Relationship between prion propensity and the rates of individual molecular steps of fibril assembly.

Peptides and proteins possess an inherent propensity to self-assemble into generic fibrillar nanostructures known as amyloid fibrils, some of which are involved in medical conditions such as Alzheimer disease. In certain cases, such structures can self-propagate in living systems as prions and transmit characteristic traits to the host organism. The mechanisms that allow certain amyloid species but not others to function as prions are not fully understood. Much progress in understanding the prion phenomenon has been achieved through the study of prions in yeast as this system has proved to be experimentally highly tractable; but quantitative understanding of the biophysics and kinetics of the assembly process has remained challenging. Here, we explore the assembly of two closely related homologues of the Ure2p protein from Saccharomyces cerevisiae and Saccharomyces paradoxus, and by using a combination of kinetic theory with solution and biosensor assays, we are able to compare the rates of the individual microscopic steps of prion fibril assembly. We find that for these proteins the fragmentation rate is encoded in the structure of the seed fibrils, whereas the elongation rate is principally determined by the nature of the soluble precursor protein. Our results further reveal that fibrils that elongate faster but fracture less frequently can lose their ability to propagate as prions. These findings illuminate the connections between the in vitro aggregation of proteins and the in vivo proliferation of prions, and provide a framework for the quantitative understanding of the parameters governing the behavior of amyloid fibrils in normal and aberrant biological pathways.

On the relationship between hidden Markov Models and convex functional transforms for simulating scanning probe Microscopy

Models for simulating Scanning Probe Microscopy (SPM) may serve as a reference point for validating experimental data and practice. Generally, simulations use a microscopic model of the sample-probe interaction based on a first-principles approach, or a geometric model of macroscopic distortions due to the probe geometry. Examples of the latter include use of neural networks, the Legendre Transform, and dilation/erosion transforms from mathematical morphology. Dilation and the Legendre Transform fall within a general family of functional transforms, which distort a function by imposing a convex solution.In earlier work, the authors proposed a generalized approach to modeling SPM using a hidden Markov model, wherein both the sample-probe interaction and probe geometry may be taken into account. We present a discussion of the hidden Markov model and its relationship to these convex functional transforms for simulating and restoring SPM images.©2009 SPIE.