In this study we describe a novel approach to define structural domains and to characterize the local flexibility in both human and chicken prion proteins. The approach we use is based on a comprehensive theory of collective dynamics in proteins that was recently developed. This method determines the essential collective coordinates, which can be found from molecular dynamics trajectories via principal component analysis. Under this particular framework, we are able to identify the domains where atoms move coherently while at the same time to determine the local main-chain flexibility for each residue. We have verified this approach by comparing our results for the predicted dynamic domain systems with the computed main-chain flexibility profiles and the NMR-derived random coil indexes for human and chicken prion proteins. The three sets of data show excellent agreement. Additionally, we demonstrate that the dynamic domains calculated in this fashion provide a highly sensitive measure of protein collective structure and dynamics. Furthermore, such an analysis is capable of revealing structural and dynamic properties of proteins that are inaccessible to the conventional assessment of secondary structure. Using the collective dynamic simulation approach described here along with a high-temperature simulations of unfolding of human prion protein, we have explored whether locations of relatively low stability could be identified where the unfolding process could potentially be facilitated. According to our analysis, the locations of relatively low stability may be associated with the beta-sheet formed by strands S1 and S2 and the adjacent loops, whereas helix HC appears to be a relatively stable part of the protein. We suggest that this kind of structural analysis may provide a useful background for a more quantitative assessment of potential routes of spontaneous misfolding in prion proteins.

Water plays a unique role in all living organisms. Not only is it nature's ubiquitous solvent, but it also actively takes part in many cellular processes. In particular, the structure and properties of interfacial water near biomolecules like proteins are often related to the function of the respective molecule. It can therefore be highly instructive to study the local water density around solutes in cellular systems, particularly when solvent-mediated forces like the hydrophobic effect are relevant. Computational methods like molecular dynamics (MD) simulations seem well suited to study these systems at the atomic level. However, due to sampling requirements, it is not clear that MD simulations are indeed the method of choice to obtain converged densities at a given level of precision. We here compare the calculation of local water densities with two different methods, MD simulations and the three-dimensional reference interaction site model with the KovalenkoHirata closure (3D-RISM-KH). In particular, we investigate the convergence of the local water density to assess the required simulation times for different levels of resolution. Moreover, we provide a quantitative comparison of the densities calculated with MD and with 3D-RISM-KH, and investigate the effect of the choice of the water model for both methods. Our results show that 3D-RISM-KH yields density distributions that are very similar to those from MD up to a 0.5 Å resolution, but for significantly reduced computational cost. The combined use of MD and 3D-RISM-KH emerges as an auspicious perspective for efficient solvent sampling in dynamical systems.