FRET-based dynamic structural biology: Challenges, perspectives and an appeal for open-science practices

Maximum Likelihood Analysis of Diffusing Molecules with Conformational Dynamics in Single-Molecule FRET

In single-molecule Förster resonance energy transfer (FRET) experiments, characterizing conformational dynamics from photon bursts emitted by diffusing molecules can be challenging due to the interplay of molecular transitions, translational diffusion, and background noise. This paper extends the maximum likelihood analysis of photon bursts (burstML) to incorporate both conformational dynamics and diffusion through the laser spot, offering a comprehensive analysis of photon bursts from single diffusing molecules. The new approach integrates two previously developed methods: one accounting for diffusion without conformational dynamics and the other addressing conformational dynamics without diffusion. By combining these approaches, the extended burstML method allows determination of brightness, diffusion time, FRET efficiency in each state, and transition rates, even under challenging conditions, such as fast (comparable to photon count rates) and slow (one transition per several bursts) transition rates, high background noise, and unequal brightness or diffusivity of the states. The performance of burstML was demonstrated on simulated data of a two-state diffusing molecule and compared with the colorML method, which simplifies analysis by excluding translational diffusion. While colorML is computationally efficient and performs well under ideal conditions (low background noise and equal brightness and diffusivity of states), its accuracy diminishes when these conditions are not met. In contrast, burstML remains accurate across a broader range of experimental scenarios. Both burstML and colorML were applied to analyze folding of several proteins (Pin1 WW domain, FiP35 WW domain, FBP28 WW domain, villin, and a synthetic protein α3D) under various experimental conditions, highlighting where colorML differs from burstML and providing insights into the applicability of the methods in diverse experimental settings.

An oxygen-scavenging system without impact on DNA mechanical properties in single-molecule fluorescence experiments

Oxygen scavenging systems (OSSs) are critical for dye stability in single-molecule fluorescence (SMF) experiments. However, the commonly used protocatechuic acid (PCA)/protocatechuate-3,4-dioxygenase (PCD) OSS alters DNA mechanical properties, limiting its applicability. To address this limitation, we examine the bilirubin oxidase (BOD) OSS, which had not been previously used in single-molecule experiments, alongside the pyranose oxidase and catalase (POC) OSS. Our results revealed that POC OSS affected DNA mechanics in a buffer-dependent manner, while BOD OSS had no discernible effect across all tested buffer conditions. Furthermore, BOD OSS significantly extended the photobleaching lifetimes of Cy3 and Cy5 dyes and caused minimal pH changes compared to PCD OSS. Collectively, these findings highlight the superior performance of BOD OSS, suggesting its potential for widespread application, particularly in experiments combining SMF with single-molecule force spectroscopy (SMFS) measurements.

Watching lanthanide nanoparticles one at a time: characterization of their photoluminescence dynamics at the single nanoparticle level

Lanthanide nanoparticles (LnNPs) feature sharp emission lines together with millisecond emission lifetimes which make them promising luminescent probes for biosensing and bioimaging. Although LnNPs are attracting much interest, their photoluminescence properties at the single nanoparticle level remain largely unexplored. Here, we employ fluorescence correlation spectroscopy (FCS) and photoluminescence burst analysis to investigate the photodynamics of Sm- and Eu-based LnNPs with single nanoparticle sensitivity and microsecond resolution. By recording the photoluminescence intensity and the number of contributing LnNPs, we compute the photoluminescence brightness per individual nanoparticle and estimate the actual number of emitting centers per nanoparticle. Our approach overcomes the challenges associated with ensemble-averaged techniques and provides insights into LnNP photodynamics. Moreover, we demonstrate our microscope's ability to detect and analyze LnNPs at the single nanoparticle level, monitoring both photoluminescence brightness and burst duration. These findings expand our understanding of LnNPs and pave the way for advanced biosensing applications at the single nanoparticle level.

Determination of absolute intramolecular distances in proteins using anomalous X-ray scattering interferometry

Biomolecular structures are typically determined using frozen or crystalline samples. Measurement of intramolecular distances in solution can provide additional insights into conformational heterogeneity and dynamics of biological macromolecules and their complexes. The established molecular ruler techniques used for this (NMR, FRET, and EPR) are, however, limited in their dynamic range and require model assumptions to determine absolute distance or distance distributions. Here, we introduce anomalous X-ray scattering interferometry (AXSI) for intramolecular distance measurements in proteins, which are labeled at two sites with small gold nanoparticles of 0.7 nm radius. We apply AXSI to two different cysteine-variants of maltose binding protein in the presence and absence of its ligand maltose and find distances in quantitative agreement with single-molecule FRET experiments. Our study shows that AXSI enables determination of intramolecular distance distributions under virtually arbitrary solution conditions and we anticipate its broad use to characterize protein conformational ensembles and dynamics.

tttrlib: modular software for integrating fluorescence spectroscopy, imaging, and molecular modeling

SUMMARY

We introduce software for reading, writing and processing fluorescence single-molecule and image spectroscopy data and developing analysis pipelines to unify various spectroscopic analysis tools. Our software can be used for processing multiple experiment types, e.g. for time-resolved single-molecule spectroscopy, laser scanning microscopy, fluorescence correlation spectroscopy and image correlation spectroscopy. The software is file format agnostic and processes multiple time-resolved data formats and outputs. Our software eliminates the need for data conversion and mitigates data archiving issues.

AVAILABILITY AND IMPLEMENTATION

tttrlib is available via pip (https://pypi.org/project/tttrlib/) and bioconda while the open-source code is available via GitHub (https://github.com/fluorescence-tools/tttrlib). Presented examples and additional documentation demonstrating how to implement in vitro and live-cell image spectroscopy analysis are available at https://docs.peulen.xyz/tttrlib and https://zenodo.org/records/14002224.

Pretrained Deep Neural Network Kin-SiM for Single-Molecule FRET Trace Idealization

Single-molecule fluorescence resonance energy transfer (smFRET) has emerged as a pivotal technique for probing biomolecular dynamics over time at nanometer scales. Quantitative analyses of smFRET time traces remain challenging due to confounding factors such as low signal-to-noise ratios, photophysical effects such as bleaching and blinking, and the complexity of modeling the underlying biomolecular states and kinetics. The dynamic distance information shaping the smFRET trace powerfully uncovers even transient conformational changes in single biomolecules both at or far from equilibrium, relying on trace idealization to identify specific interconverting states. Conventional trace idealization methods based on hidden Markov models (HMMs) require substantial a priori knowledge of the system under study, manual intervention, and assumptions about the number of states and transition probabilities. Here, we present a deep learning framework using long short-term memory (LSTM) to automate the trace idealization, termed Kin-SiM. Our approach employs neural networks pretrained on simulated data to learn high-order correlations in the multidimensional FRET trajectories. Without user input of Markovian assumptions, the trained LSTM networks directly idealize the FRET traces to extract the number of underlying biomolecular states, their interstate dynamics, and associated kinetic parameters. On benchmark smFRET data sets, Kin-SiM achieves a performance similar to conventional HMM-based methods but with less hands-on time and lower risk of bias. We further systematically evaluate the key training factors that affect network performance to define the correct hyperparameter tuning for applying deep neural networks to smFRET data analyses.

Toward measurements of absolute membrane potential in Bacillus subtilis using fluorescence lifetime

Membrane potential (MP) changes can provide a simple readout of bacterial functional and metabolic state or stress levels. While several optical methods exist for measuring fast changes in MP in excitable cells, there is a dearth of such methods for absolute and precise measurements of steady-state MPs in bacterial cells. Conventional electrode-based methods for the measurement of MP are not suitable for calibrating optical methods in small bacterial cells. While optical measurement based on Nernstian indicators have been successfully used, they do not provide absolute or precise quantification of MP or its changes. We present a novel, calibrated MP recording approach to address this gap. In this study, we used a fluorescence lifetime-based approach to obtain a single-cell-resolved distribution of the membrane potential and its changes upon extracellular chemical perturbation in a population of bacterial cells for the first time. Our method is based on 1) a unique VoltageFluor (VF) optical transducer, whose fluorescence lifetime varies as a function of MP via photoinduced electron transfer and 2) a quantitative phasor-FLIM analysis for high-throughput readout. This method allows MP changes to be easily visualized, recorded and quantified. By artificially modulating potassium concentration gradients across the membrane using an ionophore, we have obtained a Bacillus subtilis-specific MP versus VF lifetime calibration and estimated the MP for unperturbed B. subtilis cells to be -65 mV (in minimal salts glycerol glutamate [MSgg]), -127 mV (in M9), and that for chemically depolarized cells as -14 mV (in MSgg). We observed a population-level MP heterogeneity of ∼6-10 mV indicating a considerable degree of diversity of physiological and metabolic states among individual cells. Our work paves the way for deeper insights into bacterial electrophysiology and bioelectricity research.

The conserved lysine residue in transmembrane helix 5 is pivotal for the cytoplasmic gating of the L-amino acid transporters

L-Amino acid transporters (LATs) play a key role in a wide range of physiological processes. Defects in LATs can lead to neurological disorders and aminoacidurias, while the overexpression of these transporters is related to cancer. BasC is a bacterial LAT transporter with an APC fold. In this study, to monitor the cytoplasmic motion of BasC, we developed a single-molecule Förster resonance energy transfer assay that can characterize the conformational states of the intracellular gate in solution at room temperature. Based on combined biochemical and biophysical data and molecular dynamics simulations, we propose a model in which the conserved lysine residue in TM5 supports TM1a to explore both open and closed states within the cytoplasmic gate under apo conditions. This equilibrium can be altered by substrates, mutation of conserved lysine 154 in TM5, or a transport-blocking nanobody interacting with TM1a. Overall, these findings provide insights into the transport mechanism of BasC and highlight the significance of the lysine residue in TM5 in the cytoplasmic gating of LATs.

Cellular Function of a Biomolecular Condensate Is Determined by Its Ultrastructure

Biomolecular condensates play key roles in the spatiotemporal regulation of cellular processes. Yet, the relationship between atomic features and condensate function remains poorly understood. We studied this relationship using the polar organizing protein Z (PopZ) as a model system, revealing how its material properties and cellular function depend on its ultrastructure. We revealed PopZ's hierarchical assembly into a filamentous condensate by integrating cryo-electron tomography, biochemistry, single-molecule techniques, and molecular dynamics simulations. The helical domain drives filamentation and condensation, while the disordered domain inhibits them. Phase-dependent conformational changes prevent interfilament contacts in the dilute phase and expose client binding sites in the dense phase. These findings establish a multiscale framework that links molecular interactions and condensate ultrastructure to macroscopic material properties that drive cellular function.

Indocarbocyanine-Indodicarbocyanine (sCy3-sCy5) Absorptive Interactions in Conjugates and DNA Duplexes

Sulfonated indocyanines 3 and 5 (sCy3, sCy5) are widely used to label biomolecules. Their high molar absorption coefficients and lack of spectral overlap with biopolymers make them ideal as linker components for rapid assessment of bioconjugate stoichiometry. We recently found that the determination of the sCy3:sCy5 molar ratio in a conjugate from its optical absorption spectrum is not straightforward, as the sCy3:sCy5 absorbance ratio at the maxima tends to be larger than expected. In this work, we have investigated this phenomenon in detail by studying the spectral properties of a series of sCy3-sCy5 conjugates in which the dyes are separated by linkers of various lengths, including DNA duplexes. It was found that when sCy3 and sCy5 are located in close proximity, they consistently exhibit an "abnormal" absorbance ratio. However, when the two dyes are separated by long rigid DNA-based spacers, the absorbance ratio becomes consistent with their individual molar absorption coefficients. This phenomenon should be taken into account when assessing the molar ratio of the dyes by UV-Vis spectroscopy.

Exploring the dynamics of messenger ribonucleoprotein-mediated translation repression

Translational control is crucial for well-balanced cellular function and viability of organisms. Different mechanisms have evolved to up- and down-regulate protein synthesis, including 3' untranslated region (UTR)-mediated translation repression. RNA binding proteins or microRNAs interact with regulatory sequence elements located in the 3' UTR and interfere most often with the rate-limiting initiation step of translation. Dysregulation of post-transcriptional gene expression leads to various kinds of diseases, emphasizing the significance of understanding the mechanisms of these processes. So far, only limited mechanistic details about kinetics and dynamics of translation regulation are understood. This mini-review focuses on 3' UTR-mediated translational regulation mechanisms and demonstrates the potential of using single-molecule fluorescence-microscopy for kinetic and dynamic studies of translation regulation in vivo and in vitro.

Accounting for Fast vs Slow Exchange in Single Molecule FRET Experiments Reveals Hidden Conformational States

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 time scales 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 nuclear magnetic resonance, 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. Understanding protein dynamics is crucial for understanding protein function, yet few methodologies report on protein motion at an atomic level. Combining single molecule Förster resonance energy transfer (smFRET) experiments with computer simulations could provide atomistic models of protein ensembles which are grounded in experiments, however, there has been limited agreement between the two methods to date. Here, we present an algorithm to recapitulate smFRET experiments from molecular dynamics simulations. This approach significantly improves agreement between simulations and experiments for proteins across the ordered spectrum. Moreover, with this approach we can confidently create atomic models for states observed during smFRET experiments which were otherwise difficult to model due to high amounts of flexibility, disorder, or large deviations from crystal-like states.

The potential of fluorogenicity for single molecule FRET and DyeCycling

Single Molecule Förster Resonance Energy Transfer (smFRET) is a popular technique to directly observe biomolecular dynamics in real time, offering unique mechanistic insight into proteins, ribozymes, and so forth. However, inevitable photobleaching of the fluorophores puts a stringent limit on the total time a surface-tethered molecule can be monitored, fundamentally limiting the information gain through conventional smFRET measurements. DyeCycling addresses this problem by using reversibly - instead of covalently - coupled FRET fluorophores, through which it can break the photobleaching limit and theoretically provide unlimited observation time. In this perspective paper, we discuss the potential of various fluorogenic strategies to suppress the background fluorescence caused by unbound, freely diffusing fluorophores inherent to the DyeCycling approach. In comparison to nanophotonic background suppression using zero-mode waveguides, the fluorogenic approach would enable DyeCycling experiments on regular glass slides with fluorogenic FRET probes that are quenched in solution and only fluoresce upon target binding. We review a number of fluorogenic approaches and conclude, among other things, that short-range quenching appears promising for realising fluorogenic DyeCycling on regular glass slides. We anticipate that our discussion will be relevant for all single-molecule fluorescence techniques that use reversible fluorophore binding.

Time-heterogeneity of the Förster Radius from Dipole Orientational Dynamics Impacts Single-Molecule FRET Experiments

Förster resonance energy transfer (FRET) is a quantum mechanical phenomenon involving the non-radiative transfer of energy between coupled electric dipoles. Due to the strong dependence of FRET on the distance between the dipoles, it is frequently used as a "molecular ruler" in biology, chemistry, and physics. This is done by placing dipolar molecules called dyes on molecules of interest. In time-resolved confocal single-molecule FRET (smFRET) experiments, the joint distribution of the FRET efficiency and the donor fluorescence lifetime can reveal underlying molecular conformational dynamics via deviation from their theoretical Förster relationship. This deviation is referred to as a dynamic shift. Quantifying the dynamic shift caused by the motion of the fluorescent dyes is essential to decoupling the dynamics of the studied molecules and the dyes. We develop novel Langevin models for the dye linker dynamics, including rotational dynamics, based on first principle physics and proper dye linker chemistry to match accessible volumes predicted by molecular dynamics simulations. By simulating the dyes' stochastic translational and rotational dynamics, we show that the observed dynamic shift can largely be attributed to the mutual orientational dynamics of the electric dipole moments associated with the dyes, not their accessible volume. Our models provide the most up-to-date and accurate estimation of FRET.

Probing actin-activated ATP turnover kinetics of human cardiac myosin II by single molecule fluorescence

Mechanistic insights into myosin II energy transduction in striated muscle in health and disease would benefit from functional studies of a wide range of point-mutants. This approach is, however, hampered by the slow turnaround of myosin II expression that usually relies on adenoviruses for gene transfer. A recently developed virus-free method is more time effective but would yield too small amounts of myosin for standard biochemical analyses. However, if the fluorescent adenosine triphosphate (ATP) and single molecule (sm) total internal reflection fluorescence microscopy previously used to analyze basal ATP turnover by myosin alone, can be expanded to actin-activated ATP turnover, it would appreciably reduce the required amount of myosin. To that end, we here describe zero-length cross-linking of human cardiac myosin II motor fragments (sub-fragment 1 long [S1L]) to surface-immobilized actin filaments in a configuration with maintained actin-activated ATP turnover. After optimizing the analysis of sm fluorescence events, we show that the amount of myosin produced from C2C12 cells in one 60 mm cell culture plate is sufficient to obtain both the basal myosin ATP turnover rate and the maximum actin-activated rate constant (k cat). Our analysis of many single binding events of fluorescent ATP to many S1L motor fragments revealed processes reflecting basal and actin-activated ATPase, but also a third exponential process consistent with non-specific ATP-binding outside the active site.

Single-molecule insights into repetitive helicases

Helicases are ubiquitous motors involved in almost all aspects of nucleic acid metabolism; therefore, revealing their unwinding behaviors and mechanisms is fundamentally and medically essential. In recent decades, single-molecule applications have revolutionized our ability to study helicases by avoiding the averaging of bulk assays and bridging the knowledge gap between dynamics and structures. This advancement has updated our understanding of the biochemical properties of helicases, such as their rate, directionality, processivity, and step size, while also uncovering unprecedented mechanistic insights. Among these, repetitive motion, a new feature of helicases, is one of the most remarkable discoveries. However, comprehensive reviews and comparisons are still lacking. Consequently, the present review aims to summarize repetitive helicases, compare the repetitive phenomena, and discuss the underlying molecular mechanisms. This review may provide a systematic understanding of repetitive helicases and help understand their cellular functions.

Deep learning based local feature classification to automatically identify single molecule fluorescence events

Long-term single-molecule fluorescence measurements are widely used powerful tools to study the conformational dynamics of biomolecules in real time to further elucidate their conformational dynamics. Typically, thousands or even more single-molecule traces are analyzed to provide statistically meaningful information, which is labor-intensive and can introduce user bias. Recently, several deep-learning models have been developed to automatically classify single-molecule traces. In this study, we introduce DEBRIS (Deep lEarning Based fRagmentatIon approach for Single-molecule fluorescence event identification), a deep-learning model focusing on classifying local features and capable of automatically identifying steady fluorescence signals and dynamically emerging signals of different patterns. DEBRIS efficiently and accurately identifies both one-color and two-color single-molecule events, including their start and end points. By adjusting user-defined criteria, DEBRIS becomes the pioneer in using a deep learning model to accurately classify four different types of single-molecule fluorescence events using the same trained model, demonstrating its universality and ability to enrich the current toolbox.

Foundation model for efficient biological discovery in single-molecule data

Modern data-intensive techniques offer ever deeper insights into biology, but render the process of discovery increasingly complex. For example, exploiting the unique ability of single-molecule fluorescence microscopy (SMFM)1-5. to uncover rare but critical intermediates often demands manual inspection of time traces and iterative ad hoc approaches that are difficult to systematize. To facilitate systematic and efficient discovery from SMFM data, we introduce META-SiM, a transformer-based foundation model pre-trained on diverse SMFM analysis tasks. META-SiM achieves high performance-rivaling best-in-class algorithms-on a broad range of analysis tasks including trace selection, classification, segmentation, idealization, and stepwise photobleaching analysis. Additionally, the model produces high-dimensional embedding vectors that encapsulate detailed information about each trace, which the web-based META-SiM Projector (https://www.simol-projector.org) casts into lower-dimensional space for efficient whole-dataset visualization, labeling, comparison, and sharing. Combining this Projector with the objective metric of Local Shannon Entropy enables rapid identification of condition-specific behaviors, even if rare or subtle. As a result, by applying META-SiM to an existing single-molecule Förster resonance energy transfer (smFRET) dataset6, we discover a previously unobserved intermediate state in pre-mRNA splicing. META-SiM thus removes bottlenecks, improves objectivity, and both systematizes and accelerates biological discovery in complex single-molecule data.

Single-molecule detection and super-resolution imaging with a portable and adaptable 3D-printed microscopy platform (Brick-MIC)

Over the past decades, single-molecule and super-resolution microscopy have advanced and represent essential tools for life science research. There is, however, a growing gap between the state of the art and what is accessible to biologists, biochemists, medical researchers, or labs with financial constraints. To bridge this gap, we introduce Brick-MIC, a versatile and affordable open-source 3D-printed microspectroscopy and imaging platform. Brick-MIC enables the integration of various fluorescence imaging techniques with single-molecule resolution within a single platform and exchange between different modalities within minutes. We here present variants of Brick-MIC that facilitate single-molecule fluorescence detection, fluorescence correlation spectroscopy, time-correlated single-photon counting and super-resolution imaging (STORM and PAINT). Detailed descriptions of the hardware and software components, as well as data analysis routines, are provided, to allow non-optics specialists to operate their own Brick-MIC with minimal effort and investments. We foresee that our affordable, flexible, and open-source Brick-MIC platform will be a valuable tool for many laboratories worldwide.

Increasing the accuracy of single-molecule data analysis using tMAVEN

Time-dependent single-molecule experiments contain rich kinetic information about the functional dynamics of biomolecules. A key step in extracting this information is the application of kinetic models, such as hidden Markov models (HMMs), which characterize the molecular mechanism governing the experimental system. Unfortunately, researchers rarely know the physicochemical details of this molecular mechanism a priori, which raises questions about how to select the most appropriate kinetic model for a given single-molecule data set and what consequences arise if the wrong model is chosen. To address these questions, we have developed and used time-series modeling, analysis, and visualization environment (tMAVEN), a comprehensive, open-source, and extensible software platform. tMAVEN can perform each step of the single-molecule analysis pipeline, from preprocessing to kinetic modeling to plotting, and has been designed to enable the analysis of a single-molecule data set with multiple types of kinetic models. Using tMAVEN, we have systematically investigated mismatches between kinetic models and molecular mechanisms by analyzing simulated examples of prototypical single-molecule data sets exhibiting common experimental complications, such as molecular heterogeneity, with a series of different types of HMMs. Our results show that no single kinetic modeling strategy is mathematically appropriate for all experimental contexts. Indeed, HMMs only correctly capture the underlying molecular mechanism in the simplest of cases. As such, researchers must modify HMMs using physicochemical principles to avoid the risk of missing the significant biological and biophysical insights into molecular heterogeneity that their experiments provide. By enabling the facile, side-by-side application of multiple types of kinetic models to individual single-molecule data sets, tMAVEN allows researchers to carefully tailor their modeling approach to match the complexity of the underlying biomolecular dynamics and increase the accuracy of their single-molecule data analyses.

Deep-LASI, single-molecule data analysis software

By avoiding ensemble averaging, single-molecule methods provide novel means of extracting mechanistic insights into function of material and molecules at the nanoscale. However, one of the big limitations is the vast amount of data required for analyzing and extracting the desired information, which is time-consuming and user dependent. Here, we introduce Deep-LASI, a software suite for the manual and automatic analysis of single-molecule traces, interactions, and the underlying kinetics. The software can handle data from one-, two- and three-color fluorescence data, and was particularly designed for the analysis of two- and three-color single-molecule fluorescence resonance energy transfer experiments. The functionalities of the software include: the registration of multiple-channels, trace sorting and categorization, determination of the photobleaching steps, calculation of fluorescence resonance energy transfer correction factors, and kinetic analyses based on hidden Markov modeling or deep neural networks. After a kinetic analysis, the ensuing transition density plots are generated, which can be used for further quantification of the kinetic parameters of the system. Each step in the workflow can be performed manually or with the support of machine learning algorithms. Upon reading in the initial data set, it is also possible to perform the remaining analysis steps automatically without additional supervision. Hence, the time dedicated to the analysis of single-molecule experiments can be reduced from days/weeks to minutes. After a thorough description of the functionalities of the software, we also demonstrate the capabilities of the software via the analysis of a previously published dynamic three-color DNA origami structure fluctuating between three states. With the drastic time reduction in data analysis, new types of experiments become realistically possible that complement our currently available palette of methodologies for investigating the nanoworld.

Advanced multiparametric image spectroscopy and super-resolution microscopy reveal a minimal model of CD95 signal initiation

Unraveling the concentration-dependent spatiotemporal organization of receptors in the plasma membrane is crucial to understand cell signal initiation. A paradigm of this process is the oligomerization of CD95 during apoptosis signaling, with different oligomerization models being discussed. Here, we establish the molecular-sensitive approach cell lifetime Förster resonance energy transfer image spectroscopy to determine CD95 configurations in live cells. These data are corroborated by stimulated emission depletion microscopy, confocal photobleaching step analysis, and fluorescence correlation spectroscopy. We probed CD95 interactions for concentrations of ~10 to 1000 molecules per square micrometer, over nanoseconds to hours, and molecular to cellular scales. Quantitative benchmarking was achieved establishing high-fidelity monomer and dimer controls. While CD95 alone is primarily monomeric (~96%) and dimeric (4%), the addition of ligand induces oligomerization to dimers/trimers (~15%) leading to cell death. This study highlights molecular concentration effects and oligomerization dynamics. It reveals a minimal model, where small CD95 oligomers suffice to efficiently initiate signaling.

Foundation model for efficient biological discovery in single-molecule data

Modern data-intensive techniques offer ever deeper insights into biology, but render the process of discovery increasingly complex. For example, exploiting the unique ability of single-molecule fluorescence microscopy (SMFM)1-5. to uncover rare but critical intermediates often demands manual inspection of time traces and iterative ad hoc approaches that are difficult to systematize. To facilitate systematic and efficient discovery from SMFM data, we introduce META-SiM, a transformer-based foundation model pre-trained on diverse SMFM analysis tasks. META-SiM achieves high performance-rivaling best-in-class algorithms-on a broad range of analysis tasks including trace selection, classification, segmentation, idealization, and stepwise photobleaching analysis. Additionally, the model produces high-dimensional embedding vectors that encapsulate detailed information about each trace, which the web-based META-SiM Projector (https://www.simol-projector.org) casts into lower-dimensional space for efficient whole-dataset visualization, labeling, comparison, and sharing. Combining this Projector with the objective metric of Local Shannon Entropy enables rapid identification of condition-specific behaviors, even if rare or subtle. As a result, by applying META-SiM to an existing single-molecule Förster resonance energy transfer (smFRET) dataset6, we discover a previously unobserved intermediate state in pre-mRNA splicing. META-SiM thus removes bottlenecks, improves objectivity, and both systematizes and accelerates biological discovery in complex single-molecule data.

Subtype-specific conformational landscape of NMDA receptor gating

N-methyl-D-aspartate receptors are ionotropic glutamate receptors that mediate synaptic transmission and plasticity. Variable GluN2 subunits in diheterotetrameric receptors with identical GluN1 subunits set very different functional properties. To understand this diversity, we use single-molecule fluorescence resonance energy transfer (smFRET) to measure the conformations of the ligand binding domain and modulatory amino-terminal domain of the common GluN1 subunit in receptors with different GluN2 subunits. Our results demonstrate a strong influence of the GluN2 subunits on GluN1 rearrangements, both in non-agonized and partially agonized activation intermediates, which have been elusive to structural analysis, and in the fully liganded state. Chimeric analysis reveals structural determinants that contribute to these subtype differences. Our study provides a framework for understanding the conformational landscape that supports highly divergent levels of activity, desensitization, and agonist potency in receptors with different GluN2s and could open avenues for the development of subtype-specific modulators.

FRET-guided modeling of nucleic acids

The functional diversity of RNAs is encoded in their innate conformational heterogeneity. The combination of single-molecule spectroscopy and computational modeling offers new attractive opportunities to map structural transitions within nucleic acid ensembles. Here, we describe a framework to harmonize single-molecule Förster resonance energy transfer (FRET) measurements with molecular dynamics simulations and de novo structure prediction. Using either all-atom or implicit fluorophore modeling, we recreate FRET experiments in silico, visualize the underlying structural dynamics and quantify the reaction coordinates. Using multiple accessible-contact volumes as a post hoc scoring method for fragment assembly in Rosetta, we demonstrate that FRET can be used to filter a de novo RNA structure prediction ensemble by refuting models that are not compatible with in vitro FRET measurement. We benchmark our FRET-assisted modeling approach on double-labeled DNA strands and validate it against an intrinsically dynamic manganese(II)-binding riboswitch. We show that a FRET coordinate describing the assembly of a four-way junction allows our pipeline to recapitulate the global fold of the riboswitch displayed by the crystal structure. We conclude that computational fluorescence spectroscopy facilitates the interpretability of dynamic structural ensembles and improves the mechanistic understanding of nucleic acid interactions.

Efficient, nonparametric removal of noise and recovery of probability distributions from time series using nonlinear-correlation functions: Photon and photon-counting noise

A preceding paper [M. Dhar, J. A. Dickinson, and M. A. Berg, J. Chem. Phys. 159, 054110 (2023)] shows how to remove additive noise from an experimental time series, allowing both the equilibrium distribution of the system and its Green's function to be recovered. The approach is based on nonlinear-correlation functions and is fully nonparametric: no initial model of the system or of the noise is needed. However, single-molecule spectroscopy often produces time series with either photon or photon-counting noise. Unlike additive noise, photon noise is signal-size correlated and quantized. Photon counting adds the potential for bias. This paper extends noise-corrected-correlation methods to these cases and tests them on synthetic datasets. Neither signal-size correlation nor quantization is a significant complication. Analysis of the sampling error yields guidelines for the data quality needed to recover the properties of a system with a given complexity. We show that bias in photon-counting data can be corrected, even at the high count rates needed to optimize the time resolution. Using all these results, we discuss the factors that limit the time resolution of single-molecule spectroscopy and the conditions that would be needed to push measurements into the submicrosecond region.

iMAX FRET (Information Maximized FRET) for Multipoint Single-Molecule Structural Analysis

Understanding the structure of biomolecules is vital for deciphering their roles in biological systems. Single-molecule techniques have emerged as alternatives to conventional ensemble structure analysis methods for uncovering new biology in molecular dynamics and interaction studies, yet only limited structural information could be obtained experimentally. Here, we address this challenge by introducing iMAX FRET, a one-pot method that allows ab initio 3D profiling of individual molecules using two-color FRET measurements. Through the stochastic exchange of fluorescent weak binders, iMAX FRET simultaneously assesses multiple distances on a biomolecule within a few minutes, which can then be used to reconstruct the coordinates of up to four points in each molecule, allowing structure-based inference. We demonstrate the 3D reconstruction of DNA nanostructures, protein quaternary structures, and conformational changes in proteins. With iMAX FRET, we provide a powerful approach to advance the understanding of biomolecular structure by expanding conventional FRET analysis to three dimensions.

Single-molecule detection and super-resolution imaging with a portable and adaptable 3D-printed microscopy platform (Brick-MIC)

Over the past decades, single-molecule and super-resolution microscopy have advanced and represent essential tools for life science research. There is,however, a growing gap between the state-of-the-art and what is accessible to biologists, biochemists, medical researchers or labs with financial constraints. To bridge this gap, we introduce Brick-MIC, a versatile and affordable open-source 3D-printed micro-spectroscopy and imaging platform. Brick-MIC enables the integration of various fluorescence imaging techniques with single-molecule resolution within a single platform and exchange between different modalities within minutes. We here present variants of Brick-MIC that facilitate single-molecule fluorescence detection, fluorescence correlation spectroscopy and super-resolution imaging (STORM and PAINT). Detailed descriptions of the hardware and software components, as well as data analysis routines are provided, to allow non-optics specialist to operate their own Brick-MIC with minimal effort and investments. We foresee that our affordable, flexible, and opensource Brick-MIC platform will be a valuable tool for many laboratories worldwide.

Fluorescence lifetime multiplexing with fluorogen activating protein FAST variants

In this paper, we propose a fluorescence-lifetime imaging microscopy (FLIM) multiplexing system based on the fluorogen-activating protein FAST. This genetically encoded fluorescent labeling platform employs FAST mutants that activate the same fluorogen but provide different fluorescence lifetimes for each specific protein-dye pair. All the proposed probes with varying lifetimes possess nearly identical and the smallest-in-class size, along with quite similar steady-state optical properties. In live mammalian cells, we target these chemogenetic tags to two intracellular structures simultaneously, where their fluorescence signals are clearly distinguished by FLIM. Due to the unique structure of certain fluorogens under study, their complexes with FAST mutants display a monophasic fluorescence decay, which may facilitate enhanced multiplexing efficiency by reducing signal cross-talks and providing optimal prerequisites for signal separation upon co-localized and/or spatially overlapped labeling.

Single-Molecule Imaging of Integral Membrane Protein Dynamics and Function

Integral membrane proteins (IMPs) play central roles in cellular physiology and represent the majority of known drug targets. Single-molecule fluorescence and fluorescence resonance energy transfer (FRET) methods have recently emerged as valuable tools for investigating structure-function relationships in IMPs. This review focuses on the practical foundations required for examining polytopic IMP function using single-molecule FRET (smFRET) and provides an overview of the technical and conceptual frameworks emerging from this area of investigation. In this context, we highlight the utility of smFRET methods to reveal transient conformational states critical to IMP function and the use of smFRET data to guide structural and drug mechanism-of-action investigations. We also identify frontiers where progress is likely to be paramount to advancing the field.

NMR and Single-Molecule FRET Insights into Fast Protein Motions and Their Relation to Function

Proteins often undergo large-scale conformational transitions, in which secondary and tertiary structure elements (loops, helices, and domains) change their structures or their positions with respect to each other. Simple considerations suggest that such dynamics should be relatively fast, but the functional cycles of many proteins are often relatively slow. Sophisticated experimental methods are starting to tackle this dichotomy and shed light on the contribution of large-scale conformational dynamics to protein function. In this review, we focus on the contribution of single-molecule Förster resonance energy transfer and nuclear magnetic resonance (NMR) spectroscopies to the study of conformational dynamics. We briefly describe the state of the art in each of these techniques and then point out their similarities and differences, as well as the relative strengths and weaknesses of each. Several case studies, in which the connection between fast conformational dynamics and slower function has been demonstrated, are then introduced and discussed. These examples include both enzymes and large protein machines, some of which have been studied by both NMR and fluorescence spectroscopies.

Four Parallel Pathways in T4 Ligase-Catalyzed Repair of Nicked DNA with Diverse Bending Angles

The structural diversity of biological macromolecules in different environments contributes complexity to enzymological processes vital for cellular functions. Fluorescence resonance energy transfer and electron microscopy are used to investigate the enzymatic reaction of T4 DNA ligase catalyzing the ligation of nicked DNA. The data show that both the ligase-AMP complex and the ligase-AMP-DNA complex can have four conformations. This finding suggests the parallel occurrence of four ligation reaction pathways, each characterized by specific conformations of the ligase-AMP complex that persist in the ligase-AMP-DNA complex. Notably, these complexes have DNA bending angles of ≈0°, 20°, 60°, or 100°. The mechanism of parallel reactions challenges the conventional notion of simple sequential reaction steps occurring among multiple conformations. The results provide insights into the dynamic conformational changes and the versatile attributes of T4 DNA ligase and suggest that the parallel multiple reaction pathways may correspond to diverse T4 DNA ligase functions. This mechanism may potentially have evolved as an adaptive strategy across evolutionary history to navigate complex environments.

Recovering true FRET efficiencies from smFRET investigations requires triplet state mitigation

Single-molecule fluorescence resonance energy transfer (smFRET) methods employed to quantify time-dependent compositional and conformational changes within biomolecules require elevated illumination intensities to recover robust photon emission streams from individual fluorophores. Here we show that outside the weak-excitation limit, and in regimes where fluorophores must undergo many rapid cycles of excitation and relaxation, non-fluorescing, excitation-induced triplet states with lifetimes orders of magnitude longer lived than photon-emitting singlet states degrade photon emission streams from both donor and acceptor fluorophores resulting in illumination-intensity-dependent changes in FRET efficiency. These changes are not commonly taken into consideration; therefore, robust strategies to suppress excited state accumulations are required to recover accurate and precise FRET efficiency, and thus distance, estimates. We propose both robust triplet state suppression and data correction strategies that enable the recovery of FRET efficiencies more closely approximating true values, thereby extending the spatial and temporal resolution of smFRET.

Two-color interferometric scattering (iSCAT) microscopy reveals structural dynamics in discrete plasmonic molecules

Plasmonic molecules are discrete assemblies of noble metal nanoparticles (NPs) that are of interest as transducers in optical nanosensors. So far, NPs with diameters of ∼40 nm have been the preferred building blocks for plasmonic molecules intended as optical single molecule sensors due to difficulties associated with detecting smaller NPs through elastic scattering in conventional darkfield microscopy. Here, we apply 405 nm, 445 nm two-color interferometric scattering (iSCAT) microscopy to characterize polyethylene glycol (PEG) tethered dimers of 10 nm and 20 nm Ag NPs and their monomers. Dimers of both NP sizes can be discerned from their respective monomers through changes in the average iSCAT contrast. In the case of 20 nm Ag NPs, dimer formation induces a change in the sign of the iSCAT contrast, providing a characteristic signal for detecting binding events. 20 nm Ag NP dimers with 0.4 kDa and 3.4 kDa polyethylene glycol (PEG) spacers show iSCAT contrast distributions with significantly different averages on both wavelength channels. The iSCAT contrast measured for individual PEG-tethered 10 nm or 20 nm NP dimers as a function of time shows contrast fluctuations indicative of a rich structural dynamics in the assembled plasmonic molecules, which provides an additional metric to discern dimers from monomers and paves the path to a new class of interferometric plasmon rulers.

Unraveling Burst Selection Bias in Single-Molecule FRET of Species with Unequal Brightness and Diffusivity

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.

Accounting for fast vs slow exchange in single molecule FRET experiments reveals hidden conformational states

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.

Single-Molecule Spectroscopy and Super-Resolution Mapping of Physicochemical Parameters in Living Cells

By superlocalizing the positions of millions of single molecules over many camera frames, a class of super-resolution fluorescence microscopy methods known as single-molecule localization microscopy (SMLM) has revolutionized how we understand subcellular structures over the past decade. In this review, we highlight emerging studies that transcend the outstanding structural (shape) information offered by SMLM to extract and map physicochemical parameters in living mammalian cells at single-molecule and super-resolution levels. By encoding/decoding high-dimensional information-such as emission and excitation spectra, motion, polarization, fluorescence lifetime, and beyond-for every molecule, and mass accumulating these measurements for millions of molecules, such multidimensional and multifunctional super-resolution approaches open new windows into intracellular architectures and dynamics, as well as their underlying biophysical rules, far beyond the diffraction limit.

Steady-state and time-resolved fluorescent methodologies to characterize the conformational landscape of the selectivity filter of K+ channels

A variety of equilibrium and non-equilibrium methods have been used in a multidisciplinary approach to study the conformational landscape associated with the binding of different cations to the pore of potassium channels. These binding processes, and the conformational changes resulting therefrom, modulate the functional properties of such integral membrane properties, revealing these permeant and blocking cations as true effectors of such integral membrane proteins. KcsA, a prototypic K+ channel from Streptomyces lividans, has been extensively characterized in this regard. Here, we revise several fluorescence-based approaches to monitor cation binding under different experimental conditions in diluted samples, analyzing the advantages and disadvantages of each approach. These studies have contributed to explain the selectivity, conduction, and inactivation properties of K+ channels at the molecular level, together with the allosteric communication between the two gates that control the ion channel flux, and how they are modulated by lipids.

Two-Dimensional Fluctuation Correlation Spectroscopy (2D-FlucCS): A Method to Determine the Origin of Relaxation Rate Dispersion

Relaxation rate dispersion, i.e., nonexponential or multicomponent kinetics, is observed in complex systems when measuring relaxation kinetics. Often, the origin of rate dispersion is associated with the heterogeneity in the system. However, both homogeneous (where all molecules experience the same rate but inherently nonexponential) and heterogeneous (where all molecules experience different rates) systems can exhibit rate dispersion. A multidimensional correlation analysis method has been demonstrated to detect and quantify rate dispersion observed in molecular rotation, diffusion, solvation, and reaction kinetics. One-dimensional (1D) autocorrelation function detects rate dispersion and measures its extent. Two-dimensional (2D) autocorrelation function measures the origin of rate dispersion and distinguishes homogeneous from heterogeneous. In a heterogeneous system, implicitly there exist subensembles of molecules experiencing different rates. A three-dimensional (3D) autocorrelation function measures subensemble exchange if present and reveals if the system possesses static or dynamic heterogeneity. This perspective discusses the principles, applications, and potential and also presents a future outlook of two-dimensional fluctuation correlation spectroscopy (2D-FlucCS). The method is applicable to any experiment or simulation where a time series of fluctuation in an observable (emission, scattering, current, etc.) around a mean value can be obtained in steady state (equilibrium or nonequilibrium), provided the system is ergodic.

A molecular dynamics simulation study of glycine/serine octapeptides labeled with 2,3-diazabicyclo[2.2.2]oct-2-ene fluorophore

The compound 2,3-diazabicyclo[2.2.2]oct-2-ene (DBO) is a versatile fluorophore widely used in Förster resonance energy transfer (FRET) spectroscopy studies due to its remarkable sensitivity, enabling precise donor-acceptor distance measurements, even for short peptides. Integrating time-resolved and FRET spectroscopies with molecular dynamics simulations provides a robust approach to unravel the structure and dynamics of biopolymers in a solution. This study investigates the structural behavior of three octapeptide variants: Trp-(Gly-Ser)3-Dbo, Trp-(GlyGly)3-Dbo, and Trp-(SerSer)3-Dbo, where Dbo represents the DBO-containing modified aspartic acid, using molecular dynamics simulations. Glycine- and serine-rich amino acid fragments, common in flexible protein regions, play essential roles in functional properties. Results show excellent agreement between end-to-end distances, orientational factors from simulations, and the available experimental and theoretical data, validating the reliability of the GROMOS force field model. The end-to-end distribution, modeled using three Gaussian distributions, reveals a complex shape, confirmed by cluster analysis highlighting a limited number of significant conformations dominating the peptide landscape. All peptides predominantly adopt a disordered state in the solvent, yet exhibit a compact shape, aligning with the model of disordered polypeptide chains in poor solvents. Conformations show marginal dependence on chain composition, with Ser-only chains exhibiting slightly more elongation. This study enhances our understanding of peptide behavior, providing valuable insights into their structural dynamics in solution.

Real-time single-molecule imaging of CaMKII-calmodulin interactions

The binding of calcium/calmodulin (CAM) to calcium/calmodulin-dependent protein kinase II (CaMKII) initiates an ATP-driven cascade that triggers CaMKII autophosphorylation. The autophosphorylation in turn increases the CaMKII affinity for CAM. Here, we studied the ATP dependence of CAM association with the actin-binding CaMKIIβ isoform using single-molecule total internal reflection fluorescence microscopy. Rhodamine-CAM associations/dissociations to surface-immobilized Venus-CaMKIIβ were resolved with 0.5 s resolution from video records, batch-processed with a custom algorithm. CAM occupancy was determined simultaneously with spot-photobleaching measurement of CaMKII holoenzyme stoichiometry. We show the ATP-dependent increase of the CAM association requires dimer formation for both the α and β isoforms. The study of mutant β holoenzymes revealed that the ATP-dependent increase in CAM affinity results in two distinct states. The phosphorylation-defective (T287.306-307A) holoenzyme resides only in the low-affinity state. CAM association is further reduced in the T287A holoenzyme relative to T287.306-307A. In the absence of ATP, the affinity of CAM for the T287.306-307A mutant and the wild-type monomer are comparable. The affinity of the ATP-binding impaired (K43R) mutant is even weaker. In ATP, the K43R holoenzyme resides in the low-affinity state. The phosphomimetic mutant (T287D) resides only in a 1000-fold higher-affinity state, with mean CAM occupancy of more than half of the 14-mer holoenzyme stoichiometry in picomolar CAM. ATP promotes T287D holoenzyme disassembly but does not elevate CAM occupancy. Single Poisson distributions characterized the ATP-dependent CAM occupancy of mutant holoenzymes. In contrast, the CAM occupancy of the wild-type population had a two-state distribution with both low- and high-affinity states represented. The low-affinity state was the dominant state, a result different from published in vitro assays. Differences in assay conditions can alter the balance between activating and inhibitory autophosphorylation. Bound ATP could be sufficient for CaMKII structural function, while antagonistic autophosphorylations may tune CaMKII kinase-regulated action-potential frequency decoding in vivo.

Structural highlights of macromolecular complexes and assemblies

The structures of macromolecular assemblies have given us deep insights into cellular processes and have profoundly impacted biological research and drug discovery. We highlight the structures of macromolecular assemblies that have been modeled using integrative and computational methods and describe how open access to these structures from structural archives has empowered the research community. The arsenal of experimental and computational methods for structure determination ensures a future where whole organelles and cells can be modeled.

A practical guide to time-resolved fluorescence microscopy and spectroscopy

Time-correlated single photon counting (TCSPC) coupled with confocal microscopy is a versatile biophysical tool that enables real-time monitoring of biomolecular dynamics across many timescales. With TCSPC, Fluorescence correlation spectroscopy (FCS) and pulsed interleaved excitation-Förster resonance energy transfer (PIE-FRET) are collected simultaneously on diffusing molecules to extract diffusion characteristics and proximity information. This article is a guide to calibrating FCS and PIE-FRET measurements with several biological samples including liposomes, streptavidin-coated quantum dots, proteins, and nucleic acids for reliable determination of diffusion coefficients and FRET efficiency. The FRET efficiency results are also compared to surface-attached single molecules using fluorescence lifetime imaging microscopy (FLIM-FRET). Combining the methods is a powerful approach to revealing mechanistic details of biological processes and pathways.

An optofluidic antenna for enhancing the sensitivity of single-emitter measurements

Many single-molecule investigations are performed in fluidic environments, for example, to avoid unwanted consequences of contact with surfaces. Diffusion of molecules in this arrangement limits the observation time and the number of collected photons, thus, compromising studies of processes with fast or slow dynamics. Here, we introduce a planar optofluidic antenna (OFA), which enhances the fluorescence signal from molecules by about 5 times per passage, leads to about 7-fold more frequent returns to the observation volume, and significantly lengthens the diffusion time within one passage. We use single-molecule multi-parameter fluorescence detection (sm-MFD), fluorescence correlation spectroscopy (FCS) and Förster resonance energy transfer (FRET) measurements to characterize our OFAs. The antenna advantages are showcased by examining both the slow (ms) and fast (50 μs) dynamics of DNA four-way (Holliday) junctions with real-time resolution. The FRET trajectories provide evidence for the absence of an intermediate conformational state and introduce an upper bound for its lifetime. The ease of implementation and compatibility with various microscopy modalities make OFAs broadly applicable to a diverse range of studies.

Conformational basis of subtype-specific allosteric control of NMDA receptor gating

N-methyl-D-aspartate receptors are ionotropic glutamate receptors that are integral to synaptic transmission and plasticity. Variable GluN2 subunits in diheterotetrameric receptors with identical GluN1 subunits set very different functional properties, which support their individual physiological roles in the nervous system. To understand the conformational basis of this diversity, we assessed the conformation of the common GluN1 subunit in receptors with different GluN2 subunits using single-molecule fluorescence resonance energy transfer (smFRET). We established smFRET sensors in the ligand binding domain and modulatory amino-terminal domain to study an apo-like state and partially liganded activation intermediates, which have been elusive to structural analysis. Our results demonstrate a strong, subtype-specific influence of apo and glutamate-bound GluN2 subunits on GluN1 rearrangements, suggesting a conformational basis for the highly divergent levels of receptor activity, desensitization and agonist potency. Chimeric analysis reveals structural determinants that contribute to the subtype differences. Our study provides a framework for understanding GluN2-dependent functional properties and could open new avenues for subtype-specific modulation.

Mechanism of Viral DNA Packaging in Phage T4 Using Single-Molecule Fluorescence Approaches

In all tailed phages, the packaging of the double-stranded genome into the head by a terminase motor complex is an essential step in virion formation. Despite extensive research, there are still major gaps in the understanding of this highly dynamic process and the mechanisms responsible for DNA translocation. Over the last fifteen years, single-molecule fluorescence technologies have been applied to study viral nucleic acid packaging using the robust and flexible T4 in vitro packaging system in conjunction with genetic, biochemical, and structural analyses. In this review, we discuss the novel findings from these studies, including that the T4 genome was determined to be packaged as an elongated loop via the colocalization of dye-labeled DNA termini above the portal structure. Packaging efficiency of the TerL motor was shown to be inherently linked to substrate structure, with packaging stalling at DNA branches. The latter led to the design of multiple experiments whose results all support a proposed torsional compression translocation model to explain substrate packaging. Evidence of substrate compression was derived from FRET and/or smFRET measurements of stalled versus resolvase released dye-labeled Y-DNAs and other dye-labeled substrates relative to motor components. Additionally, active in vivo T4 TerS fluorescent fusion proteins facilitated the application of advanced super-resolution optical microscopy toward the visualization of the initiation of packaging. The formation of twin TerS ring complexes, each expected to be ~15 nm in diameter, supports a double protein ring-DNA synapsis model for the control of packaging initiation, a model that may help explain the variety of ring structures reported among pac site phages. The examination of the dynamics of the T4 packaging motor at the single-molecule level in these studies demonstrates the value of state-of-the-art fluorescent tools for future studies of complex viral replication mechanisms.

Simple and Efficient Detection Scheme of Two-Color Fluorescence Correlation Spectroscopy for Protein Dynamics Investigation from Nanoseconds to Milliseconds

Nanosecond resolved fluorescence correlation spectroscopy (ns-FCS) based on two-color fluorescence detection is a powerful strategy for investigating the fast dynamics of biological macromolecules labeled with donor and acceptor fluorophores. The standard methods of ns-FCS use two single-photon avalanche diodes (SPADs) for the detection of single-color signals (four SPADs for two-color signals) to eliminate the afterpulse artifacts of SPAD at the expense of the efficiency of utilizing photon data in the calculation of correlograms. Herein, we demonstrated that hybrid photodetectors (HPDs) enable the recording of fluorescence photons in ns-FCS based on the minimal system using two HPDs for the detection of two-color signals. However, HPD exhibited afterpulses at a yield with respect to the rate of photodetection (<10-4) much lower than that of SPADs (∼10-2), which could still hamper correlation measurements. We demonstrated that the simple subtraction procedure could eliminate afterpulse artifacts. While the quantum efficiency of photodetection for HPDs is lower than that for high-performance SPADs, the developed system can be practically used for two-color ns-FCS in a time domain longer than a few nanoseconds. The fast chain dynamics of the B domain of protein A in the unfolded state was observed using the new method.