent stochastic events is insufficient for a biomolecular machine as complex as the spliceosome. In this chapter, we elaborate on the recently developed smFRET-based Single-Molecule Cluster Analysis (SiMCAn) that dissects the intricate conformational dynamics of a pre-mRNA through the splicing cycle in a model-free fashion. By leveraging hierarchical clustering techniques developed for Bioinformatics, SiMCAn efficiently analyzes large datasets to first identify common molecular behaviors. Through a second level of clustering based on the abundance of dynamic behaviors exhibited by defined functional intermediates that have been stalled by biochemical or genetic tools, SiMCAn then efficiently assigns pre-mRNA FRET states and transitions to specific splicing complexes, with the potential to find heretofore undescribed conformations. SiMCAn thus arises as a general tool to analyze dynamic cellular machines more broadly.
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Affiliation(s)
| | - M R Blanco
- Single Molecule Analysis Group and Center for RNA Biomedicine, University of Michigan, Ann Arbor, MI, United States
| | - N G Walter
- Single Molecule Analysis Group and Center for RNA Biomedicine, University of Michigan, Ann Arbor, MI, United States.
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48
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Spiegel JD, Fulle S, Kleinschmidt M, Gohlke H, Marian CM. Failure of the IDA in FRET Systems at Close Inter-Dye Distances Is Moderated by Frequent Low κ2 Values. J Phys Chem B 2016; 120:8845-62. [DOI: 10.1021/acs.jpcb.6b05754] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
| | - Simone Fulle
- BioMed X Innovation Center, Im Neuenheimer Feld 515, 69120 Heidelberg, Germany
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49
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Chen J, Pyle JR, Sy Piecco KW, Kolomeisky AB, Landes CF. A Two-Step Method for smFRET Data Analysis. J Phys Chem B 2016; 120:7128-32. [DOI: 10.1021/acs.jpcb.6b05697] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Jixin Chen
- Department
of Chemistry and Biochemistry, Ohio University, Athens, Ohio 45701, United States
| | - Joseph R. Pyle
- Department
of Chemistry and Biochemistry, Ohio University, Athens, Ohio 45701, United States
| | - Kurt Waldo Sy Piecco
- Department
of Chemistry and Biochemistry, Ohio University, Athens, Ohio 45701, United States
| | | | - Christy F. Landes
- Department
of Chemistry, Rice University, Houston, Texas 77251, United States
- Department
of Electrical and Computer Engineering, Rice University, Houston, Texas 77251, United States
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50
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Espah Borujeni A, Salis HM. Translation Initiation is Controlled by RNA Folding Kinetics via a Ribosome Drafting Mechanism. J Am Chem Soc 2016; 138:7016-23. [PMID: 27199273 DOI: 10.1021/jacs.6b01453] [Citation(s) in RCA: 93] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
RNA folding plays an important role in controlling protein synthesis as well as other cellular processes. Existing models have focused on how RNA folding energetics control translation initiation rate under equilibrium conditions but have largely ignored the effects of nonequilibrium RNA folding. We introduce a new mechanism, called "ribosome drafting", that explains how a mRNA's folding kinetics and the ribosome's binding rate collectively control its translation initiation rate. During cycles of translation, ribosome drafting emerges whenever successive ribosomes bind to a mRNA faster than the mRNA can refold, maintaining it in a nonequilibrium state with an acceleration of protein synthesis. Using computational design, time-correlated single photon counting, and expression measurements, we demonstrate that slow-folding and fast-folding RNA structures with equivalent folding energetics can vary protein synthesis rates by 1000-fold. We determine the necessary conditions for ribosome drafting by characterizing mRNAs with rationally designed ribosome binding rates, folding kinetics, and folding energetics, confirming the predictions of a nonequilibrium Markov model of translation. Our results have widespread implications, illustrating how competitive folding and assembly kinetics can shape the gene expression machinery's sequence-structure-function relationship inside cells.
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Affiliation(s)
- Amin Espah Borujeni
- Department of Chemical Engineering and ‡Department of Biological Engineering, The Pennsylvania State University , University Park, Pennsylvania 16802, United States
| | - Howard M Salis
- Department of Chemical Engineering and ‡Department of Biological Engineering, The Pennsylvania State University , University Park, Pennsylvania 16802, United States
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