Simulating oligomerization at experimental concentrations and long timescales: A Markov state model approach

A Kinetic Transition Network Model Reveals the Diversity of Protein Dimer Formation Mechanisms

Protein homodimers have been classified as three-state or two-state dimers depending on whether a folded monomer forms before association, but the details of the folding-binding mechanisms are poorly understood. Kinetic transition networks of conformational states have provided insight into the folding mechanisms of monomeric proteins, but extending such a network to two protein chains is challenging as all the relative positions and orientations of the chains need to be included, greatly increasing the number of degrees of freedom. Here, we present a simplification of the problem by grouping all states of the two chains into two layers: a dissociated and an associated layer. We combined our two-layer approach with the Wako-Saito-Muñoz-Eaton method and used Transition Path Theory to investigate the dimer formation kinetics of eight homodimers. The analysis reveals a remarkable diversity of dimer formation mechanisms. Induced folding, conformational selection, and rigid docking are often simultaneously at work, and their contribution depends on the protein concentration. Pre-folded structural elements are always present at the moment of association, and asymmetric binding mechanisms are common. Our two-layer network approach can be combined with various methods that generate discrete states, yielding new insights into the kinetics and pathways of flexible binding processes.

Unsupervised Learning Methods for Molecular Simulation Data

Unsupervised learning is becoming an essential tool to analyze the increasingly large amounts of data produced by atomistic and molecular simulations, in material science, solid state physics, biophysics, and biochemistry. In this Review, we provide a comprehensive overview of the methods of unsupervised learning that have been most commonly used to investigate simulation data and indicate likely directions for further developments in the field. In particular, we discuss feature representation of molecular systems and present state-of-the-art algorithms of dimensionality reduction, density estimation, and clustering, and kinetic models. We divide our discussion into self-contained sections, each discussing a specific method. In each section, we briefly touch upon the mathematical and algorithmic foundations of the method, highlight its strengths and limitations, and describe the specific ways in which it has been used-or can be used-to analyze molecular simulation data.

Protein aggregation: in silico algorithms and applications

Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications.

Bifurcated Hydrogen Bonds and the Fold Switching of Lymphotactin

Lymphotactin (Ltn) exists under physiological conditions in an equilibrium between two interconverting structures with distinct biological functions. Using replica-exchange-with-tunneling, we study the conversion between the 2-folds. Unlike previously proposed, we find that the fold switching does not require unfolding of lymphotactin but proceeds through a series of intermediates that remain partially structured. This process relies on two bifurcated hydrogen bonds that connect the β2 and β3 strands and ease the transition between the hydrogen bond pattern by which the central three-stranded β-sheet in the two forms differs.

Inhibition of GNNQQNY prion peptide aggregation by trehalose: a mechanistic view

Deposition of amyloid fibrils is the seminal event in the pathogenesis of numerous neurodegenerative diseases. The formation of this amyloid assembly is the manifestation of a cascade of structural transitions including toxic oligomer formation in the early stages of aggregation. Thus a viable therapeutic strategy involves the use of small molecular ligands to interfere with this assembly. In this perspective, we have explored the kinetics of aggregate formation of the fibril forming GNNQQNY peptide fragment from the yeast prion protein SUP35 using multiple all atom MD simulations with explicit solvent and provided mechanistic insights into the way trehalose, an experimentally known aggregation inhibitor, modulates the aggregation pathway. The results suggest that the assimilation process is impeded by different barriers at smaller and larger oligomeric sizes: the initial one being easily surpassed at higher temperatures and peptide concentrations. The kinetic profile demonstrates that trehalose delays the aggregation process by increasing both these activation barriers, specifically the latter one. It increases the sampling of small-sized aggregates that lack the beta sheet conformation. Analysis reveals that the barrier in the growth of larger stable oligomers causes the formation of multiple stable small oligomers which then fuse together bimolecularly. The PCA of 26 properties was carried out to deconvolute the events within the temporary lag phases, which suggested dynamism in lags involving an increase in interchain contacts and burial of SASA. The predominant growth route is monomer addition, which changes to condensation on account of a large number of depolymerisation events in the presence of trehalose. The favourable interaction of trehalose specifically with the sidechain of the peptide promotes crowding of trehalose molecules in its vicinity - the combination of both these factors imparts the observed behaviour. Furthermore, increasing trehalose concentration leads to faster expulsion of water molecules than interpeptide interactions. These expelled water molecules have larger translational movement, suggesting an entropy factor to favor the assembly process. Different conformations observed under this condition suggest the role of water molecules in guiding the morphology of the aggregates as well. A similar scenario exists on increasing peptide concentration.

Elucidation of the conformational dynamics of multi-body systems by construction of Markov state models

Constructing Markov State Models (MSMs) based on short molecular dynamics simulations is a powerful computational technique to complement experiments in predicting long-time kinetics of biomolecular processes at atomic resolution. Even though the MSM approach has been widely applied to study one-body processes such as protein folding and enzyme conformational changes, the majority of biological processes, e.g. protein-ligand recognition, signal transduction, and protein aggregation, essentially involve multiple entities. Here we review the attempts at constructing MSMs for multi-body systems, point out the challenges therein and discuss recent algorithmic progresses that alleviate these challenges. In particular, we describe an automatic kinetics based partitioning method that achieves optimal definition of the conformational states in a multi-body system, and discuss a novel maximum-likelihood approach that efficiently estimates the slow uphill kinetics utilizing pre-computed equilibrium populations of all states. We expect that these new algorithms and their combinations may boost investigations of important multi-body biological processes via the efficient construction of MSMs.

Fibril-Barrel Transitions in Cylindrin Amyloids

We introduce Replica-Exchange-with-Tunneling (RET) simulations as a tool for studies of the conversion between polymorphic amyloids. For the 11-residue amyloid-forming cylindrin peptide we show that this technique allows for a more efficient sampling of the formation and interconversion between fibril-like and barrel-like assemblies. We describe a protocol for optimized analysis of RET simulations that allows us to propose a mechanism for formation and interconversion between various cylindrin assemblies. Especially, we show that an interchain salt bridge between residues K3 and D7 is crucial for formation of the barrel structure.

Markov State Models and tICA Reveal a Nonnative Folding Nucleus in Simulations of NuG2

After reanalyzing simulations of NuG2-a designed mutant of protein G-generated by Lindorff-Larsen et al. with time structure-based independent components analysis and Markov state models as well as performing 1.5 ms of additional sampling on Folding@home, we found an intermediate with a register-shift in one of the β-sheets that was visited along a minor folding pathway. The minor folding pathway was initiated by the register-shifted sheet, which is composed of solely nonnative contacts, suggesting that for some peptides, nonnative contacts can lead to productive folding events. To confirm this experimentally, we suggest a mutational strategy for stabilizing the register shift, as well as an infrared experiment that could observe the nonnative folding nucleus.

Sequencing proteins with transverse ionic transport in nanochannels

De novo protein sequencing is essential for understanding cellular processes that govern the function of living organisms and all sequence modifications that occur after a protein has been constructed from its corresponding DNA code. By obtaining the order of the amino acids that compose a given protein one can then determine both its secondary and tertiary structures through structure prediction, which is used to create models for protein aggregation diseases such as Alzheimer's Disease. Here, we propose a new technique for de novo protein sequencing that involves translocating a polypeptide through a synthetic nanochannel and measuring the ionic current of each amino acid through an intersecting perpendicular nanochannel. We find that the distribution of ionic currents for each of the 20 proteinogenic amino acids encoded by eukaryotic genes is statistically distinct, showing this technique's potential for de novo protein sequencing.

Advances in the Simulation of Protein Aggregation at the Atomistic Scale

Protein aggregation into highly structured amyloid fibrils is associated with various diseases including Alzheimer's disease, Parkinson's disease, and type II diabetes. Amyloids can also have normal biological functions and, in the future, could be used as the basis for novel nanoscale materials. However, a full understanding of the physicochemical forces that drive protein aggregation is still lacking. Such understanding is crucial for the development of drugs that can effectively inhibit aberrant amyloid aggregation and for the directed design of functional amyloids. Atomistic simulations can help understand protein aggregation. In particular, atomistic simulations can be used to study the initial formation of toxic oligomers which are hard to characterize experimentally and to understand the difference in aggregation behavior between different amyloidogenic peptides. Here, we review the latest atomistic simulations of protein aggregation, concentrating on amyloidogenic protein fragments, and provide an outlook for the future in this field.

Dynamics of the conformational transitions during the dimerization of an intrinsically disordered peptide: a case study on the human islet amyloid polypeptide fragment

Dimerization pathways of the human islet amyloid polypeptide fragment are elucidated from extensive molecular dynamics simulations.

Energetic contributions of residues to the formation of early amyloid-β oligomers

Low-weight amyloid-β (Aβ) oligomers formed at early stages of oligomerization rather than fibril assemblies seem to be the toxic components that drive neurodegeneration in Alzheimer's disease. Unfortunately, detailed knowledge of the structure of these early oligomers at the residue level is not yet available. In this study, we performed all-atom explicit solvent molecular dynamics simulations to examine the oligomerization process of Aβ10-35 monomers when forming dimers, trimers, tetramers and octamers, with four independent simulations of a total simulated time of 3 μs for each oligomer system. The decomposition of the stability free energy by MM-GBSA methodology allowed us to unravel the network of energetic interactions that stabilize such oligomers. The contribution of the intermonomeric van der Waals term is the most significant energy feature of the oligomerization process, consistent with the so-called hydrophobic effect. Furthermore, the decomposition of the stability free energy into residues and residue-pairwise terms revealed that it is mainly apolar interactions between the three specific hydrophobic fragments 31-35 (C-terminal region), 17-20 (central hydrophobic core) and 12-14 (N-terminal region) that are responsible for such a favourable effect. The conformation in which the hydrophobic cthr-chc interaction is oriented perpendicularly is particularly important. We propose three other model substructures that favour the oligomerization process and can thus be considered as molecular targets for future inhibitors. Understanding Aβ oligomerization at the residue level could lead to more efficient design of inhibitors of this process.

Nucleation process of a fibril precursor in the C-terminal segment of amyloid-β

By extended atomistic simulations in explicit solvent and bias-exchange metadynamics, we study the aggregation process of 18 chains of the C-terminal segment of amyloid-β, an intrinsically disordered protein involved in Alzheimer's disease and prone to form fibrils. Starting from a disordered aggregate, we are able to observe the formation of an ordered nucleus rich in beta sheets. The rate limiting step in the nucleation pathway involves crossing a barrier of approximately 40 kcal/mol and is associated with the formation of a very specific interdigitation of the side chains belonging to different sheets. This structural pattern is different from the one observed experimentally in a microcrystal of the same system, indicating that the structure of a "nascent" fibril may differ from the one of an "extended" fibril.

Flexible backbone sampling methods to model and design protein alternative conformations

Sampling alternative conformations is key to understanding how proteins work and engineering them for new functions. However, accurately characterizing and modeling protein conformational ensembles remain experimentally and computationally challenging. These challenges must be met before protein conformational heterogeneity can be exploited in protein engineering and design. Here, as a stepping stone, we describe methods to detect alternative conformations in proteins and strategies to model these near-native conformational changes based on backrub-type Monte Carlo moves in Rosetta. We illustrate how Rosetta simulations that apply backrub moves improve modeling of point mutant side-chain conformations, native side-chain conformational heterogeneity, functional conformational changes, tolerated sequence space, protein interaction specificity, and amino acid covariation across protein-protein interfaces. We include relevant Rosetta command lines and RosettaScripts to encourage the application of these types of simulations to other systems. Our work highlights that critical scoring and sampling improvements will be necessary to approximate conformational landscapes. Challenges for the future development of these methods include modeling conformational changes that propagate away from designed mutation sites and modulating backbone flexibility to predictively design functionally important conformational heterogeneity.

C-terminal turn stability determines assembly differences between Aβ40 and Aβ42

Oligomerization of the amyloid β-protein (Aβ) is a seminal event in Alzheimer's disease. Aβ42, which is only two amino acids longer than Aβ40, is particularly pathogenic. Why this is so has not been elucidated fully. We report here results of computational and experimental studies revealing a C-terminal turn at Val36-Gly37 in Aβ42 that is not present in Aβ40. The dihedral angles of residues 36 and 37 in an Ile31-Ala42 peptide were consistent with β-turns, and a β-hairpin-like structure was indeed observed that was stabilized by hydrogen bonds and by hydrophobic interactions between residues 31-35 and residues 38-42. In contrast, Aβ(31-40) mainly existed as a statistical coil. To study the system experimentally, we chemically synthesized Aβ peptides containing amino acid substitutions designed to stabilize or destabilize the hairpin. The triple substitution Gly33Val-Val36Pro-Gly38Val ("VPV") facilitated Aβ42 hexamer and nonamer formation, while inhibiting formation of classical amyloid-type fibrils. These assemblies were as toxic as were assemblies from wild-type Aβ42. When substituted into Aβ40, the VPV substitution caused the peptide to oligomerize similarly to Aβ42. The modified Aβ40 was significantly more toxic than Aβ40. The double substitution d-Pro36-l-Pro37 abolished hexamer and dodecamer formation by Aβ42 and produced an oligomer size distribution similar to that of Aβ40. Our data suggest that the Val36-Gly37 turn could be the sine qua non of Aβ42. If true, this structure would be an exceptionally important therapeutic target.

Design of β-amyloid aggregation inhibitors from a predicted structural motif

Drug design studies targeting one of the primary toxic agents in Alzheimer's disease, soluble oligomers of amyloid β-protein (Aβ), have been complicated by the rapid, heterogeneous aggregation of Aβ and the resulting difficulty to structurally characterize the peptide. To address this, we have developed [Nle(35), D-Pro(37)]Aβ(42), a substituted peptide inspired from molecular dynamics simulations which forms structures stable enough to be analyzed by NMR. We report herein that [Nle(35), D-Pro(37)]Aβ(42) stabilizes the trimer and prevents mature fibril and β-sheet formation. Further, [Nle(35), D-Pro(37)]Aβ(42) interacts with WT Aβ(42) and reduces aggregation levels and fibril formation in mixtures. Using ligand-based drug design based on [Nle(35), D-Pro(37)]Aβ(42), a lead compound was identified with effects on inhibition similar to the peptide. The ability of [Nle(35), D-Pro(37)]Aβ(42) and the compound to inhibit the aggregation of Aβ(42) provides a novel tool to study the structure of Aβ oligomers. More broadly, our data demonstrate how molecular dynamics simulation can guide experiment for further research into AD.

Key residues for the oligomerization of Aβ42 protein in Alzheimer's disease

Deposition of amyloid fibrils consisting of amyloid β (Aβ) protein as senile plaques in the brain is a pathological hallmark of Alzheimer's disease. However, a growing body of evidence shows that soluble Aβ oligomers correlate better with dementia than fibrils, suggesting that Aβ oligomers may be the primary toxic species. The structure and oligomerization mechanism of these Aβ oligomers are crucial for developing effective therapeutics. Here we investigated the oligomerization of Aβ42 in the context of a fusion protein containing GroES and ubiquitin fused to the N-terminus of Aβ sequence. The presence of fusion protein partners, in combination with a denaturing buffer containing 8M urea at pH 10, is unfavorable for Aβ42 aggregation, thus allowing only the most stable structures to be observed. Transmission electron microscopy showed that Aβ42 fusion protein formed globular oligomers, which bound weakly to thioflavin T and Congo red. SDS-PAGE shows that Aβ42 fusion protein formed SDS-resistant hexamers and tetramers. In contrast, Aβ40 fusion protein remained as monomers on SDS gel, suggesting that the oligomerization of Aβ42 fusion protein is not due to the fusion protein partners. Cysteine scanning mutagenesis at 22 residue positions further revealed that single cysteine substitutions of the C-terminal hydrophobic residues (I31, I32, L34, V39, V40, and I41) led to disruption of hexamer and tetramer formation, suggesting that hydrophobic interactions between these residues are most critical for Aβ42 oligomerization.

Rationally designed turn promoting mutation in the amyloid-β peptide sequence stabilizes oligomers in solution

Enhanced production of a 42-residue beta amyloid peptide (Aβ₄₂) in affected parts of the brain has been suggested to be the main causative factor for the development of Alzheimer's Disease (AD). The severity of the disease depends not only on the amount of the peptide but also its conformational transition leading to the formation of oligomeric amyloid-derived diffusible ligands (ADDLs) in the brain of AD patients. Despite being significant to the understanding of AD mechanism, no atomic-resolution structures are available for these species due to the evanescent nature of ADDLs that hinders most structural biophysical investigations. Based on our molecular modeling and computational studies, we have designed Met35Nle and G37p mutations in the Aβ₄₂ peptide (Aβ₄₂Nle35p37) that appear to organize Aβ₄₂ into stable oligomers. 2D NMR on the Aβ₄₂Nle35p37 peptide revealed the occurrence of two β-turns in the V24-N27 and V36-V39 stretches that could be the possible cause for the oligomer stability. We did not observe corresponding NOEs for the V24-N27 turn in the Aβ_21–43Nle35p37 fragment suggesting the need for the longer length amyloid peptide to form the stable oligomer promoting conformation. Because of the presence of two turns in the mutant peptide which were absent in solid state NMR structures for the fibrils, we propose, fibril formation might be hindered. The biophysical information obtained in this work could aid in the development of structural models for toxic oligomer formation that could facilitate the development of therapeutic approaches to AD.

Free-energy landscapes and thermodynamic parameters of complex molecules from nonequilibrium simulation trajectories

Thermodynamic parameters such as free energies and heat capacities are important quantities for understanding processes involving structural transitions in complex molecules such as proteins. Computational investigations provide simulated data that can be used for calculating thermodynamic parameters. However, calculations give accurate results only if the simulations sample all of configuration space with the appropriate temperature-dependent Boltzmann equilibrium probabilities. For many systems, truly comprehensive sampling of configuration space is not computationally feasible. We present an approximation technique for the calculations that will give accurate values for thermodynamic parameters when the data is incomplete. Our work is applicable to systems in which there are two distinct, important regions of configuration space that must be sampled. Importantly, the results are also valid when the system is more complex than two-state systems. Transition pathways that involve intermediate configurations between two stable regions are allowed in this treatment, and therefore the results are valid for multistate systems.

Mapping the conformational dynamics and pathways of spontaneous steric zipper Peptide oligomerization

The process of protein misfolding and self-assembly into various, polymorphic aggregates is associated with a number of important neurodegenerative diseases. Only recently, crystal structures of several short peptides have provided detailed structural insights into -sheet rich aggregates, known as amyloid fibrils. Knowledge about early events of the formation and interconversion of small oligomeric states, an inevitable step in the cascade of peptide self-assembly, however, remains still limited. We employ molecular dynamics simulations in explicit solvent to study the spontaneous aggregation process of steric zipper peptide segments from the tau protein and insulin in atomistic detail. Starting from separated chains with random conformations, we find a rapid formation of structurally heterogeneous, -sheet rich oligomers, emerging from multiple bimolecular association steps and diverse assembly pathways. Furthermore, our study provides evidence that aggregate intermediates as small as dimers can be kinetically trapped and thus affect the structural evolution of larger oligomers. Alternative aggregate structures are found for both peptide sequences in the different independent simulations, some of which feature characteristics of the known steric zipper conformation (e.g., -sheet bilayers with a dry interface). The final aggregates interconvert with topologically distinct oligomeric states exclusively via internal rearrangements. The peptide oligomerization was analyzed through the perspective of a minimal oligomer, i.e., the dimer. Thereby all observed multimeric aggregates can be consistently mapped onto a space of reduced dimensionality. This novel method of conformational mapping reveals heterogeneous association and reorganization dynamics that are governed by the characteristics of peptide sequence and oligomer size.

Probing the topological tolerance of multimeric protein interactions: evaluation of an estrogen/synthetic ligand for FK506 binding protein conjugate

Bivalent small molecules composed of a targeting element and an element that recruits endogenous proteins have been shown to block protein-protein interactions in some systems. We have attempted to apply such an approach to disrupt the interaction of the estrogen receptor α with either its associated coactivators or its dimerization partner (i.e., another estrogen receptor). We show here that a conjugate capable of simultaneously binding both the estrogen receptor and a recruited protein (FK506 Binding Protein 12 kDa) is, however, incapable of disrupting the multimeric estrogen receptor dimer/coactivator complex both in vitro and in cell-based reporter gene assays. We postulate why it may not be possible to disrupt this particular protein-protein complex-as well as other systems having high topological tolerance-with such bivalent inhibitors.

Everything you wanted to know about Markov State Models but were afraid to ask

Simulating protein folding has been a challenging problem for decades due to the long timescales involved (compared with what is possible to simulate) and the challenges of gaining insight from the complex nature of the resulting simulation data. Markov State Models (MSMs) present a means to tackle both of these challenges, yielding simulations on experimentally relevant timescales, statistical significance, and coarse grained representations that are readily humanly understandable. Here, we review this method with the intended audience of non-experts, in order to introduce the method to a broader audience. We review the motivations, methods, and caveats of MSMs, as well as some recent highlights of applications of the method. We conclude by discussing how this approach is part of a paradigm shift in how one uses simulations, away from anecdotal single-trajectory approaches to a more comprehensive statistical approach.

Protein folded states are kinetic hubs

Understanding molecular kinetics, and particularly protein folding, is a classic grand challenge in molecular biophysics. Network models, such as Markov state models (MSMs), are one potential solution to this problem. MSMs have recently yielded quantitative agreement with experimentally derived structures and folding rates for specific systems, leaving them positioned to potentially provide a deeper understanding of molecular kinetics that can lead to experimentally testable hypotheses. Here we use existing MSMs for the villin headpiece and NTL9, which were constructed from atomistic simulations, to accomplish this goal. In addition, we provide simpler, humanly comprehensible networks that capture the essence of molecular kinetics and reproduce qualitative phenomena like the apparent two-state folding often seen in experiments. Together, these models show that protein dynamics are dominated by stochastic jumps between numerous metastable states and that proteins have heterogeneous unfolded states (many unfolded basins that interconvert more rapidly with the native state than with one another) yet often still appear two-state. Most importantly, we find that protein native states are hubs that can be reached quickly from any other state. However, metastability and a web of nonnative states slow the average folding rate. Experimental tests for these findings and their implications for other fields, like protein design, are also discussed.

Network models for molecular kinetics and their initial applications to human health

Molecular kinetics underlies all biological phenomena and, like many other biological processes, may best be understood in terms of networks. These networks, called Markov state models (MSMs), are typically built from physical simulations. Thus, they are capable of quantitative prediction of experiments and can also provide an intuition for complex conformational changes. Their primary application has been to protein folding; however, these technologies and the insights they yield are transferable. For example, MSMs have already proved useful in understanding human diseases, such as protein misfolding and aggregation in Alzheimer's disease.

Bayesian detection of intensity changes in single molecule and molecular dynamics trajectories

Single molecule spectroscopy experiments and molecular dynamics simulations have several profound features in common, chief among which is that both follow the dynamics of some degrees of freedom of a single molecule over time. The analysis is essentially the same: one investigates the changes in the degrees of freedom followed. For instance, in a single molecule fluorescence experiment, the degree of freedom is often the number of photons detected in some time period. In this article, we introduce a straightforward Bayesian method for detecting if and when changes occurred. In contrast to methods based upon maximum likelihood estimates, a Bayesian approach allows for a more systematic means not only to change point detection but also to cluster the data into states. Most importantly, the Bayesian method supplies a simpler hypothesis testing framework. Although we focus on Poisson-distributed data, the Bayesian methods outlined here can in principle be applied to data sampled from any distribution.

Bayesian single-exponential kinetics in single-molecule experiments and simulations

In this work, we develop a fully Bayesian method for the calculation of probability distributions of single-exponential rates for any single-molecule process. These distributions can even be derived when no transitions from one state to another have been observed, since in that case the data can be used to estimate a lower bound on the rate. Using a Bayesian hypothesis test, one can easily test whether a transition occurs at the same rate or at different rates in two data sets. We illustrate these methods with molecular dynamics simulations of the folding of a beta-sheet protein. However, the theory presented here can be used on any data from simulation or experiment for which a two-state description is appropriate.

Using generalized ensemble simulations and Markov state models to identify conformational states

Part of understanding a molecule's conformational dynamics is mapping out the dominant metastable, or long lived, states that it occupies. Once identified, the rates for transitioning between these states may then be determined in order to create a complete model of the system's conformational dynamics. Here we describe the use of the MSMBuilder package (now available at http://simtk.org/home/msmbuilder/) to build Markov State Models (MSMs) to identify the metastable states from Generalized Ensemble (GE) simulations, as well as other simulation datasets. Besides building MSMs, the code also includes tools for model evaluation and visualization.

Alzheimer's disease and blood-brain barrier function-Why have anti-beta-amyloid therapies failed to prevent dementia progression?

Proteopathies of the brain are defined by abnormal, disease-inducing protein deposition that leads to functional abrogation and death of neurons. Immunization trials targeting the removal of amyloid-beta plaques in Alzheimer's disease have so far failed to stop the progression of dementia, despite autopsy findings of reduced plaque load. Here, we summarize current knowledge of the relationship between AD pathology and blood-brain barrier function, and propose that the activation of the excretion function of the blood-brain barrier might help to achieve better results in trials targeting the dissolution of cerebral amyloid-beta aggregates. We further discuss a possible role of oligomers in limiting the efficacy of immunotherapy.