Single-Molecule Analysis beyond Dwell Times: Demonstration and Assessment in and out of Equilibrium

Carbon-nanotube field-effect transistors for resolving single-molecule aptamer-ligand binding kinetics

Small molecules such as neurotransmitters are critical for biochemical functions in living systems. While conventional ultraviolet-visible spectroscopy and mass spectrometry lack portability and are unsuitable for time-resolved measurements in situ, techniques such as amperometry and traditional field-effect detection require a large ensemble of molecules to reach detectable signal levels. Here we demonstrate the potential of carbon-nanotube-based single-molecule field-effect transistors (smFETs), which can detect the charge on a single molecule, as a new platform for recognizing and assaying small molecules. smFETs are formed by the covalent attachment of a probe molecule, in our case a DNA aptamer, to a carbon nanotube. Conformation changes on binding are manifest as discrete changes in the nanotube electrical conductance. By monitoring the kinetics of conformational changes in a binding aptamer, we show that smFETs can detect and quantify serotonin at the single-molecule level, providing unique insights into the dynamics of the aptamer-ligand system. In particular, we show the involvement of G-quadruplex formation and the disruption of the native hairpin structure in the conformational changes of the serotonin-aptamer complex. The smFET is a label-free approach to analysing molecular interactions at the single-molecule level with high temporal resolution, providing additional insights into complex biological processes.

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.

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.

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 physico-chemical details of this molecular mechanism a priori, which raises questions about how to select the most appropriate kinetic model for a given single-molecule dataset 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 pre-processing to kinetic modeling to plotting, and has been designed to enable the analysis of a single-molecule dataset 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 datasets 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 physico-chemical 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 datasets, 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.

Cochaperones convey the energy of ATP hydrolysis for directional action of Hsp90

The molecular chaperone and heat shock protein Hsp90 is part of many protein complexes in eukaryotic cells. Together with its cochaperones, Hsp90 is responsible for the maturation of hundreds of clients. Although having been investigated for decades, it still is largely unknown which components are necessary for a functional complex and how the energy of ATP hydrolysis is used to enable cyclic operation. Here we use single-molecule FRET to show how cochaperones introduce directionality into Hsp90's conformational changes during its interaction with the client kinase Ste11. Three cochaperones are needed to couple ATP turnover to these conformational changes. All three are therefore essential for a functional cyclic operation, which requires coupling to an energy source. Finally, our findings show how the formation of sub-complexes in equilibrium followed by a directed selection of the functional complex can be the most energy efficient pathway for kinase maturation.

Single-Molecule FRET-Resolved Protein Dynamics - from Plasmid to Data in Six Steps

Single-molecule Förster resonance energy transfer (smFRET) is a powerful technique for the detection of conformational dynamics of biomolecules. While many smFRET experiments are performed using dye-labeled DNA, here we describe a comprehensive protocol to resolve the conformational dynamics of a protein system - notably from plasmid to data. Using the example of the heat-shock protein Hsp90, we describe the protein production and threefold site-specific bioconjugation, the smFRET measurement using total internal reflection fluorescence microscopy (TIRFM), and raw data processing to reveal time-resolved protein dynamics. The described smFRET approach is readily transferrable to the study of many more all-protein systems and their conformational energy landscape.

The Onset of Molecule-Spanning Dynamics in Heat Shock Protein Hsp90

Protein dynamics have been investigated on a wide range of time scales. Nano- and picosecond dynamics have been assigned to local fluctuations, while slower dynamics have been attributed to larger conformational changes. However, it is largely unknown how fast (local) fluctuations can lead to slow global (allosteric) changes. Here, fast molecule-spanning dynamics on the 100 to 200 ns time scale in the heat shock protein 90 (Hsp90) are shown. Global real-space movements are assigned to dynamic modes on this time scale, which is possible by a combination of single-molecule fluorescence, quasi-elastic neutron scattering and all-atom molecular dynamics (MD) simulations. The time scale of these dynamic modes depends on the conformational state of the Hsp90 dimer. In addition, the dynamic modes are affected to various degrees by Sba1, a co-chaperone of Hsp90, depending on the location within Hsp90, which is in very good agreement with MD simulations. Altogether, this data is best described by fast molecule-spanning dynamics, which precede larger conformational changes in Hsp90 and might be the molecular basis for allostery. This integrative approach provides comprehensive insights into molecule-spanning dynamics on the nanosecond time scale for a multi-domain protein.

Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures

Single-molecule experiments have changed the way we explore the physical world, yet data analysis remains time-consuming and prone to human bias. Here, we introduce Deep-LASI (Deep-Learning Assisted Single-molecule Imaging analysis), a software suite powered by deep neural networks to rapidly analyze single-, two- and three-color single-molecule data, especially from single-molecule Förster Resonance Energy Transfer (smFRET) experiments. Deep-LASI automatically sorts recorded traces, determines FRET correction factors and classifies the state transitions of dynamic traces all in ~20-100 ms per trajectory. We benchmarked Deep-LASI using ground truth simulations as well as experimental data analyzed manually by an expert user and compared the results with a conventional Hidden Markov Model analysis. We illustrate the capabilities of the technique using a highly tunable L-shaped DNA origami structure and use Deep-LASI to perform titrations, analyze protein conformational dynamics and demonstrate its versatility for analyzing both total internal reflection fluorescence microscopy and confocal smFRET data.

Transition Time Determination of Single-Molecule FRET Trajectories via Wasserstein Distance Analysis in Steady-State Variations in smFRET (WAVE)

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.

Aha1 regulates Hsp90's conformation and function in a stoichiometry-dependent way

The heat shock protein 90 (Hsp90) is a molecular chaperone, which plays a key role in eukaryotic protein homeostasis. Co-chaperones assist Hsp90 in client maturation and in regulating essential cellular processes such as cell survival, signal transduction, gene regulation, hormone signaling, and neurodegeneration. Aha1 (activator of Hsp90 ATPase) is a unique co-chaperone known to stimulate the ATP hydrolysis of Hsp90, but the mechanism of their interaction is still unclear. In this report, we show that one or two Aha1 molecules can bind to one Hsp90 dimer and that the binding stoichiometry affects Hsp90's conformation, kinetics, ATPase activity, and stability. In particular, a coordination of two Aha1 molecules can be seen in stimulating the ATPase activity of Hsp90 and the unfolding of the middle domain, whereas the conformational equilibrium and kinetics are hardly affected by the stoichiometry of bound Aha1. Altogether, we show a regulation mechanism through the stoichiometry of Aha1 going far beyond a regulation of Hsp90's conformation.

A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories

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.

A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories

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.

Heterogeneous migration routes of DNA triplet repeat slip-outs

It is unclear how the length of a repetitive DNA tract determines the onset and progression of repeat expansion diseases, but the dynamics of secondary DNA structures formed by repeat sequences are believed to play an important role. It was recently shown that three-way DNA junctions containing slip-out hairpins of CAG or CTG repeats and contiguous triplet repeats in the adjacent duplex displayed single-molecule FRET (smFRET) dynamics that were ascribed to both local conformational motions and longer-range branch migration. Here we explore these so-called "mobile" slip-out structures through a detailed kinetic analysis of smFRET trajectories and coarse-grained modeling. Despite the apparent structural simplicity, with six FRET states resolvable, most smFRET states displayed biexponential dwell-time distributions, attributed to structural heterogeneity and overlapping FRET states. Coarse-grained modeling for a (GAC)₁₀ repeat slip-out included trajectories that corresponded to a complete round of branch migration; the structured free energy landscape between slippage events supports the dynamical complexity observed by smFRET. A hairpin slip-out with 40 CAG repeats, which is above the repeat length required for disease in several triplet repeat disorders, displayed smFRET dwell times that were on average double those of 3WJs with 10 repeats. The rate of secondary-structure rearrangement via branch migration, relative to particular DNA processing pathways, may be an important factor in the expansion of triplet repeat expansion diseases.

Determination of rate constants for conformational changes of RNA helicases by single-molecule FRET TIRF microscopy

RNA helicases couple nucleotide-driven conformational changes to the unwinding of RNA duplexes. Interaction partners can regulate helicase activity by altering the rate constants of these conformational changes. Single-molecule FRET experiments on donor/acceptor-labeled, immobilized molecules are ideally suited to monitor conformational changes in real time and to extract rate constants for these processes. This article provides guidance on how to design, perform, and analyze single-molecule FRET experiments by TIRF microscopy. It covers the theoretical background of FRET and single-molecule TIRF microscopy, the considerations to prepare proteins of interest for donor/acceptor labeling and surface immobilization, and the principles and procedures of data analysis, including image analysis and the determination of FRET time traces, the extraction of rate constants from FRET time traces, and the general conclusions that can be drawn from these data. A case study, using the DEAD-box protein eIF4A as an example, highlights how single-molecule FRET studies have been instrumental in understanding the role of conformational changes for duplex unwinding and for the regulation of helicase activities. Selected examples illustrate which conclusions can be drawn from the kinetic data obtained, highlight possible pitfalls in data analysis and interpretation, and outline how kinetic models can be related to functionally relevant states.

Single-Molecule Kinetic Studies of Nucleic Acids by Förster Resonance Energy Transfer

Single-molecule microscopy is often used to observe and characterize the conformational dynamics of nucleic acids (NA). Due to the large variety of NA structures and the challenges specific to single-molecule observation techniques, the data recorded in such experiments must be processed via multiple statistical treatments to finally yield a reliable mechanistic view of the NA dynamics. In this chapter, we propose a comprehensive protocol to analyze single-molecule trajectories in the scope of single-molecule Förster resonance energy transfer (FRET) microscopy. The suggested protocol yields the conformational states common to all molecules in the investigated sample, together with the associated conformational transition kinetics. The given model resolves states that are indistinguishable by their observed FRET signals and is estimated with 95% confidence using error calculations on FRET states and transition rate constants. In the end, a step-by-step user guide is given to reproduce the protocol with the Multifunctional Analysis Software to Handle single-molecule FRET data (MASH-FRET).

Theory and Analysis of Single-Molecule FRET Experiments

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.

Can DyeCycling break the photobleaching limit in single-molecule FRET?

Biomolecular systems, such as proteins, crucially rely on dynamic processes at the nanoscale. Detecting biomolecular nanodynamics is therefore key to obtaining a mechanistic understanding of the energies and molecular driving forces that control biomolecular systems. Single-molecule fluorescence resonance energy transfer (smFRET) is a powerful technique to observe in real-time how a single biomolecule proceeds through its functional cycle involving a sequence of distinct structural states. Currently, this technique is fundamentally limited by irreversible photobleaching, causing the untimely end of the experiment and thus, a narrow temporal bandwidth of ≤ 3 orders of magnitude. Here, we introduce "DyeCycling", a measurement scheme with which we aim to break the photobleaching limit in smFRET. We introduce the concept of spontaneous dye replacement by simulations, and as an experimental proof-of-concept, we demonstrate the intermittent observation of a single biomolecule for one hour with a time resolution of milliseconds. Theoretically, DyeCycling can provide > 100-fold more information per single molecule than conventional smFRET. We discuss the experimental implementation of DyeCycling, its current and fundamental limitations, and specific biological use cases. Given its general simplicity and versatility, DyeCycling has the potential to revolutionize the field of time-resolved smFRET, where it may serve to unravel a wealth of biomolecular dynamics by bridging from milliseconds to the hour range.

ELECTRONIC SUPPLEMENTARY MATERIAL

Supplementary material is available for this article at 10.1007/s12274-022-4420-5 and is accessible for authorized users.

Joint Detection of Change Points in Multichannel Single-Molecule Measurements

Recent developments in single-molecule measurement technology have expanded the capability to measure multiple parameters. These emergent modalities provide more holistic observations of complex biomolecular processes and call for new analysis methods to detect state changes in multichannel data. Here we develop an algorithm called MULLR (MUlti-channel Log-Likelihood Ratio test) to jointly identify change points in multichannel single-molecule measurements. MULLR is an extension of the popular single-channel implementation for change point detection based on a binary segmentation and log-likelihood ratio test framework. We validate the algorithm on simulated data and characterize the power of detection and false positive rate. We show that MULLR can identify change points in experimental multichannel data and naturally works with different noise statistics and time resolutions across channels. Further, we quantify the benefit of MULLR compared to single-channel analysis. We envision that the MULLR algorithm will be useful to a range of multiparameter single-molecule measurements.

FIB-milled plasmonic nanoapertures allow for long trapping times of individual proteins

We have developed a fabrication methodology for label-free optical trapping of individual nanobeads and proteins in inverted-bowtie-shaped plasmonic gold nanopores. Arrays of these nanoapertures can be reliably produced using focused ion beam (FIB) milling with gap sizes of 10–20 nm, single-nanometer variation, and with a remarkable stability that allows for repeated use. We employ an optical readout where the presence of the protein entering the trap is marked by an increase in the transmission of light through the nanoaperture from the shift of the plasmonic resonance. In addition, the optical trapping force of the plasmonic nanopores allows 20-nm polystyrene beads and proteins, such as beta-amylase and Heat Shock Protein (HSP90), to be trapped for very long times (approximately minutes). On demand, we can release the trapped molecule for another protein to be interrogated. Our work opens up new routes to acquire information on the conformation and dynamics of individual proteins.

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We demonstrate fabrication of arrays of inverted-bowtie-shaped plasmonic gold nanopores

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Arrays (>64) of bowties with 10 to 20-nm size gap and single-nanometer variation can be produced

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We optically tweeze and detect single 20-nm polystyrene beads and individual proteins

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Our system allows for long observations (approximately minutes) of protein dynamics

Nanopore electro-osmotic trap for the label-free study of single proteins and their conformations

Many strategies have been pursued to trap and monitor single proteins over time to detect the molecular mechanisms of these essential nanomachines. Single-protein sensing with nanopores is particularly attractive because it allows label-free high-bandwidth detection on the basis of ion currents. Here we present the nanopore electro-osmotic trap (NEOtrap) that allows trapping and observing single proteins for hours with submillisecond time resolution. The NEOtrap is formed by docking a DNA-origami sphere onto a passivated solid-state nanopore, which seals off a nanocavity of a user-defined size and creates an electro-osmotic flow that traps nearby particles irrespective of their charge. We demonstrate the NEOtrap's ability to sensitively distinguish proteins on the basis of size and shape, and discriminate between nucleotide-dependent protein conformations, as exemplified by the chaperone protein Hsp90. Given the experimental simplicity and capacity for label-free single-protein detection over the broad bio-relevant time range, the NEOtrap opens new avenues to study the molecular kinetics underlying protein function.

The NEOtrap - en route with a new single-molecule technique

This paper provides a perspective on potential applications of a new single-molecule technique, viz., the nanopore electro-osmotic trap (NEOtrap). This solid-state nanopore-based method uses locally induced electro-osmosis to form a hydrodynamic trap for single molecules. Ionic current recordings allow one to study an unlabeled protein or nanoparticle of arbitrary charge that can be held in the nanopore's most sensitive region for very long times. After motivating the need for improved single-molecule technologies, we sketch various possible technical extensions and combinations of the NEOtrap. We lay out diverse applications in biosensing, enzymology, protein folding, protein dynamics, fingerprinting of proteins, detecting post-translational modifications, and all that at the level of single proteins - illustrating the unique versatility and potential of the NEOtrap.

FRETboard: Semisupervised classification of FRET traces

Förster resonance energy transfer (FRET) is a useful phenomenon in biomolecular investigations, as it can be leveraged for nanoscale measurements. The optical signals produced by such experiments can be analyzed by fitting a statistical model. Several software tools exist to fit such models in an unsupervised manner but lack the flexibility to adapt to different experimental setups and require local installations. Here, we propose to fit models to optical signals more intuitively by adopting a semisupervised approach, in which the user interactively guides the model to fit a given data set, and introduce FRETboard, a web tool that allows users to provide such guidance. We show that our approach is able to closely reproduce ground truth FRET statistics in a wide range of simulated single-molecule scenarios and correctly estimate parameters for up to 11 states. On in vitro data, we retrieve parameters identical to those obtained by laborious manual classification in a fraction of the required time. Moreover, we designed FRETboard to be easily extendable to other models, allowing it to adapt to future developments in FRET measurement and analysis.

KERA: analysis tool for multi-process, multi-state single-molecule data

Molecular machines within cells dynamically assemble, disassemble and reorganize. Molecular interactions between their components can be observed at the single-molecule level and quantified using colocalization single-molecule spectroscopy, in which individual labeled molecules are seen transiently associating with a surface-tethered partner, or other total internal reflection fluorescence microscopy approaches in which the interactions elicit changes in fluorescence in the labeled surface-tethered partner. When multiple interacting partners can form ternary, quaternary and higher order complexes, the types of spatial and temporal organization of these complexes can be deduced from the order of appearance and reorganization of the components. Time evolution of complex architectures can be followed by changes in the fluorescence behavior in multiple channels. Here, we describe the kinetic event resolving algorithm (KERA), a software tool for organizing and sorting the discretized fluorescent trajectories from a range of single-molecule experiments. KERA organizes the data in groups by transition patterns, and displays exhaustive dwell time data for each interaction sequence. Enumerating and quantifying sequences of molecular interactions provides important information regarding the underlying mechanism of the assembly, dynamics and architecture of the macromolecular complexes. We demonstrate KERA's utility by analyzing conformational dynamics of two DNA binding proteins: replication protein A and xeroderma pigmentosum complementation group D helicase.

MASH-FRET: A Simplified Approach for Single-Molecule Multiplexing Using FRET

Multiplexed detection has been a big motivation in biomarker analysis as it not only saves cost and labor but also improves the reliability of diagnosis. Among the many approaches for multiplexed detection, fluorescence resonance energy transfer (FRET)-based multiplexing is gaining popularity particularly due to its low background and quantitative nature. Although several FRET-based approaches have been developed for multiplexing, they require either multiple FRET pairs in combination with multiple excitation sources or complicated algorithms to accurately assign signals for individual FRET pairs. Therefore, the need for multiple FRET pairs and multiple excitation sources not only complicates the experimental design but also increases the cost and labor. In this regard, multiplexed sensing by tuning the interdye distance of a single FRET pair could be an ideal solution if identification of multiple FRET efficiencies in a single imaging is possible. Here, implementing a program called MASH-FRET, we evaluated the rigor and capability of this program in identifying seemingly overlapped FRET populations obtained from a multiplexed detection experiment using a single FRET pair. Through MASH-FRET-enabled bootstrap-based analysis of FRET data (also called BOBA-FRET), we demonstrated that the resolution and statistical confidence of the poorly resolved or even unresolved FRET populations can be readily determined. Using simulated FRET data, we further demonstrated that the program can easily identify FRET populations separated by ∼0.1 in mean FRET values, indicating an upper limit of ∼9-fold multiplexing without the need for complicated labeling schemes and multiexcitation sources. Therefore, this paper presents a data analysis approach on an existing platform that has a great potential to simplify the technological needs for multiplexing and to broaden the scope of FRET-based single-molecule analyses.

A Python Toolbox for Unbiased Statistical Analysis of Fluorescence Intermittency of Multilevel Emitters

We report on a Python toolbox for unbiased statistical analysis of fluorescence intermittency properties of single emitters. Intermittency, that is, step-wise temporal variations in the instantaneous emission intensity and fluorescence decay rate properties, is common to organic fluorophores, II-VI quantum dots, and perovskite quantum dots alike. Unbiased statistical analysis of intermittency switching time distributions, involved levels, and lifetimes are important to avoid interpretation artifacts. This work provides an implementation of Bayesian changepoint analysis and level clustering applicable to time-tagged single-photon detection data of single emitters that can be applied to real experimental data and as a tool to verify the ramifications of hypothesized mechanistic intermittency models. We provide a detailed Monte Carlo analysis to illustrate these statistics tools and to benchmark the extent to which conclusions can be drawn on the photophysics of highly complex systems, such as perovskite quantum dots that switch between a plethora of states instead of just two.

AutoStepfinder: A fast and automated step detection method for single-molecule analysis

Single-molecule techniques allow the visualization of the molecular dynamics of nucleic acids and proteins with high spatiotemporal resolution. Valuable kinetic information of biomolecules can be obtained when the discrete states within single-molecule time trajectories are determined. Here, we present a fast, automated, and bias-free step detection method, AutoStepfinder, that determines steps in large datasets without requiring prior knowledge on the noise contributions and location of steps. The analysis is based on a series of partition events that minimize the difference between the data and the fit. A dual-pass strategy determines the optimal fit and allows AutoStepfinder to detect steps of a wide variety of sizes. We demonstrate step detection for a broad variety of experimental traces. The user-friendly interface and the automated detection of AutoStepfinder provides a robust analysis procedure that enables anyone without programming knowledge to generate step fits and informative plots in less than an hour.

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Fast, automated, and bias-free detection of steps within single-molecule trajectories

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Robust step detection without any prior knowledge on the data

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A dual-pass strategy for the detection of steps over a wide variety of scales

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A user-friendly interface for a simplified step fitting procedure

Single-molecule techniques have made it possible to track individual protein complexes in real time with a nanometer spatial resolution and a millisecond timescale. Accurate determination of the dynamic states within single-molecule time traces provides valuable kinetic information that underlie the function of biological macromolecules. Here, we present a new automated step detection method called AutoStepfinder, a versatile, robust, and easy-to-use algorithm that allows researchers to determine the kinetic states within single-molecule time trajectories without any bias.

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

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.

Hierarchical dynamics in allostery following ATP hydrolysis monitored by single molecule FRET measurements and MD simulations

We report on a study that combines advanced fluorescence methods with molecular dynamics (MD) simulations to cover timescales from nanoseconds to milliseconds for a large protein. This allows us to delineate how ATP hydrolysis in a protein causes allosteric changes at a distant protein binding site, using the chaperone Hsp90 as test system. The allosteric process occurs via hierarchical dynamics involving timescales from nano- to milliseconds and length scales from Ångstroms to several nanometers. We find that hydrolysis of one ATP is coupled to a conformational change of Arg380, which in turn passes structural information via the large M-domain α-helix to the whole protein. The resulting structural asymmetry in Hsp90 leads to the collapse of a central folding substrate binding site, causing the formation of a novel collapsed state (closed state B) that we characterise structurally. We presume that similar hierarchical mechanisms are fundamental for information transfer induced by ATP hydrolysis through many other proteins.

DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning

Single-molecule Förster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated, open-source standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histograms of biomolecule behavior, is a user-adjustable quality threshold. Integrating standard features of smFRET analysis, DeepFRET consequently outputs the common kinetic information metrics. Its classification accuracy on ground truth data reached >95% outperforming human operators and commonly used threshold, only requiring ~1% of the time. Its precise and rapid operation on real data demonstrates DeepFRET's capacity to objectively quantify biomolecular dynamics and the potential to contribute to benchmarking smFRET for dynamic structural biology.

Controlling protein function by fine-tuning conformational flexibility

In a living cell, protein function is regulated in several ways, including post-translational modifications (PTMs), protein-protein interaction, or by the global environment (e.g. crowding or phase separation). While site-specific PTMs act very locally on the protein, specific protein interactions typically affect larger (sub-)domains, and global changes affect the whole protein non-specifically. Herein, we directly observe protein regulation under three different degrees of localization, and present the effects on the Hsp90 chaperone system at the levels of conformational steady states, kinetics and protein function. Interestingly using single-molecule FRET, we find that similar functional and conformational steady states are caused by completely different underlying kinetics. We disentangle specific and non-specific effects that control Hsp90’s ATPase function, which has remained a puzzle up to now. Lastly, we introduce a new mechanistic concept: functional stimulation through conformational confinement. Our results demonstrate how cellular protein regulation works by fine-tuning the conformational state space of proteins.

Proteins play a wide variety of roles in the cell and interact with many other molecules. The behavior of proteins depends on their structure; yet, proteins are often flexible and will change shape, much like a tree in the wind. Nevertheless, for some of the activities that it performs, a protein must adopt one specific shape. Therefore, the likelihood that the protein will take on this specific shape directly determines how efficiently that protein can perform a specific job.

The shape of a protein can be regulated by changes at several levels; these could include modifying one of the amino acid building blocks that make up that protein, binding to another protein, or by placing the protein in a part of the cell that is crowded with other large molecules. Schmid and Hugel wanted to understand how these three different types of regulation affect the structure of a protein and how they relate to its activities.

The protein Hsp90 was used as a test case. It typically exists with two copies of the protein bound together, either in a parallel or a V-shape. Hsp90 plays several important roles in metabolism and can break down molecules of ATP, the so-called energy currency of the cell. All three types of regulation favored the Hsp90 pairs taking the parallel structure and increased its breakdown of ATP. The results suggest that the Hsp90 pair has a flexible structure, and that reducing this flexibility can improve Hsp90’s efficiency in carrying out its role.

It was particularly unexpected that the large-scale, unspecific effect of placing the protein in a crowded environment could have such similar results to a small-scale, precise change of a single amino acid within the protein. While all three forms of regulation help to stabilize the parallel structure for Hsp90, they do this through different mechanisms, which influence the speed and the way that the protein transitions between the two structures. Schmid and Hugel believe that these results offer a new perspective on how diversely the shape and function of proteins is controlled at the molecular level, which could have wider implications for medical diagnostics and treatment.

Nucleotide Binding and Active Site Gate Dynamics for the Hsp90 Chaperone ATPase Domain from Benchtop and High Field 19F NMR Spectroscopy

Protein turnover in cells is regulated by the ATP dependent activity of the Hsp90 chaperone. In concert with accessory proteins, ATP hydrolysis drives the obligate Hsp90 dimer through a cycle between open and closed states that is critical for assisting the folding and stability of hundreds of proteins. Cycling is initiated by ATP binding to the ATPase domain, with the chaperone and the active site gates in the dimer in open states. The chaperone then adopts a short-lived, ATP bound closed state with a closed active site gate. The structural and dynamic changes induced in the ATPase domain and active site gate upon nucleotide binding, and their impact on dimer closing are not well understood. We site-specifically ¹⁹F-labeled the ATPase domain at the active site gate to enable benchtop and high field ¹⁹F NMR spectroscopic studies. Combined with MD simulations, this allowed accurate characterization of pico- to nanosecond time scale motions of the active site gate, as well as slower micro- to millisecond time scale processes resulting from nucleotide binding. ATP binding induces increased flexibility at one of the hinges of the active site gate, a necessary prelude to release of the second hinge and eventual gate closure in the intact chaperone.

Metal ions and sugar puckering balance single-molecule kinetic heterogeneity in RNA and DNA tertiary contacts

The fidelity of group II intron self-splicing and retrohoming relies on long-range tertiary interactions between the intron and its flanking exons. By single-molecule FRET, we explore the binding kinetics of the most important, structurally conserved contact, the exon and intron binding site 1 (EBS1/IBS1). A comparison of RNA-RNA and RNA-DNA hybrid contacts identifies transient metal ion binding as a major source of kinetic heterogeneity which typically appears in the form of degenerate FRET states. Molecular dynamics simulations suggest a structural link between heterogeneity and the sugar conformation at the exon-intron binding interface. While Mg2+ ions lock the exon in place and give rise to long dwell times in the exon bound FRET state, sugar puckering alleviates this structural rigidity and likely promotes exon release. The interplay of sugar puckering and metal ion coordination may be an important mechanism to balance binding affinities of RNA and DNA interactions in general.

Single-stranded regions modulate conformational dynamics and ATPase activity of eIF4A to optimize 5'-UTR unwinding

Eukaryotic translation initiation requires unwinding of secondary structures in the 5′-untranslated region of mRNA. The DEAD-box helicase eIF4A is thought to unwind structural elements in the 5′-UTR in conjunction with eIF4G and eIF4B. Both factors jointly stimulate eIF4A activities by modulation of eIF4A conformational cycling between open and closed states. Here we examine how RNA substrates modulate eIF4A activities. The RNAs fall into two classes: Short RNAs only partially stimulate the eIF4A ATPase activity, and closing is rate-limiting for the conformational cycle. By contrast, longer RNAs maximally stimulate ATP hydrolysis and promote closing of eIF4A. Strikingly, the rate constants of unwinding do not correlate with the length of a single-stranded region preceding a duplex, but reach a maximum for RNA with a single-stranded region of six nucleotides. We propose a model in which RNA substrates affect eIF4A activities by modulating the kinetic partitioning of eIF4A between futile, unproductive, and productive cycles.

Conformational Cycling within the Closed State of Grp94, an Hsp90-Family Chaperone

The Hsp90 family of chaperones requires ATP-driven cycling to perform their function. The presence of two bound ATP molecules is known to favor a closed conformation of the Hsp90 dimer. However, the structural and mechanistic consequences of subsequent ATP hydrolysis are poorly understood. Using single-molecule FRET, we discover novel dynamic behavior in the closed state of Grp94, the Hsp90 family member resident in the endoplasmic reticulum. Under ATP turnover conditions, Grp94 populates two distinct closed states, a relatively static ATP/ATP closed state that adopts one conformation, and a dynamic ATP/ADP closed state that can adopt two conformations. We constructed a Grp94 heterodimer with one arm that is catalytically dead, to extend the lifetime of the ATP/ADP state by preventing hydrolysis of the second ATP. This construct shows prolonged periods of cycling between two closed conformations. Our results enable a quantitative description of how ATP hydrolysis influences Grp94, where sequential ATP hydrolysis steps allow Grp94 to transition between closed states with different dynamic and structural properties. This stepwise transitioning of Grp94's dynamic properties may provide a mechanism to propagate structural changes to a bound client protein.

Bayesian-Estimated Hierarchical HMMs Enable Robust Analysis of Single-Molecule Kinetic Heterogeneity

Single-molecule kinetic experiments allow the reaction trajectories of individual biomolecules to be directly observed, eliminating the effects of population averaging and providing a powerful approach for elucidating the kinetic mechanisms of biomolecular processes. A major challenge to the analysis and interpretation of these experiments, however, is the kinetic heterogeneity that almost universally complicates the recorded single-molecule signal versus time trajectories (i.e., signal trajectories). Such heterogeneity manifests as changes and/or differences in the transition rates that are observed within individual signal trajectories or across a population of signal trajectories. Because characterizing kinetic heterogeneity can provide critical mechanistic information, we have developed a computational method that effectively and comprehensively enables such analysis. To this end, we have developed a computational algorithm and software program, hFRET, that uses the variational approximation for Bayesian inference to estimate the parameters of a hierarchical hidden Markov model, thereby enabling robust identification and characterization of kinetic heterogeneity. Using simulated signal trajectories, we demonstrate the ability of hFRET to accurately and precisely characterize kinetic heterogeneity. In addition, we use hFRET to analyze experimentally recorded signal trajectories reporting on the conformational dynamics of ribosomal pre-translocation (PRE) complexes. The results of our analyses demonstrate that PRE complexes exhibit kinetic heterogeneity, reveal the physical origins of this heterogeneity, and allow us to expand the current model of PRE complex dynamics. The methods described here can be applied to signal trajectories generated using any type of signal and can be easily extended to the analysis of signal trajectories exhibiting more complex kinetic behaviors. Moreover, variations of our approach can be easily developed to integrate kinetic data obtained from different experimental constructs and/or from molecular dynamics simulations of a biomolecule of interest.

The Hsp90 Chaperone: 1H and 19F Dynamic Nuclear Magnetic Resonance Spectroscopy Reveals a Perfect Enzyme

Hsp90 is a crucial chaperone whose ATPase activity is fundamental for stabilizing and activating a diverse array of client proteins. Binding and hydrolysis of ATP by dimeric Hsp90 drive a conformational cycle characterized by fluctuations between a compact, N- and C-terminally dimerized catalytically competent closed state and a less compact open state that is largely C-terminally dimerized. We used ¹⁹F and ¹H dynamic nuclear magnetic resonance (NMR) spectroscopy to study the opening and closing kinetics of Hsp90 and to determine the k_cat for ATP hydrolysis. We derived a set of coupled ordinary differential equations describing the rate laws for the Hsp90 kinetic cycle and used these to analyze the NMR data. We found that the kinetics of closing and opening for the chaperone are slow and that the lower limit for k_cat of ATP hydrolysis is ∼1 s^-1. Our results show that the chemical step is optimized and that Hsp90 is indeed a "perfect" enzyme.

Analyzing dwell times with the Generalized Method of Moments

The Generalized Method of Moments (GMM) is a statistical method for the analysis of samples from random processes. First developed for the analysis of econometric data, the method is here formulated to extract hidden kinetic parameters from measurements of single molecule dwell times. Our method is based on the analysis of cumulants of the measured dwell times. We develop a general form of an objective function whose minimization can return estimates of decay parameters for any number of intermediates directly from the data. We test the performance of our technique using both simulated and experimental data. We also compare the performance of our method to nonlinear least-squares minimization (NL-LSQM), a commonly-used technique for analysis of single molecule dwell times. Our findings indicate that the GMM performs comparably to NL-LSQM over most of the parameter range we explore. It offers some benefits compared with NL-LSQM in that it does not require binning, exhibits slightly lower bias and variance with small sample sizes (N<20), and is somewhat superior in identifying fast decay times with these same low count data sets. Additionally, a comparison with the Classical Method of Moments (CMM) shows that the CMM can fail in many cases, whereas the GMM always returns estimates. Our results show that the GMM can be a useful tool and complements standard approaches to analysis of single molecule dwell times.

Efficient use of single molecule time traces to resolve kinetic rates, models and uncertainties

Single molecule time traces reveal the time evolution of unsynchronized kinetic systems. Especially single molecule Förster resonance energy transfer (smFRET) provides access to enzymatically important time scales, combined with molecular distance resolution and minimal interference with the sample. Yet the kinetic analysis of smFRET time traces is complicated by experimental shortcomings-such as photo-bleaching and noise. Here we recapitulate the fundamental limits of single molecule fluorescence that render the classic, dwell-time based kinetic analysis unsuitable. In contrast, our Single Molecule Analysis of Complex Kinetic Sequences (SMACKS) considers every data point and combines the information of many short traces in one global kinetic rate model. We demonstrate the potential of SMACKS by resolving the small kinetic effects caused by different ionic strengths in the chaperone protein Hsp90. These results show an unexpected interrelation between conformational dynamics and ATPase activity in Hsp90.

Analyzing the Dynamics of Single TBP-DNA-NC2 Complexes Using Hidden Markov Models

Single-pair Förster resonance energy transfer (spFRET) has become an important tool for investigating conformational dynamics in biological systems. To extract dynamic information from the spFRET traces measured with total internal reflection fluorescence microscopy, we extended the hidden Markov model (HMM) approach. In our extended HMM analysis, we incorporated the photon-shot noise from camera-based systems into the HMM. Thus, the variance in Förster resonance energy transfer (FRET) efficiency of the various states, which is typically a fitted parameter, is explicitly included in the analysis estimated from the number of detected photons. It is also possible to include an additional broadening of the FRET state, which would then only reflect the inherent flexibility of the dynamic biological systems. This approach is useful when comparing the dynamics of individual molecules for which the total intensities vary significantly. We used spFRET with the extended HMM analysis to investigate the dynamics of TATA-box-binding protein (TBP) on promoter DNA in the presence of negative cofactor 2 (NC2). We compared the dynamics of two promoters as well as DNAs of different length and labeling location. For the adenovirus major late promoter, four FRET states were observed; three states correspond to different conformations of the DNA in the TBP-DNA-NC2 complex and a four-state model in which the complex has shifted along the DNA. The HMM analysis revealed that the states are connected via a linear, four-well model. For the H2B promoter, more complex dynamics were observed. By clustering the FRET states detected with the HMM analysis, we could compare the general dynamics observed for the two promoter sequences. We observed that the dynamics from a stretched DNA conformation to a bent conformation for the two promoters were similar, whereas the bent conformation of the TBP-DNA-NC2 complex for the H2B promoter is approximately three times more stable than for the adenovirus major late promoter.

Conformational Dynamics of a Single Protein Monitored for 24 h at Video Rate

We use plasmon rulers to follow the conformational dynamics of a single protein for up to 24 h at a video rate. The plasmon ruler consists of two gold nanospheres connected by a single protein linker. In our experiment, we follow the dynamics of the molecular chaperone heat shock protein 90 (Hsp90), which is known to show "open" and "closed" conformations. Our measurements confirm the previously known conformational dynamics with transition times in the second to minute time scale and reveals new dynamics on the time scale of minutes to hours. Plasmon rulers thus extend the observation bandwidth 3-4 orders of magnitude with respect to single-molecule fluorescence resonance energy transfer and enable the study of molecular dynamics with unprecedented precision.

Effects of Inhibitors on Hsp90's Conformational Dynamics, Cochaperone and Client Interactions

The molecular chaperone and heat-shock protein Hsp90 has become a central target in anti-cancer therapy. Nevertheless, the effect of Hsp90 inhibition is still not understood at the molecular level, preventing a truly rational drug design. Here we report on the effect of the most prominent drug candidates, namely, radicicol, geldanamycin, derivatives of purine, and novobiocin, on Hsp90's characteristic conformational dynamics and the binding of three interaction partners. Unexpectedly, the global opening and closing transitions are hardly affected by Hsp90 inhibitors. Moreover, we find no significant changes in the binding of the cochaperones Aha1 and p23 nor of the model substrate Δ131Δ. This holds true for competitive and allosteric inhibitors. Therefore, direct inhibition mechanisms affecting only one molecular interaction are unlikely. We suggest that the inhibitory action observed in vivo is caused by a combination of subtle effects, which can be used in the search for novel Hsp90 inhibition mechanisms.

Reliable State Identification and State Transition Detection in Fluorescence Intensity-Based Single-Molecule Förster Resonance Energy-Transfer Data

Single-molecule Förster resonance energy transfer (smFRET) is a powerful technique to probe biomolecular structure and dynamics. A popular implementation of smFRET consists of recording fluorescence intensity time traces of surface-immobilized, chromophore-tagged molecules. This approach generates large and complex data sets, the analysis of which is to date not standardized. Here, we address a key challenge in smFRET data analysis: the generation of thermodynamic and kinetic models that describe with statistical rigor the behavior of FRET trajectories recorded from surface-tethered biomolecules in terms of the number of FRET states, the corresponding mean FRET values, and the kinetic rates at which they interconvert. For this purpose, we first perform Monte Carlo simulations to generate smFRET trajectories, in which a relevant space of experimental parameters is explored. Then, we provide an account on current strategies to achieve such model selection, as well as a quantitative assessment of their performances. Specifically, we evaluate the performance of each algorithm (change-point analysis, STaSI, HaMMy, vbFRET, and ebFRET) with respect to accuracy, reproducibility, and computing time, which yields a range of algorithm-specific referential benchmarks for various data qualities. Data simulation and analysis were performed with our MATLAB-based multifunctional analysis software for handling smFRET data (MASH-FRET).

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Single-molecule Förster resonance energy transfer (smFRET) has become a widely used biophysical technique to study the dynamics of biomolecules. For many molecular machines in a cell proteins have to act together with interaction partners in a functional cycle to fulfill their task. The extension of two-color to multi-color smFRET makes it possible to simultaneously probe more than one interaction or conformational change. This not only adds a new dimension to smFRET experiments but it also offers the unique possibility to directly study the sequence of events and to detect correlated interactions when using an immobilized sample and a total internal reflection fluorescence microscope (TIRFM). Therefore, multi-color smFRET is a versatile tool for studying biomolecular complexes in a quantitative manner and in a previously unachievable detail. Here, we demonstrate how to overcome the special challenges of multi-color smFRET experiments on proteins. We present detailed protocols for obtaining the data and for extracting kinetic information. This includes trace selection criteria, state separation, and the recovery of state trajectories from the noisy data using a 3D ensemble Hidden Markov Model (HMM). Compared to other methods, the kinetic information is not recovered from dwell time histograms but directly from the HMM. The maximum likelihood framework allows us to critically evaluate the kinetic model and to provide meaningful uncertainties for the rates. By applying our method to the heat shock protein 90 (Hsp90), we are able to disentangle the nucleotide binding and the global conformational changes of the protein. This allows us to directly observe the cooperativity between the two nucleotide binding pockets of the Hsp90 dimer.

Cooperative Nucleotide Binding in Hsp90 and Its Regulation by Aha1

The function of the molecular chaperone Hsp90 depends on large conformational changes, the rearrangement of local motifs, and the binding and hydrolysis of ATP. The size and complexity of the Hsp90 system impedes the detailed investigation of their interplay using standard methods. To overcome this limitation, we developed a three-color single-molecule FRET assay to study the interaction of Hsp90 with a fluorescently labeled reporter nucleotide in detail. It allows us to directly observe the cooperativity between the two nucleotide binding pockets in the protein dimer. Furthermore, our approach disentangles the protein conformation and the nucleotide binding state of Hsp90 and extracts the kinetics of the state transitions. Thereby, we can identify the kinetic causes mediating the cooperativity. We find that the presence of the first nucleotide prolongs the binding of the second nucleotide to Hsp90. In addition, we observe changes in the kinetics for both the open and the closed conformation of Hsp90 in dependence on the number of occupied nucleotide binding sites. Our analysis also reveals how the co-chaperone Aha1, known to accelerate Hsp90's ATPase activity, affects those transitions in a nucleotide-dependent and independent manner, thereby adding another layer of regulation to Hsp90.