Influence of temperature on formation of perfect tau fragment fibrils using PRIME20/DMD simulations

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Molecular Modeling of Self-Assembling Peptides

Peptide epitopes mediate as many as 40% of protein-protein interactions and fulfill signaling, inhibition, and activation roles within the cell. Beyond protein recognition, some peptides can self- or coassemble into stable hydrogels, making them a readily available source of biomaterials. While these 3D assemblies are routinely characterized at the fiber level, there are missing atomistic details about the assembly scaffold. Such atomistic detail can be useful in the rational design of more stable scaffold structures and with improved accessibility to functional motifs. Computational approaches can in principle reduce the experimental cost of such an endeavor by predicting the assembly scaffold and identifying novel sequences that adopt said structure. Yet, inaccuracies in physical models and inefficient sampling have limited atomistic studies to short (two or three amino acid) peptides. Given recent developments in machine learning and advances in sampling strategies, we revisit the suitability of physical models for this task. We use the MELD (Modeling Employing Limited Data) approach to drive self-assembly in combination with generic data in cases where conventional MD is unsuccessful. Finally, despite recent developments in machine learning algorithms for protein structure and sequence predictions, we find the algorithms are not yet suited for studying the assembly of short peptides.

Mapping the configurational landscape and aggregation phase behavior of the tau protein fragment PHF6

The PHF6 (Val-Gln-Ile-Val-Tyr-Lys) motif, found in all isoforms of the microtubule-associated protein tau, forms an integral part of ordered cores of amyloid fibrils formed in tauopathies and is thought to play a fundamental role in tau aggregation. Because PHF6 as an isolated hexapeptide assembles into ordered fibrils on its own, it is investigated as a minimal model for insight into the initial stages of aggregation of larger tau fragments. Even for this small peptide, however, the large length and time scales associated with fibrillization pose challenges for simulation studies of its dynamic assembly, equilibrium configurational landscape, and phase behavior. Here, we develop an accurate, bottom-up coarse-grained model of PHF6 for large-scale simulations of its aggregation, which we use to uncover molecular interactions and thermodynamic driving forces governing its assembly. The model, not trained on any explicit information about fibrillar structure, predicts coexistence of formed fibrils with monomers in solution, and we calculate a putative equilibrium phase diagram in concentration-temperature space. We also characterize the configurational and free energetic landscape of PHF6 oligomers. Importantly, we demonstrate with a model of heparin that this widely studied cofactor enhances the aggregation propensity of PHF6 by ordering monomers during nucleation and remaining associated with growing fibrils, consistent with experimentally characterized heparin-tau interactions. Overall, this effort provides detailed molecular insight into PHF6 aggregation thermodynamics and pathways and, furthermore, demonstrates the potential of modern multiscale modeling techniques to produce predictive models of amyloidogenic peptides simultaneously capturing sequence-specific effects and emergent aggregate structures.

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.

Computational Models for the Study of Protein Aggregation

Protein aggregation has been studied by many groups around the world for many years because it can be the cause of a number of neurodegenerative diseases that have no effective treatment. Obtaining the structure of related fibrils and toxic oligomers, as well as describing the pathways and main factors that govern the self-organization process, is of paramount importance, but it is also very difficult. To solve this problem, experimental and computational methods are often combined to get the most out of each method. The effectiveness of the computational approach largely depends on the construction of a reasonable molecular model. Here we discussed different versions of the four most popular all-atom force fields AMBER, CHARMM, GROMOS, and OPLS, which have been developed for folded and intrinsically disordered proteins, or both. Continuous and discrete coarse-grained models, which were mainly used to study the kinetics of aggregation, are also summarized.

Engineering β-Sheet Peptide Coassemblies for Biomaterial Applications

Peptide coassembly, wherein at least two different peptides interact to form multicomponent nanostructures, is an attractive approach for generating functional biomaterials. Current efforts seek to design pairs of peptides, A and B, that form nanostructures (e.g., β-sheets with ABABA-type β-strand patterning) while resisting self-assembly (e.g., AAAAA-type or BBBBB-type β-sheets). To confer coassembly behavior, most existing designs have been based on highly charged variants of known self-assembling peptides; like-charge repulsion limits self-assembly while opposite-charge attraction promotes coassembly. Recent analyses using solid-state NMR and coarse-grained simulations reveal that preconceived notions of structure and molecular organization are not always correct. This perspective highlights recent advances and key challenges to understanding and controlling peptide coassembly.

CATCH Peptides Coassemble into Structurally Heterogeneous β-Sheet Nanofibers with Little Preference to β-Strand Alignment

Coassembling peptides offer an additional degree of freedom in the design of nanostructured biomaterials when compared to analogous self-assembling peptides. Yet, our understanding of how amino acid sequences encodes coassembled nanofiber structure is limited. Prior work on a charge-complementary pair, CATCH+ and CATCH- peptides, detected like-peptide nearest neighbors (CATCH+:CATCH+ and CATCH-:CATCH-) within coassembled β-sheet nanofibers; these self-associated peptide pairs marked a departure from an "ideal" coassembled structure. In this work, we employ solid-state NMR, isotope-edited FTIR, and coarse-grained molecular dynamics simulations to evaluate the alignment of β-strands within CATCH peptide nanofibers. Both experimental and computational results suggest that CATCH molecules coassemble into structurally heterogeneous nanofibers, which is consistent with our observations in another coassembling system, the King-Webb peptides. Within β-sheet nanofibers, β-strands were found to have nearest neighbors aligned in-register parallel, in-register antiparallel, and out-of-register. In comparison to the King-Webb peptides, CATCH nanofibers exhibit a greater degree of structural heterogeneity. By comparing the amino acid sequences of CATCH and King-Webb peptides, we can begin to unravel sequence-to-structure relationships, which may encode more precise coassembled β-sheet nanostructures.

Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer's Disease, Parkinson's Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis

Protein misfolding and aggregation is observed in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization of all species along the pathways from monomers to fibrils is challenging by experimental and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aβ, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease (AD), Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, for many years.

Terminal Capping of an Amyloidogenic Tau Fragment Modulates Its Fibrillation Propensity

Aberrant protein folding leading to the formation of characteristic cross-β-sheet-rich amyloid structures is well known for its association with a variety of debilitating human diseases. Often, depending upon amino acid composition, only a small segment of a large protein participates in amyloid formation and is in fact capable of self-assembling into amyloid, independent of the rest of the protein. Therefore, such peptide fragments serve as useful model systems for understanding the process of amyloid formation. An important factor that has often been overlooked while using peptides to mimic full-length protein is the charge on the termini of these peptides. Here, we show the influence of terminal charges on the aggregation of an amyloidogenic peptide from microtubule-associated protein Tau, implicated in Alzheimer's disease and tauopathies. We found that modification of terminal charges by capping the peptide at one or both of the termini drastically modulates the fibrillation of the hexapeptide sequence paired helical filament 6 (PHF6) from repeat 3 of Tau, both with and without heparin. Without heparin, the PHF6 peptide capped at both termini and PHF6 capped only at the N-terminus self-assembled to form amyloid fibrils. With heparin, all capping variants of PHF6, except for PHF6 with both termini free, formed typical amyloid fibrils. However, the rate and extent of aggregation both with and without heparin as well as the morphology of aggregates were found to be highly dependent on the terminal charges. Our molecular dynamics simulations on PHF6 capping variants corroborated our experiments and provided critical insights into the mechanism of PHF6 self-assembly. Overall, our results emphasize the importance of terminal modifications in fibrillation of small peptide fragments and provide significant insights into the aggregation of a small Tau fragment, which is considered essential for Tau filament assembly.

The Early Phase of β2-Microglobulin Aggregation: Perspectives From Molecular Simulations

Protein β2-microglobulin is the causing agent of two amyloidosis, dialysis related amyloidosis (DRA), affecting the bones and cartilages of individuals with chronic renal failure undergoing long-term hemodialysis, and a systemic amyloidosis, found in one French family, which impairs visceral organs. The protein’s small size and its biomedical significance attracted the attention of theoretical scientists, and there are now several studies addressing its aggregation mechanism in the context of molecular simulations. Here, we review the early phase of β2-microglobulin aggregation, by focusing on the identification and structural characterization of monomers with the ability to trigger aggregation, and initial small oligomers (dimers, tetramers, hexamers etc.) formed in the so-called nucleation phase. We focus our analysis on results from molecular simulations and integrate our views with those coming from in vitro experiments to provide a broader perspective of this interesting field of research. We also outline directions for future computer simulation studies.

Tau R3-R4 Domain Dimer of the Wild Type and Phosphorylated Ser356 Sequences. I. In Solution by Atomistic Simulations

In Alzheimer's disease, neurofibrillary lesions correlate with cognitive deficits and consist of inclusions of tau protein with cross-β structure. A stable dimeric form of soluble tau has been evidenced in the cells, but its high-resolution structure is missing in solution. We know, however, that cryo-electron microscopy (c-EM) of full-length tau in the brain of an individual with AD displays a core of eight β-sheets with a C-shaped architecture spanning the R3-R4 repeat domain, while the rest of the protein is very flexible. To address the conformational ensemble of the dimer, we performed atomistic replica exchange molecular dynamics simulations on the tau R3-R4 domain starting from the c-EM configuration. We find that the wild type tau R3-R4 dimer explores elongated, U-shaped, V-shaped, and globular forms rather than the C-shape. Phosphorylation of Ser356, pSer356, is known to block the interaction between the tau protein and the amyloid-β42 peptide. Standard molecular dynamics simulations of this phosphorylated sequence for a total of 5 μs compared to its wild type counterpart show a modulation of the population of β-helices and accessible topologies and a decrease of intermediates near the fibril-like conformers.

Recent Advances in Coarse-Grained Models for Biomolecules and Their Applications

Molecular dynamics simulations have emerged as a powerful tool to study biological systems at varied length and timescales. The conventional all-atom molecular dynamics simulations are being used by the wider scientific community in routine to capture the conformational dynamics and local motions. In addition, recent developments in coarse-grained models have opened the way to study the macromolecular complexes for time scales up to milliseconds. In this review, we have discussed the principle, applicability and recent development in coarse-grained models for biological systems. The potential of coarse-grained simulation has been reviewed through state-of-the-art examples of protein folding and structure prediction, self-assembly of complexes, membrane systems and carbohydrates fiber models. The multiscale simulation approaches have also been discussed in the context of their emerging role in unravelling hierarchical level information of biosystems. We conclude this review with the future scope of coarse-grained simulations as a constantly evolving tool to capture the dynamics of biosystems.

Molecular insights into the surface-catalyzed secondary nucleation of amyloid-β40 (Aβ40) by the peptide fragment Aβ16-22

Understanding the structural mechanism by which proteins and peptides aggregate is crucial, given the role of fibrillar aggregates in debilitating amyloid diseases and bioinspired materials. Yet, this is a major challenge as the assembly involves multiple heterogeneous and transient intermediates. Here, we analyze the co-aggregation of Aβ₄₀ and Aβ_16-22, two widely studied peptide fragments of Aβ₄₂ implicated in Alzheimer's disease. We demonstrate that Aβ_16-22 increases the aggregation rate of Aβ₄₀ through a surface-catalyzed secondary nucleation mechanism. Discontinuous molecular dynamics simulations allowed aggregation to be tracked from the initial random coil monomer to the catalysis of nucleation on the fibril surface. Together, the results provide insight into how dynamic interactions between Aβ₄₀ monomers/oligomers on the surface of preformed Aβ_16-22 fibrils nucleate Aβ₄₀ amyloid assembly. This new understanding may facilitate development of surfaces designed to enhance or suppress secondary nucleation and hence to control the rates and products of fibril assembly.

Thermodynamic phase diagram of amyloid-β (16-22) peptide

The aggregation of monomeric amyloid β protein (Aβ) peptide into oligomers and amyloid fibrils in the mammalian brain is associated with Alzheimer's disease. Insight into the thermodynamic stability of the Aβ peptide in different polymeric states is fundamental to defining and predicting the aggregation process. Experimental determination of Aβ thermodynamic behavior is challenging due to the transient nature of Aβ oligomers and the low peptide solubility. Furthermore, quantitative calculation of a thermodynamic phase diagram for a specific peptide requires extremely long computational times. Here, using a coarse-grained protein model, molecular dynamics (MD) simulations are performed to determine an equilibrium concentration and temperature phase diagram for the amyloidogenic peptide fragment Aβ_16-22 Our results reveal that the only thermodynamically stable phases are the solution phase and the macroscopic fibrillar phase, and that there also exists a hierarchy of metastable phases. The boundary line between the solution phase and fibril phase is found by calculating the temperature-dependent solubility of a macroscopic Aβ_16-22 fibril consisting of an infinite number of β-sheet layers. This in silico determination of an equilibrium (solubility) phase diagram for a real amyloid-forming peptide, Aβ_16-22, over the temperature range of 277-330 K agrees well with fibrillation experiments and transmission electron microscopy (TEM) measurements of the fibril morphologies formed. This in silico approach of predicting peptide solubility is also potentially useful for optimizing biopharmaceutical production and manufacturing nanofiber scaffolds for tissue engineering.

Seeding and cross-seeding fibrillation of N-terminal prion protein peptides PrP(120-144)

Prion diseases are infectious neurodegenerative diseases that are capable of cross-species transmission, thus arousing public health concerns. Seed-templating propagation of prion protein is believed to underlie prion cross-species transmission pathology. Understanding the molecular fundamentals of prion propagation is key to unravelling the pathology of prion diseases. In this study, we use coarse-grained molecular dynamics to investigate the seeding and cross-seeding aggregation of three prion protein fragments PrP(120-144) originating from human (Hu), bank vole (BV), and Syrian hamster (SHa). We find that the seed accelerates the aggregation of the monomer peptides by eliminating the lag phase. The monomer aggregation kinetics are mainly determined by the structure of the seed. The stronger the hydrophobic residues on the seed associate with each other, the higher the probability that the seed recruits monomer peptides to its surface/interface. For cross-seeding aggregation, we show that Hu has a strong tendency to adopt the conformation of the BV seed and vice versa; the Hu and BV monomers have a weak tendency to adopt the conformation of the SHa seed. These two findings are consistent with Apostol et al.'s experimental findings on PrP(138-143) and partially consistent with Jones et al.'s finding on PrP(23-144). We also identify several conformational mismatches when SHa cross-seeds BV and Hu peptides, indicating the existence of a cross-seeding barrier between SHa and the other two sequences. This study sheds light on the molecular mechanism of seed-templating aggregation of prion protein fragments underlying the sequence-dependent transmission barrier in prion diseases.

Polymorphism of fibrillar structures depending on the size of assembled Aβ17-42 peptides

The size of assembled Aβ_17-42 peptides can determine polymorphism during oligomerization and fibrillization, but the mechanism of this effect is unknown. Starting from separate random monomers, various fibrillar oligomers with distinct structural characteristics were identified using discontinuous molecular dynamics simulations based on a coarse-grained protein model. From the structures observed in the simulations, two characteristic oligomer sizes emerged, trimer and paranuclei, which generated distinct structural patterns during fibrillization. A majority of the simulations for trimers and tetramers formed non-fibrillar oligomers, which primarily progress to off-pathway oligomers. Pentamers and hexamers were significantly converted into U-shape fibrillar structures, meaning that these oligomers, called paranuclei, might be potent on-pathway intermediates in fibril formation. Fibrillar oligomers larger than hexamers generated substantial polymorphism in which hybrid structures were readily formed and homogeneous fibrillar structures appeared infrequently.

Primary Fibril Nucleation of Aggregation Prone Tau Fragments PHF6 and PHF6

We performed replica exchange molecular dynamics and forward flux sampling simulations of hexapeptide VQIINK and VQIVYK systems, also known as, respectively, fragments PHF6* and PHF6 from the tau protein. Being a part of the microtubule binding region, these fragments are known to be aggregation prone, and at least one of them is a prerequisite for fibril formation of the tau protein. Using a coarse-grained force field, we establish the phase behavior of both fragments, and investigate the nucleation kinetics for the conversion into a β-sheet fibril. As the conversion is, in principle, a reversible process, we predict the rate constants for both the fibril formation and melting, and examine the corresponding mechanisms. Our simulations indicate that, while both fragments form disordered aggregates, only PHF6 is able to form β-sheet fibrils. This observation provides a possible explanation for the lack of available steric zipper crystal structures for PHF6*.

Assembly of Triblock Amphiphilic Peptides into One-Dimensional Aggregates and Network Formation

Peptide assembly plays a key role in both neurological diseases and development of novel biomaterials with well-defined nanostructures. Synthetic model peptides provide a unique platform to explore the role of intermolecular interactions in the assembly process. A triblock peptide architecture designed by the Hartgerink group is a versatile system which relies on Coulomb interactions, hydrogen bonding, and hydrophobicity to guide these peptides' assembly at three different length scales: β-sheets, double-wall ribbon-like aggregates, and finally a highly porous network structure which can support gels with ≤1% by weight peptide concentration. In this study, by using molecular dynamics simulations of a structure based implicit solvent coarse grained model, we analyzed this hierarchical assembly process. Parametrization of our CG model is based on multiple-state points from atomistic simulations, which enables this model to represent the conformational adaptability of the triblock peptide molecule based on the surrounding medium. Our results indicate that emergence of the double-wall β-sheet packing mechanism, proposed in light of the experimental evidence, strongly depends on the subtle balance of the intermolecular forces. We demonstrate that, even though backbone hydrogen bonding dominates the early nucleation stages, depending on the strength of the hydrophobic and Coulomb forces, alternative structures such as zero-dimensional aggregates with two β-sheets oriented orthogonally (which we refer to as a cross-packed structure) and β-sheets with misoriented hydrophobic side chains are also feasible. We discuss the implications of these competing structures for the three different length scales of assembly by systematically investigating the influence of density, counterion valency, and hydrophobicity.

N-terminal Prion Protein Peptides (PrP(120-144)) Form Parallel In-register β-Sheets via Multiple Nucleation-dependent Pathways

The prion diseases are a family of fatal neurodegenerative diseases associated with the misfolding and accumulation of normal prion protein (PrP^C) into its pathogenic scrapie form (PrP^Sc). Understanding the fundamentals of prion protein aggregation and the molecular architecture of PrP^Sc is key to unraveling the pathology of prion diseases. Our work investigates the early-stage aggregation of three prion protein peptides, corresponding to residues 120-144 of human (Hu), bank vole (BV), and Syrian hamster (SHa) prion protein, from disordered monomers to β-sheet-rich fibrillar structures. Using 12 μs discontinuous molecular dynamics simulations combined with the PRIME20 force field, we find that the Hu-, BV-, and SHaPrP(120-144) aggregate via multiple nucleation-dependent pathways to form U-shaped, S-shaped, and Ω-shaped protofilaments. The S-shaped HuPrP(120-144) protofilament is similar to the amyloid core structure of HuPrP(112-141) predicted by Zweckstetter. HuPrP(120-144) has a shorter aggregation lag phase than BVPrP(120-144) followed by SHaPrP(120-144), consistent with experimental findings. Two amino acid substitutions I138M and I139M retard the formation of parallel in-register β-sheet dimers during the nucleation stage by increasing side chain-side chain association and reducing side chain interaction specificity. On average, HuPrP(120-144) aggregates contain more parallel β-sheet content than those formed by BV- and SHaPrP(120-144). Deletion of the C-terminal residues 138-144 prevents formation of fibrillar structures in agreement with the experiment. This work sheds light on the amyloid core structures underlying prion strains and how I138M, I139M, and S143N affect prion protein aggregation kinetics.

Interplay between the hydrophobic effect and dipole interactions in peptide aggregation at interfaces

Amphipathic octapeptide aggregation at hydrophobic–hydrophilic interfaces is largely driven by backbone dipole interactions in peptide aggregation at interfaces.

Mechanical stress models of Alzheimer's disease pathology

INTRODUCTION

Extracellular accumulation of amyloid-β protein and intracellular accumulation of tau in brain tissues have been described in animal models of Alzheimer's disease (AD) and mechanical stress-based diseases of different mechanisms, such as traumatic brain injury (TBI), arterial hypertension (HTN), and normal pressure hydrocephalus (NPH).

METHODS

We provide a brief overview of experimental models of TBI, HTN, and NPH showing features of tau-amyloid pathology, neuroinflammation, and neuronal loss.

RESULTS

"Alzheimer-like" hallmarks found in these mechanical stress-based models were compared with AD features found in transgenic models.

DISCUSSION

The goal of this review is, therefore, to build on current concepts of onset and progression of AD lesions. We point to the importance of accumulated mechanical stress in brain as an environmental and endogenous factor that pushes protein deposition and neuronal injury over the disease threshold. We further encourage the development of preventing strategies and drug screening based on mechanical stress models.

Structural Conversion of Aβ17-42 Peptides from Disordered Oligomers to U-Shape Protofilaments via Multiple Kinetic Pathways

Discovering the mechanisms by which proteins aggregate into fibrils is an essential first step in understanding the molecular level processes underlying neurodegenerative diseases such as Alzheimer's and Parkinson's. The goal of this work is to provide insights into the structural changes that characterize the kinetic pathways by which amyloid-β peptides convert from monomers to oligomers to fibrils. By applying discontinuous molecular dynamics simulations to PRIME20, a force field designed to capture the chemical and physical aspects of protein aggregation, we have been able to trace out the entire aggregation process for a system containing 8 Aβ17-42 peptides. We uncovered two fibrillization mechanisms that govern the structural conversion of Aβ17-42 peptides from disordered oligomers into protofilaments. The first mechanism is monomeric conversion templated by a U-shape oligomeric nucleus into U-shape protofilament. The second mechanism involves a long-lived and on-pathway metastable oligomer with S-shape chains, having a C-terminal turn, en route to the final U-shape protofilament. Oligomers with this C-terminal turn have been regarded in recent experiments as a major contributing element to cell toxicity in Alzheimer's disease. The internal structures of the U-shape protofilaments from our PRIME20/DMD simulation agree well with those from solid state NMR experiments. The approach presented here offers a simple molecular-level framework to describe protein aggregation in general and to visualize the kinetic evolution of a putative toxic element in Alzheimer's disease in particular.

Simulations of Protein Aggregation: Insights from Atomistic and Coarse-Grained Models

This Perspective highlights recent computational approaches to protein aggregation, from coarse-grained models to atomistic simulations, using the islet amyloid polypeptide (IAPP) as a case study. We review salient open questions where simulations can make an impact, discuss the successes and challenges met by simulations, and explore new directions.

Conformational ensemble and polymorphism of the all-atom Alzheimer's Aβ(37-42) amyloid peptide oligomers

Although the Aβ(37-42) peptide has two opposite terminal charges, counterintuitively its current fibril amyloid structure reveals in register parallel β-strands, as formed by the full length Aβ peptide. In this study, we carried out a replica exchange molecular dynamics simulation of 16 all-atom Aβ(37-42) peptides in explicit water starting from randomized and dispersed chains. The extensive conformational sampling (48 replicas, 460 ns/replica) with a total simulation time of 23 μs allows us to obtain a full picture on the equilibrium conformational distribution of oligomers and β-sheet sizes and gain some insights into the oligomerization process at 300 K. At the peptide concentration of 12 mM, self-assembly is described by the condensation-polymerization mechanism with conversion from micelle-like to high β-sheet structures. At equilibrium, the oligomer distribution consists of large aggregates and free monomers, representing 70% and 25% of all species, respectively. Though the formation/dissociation of β-strand is high, the population of 4-5 fully parallel β-strands, consistent with the arrangement in the current fibril, is marginal and that of 4-5 fully antiparallel β-strands, consistent with amyloid polymorphism, is non-negligible. However, the system adopts essentially mixed parallel/antiparallel β-strands. This indicates that a system of 16 Aβ(37-42) chains in explicit solvent still does not form more stable species that will irreversibly grow to a fibril, independently of polymorphism. Our results also suggest that the Aβ(37-42) fibril may display packing polymorphism with antiparallel β-strands, in addition to the experimentally observed in register parallel β-strands.