The growth of amyloid fibrils from Aβ peptide, one of the key pathogenic players in Alzheimer's disease, is believed to follow a nucleation-elongation mechanism. Fibril elongation is often described as a "dock-lock" procedure, where a disordered monomer adsorbs to an existing fibril in a relatively fast process (docking), followed by a slower conformational transition toward the ordered state of the template (locking). Here, we use molecular dynamics simulations of an ordered pentamer of Aβ42 at fully atomistic resolution, which includes solvent, to characterize the elongation process. We construct a Markov state model from an ensemble of short trajectories generated by an advanced sampling algorithm that efficiently diversifies a subset of the system without any bias forces. This subset corresponds to selected dihedral angles of the peptide chain at the fibril tip favored to be the fast growing one experimentally. From the network model, we extract distinct locking pathways covering time scales in the high microsecond regime. Slow steps are associated with the exchange of hydrophobic contacts, between nonnative and native intermolecular contacts as well as between intra- and intermolecular ones. The N-terminal segments, which are disordered in fibrils and typically considered inert, are able to shield the lateral interfaces of the pentamer. We conclude by discussing our findings in the context of a refined dock-lock model of Aβ fibril elongation, which involves structural disorder for more than one monomer at the growing tip.

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We present a computational study on the driven transport of the Maltose Binding Protein (MBP) across nanochannels in the framework of coarse-grained modeling. The work is motivated by recent experiments on voltage-driven transport of MBP across nanopores exploring the influence of denaturation on translocation pathways. Our simplified approach allows a statistical mechanical interpretation of the process which may be convenient also to the experiments. Specifically, we identify and characterize short and long channel blockades, associated to the translocation of denaturated and folded MBP conformations, respectively. We show that long blockades are related to long stall events where MBP undergoes specific and reproducible structural rearrangements. To clarify the origin of the stalls, the stick-and-slip translocation is compared to mechanical unfolding pathways obtained via steered molecular dynamics. This comparison clearly shows the translocation pathway to significantly differ from free-space unfolding dynamics and strongly suggests that stalling events are preferentially determined by the MBP regions with higher density of long-range native interactions. This result might constitute a possible criterion to predict a priori some statistical features of protein translocation from the structural analysis.

Coarse-grained simulations of protein translocation across narrow pores suggest that the transport is characterized by long stall events. The translocation bottlenecks and the associated free-energy barriers are found to be strictly related to the structural properties of the protein native structure. The ascending ramps of the free-energy profile systematically correspond to regions of the chain denser in long range native contacts formed with the untranslocated portion of the protein. These very regions are responsible for the stalls occurring during the protein transport along the nanopore. The decomposition of the free energy in internal energyand entropic terms shows that the dominant energetic contribution can be estimated on the base of the protein native structure only. Interestingly, the essential features of the dynamics are retained in a reduced phenomenological model of the process describing the evolution of a suitable collective variable in the associated free-energy landscape.

<div> <div>SPUX (Scalable Package for Uncertainty Quantification in "X") is a modular framework for Bayesian inference and uncertainty quantification. The SPUX framework aims at harnessing high performance scientific computing to tackle complex aquatic dynamical systems rich in intrinsic uncertainties,</div> <div>such as ecological ecosystems, hydrological catchments, lake dynamics, subsurface flows, urban floods, etc. The challenging task of quantifying input, output and/or parameter uncertainties in such stochastic models is tackled using Bayesian inference techniques, where numerical sampling and filtering algorithms assimilate prior expert knowledge and available experimental data. The SPUX framework greatly simplifies uncertainty quantification for realistic computationally costly models and provides an accessible, modular, portable, scalable, interpretable and reproducible scientific workflow. To achieve this, SPUX can be coupled to any serial or parallel model written in any programming language (e.g. Python, R, C/C++, Fortran, Java), can be installed either on a laptop or on a parallel cluster, and has built-in support for automatic reports, including algorithmic and computational performance metrics. I will present key SPUX concepts using a simple random walk example, and showcase recent realistic applications for catchment and lake models. In particular, uncertainties in model parameters, meteorological inputs, and data observation processes are inferred by assimilating available in-situ and remotely sensed datasets.</div> </div>