intermediate state
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Hemostasis in the arterial circulation is mediated by binding of the A1 domain of the ultralong protein von Willebrand factor to GPIbα on platelets to form a platelet plug. A1 is activated by tensile force on VWF concatemers imparted by hydrodynamic drag force. The A1 core is protected from force-induced unfolding by a long-range disulfide that links cysteines near its N and C-termini. The O-glycosylated linkers between A1 and its neighboring domains, which transmit tensile force to A1, are reported to regulate A1 activation for binding to GPIb, but the mechanism is controversial and incompletely defined. Here, we study how these linkers, and their polypeptide and O-glycan moieties, regulate A1 affinity by measuring affinity, kinetics, thermodynamics, hydrogen deuterium exchange (HDX), and unfolding by temperature and urea. The N-linker lowers A1 affinity 40-fold with a stronger contribution from its O-glycan than polypeptide moiety. The N-linker also decreases HDX in specific regions of A1 and increases thermal stability and the energy gap between its native state and an intermediate state, which is observed in urea-induced unfolding. The C-linker also decreases affinity of A1 for GPIbα, but in contrast to the N-linker, has no significant effect on HDX or A1 stability. Among different models for A1 activation, our data are consistent with the model that the intermediate state has high affinity for GPIbα, which is induced by tensile force physiologically and regulated allosterically by the N-linker.

The measurement of thermodynamic properties of chemical or biological reactions were often confined to experimental means, which produced overall measurements of properties being investigated, but were usually susceptible to pitfalls of being too general. Among the thermodynamic properties that are of interest, reaction rates hold the greatest significance, as they play a critical role in reaction processes where speed is of essence, especially when fast association may enhance binding affinity of reaction molecules. Association reactions with high affinities often involve the formation of a intermediate state, which can be demonstrated by a hyperbolic reaction curve, but whose low abundance in reaction mixture often preclude the possibility of experimental measurement. Therefore, we resorted to computational methods using predefined reaction models that model the intermediate state as the reaction progresses. Here, we present a novel method called AKPE (ANN-Dependent Kinetic Parameter Extraction), our goal is to investigate the association/dissociation rate constants and the concentration dynamics of lowly-populated states (intermediate states) in the reaction landscape. To reach our goal, we simulated the chemical or biological reactions as system of differential equations, employed artificial neural networks (ANN) to model experimentally measured data, and utilized Particle Swarm Optimization (PSO) algorithm to obtain the globally optimum parameters in both the simulation and data fitting. In the Results section, we have successfully modeled a protein association reaction using AKPE, obtained the kinetic rate constants of the reaction, and constructed a full concentration versus reaction time curve of the intermediate state during the reaction. Furthermore, judging from the various validation methods that the method proposed in this paper has strong robustness and accuracy.

Unstable states studied in kinetic, time-resolved and ligand-based crystallography are often characterized by a low occupancy, hindering structure determination by conventional methods. To automatically extract such structures, we developed Xtrapol8, a program which (i) applies various flavors of Bayesian-statistics weighting to generate the most informative Fourier difference maps; (ii) determines the occupancy of the intermediate state; (iii) calculates various types of extrapolated structure factors, and (iv) refines the corresponding structures.

Bacteriophage endolysins degrade the bacterial peptidoglycan and are considered enzymatic alternatives to small molecule antibiotics. In particular, the multimeric streptococcal endolysin PlyC has appealing antibacterial properties. However, a comprehensive thermal analysis of PlyC is lacking, which is necessary for evaluating long-term stability and downstream therapeutic potential. Biochemical and kinetic-based methods were used in combination with differential scanning calorimetry to investigate the structural, kinetic and thermodynamic stability of PlyC and its various subunits and domains. The PlyC holoenzyme structure is irreversibly compromised due to partial unfolding and aggregation at 46°C. Unfolding of the catalytic subunit, PlyCA, instigates this event, resulting in the kinetic inactivation of the endolysin. In contrast to PlyCA, the PlyCB octamer (the cell wall binding domain) is thermostable, denaturing at ~75°C. Isolation of PlyCA or PlyCB alone altered their thermal properties. Contrary to the holoenzyme, PlyCA alone unfolds uncooperatively and is thermodynamically destabilized whereas the PlyCB octamer reversibly dissociates into monomers and forms an intermediate state at 74°C in phosphate buffered saline, with each subunit subsequently denaturing at 927°C. Adding folded PlyCA to an intermediate state PlyCB, followed by cooling, allowed for in vitro reconstitution of the active holoenzyme.

Abstract Superconductors and magnetic materials, including molecules, are key ingredients for quantum and advanced spintronic applications. However, only a little is known about how these materials are mutually influenced at their interface in hybrid architectures. Here, we show that a single layer of magnetic molecules, the Terbium(III) bis-phthalocyaninato (TbPc2) complexes, deposited on a superconducting Pb(111) surface is sensitive to the topology of the intermediate state of the superconductor, namely to the presence and evolution of superconducting and normal domains due to the magnetic field screening and penetration. The evidence of this sensitivity is found in the magnetisation dynamics of the TbPc2 sub-monolayer in its paramagnetic regime showing the fingerprint of the topological hysteresis of the superconducting substrate. This study reveals the great potentialities hold by thin layers of magnetic molecules for sensing local magnetic field variation in hybrid molecular/superconductor architectures, including spin resonators or spin injection devices for spintronics applications.

Mouse embryonic stem cells (ESCs) cycle in and out of 2-cell-like (2C-like) state in culture. The molecular mechanism governing the exit of 2C-like state remains obscure, partly due to the lack of a reporter system that can genetically mark intermediate states during exiting process. Here, we identify an intermediate state that is marked by the co-expression of MERVL::tdTomato and OCT4-GFP (MERLOT) during 2C-like-to-pluripotent state transition (2CLPT). Transcriptome and epigenome analyses demonstrate that MERLOT cells cluster closely with 8-16 cell stage mouse embryos, suggesting that 2CLPT partly mimics early preimplantation development. Through a CRISPRa screen, we identify an ARRDC3-NEDD4-OCT4 regulatory axis that plays an essential role in controlling 2CLPT. Furthermore, re-evaluating previously reported 2C-like state regulators reveals dual function of Chaf1a in regulating the entry and exit of 2C-like state. Finally, ATAC-Seq footprinting analysis uncovers Klf3 as an essential transcription factor required for efficient 2CLPT. Together, our study identifies a genetically traceable intermediate state during 2CLPT and provides a valuable tool to study molecular mechanisms regulating this process.

Abstract We consider pattern-forming fronts in the complex Ginzburg–Landau equation with a traveling spatial heterogeneity which destabilises, or quenches, the trivial ground state while progressing through the domain. We consider the regime where the heterogeneity propagates with speed c just below the linear invasion speed of the pattern-forming front in the associated homogeneous system. In this situation, the front locks to the interface of the heterogeneity leaving a long intermediate state lying near the unstable ground state, possibly allowing for growth of perturbations. This manifests itself in the spectrum of the linearisation about the front through the accumulation of eigenvalues onto the absolute spectrum associated with the unstable ground state. As the quench speed c increases towards the linear invasion speed, the absolute spectrum stabilises with the same rate at which eigenvalues accumulate onto it allowing us to rigorously establish spectral stability of the front in L 2 ( R ) . The presence of unstable absolute spectrum poses a technical challenge as spatial eigenvalues along the intermediate state no longer admit a hyperbolic splitting and standard tools such as exponential dichotomies are unavailable. Instead, we projectivise the linear flow, and use Riemann surface unfolding in combination with a superposition principle to study the evolution of subspaces as solutions to the associated matrix Riccati differential equation on the Grassmannian manifold. Eigenvalues can then be identified as the roots of the meromorphic Riccati–Evans function, and can be located using winding number and parity arguments.

Abstract Background Clinical trials routinely have patients lost to follow up. We propose a methodology to understand their possible effect on the results of statistical tests by altering the concept of the fragility index to treat the outcomes of observed patients as fixed but incorporate the potential outcomes of patients lost to follow up as random and subject to modification. Methods We reanalyse the statistical results of three clinical trials on coronary artery bypass grafting (CABG) to study the possible effect of patients lost to follow up on the treatment effect statistical significance. To do so, we introduce the LTFU-aware fragility indices as a measure of the robustness of a clinical trial’s statistical results with respect to patients lost to follow up. Results The analyses illustrate that clinical trials can either be completely robust to the outcomes of patients lost to follow up, extremely sensitive to the outcomes of patients lost to follow up, or in an intermediate state. When a clinical trial is in an intermediate state, the LTFU-aware fragility indices provide an interpretable measure to quantify the degree of fragility or robustness. Conclusions The LTFU-aware fragility indices allow researchers to rigorously explore the outcomes of patients who are lost to follow up, when their data is the appropriate kind. The LTFU-aware fragility indices are sensitivity measures in a way that the original fragility index is not.