Kinetic definition of protein folding transition state ensembles and reaction coordinates

Using distributed molecular dynamics simulations we located four distinct folding transitions for a 39-residue betabetaalphabeta protein fold. To characterize the nature of each room temperature transition, we calculated the probability of transmission for 500 points along each free energy barrier. We introduced a method for determining transition states by employing the transmission probability, Ptrans, and determined which conformations were transition state ensemble members (Ptrans approximately 0.5). The transmission probability may be used to characterize the barrier in several ways. For example, we ran simulations at 82 degrees C, determined the change in Ptrans with temperature for all 2,000 conformations, and quantified Hammond behavior directly using Ptrans correlation. Additionally, we propose that diffusion along Ptrans may provide the configurational diffusion rate at the top of the barrier. Specifically, given a transition state conformation x0 with estimated Ptrans=0.5, we selected a large set of subsequent conformations from independent trajectories, each exactly a small time deltat after x0 (250 ps). Calculating Ptrans for the new trial conformations, we generated the P(Ptrans|deltat=250 ps) distribution that reflected diffusion. This approach provides a novel perspective on the diffusive nature of a protein folding transition and provides a framework for a quantitative study of activated relaxation kinetics.     

A J-modulated protonless NMR experiment characterizes the conformational ensemble of the intrinsically disordered protein WIP

Intrinsically disordered proteins (IDPs) are multi-conformational polypeptides that lack a single stable three-dimensional structure. It has become increasingly clear that the versatile IDPs play key roles in a multitude of biological processes, and, given their flexible nature, NMR is a leading method to investigate IDP behavior on the molecular level. Here we present an IDP-tailored J-modulated experiment designed to monitor changes in the conformational ensemble characteristic of IDPs by accurately measuring backbone one- and two-bond J(¹⁵N,¹³Cα) couplings. This concept was realized using a unidirectional (H)NCO ¹³C-detected experiment suitable for poor spectral dispersion and optimized for maximum coverage of amino acid types. To demonstrate the utility of this approach we applied it to the disordered actin-binding N-terminal domain of WASp interacting protein (WIP), a ubiquitous key modulator of cytoskeletal changes in a range of biological systems. One- and two-bond J(¹⁵N,¹³Cα) couplings were acquired for WIP residues 2–65 at various temperatures, and in denaturing and crowding environments. Under native conditions fitted J-couplings identified in the WIP conformational ensemble a propensity for extended conformation at residues 16–23 and 45–60, and a helical tendency at residues 28–42. These findings are consistent with a previous study of the based upon chemical shift and RDC data and confirm that the WIP^2–65 conformational ensemble is biased towards the structure assumed by this fragment in its actin-bound form. The effects of environmental changes upon this ensemble were readily apparent in the J-coupling data, which reflected a significant decrease in structural propensity at higher temperatures, in the presence of 8 M urea, and under the influence of a bacterial cell lysate. The latter suggests that crowding can cause protein unfolding through protein–protein interactions that stabilize the unfolded state. We conclude that J-couplings are a useful measureable in characterizing structural ensembles in IDPs, and that the proposed experiment provides a practical method for accurately performing such measurements, once again emphasizing the power of NMR in studying IDP behavior.     

Novel strategy to improve hepatocyte differentiation stability through synchronized behavior-driven mechanical memory of iPSCs

Cellular homeostasis is assumed to be regulated by the coordination of dynamic behaviors. Lack of efficient methods for synchronizing large quantities of cells makes studying cell culture strategies for bioprocess development challenging. Here, we demonstrate a novel application of botulinum hemagglutinin (HA), an E-cadherin function-blocking agent, to synchronize behavior-driven mechanical memory in human induced pluripotent stem cell (hiPSC) cultures. Application of HA to hiPSCs resulted in a decrease in actin bundling and disruption of colony formation in a concentration-and time-dependent manner. Interestingly, cytoskeleton rearrangement in cells with prolonged exposure to HA resulted in mechanical memory synchronization with Yes-associated protein, which increased pluripotent cell homogeneity. Synchronized hiPSCs have higher capability to differentiate into functional hepatocytes than unsynchronized hiPSCs, resulting in improved efficiency and robustness of hepatocyte differentiation. Thus, our strategy for cell behavior synchronization before differentiation induction provides an approach against the instability of differentiation of pluripotent cells.     

Dynamic changes in social dominance and mPOA GnRH expression in male mice following social opportunity

Social competence - the ability of animals to dynamically adjust their social behavior dependent on the current social context – is fundamental to the successful establishment and maintenance of social relationships in group-living species. The social opportunity paradigm, where animals rapidly ascend a social hierarchy following the removal of more dominant individuals, is a well-established approach for studying the neural and neuroendocrine mechanisms underlying socially competent behavior. In the current study, we demonstrate that this paradigm can be successfully adapted for studying socially competent behavior in laboratory mice. Replicating our previous reports, we show that male laboratory mice housed in a semi-natural environment form stable linear social hierarchies. Novel to the current study, we find that subdominant male mice immediately respond to the removal of the alpha male from a hierarchy by initiating a dramatic increase in aggressive behavior towards more subordinate individuals. Consequently, subdominants assume the role of the alpha male. Analysis of brain gene expression in individuals 1 h following social ascent indicates elevated gonadotropin-releasing hormone (GnRH) mRNA levels in the medial preoptic area (mPOA) of the hypothalamus compared to individuals that do not experience a social opportunity. Moreover, hormonal analyses indicate that subdominant individuals have increased circulating plasma testosterone levels compared to subordinate individuals. Our findings demonstrate that male mice are able to dynamically and rapidly adjust both behavior and neuroendocrine function in response to changes in social context. Further, we establish the social opportunity paradigm as an ethologically relevant approach for studying social competence and behavioral plasticity in mammals.     

Folding energetics of ligand binding proteins. I. Theoretical model

Heat capacity curves as obtained from differential scanning calorimetry are an outstanding source for molecular information on protein folding and ligand-binding energetics. However, deconvolution of C(p) data of proteins in the presence of ligands can be compromised by indeterminacies concerning the correct choice of the statistical thermodynamic ensemble. By convent, the assumption of constant free ligand concentration has been used to derive formulae for the enthalpy. Unless the ligand occurs at large excess, this assumption is incorrect. Still the relevant ensemble is the grand canonical ensemble. We derive formulae for both constraints, constancy of total or free ligand concentration and illustrate the equations by application to the typical equilibrium Nx <=> N + x <=> D + x. It is demonstrated that as long as the thermodynamic properties of the ligand can be completely corrected for by performing a reference measurement, the grand canonical approach provides the proper and mathematically significantly simpler choice. We demonstrate on the two cases of sequential or independent ligand-binding the fact, that similar binding mechanisms result in different and distinguishable heat capacity equations. Finally, we propose adequate strategies for DSC experiments as well as for obtaining first estimates of the characteristic thermodynamic parameters, which can be used as starting values in a global fit of DSC data.     

Single-molecule FRET study of denaturant induced unfolding of RNase H

Single-molecule fluorescence (F?rster) resonance energy transfer (FRET) experiments were performed on surface-immobilized RNase H molecules as a function of the concentration of the chemical denaturant guanidinium chloride (GdmCl). For comparison, we measured ensemble FRET on RNase H solutions. The single-molecule approach allowed us to study FRET distributions of the subpopulation of unfolded molecules without interference from the folded population. The unfolded ensemble experienced a continuous shift of the FRET efficiency distribution with increasing concentration of GdmCl, indicating a heterogeneous population of expanding, unfolded polypeptide chains. We have analyzed the behavior of the unfolded state quantitatively with a model in which the unfolded state is described by a continuum of substates, with the free energy of each substate linearly coupled to its m-value, the proportionality coefficient between free energy and denaturant activity. By fitting this model to the data, we have derived energetic and structural parameters that describe the unfolded state ensemble. Specifically, we have found that the average size of the unfolded state increases from 23-38 A between 0 and 6 M denaturant. Excellent agreement was achieved between the fitted model and our FRET measurements, and with previously published nuclear magnetic resonance and small-angle X-ray scattering data.     

Quantification of leakage from large unilamellar lipid vesicles by fluorescence correlation spectroscopy

Fluorescence correlation spectroscopy (FCS) is a powerful experimental technique that in recent years has found numerous applications for studying biological phenomena. In this article, we scrutinize one of these applications, namely, FCS as a technique for studying leakage of fluorescent molecules from large unilamellar lipid vesicles. Specifically, we derive the mathematical framework required for using FCS to quantify leakage of fluorescent molecules from large unilamellar lipid vesicles, and we describe the appropriate methodology for successful completion of FCS experiments. By use of this methodology, we show that FCS can be used to accurately quantify leakage of fluorescent molecules from large unilamellar lipid vesicles, including leakage of fluorescent molecules of different sizes. To demonstrate the applicability of FCS, we have investigated the antimicrobial peptide mastoparan X. We show that mastoparan X forms transient transmembrane pores in POPC/POPG (3:1) vesicles, resulting in size-dependent leakage of molecules from the vesicles. We conclude the paper by discussing some of the advantages and limitations of FCS as compared to other existing methods to measure leakage from large unilamellar lipid vesicles.     

Systemic properties of ensembles of metabolic networks: application of graphical and statistical methods to simple unbranched pathways

MOTIVATION: Mathematical models are the only realistic method for representing the integrated dynamic behavior of complex biochemical networks. However, it is difficult to obtain a consistent set of values for the parameters that characterize such a model. Even when a set of parameter values exists, the accuracy of the individual values is questionable. Therefore, we were motivated to explore statistical techniques for analyzing the properties of a given model when knowledge of the actual parameter values is lacking. RESULTS: The graphical and statistical methods presented in the previous paper are applied here to simple unbranched biosynthetic pathways subject to control by feedback inhibition. We represent these pathways within a canonical nonlinear formalism that provides a regular structure that is convenient for randomly sampling the parameter space. After constructing a large ensemble of randomly generated sets of parameter values, the structural and behavioral properties of the model with these parameter sets are examined statistically and classified. The results of our analysis demonstrate that certain properties of these systems are strongly correlated, thereby revealing aspects of organization that are highly probable independent of selection. Finally, we show how specification of a given behavior affects the distribution of acceptable parameter values.     

Titin (Visco-) Elasticity in Skeletal Muscle Myofibrils

Titin is the third most abundant protein in sarcomeres and fulfills a number of mechanical and signaling functions. Specifically, titin is responsible for most of the passive forces in sarcomeres and the passive visco-elastic behaviour of myofibrils and muscles. It has been suggested, based on mechanical testing of isolated titin molecules, that titin is an essentially elastic spring if Ig domain un/refolding is prevented either by working at short titin lengths, prior to any unfolding of Ig domains, or at long sarcomere (and titin) lengths when Ig domain un/refolding is effectively prevented. However, these properties of titin, and by extension of muscles, have not been tested with titin in its natural structural environment within a sarcomere. The purpose of this study was to gain insight into the Ig domain un/refolding kinetics and test the idea that titin could behave essentially elastically at any sarcomere length by preventing Ig domain un/refolding during passive stretch-shortening cycles. Although not completely successful, we demonstrate here that titin’s visco-elastic properties appear to depend on the Ig domain un/refolding kinetics and that indeed, titin (and thus myofibrils) can become virtually elastic when Ig domain un/refolding is prevented.     

Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks

The ensemble modeling (EM) approach has shown promise in capturing kinetic and regulatory effects in the modeling of metabolic networks. Efficacy of the EM procedure relies on the identification of model parameterizations that adequately describe all observed metabolic phenotypes upon perturbation. In this study, we propose an optimization-based algorithm for the systematic identification of genetic/enzyme perturbations to maximally reduce the number of models retained in the ensemble after each round of model screening. The key premise here is to design perturbations that will maximally scatter the predicted steady-state fluxes over the ensemble parameterizations. We demonstrate the applicability of this procedure for an Escherichia coli metabolic model of central metabolism by successively identifying single, double, and triple enzyme perturbations that cause the maximum degree of flux separation between models in the ensemble. Results revealed that optimal perturbations are not always located close to reaction(s) whose fluxes are measured, especially when multiple perturbations are considered. In addition, there appears to be a maximum number of simultaneous perturbations beyond which no appreciable increase in the divergence of flux predictions is achieved. Overall, this study provides a systematic way of optimally designing genetic perturbations for populating the ensemble of models with relevant model parameterizations.     

Dynamic patterns of correlated activity in the prefrontal cortex encode information about social behavior

New technologies make it possible to measure activity from many neurons simultaneously. One approach is to analyze simultaneously recorded neurons individually, then group together neurons which increase their activity during similar behaviors into an “ensemble.” However, this notion of an ensemble ignores the ability of neurons to act collectively and encode and transmit information in ways that are not reflected by their individual activity levels. We used microendoscopic GCaMP imaging to measure prefrontal activity while mice were either alone or engaged in social interaction. We developed an approach that combines a neural network classifier and surrogate (shuffled) datasets to characterize how neurons synergistically transmit information about social behavior. Notably, unlike optimal linear classifiers, a neural network classifier with a single linear hidden layer can discriminate network states which differ solely in patterns of coactivity, and not in the activity levels of individual neurons. Using this approach, we found that surrogate datasets which preserve behaviorally specific patterns of coactivity (correlations) outperform those which preserve behaviorally driven changes in activity levels but not correlated activity. Thus, social behavior elicits increases in correlated activity that are not explained simply by the activity levels of the underlying neurons, and prefrontal neurons act collectively to transmit information about socialization via these correlations. Notably, this ability of correlated activity to enhance the information transmitted by neuronal ensembles is diminished in mice lacking the autism-associated gene Shank3. These results show that synergy is an important concept for the coding of social behavior which can be disrupted in disease states, reveal a specific mechanism underlying this synergy (social behavior increases correlated activity within specific ensembles), and outline methods for studying how neurons within an ensemble can work together to encode information.

Behaviorally-specific patterns of correlated activity between prefrontal neurons normally enhance the information that neuronal ensembles transmit about social behavior. This study shows that in a mouse model of autism, individual neurons continue to encode social information, but this additional information carried by patterns of correlated activity is lost.     

Attenuated protein expression vectors for use in siRNA rescue experiments

Transient transfection of small interfering RNA (siRNA) provides a powerful approach for studying cellular protein functions, particularly when the target protein can be re-expressed from an exogenous siRNA-resistant construct in order to rescue the knockdown phenotype, confirm siRNA target specificity, and support mutational analyses. Rescue experiments often fail, however, when siRNA-resistant constructs are expressed at suboptimal levels. Here, we describe an ensemble of mammalian protein expression vectors with CMV promoters of differing strengths. Using CHMP2A rescue of HIV-1 budding, we show that these vectors can combine high-transfection efficiencies with tunable protein expression levels to optimize the rescue of cellular phenotypes induced by siRNA transfection.     

COCO: a simple tool to enrich the representation of conformational variability in NMR structures

NMR structures are typically deposited in databases such as the PDB in the form of an ensemble of structures. Generally, each of the models in such an ensemble satisfies the experimental data and is equally valid. No unique solution can be calculated because the experimental NMR data is insufficient, in part because it reflects the conformational variability and dynamical behavior of the molecule in solution. Even for relatively rigid molecules, the limited number of structures that are typically deposited cannot completely encompass the structural diversity allowed by the observed NMR data, but they can be chosen to try and maximize its representation. We describe here the adaptation and application of techniques more commonly used to examine large ensembles from molecular dynamics simulations, to the analysis of NMR ensembles. The approach, which is based on principal component analysis, we call COCO ("Complementary Coordinates"). The COCO approach analyses the distribution of an NMR ensemble in conformational space, and generates a new ensemble that fills "gaps" in the distribution. The method is very rapid, and analysis of a 25-member ensemble and generation of a new 25 member ensemble typically takes 1-2 min on a conventional workstation. Applied to the 545 structures in the RECOORD database, we find that COCO generates new ensembles that are as structurally diverse-both from each other and from the original ensemble-as are the structures within the original ensemble. The COCO approach does not explicitly take into account the NMR restraint data, yet in tests on selected structures from the RECOORD database, the COCO ensembles are frequently good matches to this data, and certainly are structures that can be rapidly refined against the restraints to yield high-quality, novel solutions. COCO should therefore be a useful aid in NMR structure refinement and in other situations where a richer representation of conformational variability is desired-for example in docking studies. COCO is freely accessible via the website www.ccpb.ac.uk/COCO.     

Ensemble Tractography

Tractography uses diffusion MRI to estimate the trajectory and cortical projection zones of white matter fascicles in the living human brain. There are many different tractography algorithms and each requires the user to set several parameters, such as curvature threshold. Choosing a single algorithm with specific parameters poses two challenges. First, different algorithms and parameter values produce different results. Second, the optimal choice of algorithm and parameter value may differ between different white matter regions or different fascicles, subjects, and acquisition parameters. We propose using ensemble methods to reduce algorithm and parameter dependencies. To do so we separate the processes of fascicle generation and evaluation. Specifically, we analyze the value of creating optimized connectomes by systematically combining candidate streamlines from an ensemble of algorithms (deterministic and probabilistic) and systematically varying parameters (curvature and stopping criterion). The ensemble approach leads to optimized connectomes that provide better cross-validated prediction error of the diffusion MRI data than optimized connectomes generated using a single-algorithm or parameter set. Furthermore, the ensemble approach produces connectomes that contain both short- and long-range fascicles, whereas single-parameter connectomes are biased towards one or the other. In summary, a systematic ensemble tractography approach can produce connectomes that are superior to standard single parameter estimates both for predicting the diffusion measurements and estimating white matter fascicles.     

Determining the binding capability of the mouse major urinary proteins using 2-naphthol as a fluorescent probe

The mouse major urinary proteins (MUPs) are an ensemble of isoforms secreted by adult male mice and involved in sexual olfactory communication. MUPs belong to the lipocalin superfamily, whose conserved structure is a beta-barrel made of eight antiparallel beta-strands forming a hydrophobic pocket that accommodates small organic molecules. A detailed knowledge of the molecular mechanism associated to the binding of those molecules can guide protein engineering to devise mutated proteins where the ligand specificity, binding affinity, and release rate can be modulated. Proteins with such peculiar properties may have interesting biotechnological applications for pest control, as well as in food and cosmetic industries. In this work, we demonstrate that the fluorescent molecule 2-naphthol binds to the natural ligand's binding site of MUPs with high affinity. In addition, we show that 2-naphthol binds to MUPs in its protonated form, that its fluorescence is blue-shifted, and the quantum yield is increased, thus confirming the high hydrophobicity of the protein pocket and the absence of proton acceptors inside the binding site. At large the results presented, besides demonstrating that the use of 2-naphthol provides a convenient and quick method for testing MUPs binding activity and to ascertain the quality of the protein preparation, suggest that MUPs can represent an interesting system for studying the photophysical characteristics of fluorescent molecules in a highly hydrophobic environment.     

Microtubule-dependent transport and organization of sarcomeric myosin during skeletal muscle differentiation

It has been proposed that microtubules (MTs) participate in skeletal muscle cell differentiation. However, it is still unclear how this happens. To examine whether MTs could participate directly in the organization of thick and thin filaments into sarcomeres, we observed the concomitant reorganization and dynamics of MTs with the behavior of sarcomeric actin and myosin by time-lapse confocal microscopy. Using green fluorescent protein (GFP)-EB1 protein to label MT plus ends, we determined that MTs become organized into antiparallel arrays along fusing myotubes. Their dynamics and orientation was found to be different across the thickness of the myotubes. We observed fast movements of Dsred-myosin along GFP-MTs. Comparison of GFP-EB1 and Dsred-myosin dynamics revealed that myosin moved toward MT plus ends. Immuno-electron microscopy experiments confirmed that myosin was actually associated with MTs in myotubes. Finally, we confirmed that MTs were required for the stabilization of myosin-containing elements prior to incorporation into mature sarcomeres. Collectively, our results strongly suggest that MTs become organized into a scaffold that provides directional cues for the positioning and organization of myosin filaments during sarcomere formation.     

Analytical methods for structural ensembles and dynamics of intrinsically disordered proteins

Intrinsically disordered proteins, proteins that do not have a well-defined three-dimensional structure, make up a significant proportion of our proteome and are particularly prevalent in signaling and regulation. Although their importance has been realized for two decades, there is a lack of high-resolution experimental data. Molecular dynamics simulations have been crucial in reaching our current understanding of the dynamical structural ensemble sampled by intrinsically disordered proteins. In this review, we discuss enhanced sampling simulation methods that are particularly suitable to characterize the structural ensemble, along with examples of applications and limitations. The dynamics within the ensemble can be rigorously analyzed using Markov state models. We discuss recent developments that make Markov state modeling a viable approach for studying intrinsically disordered proteins. Finally, we briefly discuss challenges and future directions when applying molecular dynamics simulations to study intrinsically disordered proteins.     

Quaternary dynamics of αB-crystallin as a direct consequence of localised tertiary fluctuations in the C-terminus

The majority of proteins exist in vivo within macromolecular assemblies whose functions are dependent on dynamical processes spanning a wide range of time scales. One such assembly is formed by the molecular chaperone αB-crystallin that exists in a variety of exchanging oligomeric states, centred on a mass of approximately 560 kDa. For many macromolecular assemblies, including αB-crystallin, the inherent dynamics, heterogeneity and high mass contribute to difficulties in quantitative studies. Here, we demonstrate a strategy based on correlating solution-state nuclear magnetic resonance spectroscopy and mass spectrometry data to characterize simultaneously the organization and dynamics of the polydisperse αB-crystallin ensemble. We show that protomeric dimers assemble into oligomers via the binding of extended C-termini, with each monomer donating and receiving one terminus. Moreover, we establish that the C-termini undergo millisecond fluctuations that regulate the interconversion of oligomeric forms. The combined biophysical approach allows construction of an energy profile for a single monomer that completely describes the equilibrium dynamics of the ensemble. It also facilitates an analysis of dynamics spanning the millisecond to hour time scales and secondary to quaternary structural levels, and provides an approach for, obtaining simultaneously detailed structural, thermodynamic and kinetic information on a heterogeneous protein assembly.     

Sensitive single-molecule protein quantification and protein complex detection in a microarray format

Single-molecule protein analysis provides sensitive protein quantitation with a digital read-out and is promising for studying biological systems and detecting biomarkers clinically. However, current single-molecule platforms rely on the quantification of one protein at a time. Conventional antibody microarrays are scalable to detect many proteins simultaneously, but they rely on less sensitive and less quantitative quantification by the ensemble averaging of fluorescent molecules. Here, we demonstrate a single-molecule protein assay in a microarray format enabled by an ultra-low background surface and single-molecule imaging. The digital read-out provides a highly sensitive, low femtomolar limit of detection and four orders of magnitude of dynamic range through the use of hybrid digital-analog quantification. From crude cell lysate, we measured levels of p53 and MDM2 in parallel, proving the concept of a digital antibody microarray for use in proteomic profiling. We also applied the single-molecule microarray to detect the p53-MDM2 protein complex in cell lysate. Our study is promising for development and application of single-molecule protein methods because it represents a technological bridge between single-plex and highly multiplex studies.