An Atomistic View of Amyloidogenic Self-assembly: Structure and Dynamics of Heterogeneous Conformational States in the Pre-nucleation Phase

Exploring the Aggregation Propensity of PHF6 Peptide Segments of the Tau Protein Using Ion Mobility Mass Spectrometry Techniques

Peptide and protein aggregation involves the formation of oligomeric species, but the complex interplay between oligomers of different conformations and sizes complicates their structural elucidation. Using ion mobility mass spectrometry (IM-MS), we aim to reveal these early steps of aggregation for the Ac-PHF6-NH2 peptide segment from tau protein, thereby distinguishing between different oligomeric species and gaining an understanding of the aggregation pathway. An important factor that is often neglected, but which can alter the aggregation propensity of peptides, is the terminal capping groups. Here, we demonstrate the use of IM-MS to probe the early stages of aggregate formation of Ac-PHF6-NH2, Ac-PHF6, PHF6-NH2, and uncapped PHF6 peptide segments. The aggregation propensity of the four PHF6 segments is confirmed using thioflavin T fluorescence assays and transmission electron microscopy. A novel approach based on post-IM fragmentation and quadrupole selection on the TIMS-Qq-ToF (trapped ion mobility) spectrometer was developed to enhance oligomer assignment, especially for the higher-order aggregates. This approach pushes the limits of IM identification of isobaric species, whose signatures appear closer to each other with increasing oligomer size, and provides new insights into the interpretation of IM-MS data. In addition, TIMS collision cross section values are compared with traveling wave ion mobility (TWIMS) data to evaluate potential instrumental bias in the trapped ion mobility results. The two IM-MS instrumental platforms are based on different ion mobility principles and have different configurations, thereby providing us with valuable insight into the preservation of weakly bound biomolecular complexes such as peptide aggregates.

Rational Design of TDP-43 Derived α-Helical Peptide Inhibitors: An In Silico Strategy to Prevent TDP-43 Aggregation in Neurodegenerative Disorders

TDP-43, an essential RNA/DNA-binding protein, is central to the pathology of neurodegenerative diseases, such as amyotrophic lateral sclerosis and frontotemporal dementia. Pathological mislocalization and aggregation of TDP-43 disrupt RNA splicing, mRNA stability, and mRNA transport, thereby impairing neuronal function and survival. The formation of amyloid-like TDP-43 filaments is largely facilitated by the destabilization of an α-helical segment within the disordered C-terminal region. In this study, we hypothesized that preventing the destabilization of the α-helical domain could potentially halt the growth of these pathological filaments. To explore this, we utilized a range of in silico techniques to design and evaluate peptide-based therapeutics that bind to pathological TDP-43 amyloid-like filament crystal structures and resist β sheet conversion. Our computational approaches, including biophysical and secondary structure property prediction, molecular docking, 3D structure prediction, and molecular dynamics simulations, were used to assess the structure, stability, and binding affinity of these peptides in relation to pathological TDP-43 filaments. The results of our in silico analyses identified a selection of promising peptides which displayed a stable α-helical structure, exhibited an increased number of intramolecular hydrogen bonds within the helical domain, and demonstrated high binding affinities for pathological TDP-43 amyloid-like filaments. Molecular dynamics simulations provided further support for the structural and thermodynamic stability of these peptides, as they exhibited lower root-mean-square deviation and more favorable free energy landscapes over 300 ns. These findings establish α-helical propensity peptides as potential lead molecules for the development of novel therapeutics against TDP-43 aggregation. This structure-based computational approach for the rational design of peptide inhibitors opens a new direction in the search for effective interventions for ALS, FTD, and other related neurodegenerative diseases. The peptides identified as the most promising candidates in this study are currently subject to further testing and validation through both in vitro and in vivo experiments.

Prion-like Aggregation of the Heptapeptide GNNQQNY into Amyloid Nanofiber Is Governed by Configuration Entropy

A major cause of prion infectivity is the early formation of small, fibril-like aggregates consisting of the heptapeptide GNNQQNY. The prion aggregates exhibit a unique stacking mode in which the hydrophobic tyrosine (Y) is exposed outward, forming a bilayer β-sheet-stacking zipper structure. This stacking mode of the prion peptides, termed "Y-outward" structure for convenience, goes against the common understanding that, for other amyloid-forming peptides, the hydrophobic residues should be hidden within the peptide fibril, referred to as "Y-inward" structure. To explore the extraordinary stacking behaviors of the prion GNNQQNY peptides, two fibril models are constructed in a fashion of "Y-outward" and "Y-inward" stackings and then studied in silico to examine their thermodynamic stabilities and disaggregation pathways. The "Y-inward" structure indeed exhibits stronger thermodynamic stability than the "Y-outward" structure, according to potential energy and stacking energy calculations. To show how the peptide fibrils dissociate, we illustrated two disaggregation pathways. A dihedral-based free energy landscape was then calculated to examine the conformational degrees of freedom of the GNNQQNY chains in the "Y-outward" and "Y-inward" structures. Peptide chains lose more configurational entropy in the "Y-inward" structure than in the "Y-outward" structure, indicating that the prion peptides are prone to aggregate in a fashion of "Y-outward" stacking pattern due to its low conformational constraints. The prion-like aggregation of the GNNQQNY peptides into amyloid fibrils is primarily governed by the configuration entropy.

Multistep molecular mechanisms of Aβ16-22 fibril formation revealed by lattice Monte Carlo simulations

As a model of self-assembly from disordered monomers to fibrils, the amyloid-β fragment Aβ16-22 was subject to past numerous experimental and computational studies. Because dynamics information between milliseconds and seconds cannot be assessed by both studies, we lack a full understanding of its oligomerization. Lattice simulations are particularly well suited to capture pathways to fibrils. In this study, we explored the aggregation of 10 Aβ16-22 peptides using 65 lattice Monte Carlo simulations, each simulation consisting of 3 × 109 steps. Based on a total of 24 and 41 simulations that converge and do not converge to the fibril state, respectively, we are able to reveal the diversity of the pathways leading to fibril structure and the conformational traps slowing down the fibril formation.

Multiscale Exploration of Concentration-Dependent Amyloid-β(16-21) Amyloid Nucleation

Atomic descriptions of peptide aggregation nucleation remain lacking due to the difficulty of exploring complex configurational spaces on long time scales. To elucidate this process, we develop a multiscale approach combining a metadynamics-based method with cluster statistical mechanics to derive concentration-dependent free energy surfaces of nucleation at near-atomic resolution. A kinetic transition network of nucleation is then constructed and employed to systematically explore nucleation pathways and kinetics through stochastic simulations. This approach is applied to describe Aβ_16-21 amyloid nucleation, revealing a two-step mechanism involving disordered aggregates at millimolar concentration, and an unexpected mechanism at submillimolar concentrations that exhibits kinetics reminiscent of classical nucleation but atypical pathways involving growing clusters with structured cores wrapped by disordered surface. When this atypical mechanism is operative, critical nucleus size can be reflected by the nucleation reaction order. Collectively, our approach paves the way for a more quantitative and detailed understanding of peptide aggregation nucleation.

Development of a Hybrid-Resolution Force Field for Peptide Self-Assembly Simulations: Optimizing Peptide-Peptide and Peptide-Solvent Interactions

Atomic descriptions of peptide self-assembly are crucial to an understanding of disease-related peptide aggregation and the design of peptide-assembled materials. Obtaining these descriptions through computer simulation is challenging because current force fields, which were not designed for this process and are often unable to describe correctly peptide self-assembly behavior and the sequence dependence. Here, we developed a framework using dipeptide aggregation as a model system to improve force fields for simulations of self-assembly. Aggregation-related structural properties were designed and used to guide the optimization of peptide-peptide and peptide-solvent interactions. With this framework, we developed a self-assembly force field, termed PACE-ASM, by reoptimizing a hybrid-resolution force field that was originally developed for folding simulation. With its applicability in folding simulations, the new PACE was used to simulate the self-assembly of two disease-related short peptides, Aβ_16-21 and PHF6, into β-sheet-rich cross-β amyloids. These simulations reproduced the crystal structures of Aβ_16-21 and PHF6 amyloids at near-atomic resolution and captured the difference in packing orientations between the two sequences, a task which is challenging even with all-atom force fields. Apart from cross-β amyloids, the self-assembly of emerging helix-rich cross-α amyloids by another peptide PSMα3 can also be correctly described with the new PACE, manifesting the versatility of the force field. We demonstrated that the ability of the PACE-ASM to model peptide self-assembly is based largely on its improved description of peptide-peptide and peptide-solvent interactions. This was achieved with our optimization framework that can readily identify and address the deficiency in describing these interactions.

How the Choice of Force-Field Affects the Stability and Self-Assembly Process of Supramolecular CTA Fibers

In recent years, computational methods have become an essential element of studies focusing on the self-assembly process. Although they provide unique insights, they face challenges, from which two are the most often mentioned in the literature: the temporal and spatial scale of the self-assembly. A less often mentioned issue, but not less important, is the choice of the force-field. The repetitive nature of the supramolecular structure results in many similar interactions. Consequently, even a small deviation in these interactions can lead to significant energy differences in the whole structure. However, studies comparing different force-fields for self-assembling systems are scarce. In this article, we compare molecular dynamics simulations for trifold hydrogen-bonded fibers performed with different force-fields, namely GROMOS, CHARMM General Force Field (CGenFF), CHARMM Drude, General Amber Force-Field (GAFF), Martini, and polarized Martini. Briefly, we tested the force-fields by simulating: (i) spontaneous self-assembly (none form a fiber within 500 ns), (ii) stability of the fiber (observed for CHARMM Drude, GAFF, MartiniP), (iii) dimerization (observed for GROMOS, GAFF, and MartiniP), and (iv) oligomerization (observed for CHARMM Drude and MartiniP). This system shows that knowledge of the force-field behavior regarding interactions in oligomer and larger self-assembled structures is crucial for designing efficient simulation protocols for self-assembling systems.

Dynamics of Amyloid Formation from Simplified Representation to Atomistic Simulations

Amyloid fibril formation is an intrinsic property of short peptides, non-disease proteins, and proteins associated with neurodegenerative diseases. Aggregates of the Aβ and tau proteins, the α-synuclein protein, and the prion protein are observed in the brain of Alzheimer's, Parkinson's, and prion disease patients, respectively. Due to the transient short-range and long-range interactions of all species and their high aggregation propensities, the conformational ensemble of these devastating proteins, the exception being for the monomeric prion protein, remains elusive by standard structural biology methods in bulk solution and in lipid membranes. To overcome these limitations, an increasing number of simulations using different sampling methods and protein models have been performed. In this chapter, we first review our main contributions to the field of amyloid protein simulations aimed at understanding the early aggregation steps of short linear amyloid peptides, the conformational ensemble of the Aβ40/42 dimers in bulk solution, and the stability of Aβ aggregates in lipid membrane models. Then we focus on our studies on the interactions of amyloid peptides/inhibitors to prevent aggregation, and long amyloid sequences, including new results on a monomeric tau construct.

Weight average approaches for predicting dynamical properties of biomolecules

Recent advances in atomistic molecular dynamics (MD) simulations of biomolecules allow us to explore their conformational spaces widely, observing large-scale conformational fluctuations or transitions between distinct structures. To reproduce or refine experimental data using MD simulations, structure ensembles, which are characterized by multiple structures and their statistical weights on the rugged free-energy landscapes, are often used. Here, we summarize weight average approaches for various experimental measurements. Weight average approaches are now applied to hybrid quantum mechanics/molecular mechanics MD simulations to predict fast vibrational motions in a protein with a high accuracy for better understanding of molecular functions from atomic structures.

MELD-accelerated molecular dynamics help determine amyloid fibril structures

It is challenging to determine the structures of protein fibrils such as amyloids. In principle, Molecular Dynamics (MD) modeling can aid experiments, but normal MD has been impractical for these large multi-molecules. Here, we show that MELD accelerated MD (MELD x MD) can give amyloid structures from limited data. Five long-chain fibril structures are accurately predicted from NMR and Solid State NMR (SSNMR) data. Ten short-chain fibril structures are accurately predicted from more limited restraints information derived from the knowledge of strand directions. Although the present study only tests against structure predictions - which are the most detailed form of validation currently available - the main promise of this physical approach is ultimately in going beyond structures to also give mechanical properties, conformational ensembles, and relative stabilities.

Aggregation of Aβ40/42 chains in the presence of cyclic neuropeptides investigated by molecular dynamics simulations

Alzheimer’s disease is associated with the formation of toxic aggregates of amyloid beta (Aβ) peptides. Despite tremendous efforts, our understanding of the molecular mechanisms of aggregation, as well as cofactors that might influence it, remains incomplete. The small cyclic neuropeptide somatostatin-14 (SST₁₄) was recently found to be the most selectively enriched protein in human frontal lobe extracts that binds Aβ₄₂ aggregates. Furthermore, SST₁₄’s presence was also found to promote the formation of toxic Aβ₄₂ oligomers in vitro. In order to elucidate how SST₁₄ influences the onset of Aβ oligomerization, we performed all-atom molecular dynamics simulations of model mixtures of Aβ₄₂ or Aβ₄₀ peptides with SST₁₄ molecules and analyzed the structure and dynamics of early-stage aggregates. For comparison we also analyzed the aggregation of Aβ₄₂ in the presence of arginine vasopressin (AVP), a different cyclic neuropeptide. We observed the formation of self-assembled aggregates containing the Aβ chains and small cyclic peptides in all mixtures of Aβ₄₂–SST₁₄, Aβ₄₂–AVP, and Aβ₄₀–SST₁₄. The Aβ₄₂–SST₁₄ mixtures were found to develop compact, dynamically stable, but small aggregates with the highest exposure of hydrophobic residues to the solvent. Differences in the morphology and dynamics of aggregates that comprise SST₁₄ or AVP appear to reflect distinct (1) regions of the Aβ chains they interact with; (2) propensities to engage in hydrogen bonds with Aβ peptides; and (3) solvent exposures of hydrophilic and hydrophobic groups. The presence of SST₁₄ was found to impede aggregation in the Aβ₄₂–SST₁₄ system despite a high hydrophobicity, producing a stronger “sticky surface” effect in the aggregates at the onset of Aβ₄₂–SST₁₄ oligomerization.

Improper folding of proteins causes disorders known as protein misfolding diseases. Under normal conditions most proteins adopt particular folds, which allow them functioning properly. However, for reasons that are not yet fully understood, proteins may misfold and aggregate, forming deposits known as amyloid fibrils, which accumulate in the brain or other tissues. This process affects functioning of the nervous system, gradually causing loss of cognitive abilities. Alzheimer’s disease is one of the most common diseases from this group. A better understanding of the aggregation of peptides implicated in Alzheimer’s disease, known as amyloid beta (Aβ) peptides, may facilitate the development of treatments that ameliorate or prevent the disease. We use detailed molecular dynamics simulations to investigate the influence of somatostatin-14 (SST₁₄), a cyclic neuropeptide that might be involved in the amyloidogenic aggregation of Aβ, on molecular processes occurring at the onset of Aβ aggregation. Results of these simulations explain how the presence of SST₁₄ might alter pathways of aggregation of Aβ, shedding light upon the possible role of extrinsic factors in the aggregation at a molecular level.

Acetylation of Aβ42 at Lysine 16 Disrupts Amyloid Formation

The residue lysine 28 (K28) is known to form an important salt bridge that stabilizes the Aβ amyloid structure, and acetylation of lysine 28 (K28Ac) slows the Aβ42 fibrillization rate but does not affect fibril morphology. On the other hand, acetylation of lysine 16 (K16Ac) residue greatly diminishes the fibrillization property of Aβ42 peptide and also affects its toxicity. This is due to the fact that lysine 16 acetylated amyloid beta peptide forms amorphous aggregates instead of amyloid fibrils. This is likely a result of increased hydrophobicity of the K16-A21 region due to K16 acetylation, as confirmed by molecular dynamic simulation studies. The calculated results show that the hydrophobic patches of aggregates from acetylated peptides were different when compared to wild-type (WT) peptide. K16Ac and double acetylated (KKAc) peptide aggregates show significantly higher cytotoxicity compared to the WT or K28Ac peptide aggregates alone. However, the heterogeneous mixture of WT and acetylated Aβ42 peptide aggregates exhibited higher free radical formation as well as cytotoxicity, suggesting dynamic interactions between different species could be a critical contributor to Aβ pathology.

Combining molecular dynamics simulations and experimental analyses in protein misfolding

The fold of a protein determines its function and its misfolding can result in loss-of-function defects. In addition, for certain proteins their misfolding can lead to gain-of-function toxicities resulting in protein misfolding diseases such as Alzheimer's, Parkinson's, or the prion diseases. In all of these diseases one or more proteins misfold and aggregate into disease-specific assemblies, often in the form of fibrillar amyloid deposits. Most, if not all, protein misfolding diseases share a fundamental molecular mechanism that governs the misfolding and subsequent aggregation. A wide variety of experimental methods have contributed to our knowledge about misfolded protein aggregates, some of which are briefly described in this review. The misfolding mechanism itself is difficult to investigate, as the necessary timescale and resolution of the misfolding events often lie outside of the observable parameter space. Molecular dynamics simulations fill this gap by virtue of their intrinsic, molecular perspective and the step-by-step iterative process that forms the basis of the simulations. This review focuses on molecular dynamics simulations and how they combine with experimental analyses to provide detailed insights into protein misfolding and the ensuing diseases.

Aggregation of disease-related peptides

Protein misfolding and aggregation of amyloid proteins is the fundamental cause of more than 20 diseases. Molecular mechanisms of the self-assembly and the formation of the toxic aggregates are still elusive. Computer simulations have been intensively used to study the aggregation of amyloid peptides of various amino acid lengths related to neurodegenerative diseases. We review atomistic and coarse-grained simulations of short amyloid peptides aimed at determining their transient oligomeric structures and the early and late aggregation steps.

Structural Analysis of a Trimer of β2-Microgloblin Fragment by Molecular Dynamics Simulations

A peptide β₂-m_21-31, which is a fragment from residue 21 to residue 31 of β₂-microgloblin, is experimentally known to self-assemble and form amyloid fibrils. In order to understand the mechanism of amyloid fibril formations, we applied the replica-exchange molecular dynamics method to the system consisting of three fragments of β₂-m_21-31. From the analyses on the temperature dependence, we found that there is a clear phase transition temperature in which the peptides aggregate with each other. Moreover, we found by the free energy analyses that there are two major stable states: One of them is like amyloid fibrils and the other is amorphous aggregates.

Computational insights into lipid assisted peptide misfolding and aggregation in neurodegeneration

An overview of recent advances in computational investigation of peptide–lipid interactions in neurodegeneration – Alzheimer's, Parkinson's and Huntington's disease.

On the Applicability of Force Fields To Study the Aggregation of Amyloidogenic Peptides Using Molecular Dynamics Simulations

Molecular dynamics simulations play an essential role in understanding biomolecular processes such as protein aggregation at temporal and spatial resolutions which are not attainable by experimental methods. For a correct modeling of protein aggregation, force fields must accurately represent molecular interactions. Here, we study the effect of five different force fields on the oligomer formation of Alzheimer's Aβ_16-22 peptide and two of its mutants: Aβ_16-22(F19V,F20V), which does not form fibrils, and Aβ_16-22(F19L) which forms fibrils faster than the wild type. We observe that while oligomer formation kinetics depends strongly on the force field, structural properties, such as the most relevant protein-protein contacts, are similar between them. The oligomer formation kinetics obtained with different force fields differ more from each other than the kinetics between aggregating and nonaggregating peptides simulated with a single force field. We discuss the difficulties in comparing atomistic simulations of amyloid oligomer formation with experimental observables.

Zn2+-triggered self-assembly of Gonadorelin [6-D-Phe] to produce nanostructures and fibrils

A synthetic derivative, GnRH [6-D-Phe], stable against enzymatic degradation, self-assembles and forms nanostructures and fibrils upon a pH shift in the presence of different concentrations of Zn²⁺in vitro. Attenuated Total Reflection Fourier Transform Infrared spectroscopy (ATR–FTIR) revealed the existence of higher order assembly of Zn²⁺: GnRH [6-D-Phe]. Nuclear Magnetic Resonance spectroscopy (NMR) indicated a weak interaction between Zn²⁺ and GnRH [6-D-Phe]. Atomic Force Microscopy (AFM) showed the existence of GnRH [6-D-Phe] oligomers and fibrils. Molecular Dynamic (MD) simulation of the 10:1 Zn²⁺: GnRH [6-D-Phe] explored the interaction and dimerization processes. In contrast to already existing short peptide fibrils, GnRH [6-D-Phe] nanostructures and fibrils form in a Tris-buffered pH environment in a controlled manner through a temperature reduction and a pH shift. The lyophilized Zn²⁺: GnRH [6-D-Phe] assembly was tested as a platform for the sustained delivery of GnRH [6-D-Phe] and incorporated into two different oil vehicle matrices. The in vitro release was slow and continuous over 14 days and not influenced by the oil matrix.

Hetero-assembly of a dual β-amyloid variant peptide system

Creation of a dual peptide system where beta-amyloid variants hetero-assemble but do not homo-assemble, sharing similarities with typical amyloid self-assemblies.

Resolving the Atomistic Modes of Anle138b Inhibitory Action on Peptide Oligomer Formation

The diphenyl-pyrazole compound anle138b is a known inhibitor of oligomeric aggregate formation in vitro and in vivo. Therefore, anle138b is considered a promising drug candidate to beneficially interfere with neurodegenerative processes causing devastating pathologies in humans. The atomistic details of the aggregation inhibition mechanism, however, are to date unknown since the ensemble of small nonfibrillar aggregates is structurally heterogeneous and inaccessible to direct structural characterization. Here, we set out to elucidate anle138b's mode of action using all-atom molecular dynamics simulations on the multi-microsecond time scale. By comparing simulations of dimeric to tetrameric aggregates from fragments of four amyloidogenic proteins (Aβ, hTau40, hIAPP, and Sup35N) in the presence and absence of anle138b, we show that the compound reduces the overall number of intermolecular hydrogen bonds, disfavors the sampling of the aggregated state, and remodels the conformational distributions within the small oligomeric peptide aggregates. Most notably, anle138b preferentially interacts with the disordered structure ensemble via its pyrazole moiety, thereby effectively blocking interpeptide main chain interactions and impeding the spontaneous formation of ordered β-sheet structures, in particular those with out-of-register antiparallel β-strands. The structurally very similar compound anle234b was previously identified as inactive by in vitro experiments. Here, we show that anle234b has no significant effect on the aggregation process in terms of reducing the β-structure content. Moreover, we demonstrate that the hydrogen bonding capabilities are autoinhibited due to steric effects imposed by the molecular geometry of anle234b and thereby indirectly confirm the proposed inhibitory mechanism of anle138b. We anticipate that the prominent binding of anle138b to partially disordered and dynamical aggregate structures is a generic basis for anle138b's ability to suppress toxic oligomer formation in a wide range of amyloidogenic peptides and proteins.

Force field development and simulations of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) play important roles in many physiological processes such as signal transduction and transcriptional regulation. Computer simulations that are based on empirical force fields have been increasingly used to understand the biophysics of disordered proteins. In this review, we focus on recent improvement of protein force fields, including polarizable force fields, concerning their accuracy in modeling intrinsically disordered proteins. Some recent benchmarks and applications of these force fields are also overviewed.