FRET-based dynamic structural biology: Challenges, perspectives and an appeal for open-science practices
- 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 Jerusalem, Israel
- Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-Universität, Germany
- Dynamic Bioimaging Lab, Advanced Optical Microscopy Centre and Biomedical Research Institute (BIOMED), Hasselt University, Belgium
- Department of Chemistry, University of Sheffield, United Kingdom
- Department of Chemistry and iNANO center, Aarhus University, Denmark
- Department of Structural Biology, St. Jude Children's Research Hospital, United States
- Laserinstitut HS Mittweida, University of Applied Science Mittweida, Germany
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, United States
- Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Germany
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, United States
- Department of Cancer Biology, University of Cincinnati School of Medicine, United States
- Department of Biochemistry and Molecular Biology and The Institute for Biophysical Dynamics, University of Chicago, United States
- Department of Chemistry, Columbia University, United States
- Department of Biophysics and Biophysical Chemistry, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Howard Hughes Medical Institute, United States
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Israel
- Department of Chemistry & Nanoscience Centre, University of Copenhagen, Denmark
- Denmark Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
- Department of Physics and Astronomy, and Institute of Applied Physics, Seoul National University, Republic of Korea
- Center for Molecular Spectroscopy and Dynamics, Institute for Basic Science and Department of Physics, Korea University, Republic of Korea
- Institute of Physical Chemistry and Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Germany
- Department of Chemistry and Biochemistry, and Department of Physiology, University of California, Los Angeles, United States
- Department of BioNanoScience, Kavli Institute of Nanoscience, Delft University of Technology, Netherlands
- Biological Physics Research Group, Clarendon Laboratory, Department of Physics, University of Oxford, United Kingdom
- School of Physics, Georgia Institute of Technology, United States
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, United States
- School of Chemistry, Seoul National University, Republic of Korea
- Department of Chemistry, Pennsylvania State University, United States
- Departments of Biology and Chemistry, Johannes Gutenberg University, Germany
- Institute of Molecular Biology (IMB), Germany
- Centre de Biologie Structurale (CBS), CNRS, INSERM, Universitié de Montpellier, France
- Institüt of Biophysics, Ulm University, Germany
- Department of Biophysics, Johns Hopkins University, United States
- Department of Biochemistry and Department of Physics, University of Zurich, Switzerland
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States
- Physical Chemistry, Department of Chemistry, Center for Nanoscience (CeNS), Center for Integrated Protein Science Munich (CIPSM) and Nanosystems Initiative Munich (NIM), Ludwig-Maximilians-Universität, Germany
- Warwick Medical School, University of Warwick, United Kingdom
- Biological Optical Microscopy Platform (BOMP), University of Melbourne, Australia
- College of Chemistry and Molecular Engineering, PKU-Tsinghua Center for Life Sciences, Beijing National Laboratory for Molecular Sciences, Peking University, China
- Department of Molecular Biosciences, Northwestern University, United States
- Department of Physiology, CaliforniaNanoSystems Institute, University of California, Los Angeles, United States
Figures
Figure 1
Workflow of modeling dynamic structures from FRET measurements.
(A) Integrative modeling requires structural and dynamic information. Prior information from conventional approaches (X-ray, NMR, cryoEM) together with computational tools defines the space of …
Figure 2
Different smFRET modalities.
(A) Confocal smFRET measurements on freely-diffusing molecules. (i) A schematic of a single-color excitation confocal microscope with point detectors used for two-color detection. The excitation …
Figure 3
Exemplary methods for following smFRET dynamics on different timescales.
Top: Biomolecular dynamics cover a wide range of timescales. Biomolecular rotations occur in the pico- to nanosecond range, while conformational changes take place in nano- to microseconds (ns-µs), …
Figure 4
Exemplary selection of approaches to detect and quantify conformational dynamics in smFRET.
(A) Dynamics in a three-state system are detected using the two-dimensional distribution of the FRET efficiency and donor fluorescence lifetime (Reproduced from Gopich and Szabo, 2012. Further …
Figure 5
Dye models for FRET.
(A) The different kinetic averaging regimes for rotation and diffusion are shown schematically. In the dynamic averaging regime, rotation and diffusion happen on a timescale faster than the FRET …
Figure 6
Approaches to integrative modeling using FRET restraints.
(A) Rigid bodies, representing either different domains of a protein or different proteins within a complex, are arranged by translation and rotation to satisfy the FRET restraints. Adapted from …
Figure 7
Concept for data storage following the FAIR principle.
All essential information should be contained in two data files, one for raw data and a second with the essential information regarding the associated analysis. (Left) Structure of proposed data …
Figure 8
Disseminating the results of smFRET studies.
Recommended categories for data and method-specific information (metadata), which are needed for documentation of smFRET studies where the authors want to archive their obtained kinetic/structural …
Figure 9
Using smFRET to investigate the structure and dynamics of ultrahigh-affinity IDP complexes.
(A) SmFRET efficiency histograms for FRET between a donor label (Alexa488) attached at various positions to the linker histone H1 (shown in blue) with the IDP ProTα (shown in red) labeled at …
Tables
Table 1
List of available software for smFRET analysis.
| Software | Type | Description | URL |
|---|---|---|---|
| ALiX | Confocal | ALiX is developed for basic research on diffusing two-color smFRET in single or multiple spot geometries (Ingargiola et al., 2017). | https://sites.google.com/a/g.ucla.edu/alix/ |
| Fretica | Confocal | Fretica, a Mathematica package with a backend written in C++, is a user-extendable toolbox that supports MFD, PIE/ALEX, PCH (Huang et al., 2004; Müller et al., 2000), FIDA (Gopich and Szabo, 2005b; Kask et al., 1999), PDA (Antonik et al., 2006; Ernst et al., 2020), recurrence analysis (Hoffmann et al., 2011), fluorescence lifetime fitting, FLIM, FCS, FLCS (Arbour and Enderlein, 2010; Dertinger et al., 2007), dual-focus FCS, nsFCS (Nettels et al., 2007; Schuler and Hofmann, 2013), maximum likelihood estimation from photon-by-photon (Gopich and Szabo, 2009) and binned trajectories, simulation of confocal experiments and more (Nettels and Schuler, 2020; https://schuler.bioc.uzh.ch/programs/). | https://schuler.bioc.uzh.ch/programs/ |
| FRET_3colorCW | Confocal | C++ and MATLAB GUI-based CPU-GPU co-parallelization software package for an enhanced maximum likelihood analysis of two- and three-color fluorescence photon trajectories generated by continuous-wave donor excitation (Yoo et al., 2020). | https://github.com/hoisunglab/FRET_3colorCW |
| gSMFRETda | Confocal | gSMFRETda is a GPU-capable program for Monte Carlo simulations of PDA. It can sample dwell time and other parameters in fine grids, thus allowing the analysis of rapid dynamic interconversions (Liu et al., 2019). | https://github.com/liu-kan/gSMFRETda |
| H2MM | Confocal | H2MM is a maximum likelihood estimation algorithm for a photon-by-photon analysis of smFRET experiments (Pirchi et al., 2016). | http://pubs.acs.org/doi/suppl/10.1021/acs.jpcb.6b10726/suppl_file/jp6b10726_si_002.zip |
| MFD Spectroscopy and Imaging | Confocal | A modular software package for confocal fluorescence spectroscopy and imaging experiments using multiparameter fluorescence detection (MFD) with all tools (FCS, fFCS, PDA, seTCSPC, trace analysis, 2D simulation of MFD diagrams, Burbulator) and multiparameter fluorescence image spectroscopy (MFIS) (Antonik et al., 2006; Felekyan et al., 2005; Kühnemuth and Seidel, 2001; Widengren et al., 2006; Felekyan et al., 2020). | https://www.mpc.hhu.de/software/3-software-package-for-mfd-fcs-and-mfis |
| OpenSMFS | Confocal | A collection of tools (Ingargiola et al., 2016b) for solution-based single-molecule fluorescence spectroscopy, including smFRET, FCS, MC-DEPI (Ingargiola et al., 2018b). | https://github.com/OpenSMFS |
| smfBox | Confocal | A confocal smFRET platform, providing build instructions and open-source acquisition software (Ambrose et al., 2020). | https://craggslab.github.io/smfBox/ |
| rFRET | Confocal Imaging | rFRET is a comprehensive, MATLAB-based program for analyzing ratiometric microscopic FRET experiments (Nagy et al., 2016). | https://peternagy.webs.com/fret#rfret |
| ChiSurf | Confocal Imaging Ensemble | ChiSurf is a fluorescence analysis platform for the analysis of time-resolved fluorescence decays (Peulen et al., 2017). | https://github.com/Fluorescence-Tools/ChiSurf/wiki |
| PAM - PIE Analysis with MATLAB | Confocal Imaging Ensemble | PAM (PIE analysis with MATLAB) is a GUI-based software package for the analysis of fluorescence experiments and supports a large number of analysis methods ranging from single-molecule methods to imaging (Schrimpf et al., 2018; Barth et al., 2020). | RRID:SCR_020966, https://www.cup.uni-muenchen.de/pc/lamb/software/pam.html |
| AutoSiM | TIRF | AutoSiM is a deep-learning developed MATLAB program for automatically selecting and sorting smFRET traces (Li et al., 2020a). | https://doi.org/10.7302/ck2m-qf69 |
| BIASD | TIRF | BIASD uses Bayesian inference to infer transition rates that are more than three orders of magnitude larger than the acquisition rate of the experimental smFRET data (Kinz-Thompson and Gonzalez, 2018). | http://github.com/ckinzthompson/biasd |
| DeepFRET | TIRF | smFRET software based on deep-learning for automatic trace selection and classification. It includes all common features: image analysis, background-corrected trace-extraction, hidden Markov analysis, correction factor application, and data visualization (Thomsen et al., 2020). | https://github.com/hatzakislab/DeepFRET-GUI |
| ebFRET | TIRF | ebFRET performs combined analysis on multiple smFRET time-series to learn a set of rates and states (van de Meent et al., 2014). | https://ebfret.github.io/ |
| FRETboard | TIRF | smFRET data preprocessing and analysis using algorithms of choice and user supervision. Also offered as a web-based user interface (de Lannoy et al., 2020). | https://github.com/cvdelannoy/FRETboard |
| HaMMy | TIRF | smFRET analysis and hidden Markov modeling (McKinney et al., 2006). | http://ha.med.jhmi.edu/resources/ |
| hFRET | TIRF | hFRET uses variational Bayesian inference to estimate the parameters of a hierarchical hidden Markov model, thereby enabling robust identification and characterization of kinetic heterogeneity (Hon and Gonzalez, 2019). | https://github.com/GonzalezBiophysicsLab/hFRET |
| iSMS | TIRF | iSMS is a user-interfaced software package for smFRET data analysis. It includes extraction of time-traces from movies, traces grouping/selection tools according to defined criteria, application of corrections, data visualization and analysis with hidden Markov modeling, and import/export possibilities in different formats for data-sharing (Preus et al., 2015; Preuss et al., 2020). | http://isms.au.dk/ |
| MASH-FRET | TIRF | MASH-FRET is a MATLAB-based software package for the simulation (Börner et al., 2018) and analysis of single-molecule FRET videos and trajectories (video processing [Hadzic et al., 2016], histogram analysis [König et al., 2013], and transitions analysis [Hadzic et al., 2018; König et al., 2013]). | https://rna-fretools.github.io/MASH-FRET/ |
| miCUBE | TIRF | TIRF smFRET platform, providing detailed build instructions and open-source acquisition software (Martens et al., 2019). | https://hohlbeinlab.github.io/miCube/index.html |
| SMACKS | TIRF | SMACKS (single-molecule analysis of complex kinetic sequences) is a maximum-likelihood approach to extract kinetic rate models from noisy single-molecule data (Schmid et al., 2016). | https://www.singlemolecule.uni-freiburg.de/software/smacks |
| smCamera | TIRF | smFRET data acquisition (Windows. exe) and analysis (IDL, MATLAB) with example data (Roy et al., 2008). | http://ha.med.jhmi.edu/resources/ |
| SPARTAN | TIRF | Automated analysis of smFRET multiple single-molecule recordings. It includes extraction of traces from movies, trace selection according to defined criteria, application of corrections, hidden Markov modeling, simulations, and data visualization (Juette et al., 2016). | https://www.scottcblanchardlab.com/software |
| STaSI | TIRF | STaSI uses the Student’s t-test and groups the segments into states by hierarchical clustering (Shuang et al., 2014). | https://github.com/LandesLab/STaSI |
| TwoTone | TIRF | A TIRF-FRET analysis package for the automatic analysis of single-molecule FRET movies (Holden et al., 2011). | https://groups.physics.ox.ac.uk/genemachines/group/Main.Software.html |
| vbFRET | TIRF | vbFRET uses variational Bayesian inference to learn hidden Markov models from individual, smFRET time trajectories (Bronson et al., 2009). | http://www.columbia.edu/cu/chemistry/groups/gonzalez/software.html |
| Fast NPS | Modeling | A nano-positioning system for macromolecular structural analysis (Eilert et al., 2017). | http://dx.doi.org/10.17632/7ztzj63r68.1 |
| Fluordynamics | Modeling | Fluordynamics is a PyMOL plugin to label biomolecules with organic fluorophores for all-atom molecular dynamics simulations. It builds on AMBERDYES (Schepers and Gohlke, 2020) and extends the force field to common nucleic acid linker chemistries (Steffen et al., 2016). | https://github.com/RNA-FRETools/fluordynamics |
| FPS | Modeling | A toolkit for FRET restrained modeling of biomolecules and their complexes for quantitative applications in structural biology (Kalinin et al., 2012). | https://www.mpc.hhu.de/software/1-fret-positioning-and-screening-fps |
| FRETraj | Modeling | FRETraj is a Python API to the LabelLib package, which integrates into PyMOL to interactively calculate accessible-contact volumes and predict FRET efficiencies (Steffen et al., 2016). | https://github.com/RNA-FRETools/fretraj |
| FRETrest in Amber20 | Modeling | FRETrest is a set of helper scripts for generating FRET-restraints for Molecular Dynamics (MD) simulations performed with the AMBER Software Suite (Dimura et al., 2020). | http://ambermd.org/doc12/Amber20.pdf |
| LabelLib | Modeling | LabelLib is a C++ library for the simulation of the accessible volume (AV) of small probes flexibly coupled to biomolecules (Dimura et al., 2016; Kalinin et al., 2012). | https://github.com/Fluorescence-Tools/LabelLib |
Table 2
A list of repositories for citable data storage.
| Repository name | URL | Size limits | Fee/Costs |
|---|---|---|---|
| Dryad Digital Repository | https://datadryad.org/stash | No | $120 USD for first 20 GB, and $50 USD for each additional 10 GB |
| figshare | https://figshare.com/ | 1 TB per dataset | It varies with publishers |
| Harvard Dataverse | https://dataverse.harvard.edu/ | 2.5 GB per file, 10 GB per dataset | Free |
| Open Science Framework | https://osf.io/ | 5 GB per file, multiple files can be uploaded | Free |
| Zenodo | https://zenodo.org/ | 50 GB per dataset | Donation |
| Mendeley Data | https://data.mendeley.com/ | 10 GB per dataset | Free |
