Biophysical reports最新文献

The thermodynamics of molecular recognition by proteins is a central determinant of complex biochemistry. For over a half-century, detailed cryogenic structures have provided deep insight into the energetic contributions to ligand binding by proteins. More recently, a dynamical proxy based on NMR-relaxation methods has revealed an unexpected richness in the contributions of conformational entropy to the thermodynamics of ligand binding. Here, we report the pressure dependence of fast internal motion within the ribonuclease barnase and its complex with the protein barstar. In what we believe is a first example, we find that protein dynamics are conserved along the pressure-binding thermodynamic cycle. The femtomolar affinity of the barnase-barstar complex exists despite a penalty by -TΔS_conf of +11.7 kJ/mol at ambient pressure. At high pressure, however, the overall change in side-chain dynamics is zero, and binding occurs with no conformational entropy penalty, suggesting an important role of conformational dynamics in the adaptation of protein function to extreme environments. Distinctive clustering of the pressure sensitivity is observed in response to both pressure and binding, indicating the presence of conformational heterogeneity involving less efficiently packed alternative conformation(s). The structural segregation of dynamics observed in barnase is striking and shows how changes in both the magnitude and the sign of regional contributions of conformational entropy to the thermodynamics of protein function are possible.

Human aquaporin 1 (hAQP1) forms homotetrameric channels that facilitate fluxes of water and small solutes across cell membranes. In addition to water channel activity, hAQP1 displays non-selective monovalent cation-channel activity gated by intracellular cyclic GMP. Dual water and ion-channel activity of hAQP1, thought to regulate cell shape and volume, could offer a target for novel therapeutics relevant to controlling cancer cell invasiveness. This study probed properties of hAQP1 ion channels using proteoliposomes, which, unlike conventional cell-based systems such as Xenopus laevis oocytes, are relatively free of background ion channels. Histidine-tagged recombinant hAQP1 protein was synthesized and purified from the methylotrophic yeast, Pichia pastoris, and reconstituted into proteoliposomes for biophysical analyses. Osmotic water channel activity confirmed correct folding and channel assembly. Ion-channel activity of hAQP1-Myc-His₆ was recorded by patch-clamp electrophysiology with excised patches. In symmetrical potassium, the hAQP1-Myc-His₆ channels displayed coordinated gating, a single-channel conductance of approximately 75 pS, and multiple subconductance states. Applicability of this method for structure-function analyses was tested using hAQP1-Myc-His₆ ^D48A/D185A channels modified by site-directed mutations of charged Asp residues estimated to be adjacent to the central ion-conducting pore of the tetramer. No differences in conductance were detected between mutant and wild-type constructs, suggesting the open-state conformation could differ substantially from expectations based on crystal structures. Nonetheless, the method pioneered here for AQP1 demonstrates feasibility for future work defining structure-function relationships, screening pharmacological inhibitors, and testing other classes in the broad family of aquaporins for previously undiscovered ion-conducting capabilities.

Cell-matrix and cell-cell adhesion play important roles in a wide variety of physiological processes, from the single-cell level to the large scale, multicellular organization of tissues. Cells actively apply forces to their environment, either extracellular matrix or neighboring cells, as well as sense its biophysical properties. The fluctuations associated with these active processes occur on an energy scale much larger than that of ordinary thermal equilibrium fluctuations, yet their statistical properties and characteristic scales are not fully understood. Here, we compare measurements of the energy scale of active cellular fluctuations-an effective cellular temperature-in four different biophysical settings, involving both single-cell and cell-aggregate experiments under various control conditions, different cell types, and various biophysical observables. The results indicate that a similar energy scale of active fluctuations might characterize the same cell type in different settings, though it may vary among different cell types, being approximately six to eight orders of magnitude larger than the ordinary thermal energy at room temperature. These findings call for extracting the energy scale of active fluctuations over a broader range of cell types, experimental settings, and biophysical observables and for understanding the biophysical origin and significance of such cellular energy scales.

A central endeavor in bioengineering concerns the construction of multistrain microbial consortia with desired properties. Typically, a gene network is partitioned between strains, and strains communicate via quorum sensing, allowing for complex behaviors. Yet a fundamental question of how emergent spatiotemporal patterning in multistrain microbial consortia affects consortial dynamics is not understood well. Here, we propose a computationally tractable and straightforward modeling framework that explicitly allows linking spatiotemporal patterning to consortial dynamics. We validate our model against previously published results and make predictions of how spatial heterogeneity impacts interstrain communication. By enabling the investigation of spatial patterns effects on microbial dynamics, our modeling framework informs experimentalists, helps advance the understanding of complex microbial systems, and supports the development of applications involving them.

Fluorogenic labeling via bioorthogonal tetrazine chemistry has proven to be highly successful in fluorescence microscopy of living cells. To date, trans-cyclooctene (TCO) and bicyclonyne have been found to be the most useful substrates for live-cell labeling owing to their fast labeling kinetics, high biocompatibility, and bioorthogonality. Recent kinetic studies of fluorogenic click reactions with TCO derivatives showed a transient fluorogenic effect but could not explain the reaction sequence and the contributions of different intermediates. More recently, fluorescence quenching by potential intermediates has been investigated, suggesting their occurrence in the reaction sequence. However, in situ studies of the click reaction that directly relate these observations to the known reaction sequence are still missing. In this study, we developed a single-molecule fluorescence detection framework to investigate fluorogenic click reactions. In combination with data from ultra-performance liquid chromatography-tandem mass spectrometry, this explains the transient intensity increase by relating fluorescent intermediates to the known reaction sequence of TCO with fluorogenic tetrazine dyes. More specifically, we confirm that the reaction of TCO with tetrazine rapidly forms a fluorescent 4,5-dihydropyridazine species that slowly tautomerizes to a weakly fluorescent 1,4-dihydropyridazine, explaining the observed drop in fluorescence intensity. On a much slower timescale of hours/days, the fluorescence intensity may be recovered by oxidation of the intermediate to a pyridazine. Our findings are of importance for quantitative applications in fluorescence microscopy and spectroscopy as the achieved peak intensity with TCO depends on the specific experimental settings. They clearly indicate the requirement for more robust benchmarking of click reactions with tetrazine dyes and the need for alternative dienophiles with fast reaction kinetics and stable fluorescence emission to further applications in advanced fluorescence microscopy.

Here we adapt the Bayesian nonparametrics (BNP) framework presented in the first companion article to analyze kinetics from single-photon, single-molecule Förster resonance energy transfer (smFRET) traces generated under continuous illumination. Using our sampler, BNP-FRET, we learn the escape rates and the number of system states given a photon trace. We benchmark our method by analyzing a range of synthetic and experimental data. Particularly, we apply our method to simultaneously learn the number of system states and the corresponding kinetics for intrinsically disordered proteins using two-color FRET under varying chemical conditions. Moreover, using synthetic data, we show that our method can deduce the number of system states even when kinetics occur at timescales of interphoton intervals.

We present a unified conceptual framework and the associated software package for single-molecule Förster resonance energy transfer (smFRET) analysis from single-photon arrivals leveraging Bayesian nonparametrics, BNP-FRET. This unified framework addresses the following key physical complexities of a single-photon smFRET experiment, including: 1) fluorophore photophysics; 2) continuous time kinetics of the labeled system with large timescale separations between photophysical phenomena such as excited photophysical state lifetimes and events such as transition between system states; 3) unavoidable detector artefacts; 4) background emissions; 5) unknown number of system states; and 6) both continuous and pulsed illumination. These physical features necessarily demand a novel framework that extends beyond existing tools. In particular, the theory naturally brings us to a hidden Markov model with a second-order structure and Bayesian nonparametrics on account of items 1, 2, and 5 on the list. In the second and third companion articles, we discuss the direct effects of these key complexities on the inference of parameters for continuous and pulsed illumination, respectively.

Förster resonance energy transfer (FRET) using pulsed illumination has been pivotal in leveraging lifetime information in FRET analysis. However, there remain major challenges in quantitative single-photon, single-molecule FRET (smFRET) data analysis under pulsed illumination including 1) simultaneously deducing kinetics and number of system states; 2) providing uncertainties over estimates, particularly uncertainty over the number of system states; and 3) taking into account detector noise sources such as cross talk and the instrument response function contributing to uncertainty; in addition to 4) other experimental noise sources such as background. Here, we implement the Bayesian nonparametric framework described in the first companion article that addresses all aforementioned issues in smFRET data analysis specialized for the case of pulsed illumination. Furthermore, we apply our method to both synthetic as well as experimental data acquired using Holliday junctions.

Neuronal function requires continuous distribution of ion channels and other proteins throughout large cell morphologies. Protein distribution is complicated by immobilization of freely diffusing subunits such as on lipid rafts or in postsynaptic densities. Here, we infer rates of immobilization for the voltage-gated potassium channel Kv4.2. Fluorescence recovery after photobleaching quantifies protein diffusion kinetics, typically reported as a recovery rate and mobile fraction. We show that, implicit in the fluorescence recovery, are rates of particle transfer between mobile and immobile fractions (im/mobilization). We performed photobleaching of fluorescein-tagged ion channel Kv4.2-sGFP2 in over 450 dendrites of rat hippocampal cells. Using mass-action models, we infer rates of Kv4.2-sGFP2 im/mobilization. Using a realistic neuron morphology, we show how these rates shape the speed and profile of subunit distribution. The experimental protocol and model inference introduced here is widely applicable to other cargo and experimental systems.