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Register a new userHamiltonian Dynamics of Saturated Elongation in Amyloid Fiber Formation
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Elongation is a fundament process in amyloid fiber growth, which is normally characterized by a linear relationship between the fiber elongation rate and the monomer concentration. However, in high concentration regions, a sub-linear dependence was often observed, which could be explained by a universal saturation mechanism. In this paper, we modeled the saturated elongation process through a Michaelis-Menten like mechanism, which is constituted by two sub-steps -- unspecific association and dissociation of a monomer with the fibril end, and subsequent conformational change of the associated monomer to fit itself to the fibrillar structure. Typical saturation concentrations were found to be $7-70mu M$ for A$beta$40, $alpha$-synuclein and etc. Furthermore, by using a novel Hamiltonian formulation, analytical solutions valid for both weak and strong saturated conditions were constructed and applied to the fibrillation kinetics of $alpha$-synuclein and silk fibroin.
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Controlling the self-assembly of supramolecular structures is vital for living cells, and a central challenge for engineering at the nano- and microscales. Nevertheless, even particles without optimized shapes can robustly form well-defined morphologies. This is the case in numerous medical conditions where normally soluble proteins aggregate into fibers. Beyond the diversity of molecular mechanisms involved, we propose that fibers generically arise from the aggregation of irregular particles with short-range interactions. Using a minimal model of ill-fitting, sticky particles, we demonstrate robust fiber formation for a variety of particle shapes and aggregation conditions. Geometrical frustration plays a crucial role in this process, and accounts for the range of parameters in which fibers form as well as for their metastable character.
Here, we discuss a collection of cutting-edge techniques and applications in use today by some of the leading experts in the field of correlative approaches in single-molecule biophysics. A key difference in emphasis, compared with traditional single-molecule biophysics approaches detailed previously, is on the emphasis of the development and use of complex methods which explicitly combine multiple approaches to increase biological insights at the single-molecule level. These so-called correlative single-molecule biophysics methods rely on multiple, orthogonal tools and analysis, as opposed to any one single driving technique. Importantly, they span both in vivo and in vitro biological systems as well as the interfaces between theory and experiment in often highly integrated ways, very different to earlier traditional non-integrative approaches. The first applications of correlative single-molecule methods involved adaption of a range of different experimental technologies to the same biological sample whose measurements were synchronised. However, now we find a greater flora of integrated methods emerging that include approaches applied to different samples at different times and yet still permit useful molecular-scale correlations to be performed. The resultant findings often enable far greater precision of length and time scales of measurements, and a more understanding of the interplay between different processes in the same cell. Many new correlative single-molecule biophysics techniques also include more complex, physiologically relevant approaches as well as increasing number that combine advanced computational methods and mathematical analysis with experimental tools. Here we review the motivation behind the development of correlative single-molecule microscopy methods, its history and recent progress in the field.
Many proteins have the potential to aggregate into amyloid fibrils, which are associated with a wide range of human disorders including Alzheimers and Parkinsons disease. In contrast to that of folded proteins, the thermodynamic stability of amyloid fibrils is not well understood: specifically the balance between entropic and enthalpic terms, including the chain entropy and the hydrophobic effect, are poorly characterised. Using simulations of a coarse-grained protein model we delineate the enthalpic and entropic contributions dominating amyloid fibril elongation, predicting a characteristic temperature-dependent enthalpic signature. We confirm this thermodynamic signature by performing calorimetric experiments and a meta-analysis over published data. From these results, we can also elucidate the necessary conditions to observe cold denaturation of amyloid fibrils. Overall, we show that amyloid fibril elongation is associated with a negative heat capacity, the magnitude of which correlates closely with the hydrophobic surface area that is buried upon fibril formation, highlighting the importance of hydrophobicity for fibril stability.
Protein aggregation in the form of amyloid fibrils has important biological and technological implications. Although the self-assembly process is highly efficient, aggregates not in the fibrillar form would also occur and it is important to include these disordered species when discussing the thermodynamic equilibrium behavior of the system. Here, we initiate such a task by considering a mixture of monomeric proteins and the corresponding aggregates in the disordered form (micelles) and in the fibrillar form (amyloid fibrils). Starting with a model on the respective binding free energies for these species, we calculate their concentrations at thermal equilibrium. We then discuss how the incorporation of the disordered structure furthers our understanding on the various amyloid promoting factors observed empirically, and on the kinetics of fibrilization.
Numerous biological approaches are available to characterise the mechanisms which govern the formation of human embryonic stem cell (hESC) colonies. To understand how the kinematics of single and pairs of hESCs impact colony formation, we study their mobility characteristics using time-lapse imaging. We perform a detailed statistical analysis of their speed, survival, directionality, distance travelled and diffusivity. We confirm that single and pairs of cells migrate as a diffusive random walk. Moreover, we show that the presence of Cell Tracer significantly reduces hESC mobility. Our results open the path to employ the theoretical framework of the diffusive random walk for the prognostic modelling and optimisation of the growth of hESC colonies. Indeed, we employ this random walk model to estimate the seeding density required to minimise the occurrence of hESC colonies arising from more than one founder cell and the minimal cell number needed for successful colony formation. We believe that our prognostic model can be extended to investigate the kinematic behaviour of somatic cells emerging from hESC differentiation and to enable its wide application in phenotyping of pluripotent stem cells for large scale stem cell culture expansion and differentiation platforms.
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