1
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Gopich IV, Chung HS. Unraveling Burst Selection Bias in Single-Molecule FRET of Species with Unequal Brightness and Diffusivity. J Phys Chem B 2024; 128:5576-5589. [PMID: 38833567 DOI: 10.1021/acs.jpcb.4c01178] [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: 06/06/2024]
Abstract
Single-molecule free diffusion experiments enable accurate quantification of coexisting species or states. However, unequal brightness and diffusivity introduce a burst selection bias and affect the interpretation of experimental results. We address this issue with a photon-by-photon maximum likelihood method, burstML, which explicitly considers burst selection criteria. BurstML accurately estimates parameters, including photon count rates, diffusion times, Förster resonance energy transfer (FRET) efficiencies, and population, even in cases where species are poorly distinguished in FRET efficiency histograms. We develop a quantitative theory that determines the fraction of photon bursts corresponding to each species and thus obtain accurate species populations from the measured burst fractions. In addition, we provide a simple approximate formula for burst fractions and establish the range of parameters where unequal brightness and diffusivity can significantly affect the results obtained by conventional methods. The performance of the burstML method is compared with that of a maximum likelihood method that assumes equal species brightness and diffusivity, as well as standard Gaussian fitting of FRET efficiency histograms, using both simulated and real single-molecule data for cold-shock protein, protein L, and protein G. The burstML method enhances the accuracy of parameter estimation in single-molecule fluorescence studies.
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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, Maryland 20892, United States
| | - Hoi Sung Chung
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
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2
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Miller JJ, Mallimadugula UL, Zimmerman MI, Stuchell-Brereton MD, Soranno A, Bowman GR. Accounting for fast vs slow exchange in single molecule FRET experiments reveals hidden conformational states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.03.597137. [PMID: 38895430 PMCID: PMC11185552 DOI: 10.1101/2024.06.03.597137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Proteins are dynamic systems whose structural preferences determine their function. Unfortunately, building atomically detailed models of protein structural ensembles remains challenging, limiting our understanding of the relationships between sequence, structure, and function. Combining single molecule Förster resonance energy transfer (smFRET) experiments with molecular dynamics simulations could provide experimentally grounded, all-atom models of a protein's structural ensemble. However, agreement between the two techniques is often insufficient to achieve this goal. Here, we explore whether accounting for important experimental details like averaging across structures sampled during a given smFRET measurement is responsible for this apparent discrepancy. We present an approach to account for this time-averaging by leveraging the kinetic information available from Markov state models of a protein's dynamics. This allows us to accurately assess which timescales are averaged during an experiment. We find this approach significantly improves agreement between simulations and experiments in proteins with varying degrees of dynamics, including the well-ordered protein T4 lysozyme, the partially disordered protein apolipoprotein E (ApoE), and a disordered amyloid protein (Aβ40). We find evidence for hidden states that are not apparent in smFRET experiments because of time averaging with other structures, akin to states in fast exchange in NMR, and evaluate different force fields. Finally, we show how remaining discrepancies between computations and experiments can be used to guide additional simulations and build structural models for states that were previously unaccounted for. We expect our approach will enable combining simulations and experiments to understand the link between sequence, structure, and function in many settings.
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Affiliation(s)
- Justin J. Miller
- Departments of Biochemistry & Biophysics and Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
| | - Upasana L. Mallimadugula
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
| | - Maxwell I. Zimmerman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
| | - Melissa D. Stuchell-Brereton
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
| | - Andrea Soranno
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
| | - Gregory R. Bowman
- Departments of Biochemistry & Biophysics and Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States
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3
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Saurabh A, Xu LWQ, Pressé S. On the statistical foundation of a recent single molecule FRET benchmark. Nat Commun 2024; 15:3627. [PMID: 38688904 PMCID: PMC11061128 DOI: 10.1038/s41467-024-47733-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 04/09/2024] [Indexed: 05/02/2024] Open
Affiliation(s)
- Ayush Saurabh
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
| | - Lance W Q Xu
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
| | - Steve Pressé
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA.
- Department of Physics, Arizona State University, Tempe, AZ, USA.
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA.
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4
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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. Reply to: On the statistical foundation of a recent single molecule FRET benchmark. Nat Commun 2024; 15:3626. [PMID: 38688911 PMCID: PMC11061175 DOI: 10.1038/s41467-024-47734-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 04/09/2024] [Indexed: 05/02/2024] Open
Affiliation(s)
- Markus Götz
- PicoQuant GmbH, Rudower Chaussee 29, 12489, Berlin, Germany.
| | - Anders Barth
- 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, University of Copenhagen, 2100, Copenhagen, Denmark
- Novo Nordisk Center for optimised oligo escape and control of disease University of Copenhagen, 2100 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, USA
| | - Nikos S Hatzakis
- Department of Chemistry, University of Copenhagen, 2100, Copenhagen, Denmark
- Novo Nordisk Center for optimised oligo escape and control of disease University of Copenhagen, 2100 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 B Sletfjerding
- Department of Chemistry, University of Copenhagen, 2100, Copenhagen, Denmark
- Novo Nordisk Center for optimised oligo escape and control of disease University of Copenhagen, 2100 Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Johannes Thomsen
- Department of Chemistry, University of Copenhagen, 2100, Copenhagen, Denmark
- Novo Nordisk Center for optimised oligo escape and control of disease University of Copenhagen, 2100 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.
- Department of Chemistry, University of Basel, Basel, Switzerland.
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5
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Grabenhorst L, Sturzenegger F, Hasler M, Schuler B, Tinnefeld P. Single-Molecule FRET at 10 MHz Count Rates. J Am Chem Soc 2024; 146:3539-3544. [PMID: 38266173 DOI: 10.1021/jacs.3c13757] [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: 01/26/2024]
Abstract
A bottleneck in many studies utilizing single-molecule Förster resonance energy transfer is the attainable photon count rate, as it determines the temporal resolution of the experiment. As many biologically relevant processes occur on time scales that are hardly accessible with currently achievable photon count rates, there has been considerable effort to find strategies to increase the stability and brightness of fluorescent dyes. Here, we use DNA nanoantennas to drastically increase the achievable photon count rates and observe fast biomolecular dynamics in the small volume between two plasmonic nanoparticles. As a proof of concept, we observe the coupled folding and binding of two intrinsically disordered proteins, which form transient encounter complexes with lifetimes on the order of 100 μs. To test the limits of our approach, we also investigated the hybridization of a short single-stranded DNA to its complementary counterpart, revealing a transition path time of 17 μs at photon count rates of around 10 MHz, which is an order-of-magnitude improvement compared to the state of the art. Concomitantly, the photostability was increased, enabling many seconds long megahertz fluorescence time traces. Due to the modular nature of the DNA origami method, this platform can be adapted to a broad range of biomolecules, providing a promising approach to study previously unobservable ultrafast biophysical processes.
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Affiliation(s)
- Lennart Grabenhorst
- Department of Chemistry and Center for NanoScience, Ludwig-Maximilians-Universität München, 81377 München, Germany
| | | | - Moa Hasler
- Department of Biochemistry, University of Zurich, 8057 Zurich, Switzerland
| | - Benjamin Schuler
- Department of Biochemistry, University of Zurich, 8057 Zurich, Switzerland
- Department of Physics, University of Zurich, 8057 Zurich, Switzerland
| | - Philip Tinnefeld
- Department of Chemistry and Center for NanoScience, Ludwig-Maximilians-Universität München, 81377 München, Germany
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6
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Chen YT, Yang H, Chu JW. Trajectory Statistical Learning of the Potential Mean of Force and Diffusion Coefficient from Molecular Dynamics Simulations. J Phys Chem B 2024; 128:56-66. [PMID: 38165090 DOI: 10.1021/acs.jpcb.3c05245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Central to studying the conformational changes of a complex protein is understanding the dynamics and energetics involved. Phenomenologically, structural dynamics can be formulated using an overdamped Langevin model along an observable, e.g., the distance between two residues in the protein. The Langevin model is specified by the deterministic force (the potential of mean force, PMF) and stochastic force (characterized by the diffusion coefficient, D). It is therefore of great interest to be able to extract both PMF and D from an observable time series but under the same computational framework. Here, we approach this challenge in molecular dynamics (MD) simulations by treating it as a missing-data Bayesian estimation problem. An important distinction in our methodology is that the entire MD trajectory, as opposed to the individual data elements, is used as the statistical variable in Bayesian imputation. This idea is implemented through an eigen-decomposition procedure for a time-symmetrized Fokker-Planck equation, followed by maximizing the likelihood for parameter estimation. The mathematical expressions for the functional derivatives used in learning PMF and D also provide new physical insights for the manner by which the information on both the deterministic and stochastic forces is encoded in the dynamics data. An all-atom MD simulation of a nontrivial biomolecule case is used to illustrate the application of this approach. We show that, interestingly, the results of trajectory statistical learning can motivate new order parameters for an improved description of the kinetic bottlenecks in conformational changes. Complementing purely data-driven or black-box methods, this work underscores the advantages of physics-based machine learning in gaining chemical insights from quantitative parameter estimation.
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Affiliation(s)
- Yi-Tsao Chen
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan, Republic of China
| | - Haw Yang
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States
| | - Jhih-Wei Chu
- Institute of Bioinformatics and Systems Biology, Department of Biological Science and Technology, Institute of Molecular Medicine and Bioengineering, and Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan, Republic of China
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7
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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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8
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Terterov I, Nettels D, Makarov DE, Hofmann H. Time-resolved burst variance analysis. BIOPHYSICAL REPORTS 2023; 3:100116. [PMID: 37559939 PMCID: PMC10406964 DOI: 10.1016/j.bpr.2023.100116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 07/03/2023] [Indexed: 08/11/2023]
Abstract
Quantifying biomolecular dynamics has become a major task of single-molecule fluorescence spectroscopy methods. In single-molecule Förster resonance energy transfer (smFRET), kinetic information is extracted from the stream of photons emitted by attached donor and acceptor fluorophores. Here, we describe a time-resolved version of burst variance analysis that can quantify kinetic rates at microsecond to millisecond timescales in smFRET experiments of diffusing molecules. Bursts are partitioned into segments with a fixed number of photons. The FRET variance is computed from these segments and compared with the variance expected from shot noise. By systematically varying the segment size, dynamics at different timescales can be captured. We provide a theoretical framework to extract kinetic rates from the decay of the FRET variance with increasing segment size. Compared to other methods such as filtered fluorescence correlation spectroscopy, recurrence analysis of single particles, and two-dimensional lifetime correlation spectroscopy, fewer photons are needed to obtain reliable timescale estimates, which reduces the required measurement time.
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Affiliation(s)
- Ivan Terterov
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Daniel Nettels
- Department of Biochemistry and Department of Physics, University of Zurich, Zurich, Switzerland
| | - Dmitrii E. Makarov
- Department of Chemistry and Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas
| | - Hagen Hofmann
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot, Israel
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9
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Xu (徐伟青) LW, Jazani S, Kilic Z, Pressé S. Single-Molecule Reaction-Diffusion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.05.556378. [PMID: 37732202 PMCID: PMC10508780 DOI: 10.1101/2023.09.05.556378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
We propose to capture reaction-diffusion on a molecule-by-molecule basis from the fastest acquirable timescale, namely individual photon arrivals. We illustrate our method on intrinsically disordered human proteins, the linker histone H1.0 as well as its chaperone prothymosin α , as these diffuse through an illuminated confocal spot and interact forming larger ternary complexes on millisecond timescales. Most importantly, single-molecule reaction-diffusion, smRD, reveals single molecule properties without trapping or otherwise confining molecules to surfaces. We achieve smRD within a Bayesian paradigm and term our method Bayes-smRD. Bayes-smRD is further free of the average, bulk, results inherent to the analysis of long photon arrival traces by fluorescence correlation spectroscopy. In learning from thousands of photon arrivals continuous spatial positions and discrete conformational and photophysical state changes, Bayes-smRD estimates kinetic parameters on a molecule-by-molecule basis with two to three orders of magnitude less data than tools such as fluorescence correlation spectroscopy thereby also dramatically reducing sample photodamage.
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Affiliation(s)
- Lance W.Q. Xu (徐伟青)
- Center for Biological Physics, Arizona State University, Tempe, AZ 85287, USA
- Department of Physics, Arizona State University, Tempe, AZ 85287, USA
| | - Sina Jazani
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins Medicine, Baltimore, MD 21205, USA
| | - Zeliha Kilic
- Department of Structural Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Steve Pressé
- Center for Biological Physics, Arizona State University, Tempe, AZ 85287, USA
- Department of Physics, Arizona State University, Tempe, AZ 85287, USA
- School of Molecular Science, Arizona State University, Tempe, AZ 85287, USA
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10
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Suddala KC, Yoo J, Fan L, Zuo X, Wang YX, Chung HS, Zhang J. Direct observation of tRNA-chaperoned folding of a dynamic mRNA ensemble. Nat Commun 2023; 14:5438. [PMID: 37673863 PMCID: PMC10482949 DOI: 10.1038/s41467-023-41155-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 08/24/2023] [Indexed: 09/08/2023] Open
Abstract
T-box riboswitches are multi-domain noncoding RNAs that surveil individual amino acid availabilities in most Gram-positive bacteria. T-boxes directly bind specific tRNAs, query their aminoacylation status to detect starvation, and feedback control the transcription or translation of downstream amino-acid metabolic genes. Most T-boxes rapidly recruit their cognate tRNA ligands through an intricate three-way stem I-stem II-tRNA interaction, whose establishment is not understood. Using single-molecule FRET, SAXS, and time-resolved fluorescence, we find that the free T-box RNA assumes a broad distribution of open, semi-open, and closed conformations that only slowly interconvert. tRNA directly binds all three conformers with distinct kinetics, triggers nearly instantaneous collapses of the open conformations, and returns the T-box RNA to their pre-binding conformations upon dissociation. This scissors-like dynamic behavior is enabled by a hinge-like pseudoknot domain which poises the T-box for rapid tRNA-induced domain closure. This study reveals tRNA-chaperoned folding of flexible, multi-domain mRNAs through a Venus flytrap-like mechanism.
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Affiliation(s)
- Krishna C Suddala
- Laboratory of Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 20892, USA
| | - Janghyun Yoo
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 20892, USA
| | - Lixin Fan
- Basic Science Program, Frederick National Laboratory for Cancer Research, Small-Angle X-Ray Scattering Core Facility of National Cancer Institute, Frederick, MD, 21702, USA
| | - Xiaobing Zuo
- X-ray Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Yun-Xing Wang
- Basic Science Program, Frederick National Laboratory for Cancer Research, Small-Angle X-Ray Scattering Core Facility of National Cancer Institute, Frederick, MD, 21702, USA
- Structural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD, 21702, USA
| | - Hoi Sung Chung
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 20892, USA.
| | - Jinwei Zhang
- Laboratory of Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 20892, USA.
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11
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Meng F, Kim JY, Gopich IV, Chung HS. Single-molecule FRET and molecular diffusion analysis characterize stable oligomers of amyloid-β 42 of extremely low population. PNAS NEXUS 2023; 2:pgad253. [PMID: 37564361 PMCID: PMC10411938 DOI: 10.1093/pnasnexus/pgad253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 07/12/2023] [Accepted: 07/21/2023] [Indexed: 08/12/2023]
Abstract
Soluble oligomers produced during protein aggregation have been thought to be toxic species causing various diseases. Characterization of these oligomers is difficult because oligomers are a heterogeneous mixture, which is not readily separable, and may appear transiently during aggregation. Single-molecule spectroscopy can provide valuable information by detecting individual oligomers, but there have been various problems in determining the size and concentration of oligomers. In this work, we develop and use a method that analyzes single-molecule fluorescence burst data of freely diffusing molecules in solution based on molecular diffusion theory and maximum likelihood method. We demonstrate that the photon count rate, diffusion time, population, and Förster resonance energy transfer (FRET) efficiency can be accurately determined from simulated data and the experimental data of a known oligomerization system, the tetramerization domain of p53. We used this method to characterize the oligomers of the 42-residue amyloid-β (Aβ42) peptide. Combining peptide incubation in a plate reader and single-molecule free-diffusion experiments allows for the detection of stable oligomers appearing at various stages of aggregation. We find that the average size of these oligomers is 70-mer and their overall population is very low, less than 1 nM, in the early and middle stages of aggregation of 1 µM Aβ42 peptide. Based on their average size and long diffusion time, we predict the oligomers have a highly elongated rod-like shape.
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Affiliation(s)
- Fanjie Meng
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892-0520, USA
| | - Jae-Yeol Kim
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892-0520, USA
| | - Irina V Gopich
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892-0520, USA
| | - Hoi Sung Chung
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892-0520, USA
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12
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Gopich IV, Kim JY, Chung HS. Analysis of photon trajectories from diffusing single molecules. J Chem Phys 2023; 159:024119. [PMID: 37431909 PMCID: PMC10474944 DOI: 10.1063/5.0153114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/19/2023] [Indexed: 07/12/2023] Open
Abstract
In single-molecule free diffusion experiments, molecules spend most of the time outside a laser spot and generate bursts of photons when they diffuse through the focal spot. Only these bursts contain meaningful information and, therefore, are selected using physically reasonable criteria. The analysis of the bursts must take into account the precise way they were chosen. We present new methods that allow one to accurately determine the brightness and diffusivity of individual molecule species from the photon arrival times of selected bursts. We derive analytical expressions for the distribution of inter-photon times (with and without burst selection), the distribution of the number of photons in a burst, and the distribution of photons in a burst with recorded arrival times. The theory accurately treats the bias introduced due to the burst selection criteria. We use a Maximum Likelihood (ML) method to find the molecule's photon count rate and diffusion coefficient from three kinds of data, i.e., the bursts of photons with recorded arrival times (burstML), inter-photon times in bursts (iptML), and the numbers of photon counts in a burst (pcML). The performance of these new methods is tested on simulated photon trajectories and on an experimental system, the fluorophore Atto 488.
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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, Maryland 20892, USA
| | - Jae-Yeol Kim
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Hoi Sung Chung
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA
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13
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Liebermann DG, Jungwirth J, Riven I, Barak Y, Levy D, Horovitz A, Haran G. From Microstates to Macrostates in the Conformational Dynamics of GroEL: A Single-Molecule Förster Resonance Energy Transfer Study. J Phys Chem Lett 2023:6513-6521. [PMID: 37440608 PMCID: PMC10388350 DOI: 10.1021/acs.jpclett.3c01281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/15/2023]
Abstract
The chaperonin GroEL is a multisubunit molecular machine that assists in protein folding in the Escherichia coli cytosol. Past studies have shown that GroEL undergoes large allosteric conformational changes during its reaction cycle. Here, we report single-molecule Förster resonance energy transfer measurements that directly probe the conformational transitions of one subunit within GroEL and its single-ring variant under equilibrium conditions. We find that four microstates span the conformational manifold of the protein and interconvert on the submillisecond time scale. A unique set of relative populations of these microstates, termed a macrostate, is obtained by varying solution conditions, e.g., adding different nucleotides or the cochaperone GroES. Strikingly, ATP titration studies demonstrate that the partition between the apo and ATP-ligated conformational macrostates traces a sigmoidal response with a Hill coefficient similar to that obtained in bulk experiments of ATP hydrolysis. These coinciding results from bulk measurements for an entire ring and single-molecule measurements for a single subunit provide new evidence for the concerted allosteric transition of all seven subunits.
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14
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Agam G, Gebhardt C, Popara M, Mächtel R, Folz J, Ambrose B, Chamachi N, Chung SY, Craggs TD, de Boer M, Grohmann D, Ha T, Hartmann A, Hendrix J, Hirschfeld V, Hübner CG, Hugel T, Kammerer D, Kang HS, Kapanidis AN, Krainer G, Kramm K, Lemke EA, Lerner E, Margeat E, Martens K, Michaelis J, Mitra J, Moya Muñoz GG, Quast RB, Robb NC, Sattler M, Schlierf M, Schneider J, Schröder T, Sefer A, Tan PS, Thurn J, Tinnefeld P, van Noort J, Weiss S, Wendler N, Zijlstra N, Barth A, Seidel CAM, Lamb DC, Cordes T. Reliability and accuracy of single-molecule FRET studies for characterization of structural dynamics and distances in proteins. Nat Methods 2023; 20:523-535. [PMID: 36973549 PMCID: PMC10089922 DOI: 10.1038/s41592-023-01807-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 01/31/2023] [Indexed: 03/29/2023]
Abstract
Single-molecule Förster-resonance energy transfer (smFRET) experiments allow the study of biomolecular structure and dynamics in vitro and in vivo. We performed an international blind study involving 19 laboratories to assess the uncertainty of FRET experiments for proteins with respect to the measured FRET efficiency histograms, determination of distances, and the detection and quantification of structural dynamics. Using two protein systems with distinct conformational changes and dynamics, we obtained an uncertainty of the FRET efficiency ≤0.06, corresponding to an interdye distance precision of ≤2 Å and accuracy of ≤5 Å. We further discuss the limits for detecting fluctuations in this distance range and how to identify dye perturbations. Our work demonstrates the ability of smFRET experiments to simultaneously measure distances and avoid the averaging of conformational dynamics for realistic protein systems, highlighting its importance in the expanding toolbox of integrative structural biology.
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Affiliation(s)
- Ganesh Agam
- Department of Chemistry, Ludwig-Maximilians University München, München, Germany
| | - Christian Gebhardt
- Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians University München, Planegg-Martinsried, Germany
| | - Milana Popara
- Molecular Physical Chemistry, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Rebecca Mächtel
- Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians University München, Planegg-Martinsried, Germany
| | - Julian Folz
- Molecular Physical Chemistry, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | | | - Neharika Chamachi
- B CUBE - Center for Molecular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Sang Yoon Chung
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA
| | | | - Marijn de Boer
- Molecular Microscopy Research Group, Zernike Institute for Advanced Materials, University of Groningen, AG Groningen, the Netherlands
| | - Dina Grohmann
- Department of Biochemistry, Genetics and Microbiology, Institute of Microbiology, Single-Molecule Biochemistry Laboratory, University of Regensburg, Regensburg, Germany
| | - Taekjip Ha
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine and Howard Hughes Medical Institute, Baltimore, MD, USA
| | - Andreas Hartmann
- B CUBE - Center for Molecular Bioengineering, Technische Universität Dresden, Dresden, Germany
| | - Jelle Hendrix
- Dynamic Bioimaging Laboratory, Advanced Optical Microscopy Center and Biomedical Research Institute, Hasselt University, Agoralaan C (BIOMED), Hasselt, Belgium
- Department of Chemistry, KU Leuven, Leuven, Belgium
| | | | | | - Thorsten Hugel
- Institute of Physical Chemistry, University of Freiburg, Freiburg, Germany
- Signalling Research Centers BIOSS and CIBSS, University of Freiburg, Freiburg, Germany
| | - Dominik Kammerer
- Department of Physics, Clarendon Laboratory, University of Oxford, Oxford, UK
- Kavli Institute of Nanoscience Discovery, University of Oxford, Oxford, UK
| | - Hyun-Seo Kang
- Bayerisches NMR Zentrum, Department of Bioscience, School of Natural Sciences, Technical University of München, Garching, Germany
| | - Achillefs N Kapanidis
- Department of Physics, Clarendon Laboratory, University of Oxford, Oxford, UK
- Kavli Institute of Nanoscience Discovery, University of Oxford, Oxford, UK
| | - Georg Krainer
- B CUBE - Center for Molecular Bioengineering, Technische Universität Dresden, Dresden, Germany
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Kevin Kramm
- Department of Biochemistry, Genetics and Microbiology, Institute of Microbiology, Single-Molecule Biochemistry Laboratory, University of Regensburg, Regensburg, Germany
| | - Edward A Lemke
- Biocenter, Johannes Gutenberg University Mainz, Mainz, Germany
- Institute of Molecular Biology, Mainz, Germany
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Eitan Lerner
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, and The Center for Nanoscience and Nanotechnology, Faculty of Mathematics and Science, The Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Emmanuel Margeat
- Centre de Biologie Structurale (CBS), University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Kirsten Martens
- Biological and Soft Matter Physics, Huygens-Kamerlingh Onnes Laboratory, Leiden University, Leiden, the Netherlands
| | | | - Jaba Mitra
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins University School of Medicine and Howard Hughes Medical Institute, Baltimore, MD, USA
- Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Gabriel G Moya Muñoz
- Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians University München, Planegg-Martinsried, Germany
| | - Robert B Quast
- Centre de Biologie Structurale (CBS), University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Nicole C Robb
- Department of Physics, Clarendon Laboratory, University of Oxford, Oxford, UK
- Kavli Institute of Nanoscience Discovery, University of Oxford, Oxford, UK
- Warwick Medical School, The University of Warwick, Coventry, UK
| | - Michael Sattler
- Bayerisches NMR Zentrum, Department of Bioscience, School of Natural Sciences, Technical University of München, Garching, Germany
- Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Center Munich, Munich, Germany
| | - Michael Schlierf
- B CUBE - Center for Molecular Bioengineering, Technische Universität Dresden, Dresden, Germany
- Cluster of Excellence Physics of Life, Technische Universität Dresden, Dresden, Germany
| | - Jonathan Schneider
- Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians University München, Planegg-Martinsried, Germany
| | - Tim Schröder
- Department of Chemistry, Ludwig-Maximilians University München, München, Germany
| | - Anna Sefer
- Institute for Biophysics, Ulm University, Ulm, Germany
| | - Piau Siong Tan
- Biocenter, Johannes Gutenberg University Mainz, Mainz, Germany
- Institute of Molecular Biology, Mainz, Germany
| | - Johann Thurn
- Institute of Physical Chemistry, University of Freiburg, Freiburg, Germany
- Institute of Technical Physics, German Aerospace Center (DLR), Stuttgart, Germany
| | - Philip Tinnefeld
- Department of Chemistry, Ludwig-Maximilians University München, München, Germany
| | - John van Noort
- Biological and Soft Matter Physics, Huygens-Kamerlingh Onnes Laboratory, Leiden University, Leiden, the Netherlands
| | - Shimon Weiss
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA
- California NanoSystems Institute, University of California, Los Angeles, CA, USA
| | - Nicolas Wendler
- Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians University München, Planegg-Martinsried, Germany
| | - Niels Zijlstra
- Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians University München, Planegg-Martinsried, Germany
| | - Anders Barth
- Molecular Physical Chemistry, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
- Department of Bionanoscience, Kavli Institute of Nanoscience, Delft University of Technology, Delft, the Netherlands.
| | - Claus A M Seidel
- Molecular Physical Chemistry, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
| | - Don C Lamb
- Department of Chemistry, Ludwig-Maximilians University München, München, Germany.
| | - Thorben Cordes
- Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians University München, Planegg-Martinsried, Germany.
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15
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Godec A, Makarov DE. Challenges in Inferring the Directionality of Active Molecular Processes from Single-Molecule Fluorescence Resonance Energy Transfer Trajectories. J Phys Chem Lett 2023; 14:49-56. [PMID: 36566432 DOI: 10.1021/acs.jpclett.2c03244] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
We discuss some of the practical challenges that one faces in using stochastic thermodynamics to infer directionality of molecular machines from experimental single-molecule trajectories. Because of the limited spatiotemporal resolution of single-molecule experiments and because both forward and backward transitions between the same pairs of states cannot always be detected, differentiating between the forward and backward directions of, e.g., an ATP-consuming molecular machine that operates periodically, turns out to be a nontrivial task. Using a simple extension of a Markov-state model that is commonly employed to analyze single-molecule transition-path measurements, we illustrate how irreversibility can be hidden from such measurements but in some cases can be uncovered when non-Markov effects in low-dimensional single-molecule trajectories are considered.
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Affiliation(s)
- Aljaž Godec
- Mathematical bioPhysics Group, Max Planck Institute for Multidisciplinary Sciences, 37077Göttingen, Germany
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16
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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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17
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Saurabh A, Safar M, Fazel M, Sgouralis I, Pressé S. Single-photon smFRET: II. Application to continuous illumination. BIOPHYSICAL REPORTS 2022; 3:100087. [PMID: 36582656 PMCID: PMC9792399 DOI: 10.1016/j.bpr.2022.100087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 11/01/2022] [Accepted: 11/21/2022] [Indexed: 12/03/2022]
Abstract
Here we adapt the Bayesian nonparametrics (BNP) framework presented in the first companion article to analyze kinetics from single-photon, single-molecule Förster resonance energy transfer (smFRET) traces generated under continuous illumination. Using our sampler, BNP-FRET, we learn the escape rates and the number of system states given a photon trace. We benchmark our method by analyzing a range of synthetic and experimental data. Particularly, we apply our method to simultaneously learn the number of system states and the corresponding kinetics for intrinsically disordered proteins using two-color FRET under varying chemical conditions. Moreover, using synthetic data, we show that our method can deduce the number of system states even when kinetics occur at timescales of interphoton intervals.
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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
| | - Matthew Safar
- Center for Biological Physics, Arizona State University, Tempe, Arizona,Department of Mathematics and Statistical Science, Arizona State University, Tempe, Arizona
| | - Mohamadreza Fazel
- Center for Biological Physics, Arizona State University, Tempe, Arizona,Department of Physics, Arizona State University, Tempe, Arizona
| | - Ioannis Sgouralis
- Department of Mathematics, University of Tennessee Knoxville, Knoxville, Tennessee
| | - 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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18
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Makarov DE, Berezhkovskii A, Haran G, Pollak E. The Effect of Time Resolution on Apparent Transition Path Times Observed in Single-Molecule Studies of Biomolecules. J Phys Chem B 2022; 126:7966-7974. [PMID: 36194758 PMCID: PMC9574923 DOI: 10.1021/acs.jpcb.2c05550] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
Single-molecule experiments have now achieved a time
resolution
allowing observation of transition paths, the brief trajectory segments
where the molecule undergoing an unfolding or folding transition enters
the energetically or entropically unfavorable barrier region from
the folded/unfolded side and exits to the unfolded/folded side, thereby
completing the transition. This resolution, however, is yet insufficient
to identify the precise entrance/exit events that mark the beginning
and the end of a transition path: the nature of the diffusive dynamics
is such that a molecular trajectory will recross the boundary between
the barrier region and the folded/unfolded state, multiple times,
at a time scale much shorter than that of the typical experimental
resolution. Here we use theory and Brownian dynamics simulations to
show that, as a result of such recrossings, the apparent transition
path times are generally longer than the true ones. We quantify this
effect using a simple model where the observed dynamics is a moving
average of the true dynamics and discuss experimental implications
of our results.
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Affiliation(s)
| | - Alexander Berezhkovskii
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland20892, United States
| | - Gilad Haran
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot76100, Israel
| | - Eli Pollak
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot76100, Israel
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19
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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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20
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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: 23] [Impact Index Per Article: 11.5] [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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21
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Opanasyuk O, Barth A, Peulen TO, Felekyan S, Kalinin S, Sanabria H, Seidel CA. Unraveling multi-state molecular dynamics in single-molecule FRET experiments. II. Quantitative analysis of multi-state kinetic networks. J Chem Phys 2022; 157:031501. [DOI: 10.1063/5.0095754] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Single-molecule Förster Resonance Energy Transfer (smFRET) experiments are ideally suited to resolve the structural dynamics of biomolecules. A significant challenge to date is capturing and quantifying the exchange between multiple conformational states, mainly when these dynamics occur on the sub-millisecond timescale. Many methods for quantitative analysis are challenged if more than two states are involved, and the appropriate choice of the number of states in the kinetic network is difficult. An additional complication arises if dynamically active molecules coexist with pseudo-static molecules in similar conformational states with undistinguishable FRET efficiencies. To address these problems, we developed a quantitative integrative analysis framework that combines the information from FRET-lines that relate average fluorescence lifetimes and intensities in two-dimensional burst frequency histograms, fluorescence decays obtained by time-correlated single photon counting, photon distribution analysis of the intensities and fluorescence correlation spectroscopy. Individually, these methodologies provide ambiguous results for the characterization of dynamics in complex kinetic networks. However, the global analysis approach enables accurate determination of the number of states, their kinetic connectivity, the transition rate constants, and species fractions. To challenge the potential of smFRET experiments studying multi-state kinetic networks, we apply our integrative framework using a set of synthetic data for three-state systems with different kinetic connectivity and exchange rates. Our methodology paves the way towards an integrated analysis of multiparameter smFRET experiments that spans all dimensions of the experimental data. Finally, we propose a workflow for the analysis and show examples that demonstrate the usefulness of this toolkit for dynamic structural biology.
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Affiliation(s)
| | | | | | - Suren Felekyan
- PC-II, Heinrich Heine University Düsseldorf Department of Chemistry, Germany
| | - Stanislav Kalinin
- Institut für Physikalische Chemie, Heinrich-Heine-Universität Düsseldorf, Germany
| | | | - Claus A.M. Seidel
- Institut fuer Physikalische Chemie, Heinrich-Heine-Universität Düsseldorf, Germany
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22
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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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23
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Chekmarev SF. Extraction of kinetics from equilibrium distributions of states using the Metropolis Monte Carlo method. Phys Rev E 2022; 105:034407. [PMID: 35428044 DOI: 10.1103/physreve.105.034407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/25/2022] [Indexed: 06/14/2023]
Abstract
The Metropolis Monte Carlo (MMC) method is used to extract reaction kinetics from a given equilibrium distribution of states of a complex system. The approach is illustrated by the folding/unfolding reaction for two proteins: a model β-hairpin and α-helical protein α_{3}D. For the β-hairpin, the free energy surfaces (FESs) and free energy profiles (FEPs) are employed as the equilibrium distributions of states, playing a role of the potentials of mean force to determine the acceptance probabilities of new states in the MMC simulations. Based on the FESs and PESs for a set of temperatures that were simulated with the molecular dynamics (MD) method, the MMC simulations are performed to extract folding/unfolding rates. It has been found that the rate constants and first-passage time (FPT) distributions obtained in the MMC simulations change with temperature in good agreement with those from the MD simulations. For α_{3}D protein, whose equilibrium folding/unfolding was studied with the single-molecule FRET method [Chung et al., J. Phys. Chem. A 115, 3642 (2011)1089-563910.1021/jp1009669], the FRET-efficiency histograms at different denaturant concentrations were used as the equilibrium distributions of protein states. It has been found that the rate constants for folding and unfolding obtained in the MMC simulations change with denaturant concentration in reasonable agreement with the constants that were extracted from the photon trajectories on the basis of theoretical models. The simulated FPT distributions are single-exponential, which is consistent with the assumption of two-state kinetics that was made in the theoretical models. The promising feature of the present approach is that it is based solely on the equilibrium distributions of states, without introducing any additional parameters to perform simulations, which suggests its applicability to other complex systems.
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Affiliation(s)
- Sergei F Chekmarev
- Institute of Thermophysics, SB RAS, 630090 Novosibirsk, Russia and Department of Physics, Novosibirsk State University, 630090 Novosibirsk, Russia
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24
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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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25
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Release of linker histone from the nucleosome driven by polyelectrolyte competition with a disordered protein. Nat Chem 2022; 14:224-231. [PMID: 34992286 DOI: 10.1038/s41557-021-00839-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 10/19/2021] [Indexed: 12/13/2022]
Abstract
Highly charged intrinsically disordered proteins are essential regulators of chromatin structure and transcriptional activity. Here we identify a surprising mechanism of molecular competition that relies on the pronounced dynamical disorder present in these polyelectrolytes and their complexes. The highly positively charged human linker histone H1.0 (H1) binds to nucleosomes with ultrahigh affinity, implying residence times incompatible with efficient biological regulation. However, we show that the disordered regions of H1 retain their large-amplitude dynamics when bound to the nucleosome, which enables the highly negatively charged and disordered histone chaperone prothymosin α to efficiently invade the H1-nucleosome complex and displace H1 via a competitive substitution mechanism, vastly accelerating H1 dissociation. By integrating experiments and simulations, we establish a molecular model that rationalizes the remarkable kinetics of this process structurally and dynamically. Given the abundance of polyelectrolyte sequences in the nuclear proteome, this mechanism is likely to be widespread in cellular regulation.
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26
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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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27
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Mothi N, Muñoz V. Protein Folding Dynamics as Diffusion on a Free Energy Surface: Rate Equation Terms, Transition Paths, and Analysis of Single-Molecule Photon Trajectories. J Phys Chem B 2021; 125:12413-12425. [PMID: 34735144 DOI: 10.1021/acs.jpcb.1c05401] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The rates of protein (un)folding are often described as diffusion on the projection of a hyperdimensional energy landscape onto a few (ideally one) order parameters. Testing such an approximation by experiment requires resolving the reactive transition paths of individual molecules, which is now becoming feasible with advanced single-molecule spectroscopic techniques. This has also sparked the interest of theorists in better understanding reactive transition paths. Here we focus on these issues aiming to establish (i) practical guidelines for the mechanistic interpretation of transition path times (TPT) and (ii) methods to extract the free energy surface and protein dynamics from the maximum likelihood analysis of photon trajectories (MLA-PT). We represent the (un)folding rates as diffusion on a 1D free energy surface with the FRET efficiency as a reaction coordinate proxy. We then perform diffusive kinetic simulations on surfaces with two minima and a barrier, but with different shapes (curvatures, barrier height, and symmetry), coupled to stochastic simulations of photon emissions that reproduce current SM-FRET experiments. From the analysis of transition paths, we find that the TPT is inversely proportional to the barrier height (difference in free energy between minimum and barrier top) for any given surface shape, and that dividing the TPT into climb and descent segments provides key information about the barrier's symmetry. We also find that the original MLA-PT procedure used to determine the TPT from experiments underestimates its value, particularly for the cases with smaller barriers (e.g., fast folders), and we suggest a simple strategy to correct for this bias. Importantly, we also demonstrate that photon trajectories contain enough information to extract the 1D free energy surface's shape and dynamics (if TPT is >4-5-fold longer than the interphoton time) using the MLA-PT directly implemented with a diffusive free energy surface model. When dealing with real (unknown) experimental data, the comparison between the likelihoods of the free energy surface and discrete kinetic three-state models can be used to evaluate the statistical significance of the estimated free energy surface.
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Affiliation(s)
- Nivin Mothi
- NSF-CREST Center for Cellular and Biomolecular Machines (CCBM), University of California, Merced, 95343 California, United States.,Chemistry and Chemical Biology Graduate Program, University of California, Merced, 95343 California, United States
| | - Victor Muñoz
- NSF-CREST Center for Cellular and Biomolecular Machines (CCBM), University of California, Merced, 95343 California, United States.,Chemistry and Chemical Biology Graduate Program, University of California, Merced, 95343 California, United States.,Department of Bioengineering, University of California, Merced, 95343 California, United States
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28
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Abstract
Flexibility in complexes between intrinsically disordered proteins and folded ligands is widespread in nature. However, timescales and spatial amplitudes of such dynamics remained unexplored for most systems. Our results show that the disordered cytoplasmic tail of the cell adhesion protein E-cadherin diffuses across the entire surface of its folded binding partner β-catenin at fast submillisecond timescales. The nanometer amplitude of these motions could allow kinases to access their recognition motifs without requiring a dissociation of the complex. We expect that the rugged energy landscape found in the E-cadherin/β-catenin complex is a defining feature of dynamic and partially disordered protein complexes. Intrinsically disordered proteins often form dynamic complexes with their ligands. Yet, the speed and amplitude of these motions are hidden in classical binding kinetics. Here, we directly measure the dynamics in an exceptionally mobile, high-affinity complex. We show that the disordered tail of the cell adhesion protein E-cadherin dynamically samples a large surface area of the protooncogene β-catenin. Single-molecule experiments and molecular simulations resolve these motions with high resolution in space and time. Contacts break and form within hundreds of microseconds without a dissociation of the complex. The energy landscape of this complex is rugged with many small barriers (3 to 4 kBT) and reconciles specificity, high affinity, and extreme disorder. A few persistent contacts provide specificity, whereas unspecific interactions boost affinity.
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29
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Bartels K, Lasitza‐Male T, Hofmann H, Löw C. Single-Molecule FRET of Membrane Transport Proteins. Chembiochem 2021; 22:2657-2671. [PMID: 33945656 PMCID: PMC8453700 DOI: 10.1002/cbic.202100106] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 05/03/2021] [Indexed: 12/31/2022]
Abstract
Uncovering the structure and function of biomolecules is a fundamental goal in structural biology. Membrane-embedded transport proteins are ubiquitous in all kingdoms of life. Despite structural flexibility, their mechanisms are typically studied by ensemble biochemical methods or by static high-resolution structures, which complicate a detailed understanding of their dynamics. Here, we review the recent progress of single molecule Förster Resonance Energy Transfer (smFRET) in determining mechanisms and timescales of substrate transport across membranes. These studies do not only demonstrate the versatility and suitability of state-of-the-art smFRET tools for studying membrane transport proteins but they also highlight the importance of membrane mimicking environments in preserving the function of these proteins. The current achievements advance our understanding of transport mechanisms and have the potential to facilitate future progress in drug design.
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Affiliation(s)
- Kim Bartels
- Centre for Structural Systems Biology (CSSB)DESY and European Molecular Biology Laboratory HamburgNotkestrasse 8522607HamburgGermany
| | - Tanya Lasitza‐Male
- Department of Structural BiologyWeizmann Institute of ScienceHerzl St. 2347610001RehovotIsrael
| | - Hagen Hofmann
- Department of Structural BiologyWeizmann Institute of ScienceHerzl St. 2347610001RehovotIsrael
| | - Christian Löw
- Centre for Structural Systems Biology (CSSB)DESY and European Molecular Biology Laboratory HamburgNotkestrasse 8522607HamburgGermany
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30
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Alston JJ, Soranno A, Holehouse AS. Integrating single-molecule spectroscopy and simulations for the study of intrinsically disordered proteins. Methods 2021; 193:116-135. [PMID: 33831596 PMCID: PMC8713295 DOI: 10.1016/j.ymeth.2021.03.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 03/25/2021] [Accepted: 03/31/2021] [Indexed: 12/21/2022] Open
Abstract
Over the last two decades, intrinsically disordered proteins and protein regions (IDRs) have emerged from a niche corner of biophysics to be recognized as essential drivers of cellular function. Various techniques have provided fundamental insight into the function and dysfunction of IDRs. Among these techniques, single-molecule fluorescence spectroscopy and molecular simulations have played a major role in shaping our modern understanding of the sequence-encoded conformational behavior of disordered proteins. While both techniques are frequently used in isolation, when combined they offer synergistic and complementary information that can help uncover complex molecular details. Here we offer an overview of single-molecule fluorescence spectroscopy and molecular simulations in the context of studying disordered proteins. We discuss the various means in which simulations and single-molecule spectroscopy can be integrated, and consider a number of studies in which this integration has uncovered biological and biophysical mechanisms.
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Affiliation(s)
- Jhullian J Alston
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis 63110, MO, USA; Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis 63130, MO, USA
| | - Andrea Soranno
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis 63110, MO, USA; Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis 63130, MO, USA.
| | - Alex S Holehouse
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis 63110, MO, USA; Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis 63130, MO, USA.
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31
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Nicholson DA, Nesbitt DJ. Pushing Camera-Based Single-Molecule Kinetic Measurements to the Frame Acquisition Limit with Stroboscopic smFRET. J Phys Chem B 2021; 125:6080-6089. [PMID: 34097408 DOI: 10.1021/acs.jpcb.1c01036] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Single-molecule fluorescence resonance energy transfer (smFRET) experiments permit detailed examination of microscopic dynamics. However, kinetic rate constants determined by smFRET are susceptible to systematic underestimation when the rate constants are comparable to the data acquisition rate. We demonstrate how such systematic errors in camera-based total internal reflection fluorescence microscopy experiments can be greatly reduced by using stroboscopic illumination/detection, allowing accurate rate constant determination up to the data sampling rate and yielding an order of magnitude increase in the dynamic range. Implementation of these stroboscopic smFRET ideas is straightforward, and the stroboscopically obtained data are compatible with multiple trajectory analysis methods, including dwell-time analysis and hidden Markov modeling. Such stroboscopic methods therefore offer a remarkably simple yet valuable addition to the smFRET toolkit, requiring only relatively modest modification to the normal data collection and analysis procedures.
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Affiliation(s)
- David A Nicholson
- National Institute of Standards and Technology and University of Colorado, JILA, Boulder, Colorado 80309, United States.,Department of Chemistry, University of Colorado, Boulder, Colorado 80309, United States
| | - David J Nesbitt
- National Institute of Standards and Technology and University of Colorado, JILA, Boulder, Colorado 80309, United States.,Department of Chemistry, University of Colorado, Boulder, Colorado 80309, United States.,Department of Physics, University of Colorado, Boulder, Colorado 80309, United States
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32
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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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33
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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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Abstract
Modern experimental kinetics of protein folding began in the early 1990s with the introduction of nanosecond laser pulses to trigger the folding reaction, providing an almost 106-fold improvement in time resolution over the stopped-flow method being employed at the time. These experiments marked the beginning of the "fast-folding" subfield that enabled investigation of the kinetics of formation of secondary structural elements and disordered loops for the first time, as well as the fastest folding proteins. When I started to work on this subject, a fast folding protein was one that folded in milliseconds. There were, moreover, no analytical theoretical models and no atomistic or coarse-grained molecular dynamics simulations to describe the mechanism. Two of the most important discoveries from my lab since then are a protein that folds in hundreds of nanoseconds, as determined from nanosecond laser temperature experiments, and the discovery that the theoretically predicted barrier crossing time is about the same for proteins that differ in folding rates by 104-fold, as determined from single molecule fluorescence measurements. We also developed what has been called the "Hückel model" of protein folding, which quantitatively explains a wide range of equilibrium and kinetic measurements. This retrospective traces the history of contributions to the "fast folding" subfield from my lab until about 3 years ago, when I left protein folding to spend the rest of my research career trying to discover an inexpensive drug for treating sickle cell disease.
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Affiliation(s)
- William A Eaton
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892-0520, United States
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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: 125] [Impact Index Per Article: 41.7] [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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Taumoefolau GH, Best RB. Estimating transition path times and shapes from single-molecule photon trajectories: A simulation analysis. J Chem Phys 2021; 154:115101. [PMID: 33752373 DOI: 10.1063/5.0040949] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
In a two-state molecular system, transition paths comprise the portions of trajectories during which the system transits from one stable state to the other. Because of their low population, it is essentially impossible to obtain information on transition paths from experiments on a large sample of molecules. However, single-molecule experiments such as laser optical tweezers or Förster resonance energy transfer (FRET) spectroscopy have allowed transition-path durations to be estimated. Here, we use molecular simulations to test the methodology for obtaining information on transition paths in single-molecule FRET by generating photon trajectories from the distance trajectories obtained in the simulation. Encouragingly, we find that this maximum likelihood analysis yields transition-path times within a factor of 2-4 of the values estimated using a good coordinate for folding, but tends to systematically underestimate them. The underestimation can be attributed partly to the fact that the large changes in the end-end distance occur mostly early in a folding trajectory. However, even if the transfer efficiency is a good reaction coordinate for folding, the assumption that the transition-path shape is a step function still leads to an underestimation of the transition-path time as defined here. We find that allowing more flexibility in the form of the transition path model allows more accurate transition-path times to be extracted and points the way toward further improvements in methods for estimating transition-path time and transition-path shape.
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Affiliation(s)
- Grace H Taumoefolau
- Laboratory of Biophotonics and Quantum Biology, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, Maryland 20852, USA
| | - Robert B Best
- Laboratory of Chemical Physics, National Institute for Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892-0520, USA
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Sottini A, Borgia A, Borgia MB, Bugge K, Nettels D, Chowdhury A, Heidarsson PO, Zosel F, Best RB, Kragelund BB, Schuler B. Polyelectrolyte interactions enable rapid association and dissociation in high-affinity disordered protein complexes. Nat Commun 2020; 11:5736. [PMID: 33184256 PMCID: PMC7661507 DOI: 10.1038/s41467-020-18859-x] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 09/14/2020] [Indexed: 02/07/2023] Open
Abstract
Highly charged intrinsically disordered proteins can form complexes with very high affinity in which both binding partners fully retain their disorder and dynamics, exemplified by the positively charged linker histone H1.0 and its chaperone, the negatively charged prothymosin α. Their interaction exhibits another surprising feature: The association/dissociation kinetics switch from slow two-state-like exchange at low protein concentrations to fast exchange at higher, physiologically relevant concentrations. Here we show that this change in mechanism can be explained by the formation of transient ternary complexes favored at high protein concentrations that accelerate the exchange between bound and unbound populations by orders of magnitude. Molecular simulations show how the extreme disorder in such polyelectrolyte complexes facilitates (i) diffusion-limited binding, (ii) transient ternary complex formation, and (iii) fast exchange of monomers by competitive substitution, which together enable rapid kinetics. Biological polyelectrolytes thus have the potential to keep regulatory networks highly responsive even for interactions with extremely high affinities.
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Affiliation(s)
- Andrea Sottini
- Department of Biochemistry, University of Zurich, Zurich, Switzerland
| | - Alessandro Borgia
- Department of Biochemistry, University of Zurich, Zurich, Switzerland
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Madeleine B Borgia
- Department of Biochemistry, University of Zurich, Zurich, Switzerland
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Katrine Bugge
- Structural Biology and NMR Laboratory (SBiNLab) and REPIN, Department of Biology, Ole Maaloes Vej 5, University of Copenhagen, 2200, Copenhagen, Denmark
| | - Daniel Nettels
- Department of Biochemistry, University of Zurich, Zurich, Switzerland
| | - Aritra Chowdhury
- Department of Biochemistry, University of Zurich, Zurich, Switzerland
| | - Pétur O Heidarsson
- Department of Biochemistry, University of Zurich, Zurich, Switzerland
- Department of Biochemistry, Science Institute, University of Iceland, Dunhagi 3, 107, Reykjavík, Iceland
| | - Franziska Zosel
- Department of Biochemistry, University of Zurich, Zurich, Switzerland
- Novo Nordisk A/S, Novo Nordisk Park, 2760, Måløv, Denmark
| | - Robert B Best
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892-0520, USA.
| | - Birthe B Kragelund
- Structural Biology and NMR Laboratory (SBiNLab) and REPIN, Department of Biology, Ole Maaloes Vej 5, University of Copenhagen, 2200, Copenhagen, Denmark.
| | - Benjamin Schuler
- Department of Biochemistry, University of Zurich, Zurich, Switzerland.
- Department of Physics, University of Zurich, Zurich, Switzerland.
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Broad distributions of transition-path times are fingerprints of multidimensionality of the underlying free energy landscapes. Proc Natl Acad Sci U S A 2020; 117:27116-27123. [PMID: 33087575 DOI: 10.1073/pnas.2008307117] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Recent single-molecule experiments have observed transition paths, i.e., brief events where molecules (particularly biomolecules) are caught in the act of surmounting activation barriers. Such measurements offer unprecedented mechanistic insights into the dynamics of biomolecular folding and binding, molecular machines, and biological membrane channels. A key challenge to these studies is to infer the complex details of the multidimensional energy landscape traversed by the transition paths from inherently low-dimensional experimental signals. A common minimalist model attempting to do so is that of one-dimensional diffusion along a reaction coordinate, yet its validity has been called into question. Here, we show that the distribution of the transition path time, which is a common experimental observable, can be used to differentiate between the dynamics described by models of one-dimensional diffusion from the dynamics in which multidimensionality is essential. Specifically, we prove that the coefficient of variation obtained from this distribution cannot possibly exceed 1 for any one-dimensional diffusive model, no matter how rugged its underlying free energy landscape is: In other words, this distribution cannot be broader than the single-exponential one. Thus, a coefficient of variation exceeding 1 is a fingerprint of multidimensional dynamics. Analysis of transition paths in atomistic simulations of proteins shows that this coefficient often exceeds 1, signifying essential multidimensionality of those systems.
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Szabó Á, Szendi-Szatmári T, Szöllősi J, Nagy P. Quo vadis FRET? Förster's method in the era of superresolution. Methods Appl Fluoresc 2020; 8:032003. [PMID: 32521530 DOI: 10.1088/2050-6120/ab9b72] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Although the theoretical foundations of Förster resonance energy transfer (FRET) were laid in the 1940s as part of the quantum physical revolution of the 20th century, it was only in the 1970s that it made its way to biology as a result of the availability of suitable measuring and labeling technologies. Thanks to its ease of application, FRET became widely used for studying molecular associations on the nanometer scale. The development of superresolution techniques at the turn of the millennium promised an unprecedented insight into the structure and function of molecular complexes. Without downplaying the significance of superresolution microscopies this review expresses our view that FRET is still a legitimate tool in the armamentarium of biologists for studying molecular associations since it offers distinct advantages and overcomes certain limitations of superresolution approaches.
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Affiliation(s)
- Ágnes Szabó
- Department of Biophysics and Cell Biology, Faculty of Medicine, University of Debrecen, Egyetem square 1, 4032 Debrecen, Hungary. MTA-DE Cell Biology and Signaling Research Group, Faculty of Medicine, University of Debrecen, Egyetem square 1, 4032 Debrecen, Hungary
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40
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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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41
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Fast three-color single-molecule FRET using statistical inference. Nat Commun 2020; 11:3336. [PMID: 32620782 PMCID: PMC7335206 DOI: 10.1038/s41467-020-17149-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 06/11/2020] [Indexed: 12/21/2022] Open
Abstract
We describe theory, experiments, and analyses of three-color Förster resonance energy transfer (FRET) spectroscopy for probing sub-millisecond conformational dynamics of protein folding and binding of disordered proteins. We devise a scheme that uses single continuous-wave laser excitation of the donor instead of alternating excitation of the donor and one of the acceptors. This scheme alleviates photophysical problems of acceptors such as rapid photobleaching, which is crucial for high time resolution experiments with elevated illumination intensity. Our method exploits the molecular species with one of the acceptors absent or photobleached, from which two-color FRET data is collected in the same experiment. We show that three FRET efficiencies and kinetic parameters can be determined without alternating excitation from a global maximum likelihood analysis of two-color and three-color photon trajectories. We implement co-parallelization of CPU-GPU processing, which leads to a significant reduction of the likelihood calculation time for efficient parameter determination.
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Kim JY, Chung HS. Disordered proteins follow diverse transition paths as they fold and bind to a partner. Science 2020; 368:1253-1257. [PMID: 32527832 DOI: 10.1126/science.aba3854] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 04/10/2020] [Indexed: 01/06/2023]
Abstract
Transition paths of macromolecular conformational changes such as protein folding are predicted to be heterogeneous. However, experimental characterization of the diversity of transition paths is extremely challenging because it requires measuring more than one distance during individual transitions. In this work, we used fast three-color single-molecule Förster resonance energy transfer spectroscopy to obtain the distribution of binding transition paths of a disordered protein. About half of the transitions follow a path involving strong non-native electrostatic interactions, resulting in a transition time of 300 to 800 microseconds. The remaining half follow more diverse paths characterized by weaker electrostatic interactions and more than 10 times shorter transition path times. The chain flexibility and non-native interactions make diverse binding pathways possible, allowing disordered proteins to bind faster than folded proteins.
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Affiliation(s)
- Jae-Yeol Kim
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Hoi Sung Chung
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
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Depletion interactions modulate the binding between disordered proteins in crowded environments. Proc Natl Acad Sci U S A 2020; 117:13480-13489. [PMID: 32487732 PMCID: PMC7306994 DOI: 10.1073/pnas.1921617117] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
The molecular environment in a biological cell is much more crowded than the conditions commonly used in biochemical and biophysical experiments in vitro. It is therefore important to understand how the conformations and interactions of biological macromolecules are affected by such crowding. Addressing these questions quantitatively, however, has been challenging owing to a lack of sufficiently detailed experimental information and theoretical concepts suitable for describing crowding, especially when polymeric crowding agents and biomolecules are involved. Here, we use the combination of extensive single-molecule experiments with established and recent theoretical concepts to investigate the interaction between two intrinsically disordered proteins. We observe pronounced effects of crowding on their interactions and provide a quantitative framework for rationalizing these effects. Intrinsically disordered proteins (IDPs) abound in cellular regulation. Their interactions are often transitory and highly sensitive to salt concentration and posttranslational modifications. However, little is known about the effect of macromolecular crowding on the interactions of IDPs with their cellular targets. Here, we investigate the influence of crowding on the interaction between two IDPs that fold upon binding, with polyethylene glycol as a crowding agent. Single-molecule spectroscopy allows us to quantify the effects of crowding on a comprehensive set of observables simultaneously: the equilibrium stability of the complex, the association and dissociation kinetics, and the microviscosity, which governs translational diffusion. We show that a quantitative and coherent explanation of all observables is possible within the framework of depletion interactions if the polymeric nature of IDPs and crowders is incorporated based on recent theoretical developments. The resulting integrated framework can also rationalize important functional consequences, for example, that the interaction between the two IDPs is less enhanced by crowding than expected for folded proteins of the same size.
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44
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Integrating Non-NMR Distance Restraints to Augment NMR Depiction of Protein Structure and Dynamics. J Mol Biol 2020; 432:2913-2929. [DOI: 10.1016/j.jmb.2020.01.023] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 01/17/2020] [Accepted: 01/17/2020] [Indexed: 11/24/2022]
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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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Tavakoli M, Jazani S, Sgouralis I, Shafraz OM, Sivasankar S, Donaphon B, Levitus M, Pressé S. Pitching single-focus confocal data analysis one photon at a time with Bayesian nonparametrics. PHYSICAL REVIEW. X 2020; 10:011021. [PMID: 34540355 PMCID: PMC8445401 DOI: 10.1103/physrevx.10.011021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Fluorescence time traces are used to report on dynamical properties of molecules. The basic unit of information in these traces is the arrival time of individual photons, which carry instantaneous information from the molecule, from which they are emitted, to the detector on timescales as fast as microseconds. Thus, it is theoretically possible to monitor molecular dynamics at such timescales from traces containing only a sufficient number of photon arrivals. In practice, however, traces are stochastic and in order to deduce dynamical information through traditional means-such as fluorescence correlation spectroscopy (FCS) and related techniques-they are collected and temporally autocorrelated over several minutes. So far, it has been impossible to analyze dynamical properties of molecules on timescales approaching data acquisition without collecting long traces under the strong assumption of stationarity of the process under observation or assumptions required for the analytic derivation of a correlation function. To avoid these assumptions, we would otherwise need to estimate the instantaneous number of molecules emitting photons and their positions within the confocal volume. As the number of molecules in a typical experiment is unknown, this problem demands that we abandon the conventional analysis paradigm. Here, we exploit Bayesian nonparametrics that allow us to obtain, in a principled fashion, estimates of the same quantities as FCS but from the direct analysis of traces of photon arrivals that are significantly smaller in size, or total duration, than those required by FCS.
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Affiliation(s)
- Meysam Tavakoli
- Department of Physics, Indiana University-Purdue University Indianapolis, IN 46202
| | - Sina Jazani
- Center for Biological Physics, Department of Physics, Arizona State University, Tempe, AZ 85287
| | - Ioannis Sgouralis
- Center for Biological Physics, Department of Physics, Arizona State University, Tempe, AZ 85287
| | - Omer M. Shafraz
- Department of Biomedical Engineering, University of California, Davis, CA 95616
| | - Sanjeevi Sivasankar
- Department of Biomedical Engineering, University of California, Davis, CA 95616
| | - Bryan Donaphon
- Biodesign Institute, Arizona State University, Tempe, AZ 85287
| | - Marcia Levitus
- Center for Biological Physics, Department of Physics, Arizona State University, Tempe, AZ 85287
- Biodesign Institute, Arizona State University, Tempe, AZ 85287 and School of Molecular Sciences, Arizona State University, Tempe, AZ 85287
| | - Steve Pressé
- Corresponding author. ; Website: http://statphysbio.physics.asu.edu
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Ozmaian M, Makarov DE. Transition path dynamics in the binding of intrinsically disordered proteins: A simulation study. J Chem Phys 2019; 151:235101. [PMID: 31864244 DOI: 10.1063/1.5129150] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Association of proteins and other biopolymers is a ubiquitous process in living systems. Recent single-molecule measurements probe the dynamics of association in unprecedented detail by measuring the properties of association transition paths, i.e., short segments of molecular trajectories between the time the proteins are close enough to interact and the formation of the final complex. Interpretation of such measurements requires adequate models for describing the dynamics of experimental observables. In an effort to develop such models, here we report a simulation study of the association dynamics of two oppositely charged, disordered polymers. We mimic experimental measurements by monitoring intermonomer distances, which we treat as "experimental reaction coordinates." While the dynamics of the distance between the centers of mass of the molecules is found to be memoryless and diffusive, the dynamics of the experimental reaction coordinates displays significant memory and can be described by a generalized Langevin equation with a memory kernel. We compute the most commonly measured property of transition paths, the distribution of the transition path time, and show that, despite the non-Markovianity of the underlying dynamics, it is well approximated as one-dimensional diffusion in the potential of mean force provided that an apparent value of the diffusion coefficient is used. This apparent value is intermediate between the slow (low frequency) and fast (high frequency) limits of the memory kernel. We have further studied how the mean transition path time depends on the ionic strength and found only weak dependence despite strong electrostatic attraction between the polymers.
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Affiliation(s)
- Masoumeh Ozmaian
- Department of Chemistry, University of Texas at Austin, Austin, Texas 78712, USA
| | - Dmitrii E Makarov
- Department of Chemistry, University of Texas at Austin, Austin, Texas 78712, USA
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Mazal H, Haran G. Single-molecule FRET methods to study the dynamics of proteins at work. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2019; 12:8-17. [PMID: 31989063 PMCID: PMC6984960 DOI: 10.1016/j.cobme.2019.08.007] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Feynman commented that "Everything that living things do can be understood in terms of the jiggling and wiggling of atoms". Proteins can jiggle and wiggle large structural elements such as domains and subunits as part of their functional cycles. Single-molecule fluorescence resonance energy transfer (smFRET) is an excellent tool to study conformational dynamics and decipher coordinated large-scale motions within proteins. smFRET methods introduced in recent years are geared toward understanding the time scales and amplitudes of function-related motions. This review discusses the methodology for obtaining and analyzing smFRET temporal trajectories that provide direct dynamic information on transitions between conformational states. It also introduces correlation methods that are useful for characterizing intramolecular motions. This arsenal of techniques has been used to study multiple molecular systems, from membrane proteins through molecular chaperones, and we examine some of these studies here. Recent exciting methodological novelties permit revealing very fast, submillisecond dynamics, whose relevance to protein function is yet to be fully grasped.
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Affiliation(s)
- Hisham Mazal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Gilad Haran
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot 7610001, Israel
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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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Lee TH. Physical Chemistry of Epigenetics: Single-Molecule Investigations. J Phys Chem B 2019; 123:8351-8362. [PMID: 31404497 PMCID: PMC6790939 DOI: 10.1021/acs.jpcb.9b06214] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Revised: 08/03/2019] [Indexed: 02/06/2023]
Abstract
The nucleosome is the fundamental building block of the eukaryotic genome, composed of an ∼147 base-pair DNA fragment wrapping around an octameric histone protein core. DNA and histone proteins are targets of enzymatic chemical modifications that serve as signals for gene regulation. These modifications are often referred to as epigenetic modifications that govern gene activities without altering the DNA sequence. Although the term epigenetics initially required inheritability, it now frequently includes noninherited histone modifications associated with gene regulation. Important epigenetic modifications for healthy cell growth and proliferation include DNA methylation, histone acetylation, methylation, phosphorylation, ubiquitination, and SUMOylation (SUMO = Small Ubiquitin-like Modifier). Our research focuses on the biophysical roles of these modifications in altering the structure and structural dynamics of the nucleosome and their implications in gene regulation mechanisms. As the changes are subtle and complex, we employ various single-molecule fluorescence approaches for their investigations. Our investigations revealed that these modifications induce changes in the structure and structural dynamics of the nucleosome and their thermodynamic and kinetic stabilities. We also suggested the implications of these changes in gene regulation mechanisms that are the foci of our current and future research.
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Affiliation(s)
- Tae-Hee Lee
- Department of Chemistry, The
Pennsylvania State University, University Park 16803, Pennsylvania, United States
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