Atomic-level characterization of the ensemble of the Aβ(1-42) monomer in water using unbiased molecular dynamics simulations and spectral algorithms

Pyroglutamylation modulates electronic properties and the conformational ensemble of the amyloid β-peptide

Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by the formation of extracellular amyloid-β (Aβ) plaques. The underlying cause of AD is unknown, however, post-translational modifications (PTMs) of Aβ have been found in AD patients and are thought to play a role in protein aggregation. One such PTM is pyroglutamylation, which can occur at two sites in Aβ, Glu3 and Glu11. This modification of Aβ involves the truncation and charge-neutralization of N-terminal glutamate, causing Aβ to become more hydrophobic and prone to aggregation. The molecular mechanism by which the introduction of pyroglutamate (pE) promotes aggregation has not been determined. To gain a greater understanding of the role that charge neutralization and truncation of the N-terminus plays on Aβ conformational sampling, we used the Drude polarizable force field (FF) to perform molecular dynamics simulations on AβpE3-42 and AβpE11-42 and comparing their properties to previous simulations of Aβ1-42. The Drude polarizable FF allows for a more accurate representation of electrostatic interactions, therefore providing novel insights into the role that charge plays in protein dynamics. Here, we report the parametrization of pE in the Drude polarizable FF and the effect of pyroglutamylation on Aβ. We found that AβpE3-42 and AβpE11-42 alter the permanent and induced dipoles of the peptide. Specifically, we found that AβpE3-42 and AβpE11-42 have modification-specific backbone and sidechain polarization response and perturbed solvation properties that shift the Aβ conformational ensemble.

Peptide Self-Assembly into Amyloid Fibrils: Unbiased All-Atom Simulations

Protein self-assembly plays an important role in biological systems, accounting for the formation of mesoscopic structures that can be highly symmetric as in the capsid of viruses or disordered as in molecular condensates or exhibit a one-dimensional fibrillar morphology as in amyloid fibrils. Deposits of the latter in tissues of individuals with degenerative diseases like Alzheimer's and Parkinson's has motivated extensive efforts to understand the sequence of molecular events accounting for their formation. These studies aim to identify on-pathway intermediates that may be the targets for therapeutic intervention. This detailed knowledge of fibril formation remains obscure, in part due to challenges with experimental analyses of these processes. However, important progress is being achieved for short amyloid peptides due to advances in our ability to perform completely unbiased all-atom simulations of the self-assembly process. This perspective discusses recent developments, their implications, and the hurdles that still need to be overcome to further advance the field.

Structure Comparison of Beta Amyloid Peptide Aβ 1-42 Isoforms. Molecular Dynamics Modeling

Beta amyloid peptide Aβ 1-42 (Aβ42) has a unique dual role in the human organism, as both the peptide with an important physiological function and one of the most toxic biological compounds provoking Alzheimer's disease (AD). There are several known Aβ42 isoforms that we discuss here that are highly neurotoxic and lead to the early onset of AD. Aβ42 is an intrinsically disordered protein with no experimentally solved structure under physiological conditions. The objective of this research was to establish the appropriate molecular dynamics (MD) methodology and model a uniform set of structures for the Aβ42 isoforms that form the core of this study. For that purpose, force field selection and verification including convergence testing for MD simulations was made. Replica exchange MD and conventional MD modeling of several Aβ42 and Aβ16 isoforms that have neurotoxic and amyloidogenic effects impacting the severity of Alzheimer's disease were carried out with the optimal force field and solvent parameters. A standardized ensemble of structures for the Aβ42 and Aβ16 isoforms covering 30-50% of the conformational ensembles extracted from the free energy minima was calculated from MD trajectories. The resulting data set of modeled structures includes Aβ42 wild type, isoD7, pS8, D7H, and H6R-Aβ42 and Aβ16 wild type, isoD7, pS8, D7H, and H6R-Aβ16. The representative structures are given in the Supporting Information; they are open for public access. In the study, we also evaluated the differences between the structures of Aβ42 isoforms and speculate on their possible relevance to the known functions. Utilizing several representative structures for a single disordered protein for docking, with their subsequent averaging by conformations, would markedly increase the reliability of docking results.

Protein Corona Formation and Aggregation of Amyloid β 1-40-Coated Gold Nanocolloids

Amyloid fibrillogenesis is a pathogenic protein aggregation process that occurs through a highly ordered process of protein-protein interactions. To better understand the protein-protein interactions involved in amyloid fibril formation, we formed nanogold colloid aggregates by stepwise additions of ∼2 nmol of amyloid β 1-40 peptide (Aβ1-40) at pH ∼3.7 and ∼25 °C. The processes of protein corona formation and building of gold colloid [diameters (d) of 20 and 80 nm] aggregates were confirmed by a red-shift of the surface plasmon resonance (SPR) band, λpeak, as the number of Aβ1-40 peptides [N(Aβ1-40)] increased. The normalized red-shift of λpeak, Δλ, was correlated with the degree of protein aggregation, and this process was approximated as the adsorption isotherm explained by the Langmuir-Freundlich model. As the coverage fraction (θ) was analyzed as a function of ϕ, which is the N(Aβ1-40) per total surface area of nanogold colloids available for adsorption, the parameters for explaining the Langmuir-Freundlich model were in good agreement for both 20 and 80 nm gold, indicating that ϕ could define the stage of the aggregation process. Surface-enhanced Raman scattering (SERS) imaging was conducted at designated values of ϕ and suggested that a protein-gold surface interaction during the initial adsorption stage may be dependent on the nanosize. The 20 nm gold case seems to prefer a relatively smaller contacting section, such as a -C-N or C═C bond, but a plane of the benzene ring may play a significant role for 80 nm gold. Regardless of the size of the particles, the β-sheet and random coil conformations were considered to be used to form gold colloid aggregates. The methodology developed in this study allows for new insights into protein-protein interactions at distinct stages of aggregation.

Aquaporin-4 Mediated Aggregation of Alzheimer's Amyloid β-Peptide

Clearance of Alzheimer's amyloid oligomers from the brain is crucial for preventing cell toxicity. Dementia complications arise as a result of apoptosis, which is caused by peptide plaques on the lipid surface of cells. Here, we employed all-atom and coarse-grained molecular dynamics simulations to investigate the aggregation of amyloid peptides at the lipid surface and the role of aquaporin-4 (AQP4) in facilitating peptide clearance from astrocytes. The network of protein-protein interactions through text mining revealed that the expression of AQP4 and amyloid aggregation were strongly correlated. It has also been revealed that the role of aquaporins in the etiology of Alzheimer's disease involves several interconnected proteins and pathways. The nature of aggregation at the surface of the 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid bilayer was revealed by the interaction of amyloid oligomers. The membrane-bound pore region of AQP4 interacts with the peptide and slows its aggregation. This interaction maintains the helical content of the peptide while lowering its toxicity at the lipid surface. The hydrophobicity of the peptide also decreased because of these interactions, which may help in the removal of the peptide from astrocytes. Long-term coarse-grained MD simulations demonstrated different features of oligomer aggregation at the surface and strong oligomer attraction to AQP4, which inhibited aggregation. Additionally, the water dynamics of aquaporins demonstrate how the selectivity filter is broken to disrupt water flow. Our findings also provide insight into the physiological alterations in brain tissue associated with Alzheimer's disease, including water retention and increased water flow in the CSF. Furthermore, in vitro thioflavin fluorescence spectroscopy revealed a slower aggregation of the peptide in the presence of AQP4.

Modeling structural interconversion in Alzheimers' amyloid beta peptide with classical and intrinsically disordered protein force fields

A comprehensive understanding of the aggregation mechanism in amyloid beta 42 (Aβ42) peptide is imperative for developing therapeutic drugs to prevent or treat Alzheimer's disease. Because of the high flexibility and lack of native tertiary structures of Aβ42, molecular dynamics (MD) simulations may help elucidate the peptide's dynamics with atomic details and collectively improve ensembles not seen in experiments. We applied microsecond-timescale MD simulations to investigate the dynamics and conformational changes of Aβ42 by using a newly developed Amber force field (ff14IDPSFF). We compared the ff14IDPSFF and the regular ff14SB force field by examining the conformational changes of two distinct Aβ42 monomers in explicit solvent. Conformational ensembles obtained by simulations depend on the force field and initial structure, Aβ42^α-helix or Aβ42^β-strand. The ff14IDPSFF sampled a high ratio of disordered structures and diverse β-strand secondary structures; in contrast, ff14SB favored helicity during the Aβ42^α-helix simulations. The conformations obtained from Aβ42^β-strand simulations maintained a balanced content in the disordered and helical structures when simulated by ff14SB, but the conformers clearly favored disordered and β-sheet structures simulated by ff14IDPSFF. The results obtained with ff14IDPSFF qualitatively reproduced the NMR chemical shifts well. In-depth peptide and cluster analysis revealed some characteristic features that may be linked to early onset of the fibril-like structure. The C-terminal region (mainly M35-V40) featured in-registered anti-parallel β-strand (β-hairpin) conformations with tested systems. Our work should expand the knowledge of force field and structure dependency in MD simulations and reveals the underlying structural mechanism-function relationship in Aβ42 peptides. Communicated by Ramaswamy H. Sarma.

Amyloidogenesis: What Do We Know So Far?

The study of protein aggregation, and amyloidosis in particular, has gained considerable interest in recent times. Several neurodegenerative diseases, such as Alzheimer's (AD) and Parkinson's (PD) show a characteristic buildup of proteinaceous aggregates in several organs, especially the brain. Despite the enormous upsurge in research articles in this arena, it would not be incorrect to say that we still lack a crystal-clear idea surrounding these notorious aggregates. In this review, we attempt to present a holistic picture on protein aggregation and amyloids in particular. Using a chronological order of discoveries, we present the case of amyloids right from the onset of their discovery, various biophysical techniques, including analysis of the structure, the mechanisms and kinetics of the formation of amyloids. We have discussed important questions on whether aggregation and amyloidosis are restricted to a subset of specific proteins or more broadly influenced by the biophysiochemical and cellular environment. The therapeutic strategies and the significant failure rate of drugs in clinical trials pertaining to these neurodegenerative diseases have been also discussed at length. At a time when the COVID-19 pandemic has hit the globe hard, the review also discusses the plausibility of the far-reaching consequences posed by the virus, such as triggering early onset of amyloidosis. Finally, the application(s) of amyloids as useful biomaterials has also been discussed briefly in this review.

Key Residue for Aggregation of Amyloid-β Peptides

It is known that oligomers of amyloid-β (Aβ) peptide are associated with Alzheimer's disease. Aβ has two isoforms: Aβ40 and Aβ42. Although the difference between Aβ40 and Aβ42 is only two additional C-terminal residues, Aβ42 aggregates much faster than Aβ40. It is unknown what role the C-terminal two residues play in accelerating aggregation. Since Aβ42 is more toxic than Aβ40, its oligomerization process needs to be clarified. Moreover, clarifying the differences between the oligomerization processes of Aβ40 and Aβ42 is essential to elucidate the key factors of oligomerization. Therefore, to investigate the dimerization process, which is the early oligomerization process, Hamiltonian replica-permutation molecular dynamics simulations were performed for Aβ40 and Aβ42. We identified a key residue, Arg5, for the Aβ42 dimerization. The two additional residues in Aβ42 allow the C-terminus to form contact with Arg5 because of the electrostatic attraction between them, and this contact stabilizes the β-hairpin. This β-hairpin promotes dimer formation through the intermolecular β-bridges. Thus, we examined the effects of amino acid substitutions of Arg5, thereby confirming that the mutations remarkably suppressed the aggregation of Aβ42. Moreover, the mutations of Arg5 suppressed the Aβ40 aggregation. It was found by analyzing the simulations that Arg5 is important for Aβ40 to form intermolecular contacts. Thus, it was clarified that the role of Arg5 in the oligomerization process varies due to the two additional C-terminal residues.

Effects of Familial Alzheimer's Disease Mutations on the Folding Free Energy and Dipole-Dipole Interactions of the Amyloid β-Peptide

Familial Alzheimer's disease (FAD) mutations of the amyloid β-peptide (Aβ) are known to lead to early onset and more aggressive Alzheimer's disease. FAD mutations such as "Iowa" (D23N), "Arctic" (E22G), "Italian" (E22K), and "Dutch" (E22Q) have been shown to accelerate Aβ aggregation relative to the wild-type (WT). The mechanism by which these mutations facilitate increased aggregation is unknown, but each mutation results in a change in the net charge of the peptide. Previous studies have used nonpolarizable force fields to study Aβ, providing some insight into how this protein unfolds. However, nonpolarizable force fields have fixed charges that lack the ability to redistribute in response to changes in local electric fields. Here, we performed polarizable molecular dynamics simulations on the full-length Aβ42 of WT and FAD mutations and calculated folding free energies of the Aβ15-27 fragment via umbrella sampling. By studying both the full-length Aβ42 and a fragment containing mutations and the central hydrophobic cluster (residues 17-21), we were able to systematically study how these FAD mutations impact secondary and tertiary structure and the thermodynamics of folding. Electrostatic interactions, including those between permanent and induced dipoles, affected side-chain properties, salt bridges, and solvent interactions. The FAD mutations resulted in shifts in the electronic structure and solvent accessibility at the central hydrophobic cluster and the hydrophobic C-terminal region. Using umbrella sampling, we found that the folding of the WT and E22 mutants is enthalpically driven, whereas the D23N mutant is entropically driven, arising from a different unfolding pathway and peptide-bond dipole response. Together, the unbiased, full-length, and umbrella sampling simulations of fragments reveal that the FAD mutations perturb nearby residues and others in hydrophobic regions to potentially alter solubility. These results highlight the role electronic polarizability plays in amyloid misfolding and the role of heterogeneous microenvironments that arise as conformational change takes place.

Characterization of the Conformations of Amyloid Beta 42 in Solution That May Mediate Its Initial Hydrophobic Aggregation

Intrinsically disordered peptides, such as amyloid β42 (Aβ42), lack a well-defined structure in solution. Aβ42 can undergo abnormal aggregation and amyloidogenesis in the brain, forming fibrillar plaques, a hallmark of Alzheimer's disease. The insoluble fibrillar forms of Aβ42 exhibit well-defined, cross β-sheet structures at the molecular level and are less toxic than the soluble, intermediate disordered oligomeric forms. However, the mechanism of initial interaction of monomers and subsequent oligomerization is not well understood. The structural disorder of Aβ42 adds to the challenges of determining the structural properties of its monomers, making it difficult to understand the underlying molecular mechanism of pathogenic aggregation. Certain regions of Aβ42 are known to exhibit helical propensity in different physiological conditions. NMR spectroscopy has shown that the Aβ42 monomer at lower pH can adopt an α-helical conformation and as the pH is increased, the peptide switches to β-sheet conformation and aggregation occurs. CD spectroscopy studies of aggregation have shown the presence of an initial spike in the amount of α-helical content at the start of aggregation. Such an increase in α-helical content suggests a mechanism wherein the peptide can expose critical non-polar residues for interaction, leading to hydrophobic aggregation with other interacting peptides. We have used molecular dynamics simulations to characterize in detail the conformational landscape of monomeric Aβ42 in solution to identify molecular properties that may mediate the early stages of oligomerization. We hypothesized that conformations with α-helical structure have a higher probability of initiating aggregation because they increase the hydrophobicity of the peptide. Although random coil conformations were found to be the most dominant, as expected, α-helical conformations are thermodynamically accessible, more so than β-sheet conformations. Importantly, for the first time α-helical conformations are observed to increase the exposure of aromatic and hydrophobic residues to the aqueous solvent, favoring their hydrophobically driven interaction with other monomers to initiate aggregation. These findings constitute a first step toward characterizing the mechanism of formation of disordered, low-order oligomers of Aβ42.

A Small Molecule Stabilizes the Disordered Native State of the Alzheimer's Aβ Peptide

The stabilization of native states of proteins is a powerful drug discovery strategy. It is still unclear, however, whether this approach can be applied to intrinsically disordered proteins. Here, we report a small molecule that stabilizes the native state of the Aβ42 peptide, an intrinsically disordered protein fragment associated with Alzheimer's disease. We show that this stabilization takes place by a disordered binding mechanism, in which both the small molecule and the Aβ42 peptide remain disordered. This disordered binding mechanism involves enthalpically favorable local π-stacking interactions coupled with entropically advantageous global effects. These results indicate that small molecules can stabilize disordered proteins in their native states through transient non-specific interactions that provide enthalpic gain while simultaneously increasing the conformational entropy of the proteins.

Pharmacological affinity fingerprints derived from bioactivity data for the identification of designer drugs

Facing the continuous emergence of new psychoactive substances (NPS) and their threat to public health, more effective methods for NPS prediction and identification are critical. In this study, the pharmacological affinity fingerprints (Ph-fp) of NPS compounds were predicted by Random Forest classification models using bioactivity data from the ChEMBL database. The binary Ph-fp is the vector consisting of a compound's activity against a list of molecular targets reported to be responsible for the pharmacological effects of NPS. Their performance in similarity searching and unsupervised clustering was assessed and compared to 2D structure fingerprints Morgan and MACCS (1024-bits ECFP4 and 166-bits SMARTS-based MACCS implementation of RDKit). The performance in retrieving compounds according to their pharmacological categorizations is influenced by the predicted active assay counts in Ph-fp and the choice of similarity metric. Overall, the comparative unsupervised clustering analysis suggests the use of a classification model with Morgan fingerprints as input for the construction of Ph-fp. This combination gives satisfactory clustering performance based on external and internal clustering validation indices.

Aggregation of a Parkinson's Disease-Related Peptide: When Does Urea Weaken Hydrophobic Interactions?

While the exact cause of neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease is not completely understood, compelling evidence implicates the aggregation of specific proteins and peptides. Co-solvents can provide molecular insight into protein aggregation mechanisms and the chemical nature of potential aggregation inhibitors. Here, we study, through molecular simulations, the hydration and binding free energies of an amphiphilic peptide from the nonamyloid-β component (NAC), a key aggregation-prone domain of α-synuclein, in water and an 8 M aqueous urea solution. Isoleucine, glycine, and serine peptides of the same length are also studied to unravel the role of urea in the hydration and aggregation of hydrophobic and hydrophilic domains. A strong impact of urea in hindering the aggregation of the NAC subdomain is observed. A slightly weaker aggregation inhibition is observed for the Gly and Ser peptides, whereas a much lower aggregation inhibitory activity is found for the Ile peptide, seemingly contrasting with urea's protein unfolding mechanism. This behavior is shown to derive from a lower profusion of urea next to the hydrophobic side chains and the backbone of the Ile's peptide in the dimeric form. As a consequence, β-sheets, formed upon aggregation, remain nearly intact. Hydrophilic neighbor groups in the amphiphilic NAC subdomain, however, are shown to anchor enough urea to weaken hydrophobic interactions and disrupt β-sheet structures. Our results indicate that urea's activity is potentiated in amphiphilic domains and that potential drugs could disrupt hydrophobic β-sheet-rich regions while not binding primarily to hydrophobic amino acids.

Molecular Dynamics Simulation Studies on the Aggregation of Amyloid-β Peptides and Their Disaggregation by Ultrasonic Wave and Infrared Laser Irradiation

Alzheimer’s disease is understood to be caused by amyloid fibrils and oligomers formed by aggregated amyloid-β (Aβ) peptides. This review article presents molecular dynamics (MD) simulation studies of Aβ peptides and Aβ fragments on their aggregation, aggregation inhibition, amyloid fibril conformations in equilibrium, and disruption of the amyloid fibril by ultrasonic wave and infrared laser irradiation. In the aggregation of Aβ, a β-hairpin structure promotes the formation of intermolecular β-sheet structures. Aβ peptides tend to exist at hydrophilic/hydrophobic interfaces and form more β-hairpin structures than in bulk water. These facts are the reasons why the aggregation is accelerated at the interface. We also explain how polyphenols, which are attracting attention as aggregation inhibitors of Aβ peptides, interact with Aβ. An MD simulation study of the Aβ amyloid fibrils in equilibrium is also presented: the Aβ amyloid fibril has a different structure at one end from that at the other end. The amyloid fibrils can be destroyed by ultrasonic wave and infrared laser irradiation. The molecular mechanisms of these amyloid fibril disruptions are also explained, particularly focusing on the function of water molecules. Finally, we discuss the prospects for developing treatments for Alzheimer’s disease using MD simulations.

Combining enhanced sampling and deep learning dimensionality reduction for the study of the heat shock protein B8 and its pathological mutant K141E

The biological functions of proteins closely depend on their conformational dynamics. This aspect is especially relevant for intrinsically disordered proteins (IDP) for which structural ensembles often offer more useful representations than individual conformations. Here we employ extensive enhanced sampling temperature replica-exchange atomistic simulations (TREMD) and deep learning dimensionality reduction to study the conformational ensembles of the human heat shock protein B8 and its pathological mutant K141E, for which no experimental 3D structures are available. First, we combined homology modelling with TREMD to generate high-dimensional data sets of 3D structures. Then, we employed a recently developed machine learning based post-processing algorithm, EncoderMap, to project the large conformational data sets into meaningful two-dimensional maps that helped us interpret the data and extract the most significant conformations adopted by both proteins during TREMD. These studies provide the first 3D structural characterization of HSPB8 and reveal the effects of the pathogenic K141E mutation on its conformational ensembles. In particular, this missense mutation appears to increase the compactness of the protein and its structural variability, at the same time rearranging the hydrophobic patches exposed on the protein surface. These results offer the possibility of rationalizing the pathogenic effects of the K141E mutation in terms of conformational changes.

The study provides the first 3D structural characterization of HSPB8 and its K141E mutant: extensive TREMD are combined with a deep learning algorithm to rationalize the disordered ensemble of structures adopted by each variant.

Characterization of Amyloidogenic Peptide Aggregability in Helical Subspace

Prototypical amyloidogenic peptides amyloid-β (Aβ) and α-synuclein (αS) can undergo helix-helix associations via partially folded helical conformers, which may influence pathological progression to Alzheimer's (AD) and Parkinson's disease (PD), respectively. At the other extreme, stable folded helical conformers have been reported to resist self-assembly and amyloid formation. Experimental characterisation of such disparities in aggregation profiles due to subtle differences in peptide stabilities is precluded by the conformational heterogeneity of helical subspace. The diverse physical models used in molecular simulations allow sampling distinct regions of the phase space and are extensive in capturing the ensemble of rich helical subspace. Robust and powerful computational predictive methods utilizing network theory and free energy mapping can model the origin of helical population shifts in amyloidogenic peptides, which highlight their inherent aggregability. In this chapter, we discuss computational models, methods, design rules, and strategies to identify the driving force behind helical self-assembly and the molecular origin of aggregation resistance in helical intermediates of Aβ42 and αS. By extensive multiscale mapping of intrapeptide interactions, we show that the computational models can capture features that are otherwise imperceptible to experiments. Our models predict that targeting terminal residues may allow modulation and control of initial pathogenic aggregability of amyloidogenic peptides.

Data Mining of Molecular Simulations Suggest Key Amino Acid Residues for Aggregation, Signaling and Drug Action

Alzheimer's disease, the most common form of dementia, currently has no cure. There are only temporary treatments that reduce symptoms and the progression of the disease. Alzheimer's disease is characterized by the prevalence of plaques of aggregated amyloid β (Aβ) peptide. Recent treatments to prevent plaque formation have provided little to relieve disease symptoms. Although there have been numerous molecular simulation studies on the mechanisms of Aβ aggregation, the signaling role has been less studied. In this study, a total of over 38,000 simulated structures, generated from molecular dynamics (MD) simulations, exploring different conformations of the Aβ42 mutants and wild-type peptides were used to examine the relationship between Aβ torsion angles and disease measures. Unique methods characterized the data set and pinpointed residues that were associated in aggregation and others associated with signaling. Machine learning techniques were applied to characterize the molecular simulation data and classify how much each residue influenced the predicted variant of Alzheimer's Disease. Orange3 data mining software provided the ability to use these techniques to generate tables and rank the data. The test and score module coupled with the confusion matrix module analyzed data with calculations of specificity and sensitivity. These methods evaluating frequency and rank allowed us to analyze and predict important residues associated with different phenotypic measures. This research has the potential to help understand which specific residues of Aβ should be targeted for drug development.

Effects of applied surface-tension on membrane-assisted Aβ aggregation

Accumulation of protein-based (Aβ) aggregates on cellular membranes with varying structural properties is commonly recognized as the key step in Alzheimer's pathogenesis. But experimental and computational challenges have made this biophysical characterization difficult. In particular, studies connecting biological membrane organization and Aβ aggregation are limited. While experiments have suggested that an increased membrane curvature results in faster Aβ peptide aggregation in the context of Alzheimer's disease, a mechanistic explanation for this relation is missing. In this work, we are leveraging molecular simulations with a physics-based coarse grained model to address and understand the relationships between curved cellular membranes and aggregation of a model template peptide Aβ 16-22. In agreement with experimental results, our simulations also suggest a positive correlation between increased peptide aggregation and membrane curvature. More curved membranes have higher lipid packing defects that engage peptide hydrophobic groups and promote faster diffusion leading to peptide fibrillar structures. In addition, we curated the effects of peptide aggregation on the membrane's structure and organization. Interfacial peptide aggregation results in heterogeneous headgroup-peptide interactions and an induced crowding effect at the lipid headgroup region, leading to a more ordered headgroup region and disordered lipid-tails at the membrane core. This work presents a mechanistic and morphological overview of the relationships between the biomembrane local structure and organization, and Aβ peptide aggregation.

Novel strategies for the fight of Alzheimer's disease targeting amyloid-β protein

Alzheimer's disease (AD), which is recognised as a devastating neurodegenerative disease throughout the world and lack of effective treatments, is a growing concern in modern society with a growing population of elderly patients. A growing number of studies reveal that abnormal accumulation and deposition of Aβ is responsible for AD. Inspired by this, strategies for the treatment of AD targeting-Aβ clearance have been discussed for a long period, exploring new drugs which is capable of destroying soluble Aβ oligomers and unsolvable Aβ aggregates. In this paper, results of recent clinical trials on several anti-amyloid-β drugs are presented and several emerging anti-amyloid AD therapies based on recent studies are reviewed. Furthermore, some of the current challenges and novel strategies to prevent AD are addressed. Herein, this review focuses on current pharmacotherapy of AD targeting-Aβ and intends to design a promising therapeutic agent for AD treatment.

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.

Role of Water Molecules and Helix Structure Stabilization in the Laser-Induced Disruption of Amyloid Fibrils Observed by Nonequilibrium Molecular Dynamics Simulations

Water plays a crucial role in the formation and destruction of biomolecular structures. The mechanism for destroying biomolecular structures was thought to be an active breaking of hydrogen bonds by water molecules. However, using nonequilibrium molecular dynamics simulations, in which an amyloid-β amyloid fibril was destroyed via infrared free-electron laser (IR-FEL) irradiation, we discovered a new mechanism, in which water molecules disrupt protein aggregates. The intermolecular hydrogen bonds formed by C═O and N-H in the fibril are broken at each pulse of laser irradiation. These bonds spontaneously re-form after the irradiation in many cases. However, when a water molecule happens to enter the gap between C═O and N-H, it inhibits the re-formation of the hydrogen bonds. Such sites become defects in the regularly aligned hydrogen bonds, from which all hydrogen bonds in the intermolecular β-sheet are broken as the fraying spreads. This role of water molecules is entirely different from other known mechanisms. This new mechanism can explain the recent experiments showing that the amyloid fibrils are not destroyed by laser irradiation under dry conditions. Additionally, we found that helix structures form more after the amyloid disruption; this is because the resonance frequency is different in a helix structure. Our findings provide a theoretical basis for the application of IR-FEL to the future treatment of amyloidosis.

Quality of force fields and sampling methods in simulating pepX peptides: a case study for intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are a group of proteins that lack well-defined structures under native conditions and carry out crucial physiological functions in various biochemical pathways. Due to the heterogeneous nature of IDPs, molecular dynamics simulations have been extensively adopted to investigate the conformational ensembles and dynamic properties of these proteins. However, their accuracy remains limited by the development of force fields and sampling algorithms. Here, we evaluated the quality of both force fields and enhanced sampling algorithms based on five short pepX peptides. Our results show that the more extended conformational ensembles sampled by the AMOEBA polarizable force field present a higher ability to reproduce experimental NMR observables than AMBER and CHARMM classical force fields. Moreover, a better agreement with experiments is achieved in the simulation of IaMD (integrated accelerated molecular dynamics) than in aMD (accelerated molecular dynamics). The results together indicate that the combination of AMOEBA force field and IaMD enhanced sampling might be a better choice for simulating IDPs. This work may provide important clues for developments and applications of force fields and enhanced sampling methods in future simulations of IDPs.

Thermodynamics and kinetics of the amyloid-β peptide revealed by Markov state models based on MD data in agreement with experiment

The amlyoid-β peptide (Aβ) is closely linked to the development of Alzheimer's disease. Molecular dynamics (MD) simulations have become an indispensable tool for studying the behavior of this peptide at the atomistic level. General key aspects of MD simulations are the force field used for modeling the peptide and its environment, which is important for accurate modeling of the system of interest, and the length of the simulations, which determines whether or not equilibrium is reached. In this study we address these points by analyzing 30-μs MD simulations acquired for Aβ40 using seven different force fields. We assess the convergence of these simulations based on the convergence of various structural properties and of NMR and fluorescence spectroscopic observables. Moreover, we calculate Markov state models for the different MD simulations, which provide an unprecedented view of the thermodynamics and kinetics of the amyloid-β peptide. This further allows us to provide answers for pertinent questions, like: which force fields are suitable for modeling Aβ? (a99SB-UCB and a99SB-ILDN/TIP4P-D); what does Aβ peptide really look like? (mostly extended and disordered) and; how long does it take MD simulations of Aβ to attain equilibrium? (at least 20-30 μs). We believe the analyses presented in this study will provide a useful reference guide for important questions relating to the structure and dynamics of Aβ in particular, and by extension other similar disordered proteins.

Small-Molecule Targeted Aβ42 Aggregate Degradation: Negatively Charged Small Molecules Are More Promising than the Neutral Ones

Heavy evidence has confirmed that Aβ42 oligomers are the most neurotoxic aggregates and play a critical role in the occurrence and development of Alzheimer's disease by causing functional neuron death, cognitive damage, and dementia. Disordered Aβ42 oligomers are challenging therapeutic targets, and no drug is currently in clinical use that modifies the properties of their monomeric states. Here, a negatively charged molecule (ER), rather than the neutral TS1 one, is identified by a molecular dynamics simulation method to be more capable of binding and sequestering the intrinsically disordered amyloid-β peptide Aβ42 in its soluble pentameric state as well as its monomeric components. Results reveal that the ERs interact with Aβ and inhibit the primary nucleation pathways in its aggregation process in entropic expansion mechanism for both Aβ42 and Aβ40 oligomers but with opposite characteristics of hydrophobic surface area (HSA). The interaction between Aβ42 oligomer and either charged ER or neutral TS1/TS0 characterizes decreased HSA, and the decrease in ER-involved case is highly visible, consistent with the observations from in silico and in vitro studies. By contrast, the presence of these inhibitors causes the HSA of Aβ40 oligomer to change undetectably and there is even a bit of increase in the histidine isomerized Aβ40 oligomer. The HSA distinction between Aβ42 and Aβ40 oligomer is possibly derived from the different effects of M35-inhibitor interaction, which is analogous to the effect of M35 oxidation. In comparison with the neutral TS1/TS0 inhibitors, ER is more prone to bind the residues located in the central (β1) and C-terminal (β2) regions of Aβ42 peptide, two key nucleation regions for Aβ intramolecular folding, intermolecular aggregation, and assembly. Notably, ER can strongly bind the charged residues, such as K16, K28, D23, to greatly disturb the potential stabilizer (e.g., salt-bridge, etc.) in metastable Aβ42 oligomers and protofibrils. These results illustrate the strategy of overcoming Alzheimer's disease from inhibiting its early stage Aβ aggregation with two kinds of small molecules to alter their behavior for therapeutic purposes and strongly recommend paying more attention to the engineering and development of negatively charged inhibitors, the long-term underappreciated ones, targeting the early stage Aβ aggregates.

Defining the Neuropathological Aggresome across in Silico, in Vitro, and ex Vivo Experiments

The loss of proteostasis over the life course is associated with a wide range of debilitating degenerative diseases and is a central hallmark of human aging. When left unchecked, proteins that are intrinsically disordered can pathologically aggregate into highly ordered fibrils, plaques, and tangles (termed amyloids), which are associated with countless disorders such as Alzheimer's disease, Parkinson's disease, type II diabetes, cancer, and even certain viral infections. However, despite significant advances in protein folding and solution biophysics techniques, determining the molecular cause of these conditions in humans has remained elusive. This has been due, in part, to recent discoveries showing that soluble protein oligomers, not insoluble fibrils or plaques, drive the majority of pathological processes. This has subsequently led researchers to focus instead on heterogeneous and often promiscuous protein oligomers. Unfortunately, significant gaps remain in how to prepare, model, experimentally corroborate, and extract amyloid oligomers relevant to human disease in a systematic manner. This Review will report on each of these techniques and their successes and shortcomings in an attempt to standardize comparisons between protein oligomers across disciplines, especially in the context of neurodegeneration. By standardizing multiple techniques and identifying their common overlap, a clearer picture of the soluble neuropathological aggresome can be constructed and used as a baseline for studying human disease and aging.

Promotion and Inhibition of Amyloid-β Peptide Aggregation: Molecular Dynamics Studies

Aggregates of amyloid-β (Aβ) peptides are known to be related to Alzheimer’s disease. Their aggregation is enhanced at hydrophilic–hydrophobic interfaces, such as a cell membrane surface and air-water interface, and is inhibited by polyphenols, such as myricetin and rosmarinic acid. We review molecular dynamics (MD) simulation approaches of a full-length Aβ peptide, Aβ40, and Aβ(16–22) fragments in these environments. Since these peptides have both hydrophilic and hydrophobic amino acid residues, they tend to exist at the interfaces. The high concentration of the peptides accelerates the aggregation there. In addition, Aβ40 forms a β-hairpin structure, and this structure accelerates the aggregation. We also describe the inhibition mechanism of the Aβ(16–22) aggregation by polyphenols. The aggregation of Aβ(16–22) fragments is caused mainly by the electrostatic attraction between charged amino acid residues known as Lys16 and Glu22. Since polyphenols form hydrogen bonds between their hydroxy and carboxyl groups and these charged amino acid residues, they inhibit the aggregation.

Effects of Ions and Small Compounds on the Structure of Aβ42 Monomers

Aggregation of amyloid-β (Aβ) proteins in the brain is a hallmark of Alzheimer's disease. This phenomenon can be promoted or inhibited by adding small molecules to the solution where Aβ is embedded. These molecules affect the ensemble of conformations sampled by Aβ monomers even before aggregation starts. Here, we perform extensive all-atom replica exchange molecular dynamics (REMD) simulations to provide a comparative study of the ensemble of conformations sampled by Aβ₄₂ monomers in solutions that promote (i.e., aqueous solution containing NaCl) and inhibit (i.e., aqueous solutions containing scyllo-inositol or 4-aminophenol) aggregation. Simulations performed in pure water are used as our reference. We find that secondary-structure content is only affected in an antagonistic manner by promoters and inhibitors at the C-terminus and the central hydrophilic core. Moreover, the end of the C-terminus binds more favorably to the central hydrophobic core region of Aβ₄₂ in NaCl adopting a type of strand-loop-strand structure that is disfavored by inhibitors. Nonpolar residues that form the dry core of larger aggregates of Aβ₄₂ (e.g., PDB ID 2BEG) are found at close proximity in these strand-loop-strand structures, suggesting that their formation could play an important role in initiating nucleation. In the presence of inhibitors, the C-terminus binds the central hydrophilic core with a higher probability than in our reference simulation. This sensitivity of the C-terminus, which is affected in an antagonistic manner by inhibitors and promoters, provides evidence for its critical role in accounting for aggregation.

A kinetic ensemble of the Alzheimer's Aβ peptide

The conformational and thermodynamic properties of disordered proteins are commonly described in terms of structural ensembles and free energy landscapes. To provide information on the transition rates between the different states populated by these proteins, it would be desirable to generalize this description to kinetic ensembles. Approaches based on the theory of stochastic processes can be particularly suitable for this purpose. Here, we develop a Markov state model and apply it to determine a kinetic ensemble of Aβ42, a disordered peptide associated with Alzheimer's disease. Through the Google Compute Engine, we generated 315-µs all-atom molecular dynamics trajectories. Using a probabilistic-based definition of conformational states in a neural network approach, we found that Aβ42 is characterized by inter-state transitions on the microsecond timescale, exhibiting only fully unfolded or short-lived, partially folded states. Our results illustrate how kinetic ensembles provide effective information about the structure, thermodynamics and kinetics of disordered proteins.

Nonequilibrium molecular dynamics simulations of infrared laser-induced dissociation of a tetrameric Aβ42 β-barrel in a neuronal membrane model

Experimental studies have reported that the amyloid-β proteins can form pores in cell membranes, and this could be one possible source of toxicity in Alzheimer's disease. Dissociation of these pores could therefore be a potential therapeutic approach. It is known that high photon density free-electron laser experiments and laser-induced nonequilibrium molecular dynamics simulations (NEMD) can dissociate amyloid fibrils at specific frequencies in vitro. Our question is whether NEMD simulations can dissociate amyloid pores in a bilayer mimicking a neuronal membrane, and as an example, we select a tetrameric Aβ42 β-barrel. Our simulations shows that the resonance between the laser field and the amide I vibrational mode of the barrel destabilises all intramolecular and intermolecular hydrogen bonds of Aβ42 and converts the β-barrel to a random/coil disordered oligomer. Starting from this disordered oligomer, extensive standard MD simulations shows sampling of disordered Aβ42 states without any increase of β-sheet and reports that the orientational order of lipids is minimally disturbed. Interestingly, the frequency to be employed to dissociate this beta-barrel is specific to the amino acid sequence. Taken together with our previous simulation results, this study indicates that infrared laser irradiation can dissociate amyloid fibrils and oligomers in bulk solution and in a membrane environment without affecting the surrounding molecules, offering therefore a promising way to retard the progression of AD.

Structural Characteristics of Monomeric Aβ42 on Fibril in the Early Stage of Secondary Nucleation Process

Amyloid-β (Aβ) aggregates are believed to be one of the main causes of Alzheimer's disease. Aβ peptides form fibrils having cross β-sheet structures mainly through primary nucleation, secondary nucleation, and elongation. In particular, self-catalyzed secondary nucleation is of great interest. Here, we investigate the adsorption of Aβ42 peptides to the Aβ42 fibril to reveal a role of adsorption as a part of secondary nucleation. We performed extensive molecular dynamics simulations based on replica exchange with solute tempering 2 (REST2) to two systems: a monomeric Aβ42 in solution and a complex of an Aβ42 peptide and Aβ42 fibril. Results of our simulations show that the Aβ42 monomer is extended on the fibril. Furthermore, we find that the hairpin structure of the Aβ42 monomer decreases but the helix structure increases by adsorption to the fibril surface. These structural changes are preferable for forming fibril-like aggregates, suggesting that the fibril surface serves as a catalyst in the secondary nucleation process. In addition, the stabilization of the helix structure of the Aβ42 monomer on the fibril indicates that the strategy of a secondary nucleation inhibitor design for Aβ40 can also be used for Aβ42.

Fluorescence-Labeled Amyloid Beta Monomer: A Molecular Dynamical Study

The aggregation process of the Amyloidβ (Aβ) peptide is one of the central questions in Alzheimers’s research. Fluorescence-labeled single-molecule detection is a novel technique concerning the early stage investigation of Aβ aggregation, where the labeling dyes are covalently bound to the Aβ monomer. As the influence of the dye on the conformational space of the Aβ monomer can be significant, its effect on the seeding process is an open question. The applied fluorescent molecule continuously switches between an active (ON) and an inactive (OFF) state, where the latter supports an extra rotational restriction at many commercially available dyes. However, only a few theoretical studies simulated the Aβ monomer in the presence of a dye and none of them considered the difference between the ON and the OFF states. Therefore, we examined the impact of a selected fluorescence dye (Alexa 568) on the conformational space of the monomeric Aβ(1–42) peptide in its ON and OFF state by replica exchange molecular dynamic simulations. Investigations on secondary structure elements as well as dye-peptide contact analysis for the monomers are presented. Experimental and theoretical NMR shifts were contrasted to qualify the calculation protocol and theoretical values of the labeled and the non-labeled peptide were also compared. We found that the first five residues have higher helical propensity in the presence of the dye, and electrostatic properties could strongly affect the connection between the dye and the peptide parts.

Differences in the free energies between the excited states of Aβ40 and Aβ42 monomers encode their aggregation propensities

The early events in the aggregation of the intrinsically disordered peptide, amyloid-β (Aβ), involve transitions from the disordered free energy ground state to assembly-competent states. Are the fingerprints of order found in the amyloid fibrils encoded in the conformations that the monomers access at equilibrium? If so, could the enhanced aggregation rate of Aβ42 compared to Aβ40 be rationalized from the sparsely populated high free energy states of the monomers? Here, we answer these questions in the affirmative using coarse-grained simulations of the self-organized polymer-intrinsically disordered protein (SOP-IDP) model of Aβ40 and Aβ42. Although both the peptides have practically identical ensemble-averaged properties, characteristic of random coils (RCs), the conformational ensembles of the two monomers exhibit sequence-specific heterogeneity. Hierarchical clustering of conformations reveals that both the peptides populate high free energy aggregation-prone ([Formula: see text]) states, which resemble the monomers in the fibril structure. The free energy gap between the ground (RC) and the [Formula: see text] states of Aβ42 peptide is smaller than that for Aβ40. By relating the populations of excited states of the two peptides to the fibril formation time scales using an empirical formula, we explain nearly quantitatively the faster aggregation rate of Aβ42 relative to Aβ40. The [Formula: see text] concept accounts for fibril polymorphs, leading to the prediction that the less stable [Formula: see text] state of Aβ42, encoding for the U-bend fibril, should form earlier than the structure with the S-bend topology, which is in accord with Ostwald's rule rationalizing crystal polymorph formation.

Tautomeric Effect of Histidine on β-Sheet Formation of Amyloid Beta 1-40: 2D-IR Simulations

Histidine state (protonated or δ or ε tautomer) has been considered the origin of abnormal misfolding and aggregation of β-amyloid (Aβ). Our previous studies reported that the δδδ isomer of Aβ (1-40) has a greater propensity for β-sheet conformation compared to other isomers. However, direct proof of the tautomeric effect has not been reported. In this context, we calculated histidine site-specific two-dimensional infrared spectroscopy of the δδδ, εεε, and πππ (all protonated histidine) systems within the framework of classical molecular dynamics simulations aiming at connecting our previous results with the current experimental observations. Our results showed that β-sheet formation is favored for the δδδ and πππ tautomers compared with the εεε tautomer, consistent with our previous studies. This result was further supported by contact map analyses and the strength of dipole coupling between the amide-I bonds of each residue. The two-dimensional infrared diagonal trace for each tautomer included three distinctive spectrally resolvable peaks near 1680, 1686, and 1693 cm^-1, as was also observed for histidine dipeptides. However, the peak positions at His6, His13, and His14 did not show a consensus trend with the histidine or protonation state but were instead affected by the presence of surrounding hydrogen bonds. Our study provides a deeper insight into the influence of tautomerism and protonation of histidine residues in Aβ (1-40) on amyloid misfolding and provides a connection between our previous simulations and experimental observations.

Laplacian score and genetic algorithm based automatic feature selection for Markov State Models in adaptive sampling based molecular dynamics

Adaptive sampling molecular dynamics based on Markov State Models use short parallel MD simulations to accelerate simulations, and are proven to identify hidden conformers. The accuracy of the predictions provided by it depends on the features extracted from the simulated data that is used to construct it. The identification of the most important features in the trajectories of the simulated system has a considerable effect on the results.

Methods

In this study, we use a combination of Laplacian scoring and genetic algorithms to obtain an optimized feature subset for the construction of the MSM. The approach is validated on simulations of three protein folding complexes, and two protein ligand binding complexes.

Results

Our experiments show that this approach produces better results when the number of samples is significantly lesser than the number of features extracted. We also observed that this method mitigates over fitting that occurs due to high dimensionality of large biosystems with shorter simulation times.

Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: Challenges and breakthroughs

Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.

Folding dynamics of Aβ42 monomer at pH 4.0–7.5 with and without physiological salt conditions – does the β1 or β2 region fold first?

The seeding region of Aβ42 monomer is jointly affected by the solution acidity, ionic distribution of the salt, and charged residues.

Intrinsic Origin of Amyloid Aggregation: Collective Effects of the Mutation and Tautomerism of Histidine

Mutation is considered an important factor in the accumulation of amyloid-β (Aβ), which is a hallmark of Alzheimer's disease (AD). A2V Aβ40 shows a higher aggregation tendency; however, the existing knowledge is not sufficient to explain the mechanism. We performed replica-exchange molecular dynamics simulations (REMD) to investigate the structural properties of A2V Aβ40 monomers and consider the tautomerism of histidine. The collective effects of the mutation and tautomerism leads A2V Aβ40 to much higher β-sheet and lower α-helix contents than WT Aβ40, which may explain the enhanced aggregation kinetics of A2V Aβ40 with respect to WT Aβ40. The current research provides new insights on understanding the pathology of AD.

Spontaneous self-assembly of amyloid β (1-40) into dimers

The self-assembly and fibrillation of amyloid β (Aβ) proteins is the neuropathological hallmark of Alzheimer's disease. However, the molecular mechanism of how disordered monomers assemble into aggregates remains largely unknown. In this work, we characterize the assembly of Aβ (1-40) monomers into dimers using long-time molecular dynamics simulations. Upon interaction, the monomers undergo conformational transitions, accompanied by change of the structure, leading to the formation of a stable dimer. The dimers are stabilized by interactions in the N-terminal region (residues 5-12), in the central hydrophobic region (residues 16-23), and in the C-terminal region (residues 30-40); with inter-peptide interactions focused around the N- and C-termini. The dimers do not contain long β-strands that are usually found in fibrils.

Effects of Metal Ions on Aβ42 Peptide Conformations from Molecular Simulation Studies

In this study, we investigate the conformational characteristics of full-length Aβ₄₂ peptide monomers in the presence of Na⁺ and Zn²⁺ metal ions using atomistic molecular dynamics (MD) simulations with an aim to explore the possible driving forces behind enhanced aggregation rates of the peptides in the presence of salts. The calculations reveal that the presence of metal ions shifts the conformational equilibrium more toward the compact ordered Aβ structures. Such compact ordered structures stabilized by distant nonlocal contacts between two crucial hydrophobic segments, hp1 and hp2, primarily through two important hydrophobic aromatic residues, Phe-19 and Phe-20, are expected to trigger the aggregation process at a faster rate by populating and stabilizing the aggregation prone structures. Formation of a significant number of such distant contacts in the presence of Na⁺ ions has also been found to result in breaking of the N-terminal helix. On the contrary, binding of Zn²⁺ ion to Aβ peptide is highly specific, which stabilizes the N-terminal helix instead of breaking it. This explains why the aggregation rate of Aβ peptides is higher in the presence of divalent Zn²⁺ ions than monovalent Na⁺ ions. Relatively higher overall stability of the most populated Aβ peptide monomers in the presence of Zn²⁺ ions has been found to be associated with specific Zn²⁺-Aβ binding and significant free energy gain.

Molecular Dynamics Simulations Combined with Nuclear Magnetic Resonance and/or Small-Angle X-ray Scattering Data for Characterizing Intrinsically Disordered Protein Conformational Ensembles

The concept of intrinsically disordered proteins (IDPs) has emerged relatively slowly, but over the past 20 years, it has become an intense research area in structural biology. Indeed, because of their considerable flexibility and structural heterogeneity, the determination of IDP conformational ensemble is particularly challenging and often requires a combination of experimental measurements and computational approaches. With the improved accuracy of all-atom force fields and the increasing computing performances, molecular dynamics (MD) simulations have become more and more reliable to generate realistic conformational ensembles. And the combination of MD simulations with experimental approaches, such as nuclear magnetic resonance (NMR) and/or small-angle X-ray scattering (SAXS) allows one to converge toward a more accurate and exhaustive description of IDP structures. In this Review, we discuss the state of the art of MD simulations of IDP conformational ensembles, with a special focus on studies that back-calculated and directly compared theoretical and experimental NMR or SAXS observables, such as chemical shifts (CS), ³J-couplings (³Jc), residual dipolar couplings (RDC), or SAXS intensities. We organize the review in three parts. In the first section, we discuss the studies which used NMR and/or SAXS data to test and validate the development of force fields or enhanced sampling techniques. In the second part, we explore different methods for the refinement of MD-derived structural ensembles, such as NMR or SAXS data-restrained MD simulations or ensemble reweighting to better fit experiments. Finally, we survey some recent studies combining MD simulations with NMR and/or SAXS measurements to investigate the relationship between IDP conformational ensemble and biological activity, as well as their implication in human diseases. From this review, we noticed that quite a few studies compared MD-generated conformational ensembles with both NMR and SAXS measurements to validate IDP structures at both local and global levels. Yet, beside the IDP propensity to form local secondary structures, their dynamic extension or compactness also appears important for their activity. Thus, we believe that a close synergy between MD simulations, NMR, and SAXS experiments would be greatly appropriate to address the challenges of characterizing the disordered structures of proteins and their complexes, relative to their biological functions.

Highly Disordered Amyloid-β Monomer Probed by Single-Molecule FRET and MD Simulation

Monomers of amyloid-β (Aβ) protein are known to be disordered, but there is considerable controversy over the existence of residual or transient conformations that can potentially promote oligomerization and fibril formation. We employed single-molecule Förster resonance energy transfer (FRET) spectroscopy with site-specific dye labeling using an unnatural amino acid and molecular dynamics simulations to investigate conformations and dynamics of Aβ isoforms with 40 (Aβ40) and 42 residues (Aβ42). The FRET efficiency distributions of both proteins measured in phosphate-buffered saline at room temperature show a single peak with very similar FRET efficiencies, indicating there is apparently only one state. 2D FRET efficiency-donor lifetime analysis reveals, however, that there is a broad distribution of rapidly interconverting conformations. Using nanosecond fluorescence correlation spectroscopy, we measured the timescale of the fluctuations between these conformations to be ∼35 ns, similar to that of disordered proteins. These results suggest that both Aβ40 and Aβ42 populate an ensemble of rapidly reconfiguring unfolded states, with no long-lived conformational state distinguishable from that of the disordered ensemble. To gain molecular-level insights into these observations, we performed molecular dynamics simulations with a force field optimized to describe disordered proteins. We find, as in experiments, that both peptides populate configurations consistent with random polymer chains, with the vast majority of conformations lacking significant secondary structure, giving rise to very similar ensemble-averaged FRET efficiencies.