Single-molecule Förster resonance energy transfer (smFRET) is increasingly being used to determine distances, structures, and dynamics of biomolecules in vitro and in vivo. However, generalized protocols and FRET standards to ensure the reproducibility and accuracy of measurements of FRET efficiencies are currently lacking. Here we report the results of a comparative blind study in which 20 labs determined the FRET efficiencies (E) of several dye-labeled DNA duplexes. Using a unified, straightforward method, we obtained FRET efficiencies with s.d. between ±0.02 and ±0.05. We suggest experimental and computational procedures for converting FRET efficiencies into accurate distances, and discuss potential uncertainties in the experiment and the modeling. Our quantitative assessment of the reproducibility of intensity-based smFRET measurements and a unified correction procedure represents an important step toward the validation of distance networks, with the ultimate aim of achieving reliable structural models of biomolecular systems by smFRET-based hybrid methods.

Development of biologically relevant crowding solutions necessitates improved understanding of how the relative size and density of mobile obstacles affect probe diffusion. Both the crowding density and relative size of each co-solute in a mixture will contribute to the measured microviscosity as assessed by altered translational mobility. Using multiphoton fluorescent correlation spectroscopy, this study addresses how excluded volume of dextran polymers from 10 to 500 kDa affect microviscosity quantified by measurements of calmodulin labeled with green fluorescent protein as the diffusing probe. Autocorrelation functions were fit using both a multiple-component model with maximum entropy method (MEMFCS) and an anomalous model. Anomalous diffusion was not detected, but fits of the data with the multiple-component model revealed separable modes of diffusion. When the dominant mode of diffusion from the MEMFCS analysis was used, we observed that increased excluded volume slows probe mobility as a simple exponential with crowder concentration. This behavior can be modeled with a single parameter, β, which depends on the dextran size composition. Two additional modes of diffusion were observed using MEMFCS and were interpreted as unique microviscosities. The fast mode corresponded to unhindered free diffusion as in buffer, whereas the slower agreed well with the bulk viscosity. At 10% crowder concentration, one finds a microviscosity approximately three times that of water, which mimics that reported for intracellular viscosity.

An analysis method of lifetime, polarization and spectrally filtered fluorescence correlation spectroscopy, referred to as filtered FCS (fFCS), is introduced. It uses, but is not limited to, multiparameter fluorescence detection to differentiate between molecular species with respect to their fluorescence lifetime, polarization and spectral information. Like the recently introduced fluorescence lifetime correlation spectroscopy (FLCS) [Chem. Phys. Lett. 2002, 353, 439–445], fFCS is based on pulsed laser excitation. However, it uses the species-specific polarization and spectrally resolved fluorescence decays to generate filters. We determined the most efficient method to generate global filters taking into account the anisotropy information. Thus, fFCS is able to distinguish species, even if they have very close or the same fluorescence lifetime, given differences in other fluorescence parameters. fFCS can be applied as a tool to compute species-specific auto- (SACF) and cross- correlation (SCCF) functions from a mixture of different species for accurate and quantitative analysis of their concentration, diffusion and kinetic properties. The computed correlation curves are also free from artifacts caused by unspecific background signal. We tested this methodology by simulating the extreme case of ligand–receptor binding processes monitored only by differences in fluorescence anisotropy. Furthermore, we apply fFCS to an experimental single-molecule FRET study of an open-to-closed conformational transition of the protein Syntaxin-1. In conclusion, fFCS and the global analysis of the SACFs and SCCF is a key tool to investigate binding processes and conformational dynamics of biomolecules in a nanosecond-to-millisecond time range as well as to unravel the involved molecular states.

p27 Kip1 is an intrinsically disordered protein (IDP) that inhibits cyclin-dependent kinase (Cdk)/cyclin complexes (e.g., Cdk2/cyclin A), causing cell cycle arrest. Cell division progresses when stably Cdk2/cyclin A-bound p27 is phosphorylated on one or two structurally occluded tyrosine residues and a distal threonine residue (T187), triggering degradation of p27. Here, using an integrated biophysical approach, we show that Cdk2/cyclin A-bound p27 samples lowly-populated conformations that provide access to the non-receptor tyrosine kinases, BCR-ABL and Src, which phosphorylate Y88 or Y88 and Y74, respectively, thereby promoting intra-assembly phosphorylation (of p27) on distal T187. Even when tightly bound to Cdk2/cyclin A, intrinsic flexibility enables p27 to integrate and process signaling inputs, and generate outputs including altered Cdk2 activity, p27 stability, and, ultimately, cell cycle progression. Intrinsic dynamics within multi-component assemblies may be a general mechanism of signaling by regulatory IDPs, which can be subverted in human disease.

We used a hybrid fluorescence spectroscopic toolkit to monitor T4 Lysozyme (T4L) in action. By unraveling the kinetic and dynamic interplay of the conformational states, we sought to elucidate the dynamic structural biology of T4L. In particular, by combining singlemolecule and ensemble multiparameter fluorescence detection, EPR spectroscopy, mutagenesis, and FRET-positioning and screening, we characterized three short-lived conformational states within the conformational landscape of the T4L over the ns-ms timescale. The use of 33 FRET-derived distance sets, to screen known T4L structures, revealed that T4L in solution mainly adopts the known open and closed states in exchange at 4 µs. A newly found minor state, undisclosed by at present more than 500 crystal structures of T4L and sampled at 230 µs, may be actively involved in the product release step in catalysis. The presented fluorescence spectroscopic toolkit is anticipated to accelerate the development of dynamic structural biology.

One of the key questions regarding intracellular diffusion is how the environment affects molecular mobility. Mostly, intracellular diffusion has been described as hindered, and the physical reasons for this behavior are: immobile barriers, molecular crowding, and binding interactions with immobile or mobile molecules. Using results from multi-photon fluorescence correlation spectroscopy, we describe how immobile barriers and crowding agents affect translational mobility. To study the hindrance produced by immobile barriers, we used sol-gels (silica nanostructures) that consist of a continuous solid phase and aqueous phase in which fluorescently tagged molecules diffuse. In the case of molecular crowding, translational mobility was assessed in increasing concentrations of 500 kDa dextran solutions. Diffusion of fluorescent tracers in both sol-gels and dextran solutions shows clear evidence of anomalous subdiffusion. In addition, data from the autocorrelation function were analyzed using the maximum entropy method as adapted to fluorescence correlation spectroscopy data and compared with the standard model that incorporates anomalous diffusion. The maximum entropy method revealed evidence of different diffusion mechanisms that had not been revealed using the anomalous diffusion model. These mechanisms likely correspond to nanostructuring in crowded environments and to the relative dimensions of the crowding agent with respect to the tracer molecule. Analysis with the maximum entropy method also revealed information about the degree of heterogeneity in the environment as reported by the behavior of diffusive molecules.

Ca2؉ -Calmodulin-dependent protein kinase II (CaMKII) is an abundant synaptic protein that was recently shown to regulate the organization of actin filaments leading to structural modifications of synapses. CaMKII is a dodecameric complex with a special architecture that provides it with unique potential for organizing the actin cytoskeleton. We report using biochemical assays that the ␤ isoform of CaMKII binds to and bundles actin filaments, and the disposition of ␤CaMKII within the actin bundles was revealed by cryoelectron tomography. In addition, ␤CaMKII was found to inhibit actin polymerization, suggesting that it either serves as a capping protein or binds monomeric actin, reducing the amount of freely available monomers to nucleate polymer assembly. By means of fluorescent cross-correlation spectroscopy, we determined that ␤CaMKII does indeed bind to monomeric actin, reaching saturation at a stoichiometry of 12:1 actin monomers per ␤CaMKII holoenzyme with a binding constant of 2.4 ؋ 10 5 M ؊1 . In cells, ␤CaMKII has a dual functional role; it can sequester monomeric actin to reduce actin polymerization and can also bundle actin filaments. Together, these effects would impact both the dynamics of actin filament assembly and enhance the rigidity of the filaments once formed, significantly impacting the structure of synapses.Actin and more than 60 different classes of actin-binding proteins form an abundant and highly regulated cytoskeletal network, constituting more than 25% of protein in non-muscle cells and over 60% in muscle cells (1). Actin is estimated to be ϳ4 mg/ml in cells, with half found in the polymerized F-actin state and the remainder in the soluble G-actin state (2). Actin undergoes rounds of polymerization and depolymerization from a soluble (G-actin) pool to a filamentous (F-actin) pool, and it is the kinetics of these forward and backward rates that determines the stability of the actin cytoskeleton. The various actin-binding proteins are, in part, responsible for regulating the kinetics of actin polymerization and depolymerization. Actin is commonly known for its structural and dynamic function in cells, such as its role in muscle contraction and cell locomotion, and has been similarly implicated for its structural role at synapses in the nervous system (3, 4). For example, actin is proposed to serve an important role in coordinating the delivery of synaptic vesicles to the pre-synaptic terminal that appear to underlie certain forms of plasticity mediated by alterations in the probability of stimulus evoked vesicle release (5-7). Additionally, several recent studies have demonstrated the importance of actin dynamics in structural reorganization of the post-synaptic spine compartment of excitatory synapses (8 -10). Several pools of actin have been identified within spines with different turnover rates, and activation of glutamate receptors on the spines regulates the kinetics of actin turnover (9, 10). Spine shape changes have been proposed to underlie forms of synaptic plasticity such as l...

FRET experiments can provide state-specific structural information of complex dynamic biomolecular assemblies. However, to overcome the sparsity of FRET experiments, they need to be combined with computer simulations. We introduce a program suite with (i) an automated design tool for FRET experiments, which determines how many and which FRET pairs should be used to minimize the uncertainty and maximize the accuracy of an integrative structure, (ii) an efficient approach for FRET-assisted coarse-grained structural modeling, and all-atom molecular dynamics simulations-based refinement, and (iii) a quantitative quality estimate for judging the accuracy of FRET-derived structures as opposed to precision. We benchmark our tools against simulated and experimental data of proteins with multiple conformational states and demonstrate an accuracy of ~3 Å RMSDCα against X-ray structures for sets of 15 to 23 FRET pairs. Free and open-source software for the introduced workflow is available at https://github.com/Fluorescence-Tools. A web server for FRET-assisted structural modeling of proteins is available at http://nmsim.de.