Perturbation of the stability of amyloid fibrils through alteration of electrostatic interactions.

The self-assembly of proteins and peptides into polymeric amyloid fibrils is a process that has important implications ranging from the understanding of protein misfolding disorders to the discovery of novel nanobiomaterials. In this study, we probe the stability of fibrils prepared at pH 2.0 and composed of the protein insulin by manipulating electrostatic interactions within the fibril architecture. We demonstrate that strong electrostatic repulsion is sufficient to disrupt the hydrogen-bonded, cross-β network that links insulin molecules and ultimately results in fibril dissociation. The extent of this dissociation correlates well with predictions for colloidal models considering the net global charge of the polypeptide chain, although the kinetics of the process is regulated by the charge state of a single amino acid. We found the fibrils to be maximally stable under their formation conditions. Partial disruption of the cross-β network under conditions where the fibrils remain intact leads to a reduction in their stability. Together, these results support the contention that a major determinant of amyloid stability stems from the interactions in the structured core, and show how the control of electrostatic interactions can be used to characterize the factors that modulate fibril stability.

Uniform stability of a particle approximation of the optimal filter derivative

Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. In many applications it may be necessary to compute the sensitivity, or derivative, of the optimal filter with respect to the static parameters of the state-space model; for instance, in order to obtain maximum likelihood model parameters of interest, or to compute the optimal controller in an optimal control problem. In Poyiadjis et al. [2011] an original particle algorithm to compute the filter derivative was proposed and it was shown using numerical examples that the particle estimate was numerically stable in the sense that it did not deteriorate over time. In this paper we substantiate this claim with a detailed theoretical study. Lp bounds and a central limit theorem for this particle approximation of the filter derivative are presented. It is further shown that under mixing conditions these Lp bounds and the asymptotic variance characterized by the central limit theorem are uniformly bounded with respect to the time index. We demon- strate the performance predicted by theory with several numerical examples. We also use the particle approximation of the filter derivative to perform online maximum likelihood parameter estimation for a stochastic volatility model.