Pathways and Intermediates of Amyloid Fibril Formation

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.

Protein misfolding and amyloid nucleation through liquid-liquid phase separation

Liquid-liquid phase separation (LLPS) is an emerging phenomenon in cell physiology and diseases. The weak multivalent interaction prerequisite for LLPS is believed to be facilitated through intrinsically disordered regions, which are prevalent in neurodegenerative disease-associated proteins. These aggregation-prone proteins also exhibit an inherent property for phase separation, resulting in protein-rich liquid-like droplets. The very high local protein concentration in the water-deficient confined microenvironment not only drives the viscoelastic transition from the liquid to solid-like state but also most often nucleate amyloid fibril formation. Indeed, protein misfolding, oligomerization, and amyloid aggregation are observed to be initiated from the LLPS of various neurodegeneration-related proteins. Moreover, in these cases, neurodegeneration-promoting genetic and environmental factors play a direct role in amyloid aggregation preceded by the phase separation. These cumulative recent observations ignite the possibility of LLPS being a prominent nucleation mechanism associated with aberrant protein aggregation. The present review elaborates on the nucleation mechanism of the amyloid aggregation pathway and the possible early molecular events associated with amyloid-related protein phase separation. It also summarizes the recent advancement in understanding the aberrant phase transition of major proteins contributing to neurodegeneration focusing on the common disease-associated factors. Overall, this review proposes a generic LLPS-mediated multistep nucleation mechanism for amyloid aggregation and its implication in neurodegeneration.

Nucleation and Growth of Amyloid Fibrils

The formation of amyloid fibrils is a complex phenomenon that remains poorly understood at the atomic scale. Herein, we perform extended unbiased all-atom simulations in explicit solvent of a short amphipathic peptide to shed light on the three mechanisms accounting for fibril formation, namely, nucleation via primary and secondary mechanisms, and fibril growth. We find that primary nucleation takes place via the formation of an intermediate state made of two laminated β-sheets oriented perpendicular to each other. The amyloid fibril spine subsequently emerges from the rotation of these β-sheets to account for peptides that are parallel to each other and perpendicular to the main axis of the fibril. Growth of this spine, in turn, takes place via a dock-and-lock mechanism. We find that peptides dock onto the fibril tip either from bulk solution or after diffusing on the fibril surface. The latter docking pathway contributes significantly to populate the fibril tip with peptides. We also find that side chain interactions drive the motion of peptides in the lock phase during growth, enabling them to adopt the structure imposed by the fibril tip with atomic fidelity. Conversely, the docked peptide becomes trapped in a local free energy minimum when docked-conformations are sampled randomly. Our simulations also highlight the role played by nonpolar fibril surface patches in catalyzing and orienting the formation of small cross-β structures. More broadly, our simulations provide important new insights into the pathways and interactions accounting for primary and secondary nucleation as well as the growth of amyloid fibrils.

Inhibition of Amyloid Nucleation by Steric Hindrance

Despite recent experiments and simulations suggesting that small-molecule inhibitors and some post-translational modifications (e.g., glycosylation and ubiquitination) can suppress the pathogenic aggregation of proteins due to steric hindrance, the effect of steric hindrance on amyloid formation has not been systematically studied. Based on Monte Carlo simulations using a coarse-grained model for amyloidogenic proteins and a hard sphere acting as steric hindrance, we investigated how steric hindrance on proteins could affect amyloid formation, particularly two steps of primary nucleation, namely, oligomerization and conformational conversion into a β-sheet-enriched nucleus. We found that steric spheres played an inhibitory role in oligomerization with the effect proportional to the sphere radius R_S, which we attributed to the decline in the nonspecific attractions between proteins. During the second step, small steric spheres facilitated the conformational conversion of proteins while large ones suppressed the conversion. The overall steric effect on amyloid nucleation was inhibitory regardless of R_S. As R_S increased, oligomeric assemblies changed from amorphous into sheet-like, structurally ordered species, reminiscent of the structure of amyloid fibrils. The oligomers with large R_S were off-pathway with their ordered structures induced by the competition between steric hindrance and nonspecific attractions of soluble proteins. Interestingly, the equimolar mixture of proteins with and without steric hindrance amplified the sterically inhibitory effect by increasing the energy barrier of protein's conformational conversion. The physical mechanisms and biological implications of the above results are discussed. Our findings improve the current understanding of how nature regulates protein aggregation and amyloid formation by steric hindrance.

Modulating Nanodroplet Formation En Route to Fibrillization of Amyloid Peptides with Designed Flanking Sequences

Soluble oligomers populating early amyloid aggregation can be regarded as nanodroplets of liquid-liquid phase separation (LLPS). Amyloid peptides typically contain hydrophobic aggregation-prone regions connected by hydrophilic linkers and flanking sequences, and such a sequence hydropathy pattern drives the formation of supramolecular structures in the nanodroplets and modulates subsequent fibrillization. Here, we studied LLPS and fibrillization of coarse-grained amyloid peptides with increasing flanking sequences. Nanodroplets assumed lamellar, cylindrical micellar, and spherical micellar structures with increasing peptide hydrophilic/hydrophobic ratios, and such morphologies governed subsequent fibrillization processes. Adding glycine-serine repeats as flanking sequences to Aβ16-22, the amyloidogenic core of amyloid-β, our computational predictions of morphological transitions were corroborated experimentally. The uncovered inter-relationships between the peptide sequence pattern, oligomer/nanodroplet morphology, and fibrillization pathway, kinetics, and structure may contribute to our understanding of pathogenic amyloidosis in aging, facilitate future efforts ameliorating amyloidosis through peptide engineering, and aid in the design of novel amyloid-based functional nanobiomaterials and nanocomposites.

Second Sphere Interactions in Amyloidogenic Diseases

Amyloids are protein aggregates bearing a highly ordered cross β structural motif, which may be functional but are mostly pathogenic. Their formation, deposition in tissues and consequent organ dysfunction is the central event in amyloidogenic diseases. Such protein aggregation may be brought about by conformational changes, and much attention has been directed toward factors like metal binding, post-translational modifications, mutations of protein etc., which eventually affect the reactivity and cytotoxicity of the associated proteins. Over the past decade, a global effort from different groups working on these misfolded/unfolded proteins/peptides has revealed that the amino acid residues in the second coordination sphere of the active sites of amyloidogenic proteins/peptides cause changes in H-bonding pattern or protein-protein interactions, which dramatically alter the structure and reactivity of these proteins/peptides. These second sphere effects not only determine the binding of transition metals and cofactors, which define the pathology of some of these diseases, but also change the mechanism of redox reactions catalyzed by these proteins/peptides and form the basis of oxidative damage associated with these amyloidogenic diseases. The present review seeks to discuss such second sphere modifications and their ramifications in the etiopathology of some representative amyloidogenic diseases like Alzheimer's disease (AD), type 2 diabetes mellitus (T2Dm), Parkinson's disease (PD), Huntington's disease (HD), and prion diseases.

Dendrimers as Antiamyloid Agents

Dendrimer–protein conjugates have significant prospects for biological applications. The complexation changes the biophysical behavior of both proteins and dendrimers. The dendrimers could influence the secondary structure of proteins, zeta-potential, distribution of charged regions on the surface, the protein–protein interactions, etc. These changes offer significant possibilities for the application of these features in nanotheranostics and biomedicine. Based on the dendrimer–protein interactions, several therapeutic applications of dendrimers have emerged. Thus, the formation of stable complexes retains the disordered proteins on the aggregation, which is especially important in neurodegenerative diseases. To clarify the origin of these properties and assess the efficiency of action, the mechanism of protein–dendrimer interaction and the nature and driving force of binding are considered in this review. The review outlines the antiamyloid activity of dendrimers and discusses the effect of dendrimer structures and external factors on their antiamyloid properties.

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.

A hybrid coarse-grained model for structure, solvation and assembly of lipid-like peptides

Reconstituted photosynthetic proteins which are activated upon exposure to solar energy hold enormous potential for powering future solid state devices and solar cells. The functionality and integration of these proteins into such devices has been successfully enabled by lipid-like peptides. Yet, a fundamental understanding of the organization of these peptides with respect to the photosynthetic proteins and themselves remains unknown and is critical for guiding the design of such light-activated devices. This study investigates the relative organization of one such peptide sequence V₆K₂ (V: valine and K: lysine) within assemblies. Given the expansive spatiotemporal scales associated with this study, a hybrid coarse-grained (CG) model which captures the structure, conformation and aggregation of the peptide is adopted. The CG model uses a combination of iterative Boltzmann inversion and force matching to provide insight into the relative organization of V₆K₂ in assemblies. The CG model reproduces the structure of a V₆K₂ peptide sequence along with its all atom (AA) solvation structure. The relative organization of multiple peptides in an assembly, as captured by CG simulations, is in agreement with corresponding results from AA simulations. Also, a backmapping procedure reintroduces the AA details of the peptides within the aggregates captured by the CG model to demonstrate the relative organization of the peptides. Furthermore, a large number of peptides self-assemble into an elongated micelle in the CG simulation, which is consistent with experimental findings. The coarse-graining procedure is tested for transferability to longer peptide sequences, and hence can be extended to other amphiphilic peptide sequences.

Diverse Aggregation Kinetics Predicted by a Coarse-Grained Peptide Model

Protein and peptide aggregation is a ubiquitous phenomenon with implications in medicine, pharmaceutical industry, and materials science. An important issue in peptide aggregation is the molecular mechanism of aggregate nucleation and growth. In many experimental studies, sigmoidal kinetics curves show a clear lag phase ascribed to nucleation; however, experimental studies also show downhill kinetics curves, where the monomers decay continuously and no lag phase can be seen. In this work, we study peptide aggregation kinetics using a coarse-grained implicit solvent model introduced in our previous work. Our simulations explore the hypothesis that the interplay between interchain attraction and intrachain bending stiffness controls the aggregation kinetics and transient aggregate morphologies. Indeed, our model reproduces the aggregation modes seen in experiment: no observed aggregation, nucleated aggregation, and rapid downhill aggregation. We find that the interaction strength is the primary parameter determining the aggregation mode, whereas the stiffness is a secondary parameter modulating the transient morphologies and aggregation rates: more attractive and stiff chains aggregate more rapidly and the transient morphologies are more ordered. We also explore the effects of the initial monomer concentration and the chain length. As the concentration decreases, the aggregation mode shifts from downhill to nucleated and no-aggregation. This concentration effect is in line with an experimental observation that the transition between downhill and nucleated kinetics is concentration-dependent. We find that longer peptides can aggregate at conditions where short peptides do not aggregate at all. It supports an experimental observation that the elongation of a homopeptide, e.g., polyglutamine, can increase the aggregation propensity.

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.

Amyloid Aggregation under the Lens of Liquid-Liquid Phase Separation

Increasing experiments suggest that amyloid peptides can undergo liquid-liquid phase separation (LLPS) before the formation of amyloid fibrils. However, the exact role of LLPS in amyloid aggregation at the molecular level remains elusive. Here, we investigated the LLPS and amyloid fibrillization of a coarse-grained peptide, capable of capturing fundamental properties of amyloid aggregation over a wide range of concentrations in molecular dynamics simulations. On the basis of the Flory-Huggins theory of polymer solutions, we determined the binodal and spinodal concentrations of LLPS in the low-concentration regime, ϕ_BL and ϕ_SL, respectively. Only at concentrations above ϕ_BL, peptides formed metastable or stable oligomers corresponding to the high-density liquid phase (HDLP) in LLPS, out of which the nucleated conformational conversion to fibril seeds occurred. Below ϕ_SL, the HDLP was metastable and transient, and the subsequent fibrillization process followed the traditional nucleation and elongation mechanisms. Only above ϕ_SL, the HDLP became stable, and the initial fibril nucleation and growth were governed by the high local peptide concentrations. The predicted saturation of amyloid aggregation half-times with increasing peptide concentration to a constant, instead of the traditional power-law scaling to zero, was confirmed by simulations and by a thioflavin-T kinetic assay and the transmission electron microscopy of islet amyloid polypeptide (IAPP) aggregation. Our study provides a unified picture of amyloid aggregation for a wide range of concentrations within the framework of LLPS, which may help us better understand the etiology of amyloid diseases, where the amyloid protein concentration can vary by ∼9 orders of magnitude depending on the organ location and facilitate the engineering of novel amyloid-based functional materials.

Proteostasis of Islet Amyloid Polypeptide: A Molecular Perspective of Risk Factors and Protective Strategies for Type II Diabetes

The possible link between hIAPP accumulation and β-cell death in diabetic patients has inspired numerous studies focusing on amyloid structures and aggregation pathways of this hormone. Recent studies have reported on the importance of early oligomeric intermediates, the many roles of their interactions with lipid membrane, pH, insulin, and zinc on the mechanism of aggregation of hIAPP. The challenges posed by the transient nature of amyloid oligomers, their structural heterogeneity, and the complex nature of their interaction with lipid membranes have resulted in the development of a wide range of biophysical and chemical approaches to characterize the aggregation process. While the cellular processes and factors activating hIAPP-mediated cytotoxicity are still not clear, it has recently been suggested that its impaired turnover and cellular processing by proteasome and autophagy may contribute significantly toward toxic hIAPP accumulation and, eventually, β-cell death. Therefore, studies focusing on the restoration of hIAPP proteostasis may represent a promising arena for the design of effective therapies. In this review we discuss the current knowledge of the structures and pathology associated with hIAPP self-assembly and point out the opportunities for therapy that a detailed biochemical, biophysical, and cellular understanding of its aggregation may unveil.

Amyloid-Like Peptide Aggregates

Self-assembly of proteins and peptides into the amyloid fold is a widespread phenomenon in the natural world. The structural hallmark of self-assembly into amyloid fibrillar assemblies is the cross-beta motif, which conveys distinct morphological and mechanical properties. The amyloid fibril formation has contrasting results depending on the organism, in the sense that it can bestow an organism with the advantages of mechanical strength and improved functionality or, on the contrary, could give rise to pathological states. In this chapter we review the existing information on amyloid-like peptide aggregates, which could either be derived from protein sequences, but also could be rationally or de novo designed in order to self-assemble into amyloid fibrils under physiological conditions. Moreover, the development of self-assembled fibrillar biomaterials that are tailored for the desired properties towards applications in biomedical or environmental areas is extensively analyzed. We also review computational studies predicting the amyloid propensity of the natural amino acid sequences and the structure of amyloids, as well as designing novel functional amyloid materials.

A two-step biopolymer nucleation model shows a nonequilibrium critical point

Biopolymer self-assembly pathways are complicated by the ability of their monomeric subunits to adopt different conformational states. This means nucleation often involves a two-step mechanism where the monomers first condense to form a metastable intermediate, which then converts to a stable polymer by conformational rearrangement of constituent monomers. Nucleation intermediates play a causative role in amyloid diseases such as Alzheimer's and Parkinson's. While existing mathematical models neglect the conversion dynamics, experiments show that conversion events frequently occur on comparable timescales to the condensation of intermediates and growth of mature polymers and thus cannot be ignored. We present a model that explicitly accounts for simultaneous assembly and conversion. To describe conversion, we propose an experimentally motivated initiation-propagation mechanism in which the stable phase arises locally within the intermediate and then spreads by nearest-neighbor interactions, in a manner analogous to one-dimensional Glauber dynamics. Our analysis shows that the competing timescales of assembly and conversion result in a nonequilibrium critical point, separating a regime where intermediates are kinetically unstable from one where conformationally mixed intermediates accumulate. This strongly affects the accumulation rate of the stable biopolymer phase. Our model is uniquely able to explain experimental phenomena such as the formation of mixed intermediates and abrupt changes in the scaling exponent γ, which relates the total monomer concentration to the accumulation rate of the stable phase. This provides a first step toward a general model of two-step biopolymer nucleation, which can quantitatively predict the concentration and composition of biologically crucial intermediates.

Theoretical and computational advances in protein misfolding

Misfolded proteins escape the cellular quality control mechanism and fail to fold properly or remain correctly folded leading to a loss in their functional specificity. Thus misfolding of proteins cause a large number of very different diseases ranging from errors in metabolism to various types of complex neurodegenerative diseases. A theoretical and computational perspective of protein misfolding is presented with a special emphasis on its salient features, mechanism and consequences. These insights quantitatively analyze different determinants of misfolding, that may be applied to design disease specific molecular targets.

Building a macro-mixing dual-basin Gō model using the Multistate Bennett Acceptance Ratio

The dual-basin Gō-model is a structural-based coarsegrained model for simulating a conformational transition between two known structures of a protein. Two parameters are required to produce a dual-basin potential mixed using two single-basin potentials, although the determination of mixing parameters is usually not straightforward. Here, we have developed an efficient scheme to determine the mixing parameters using the Multistate Bennett Acceptance Ratio (MBAR) method after short simulations with a set of parameters. In the scheme, MBAR allows us to predict observables at various unsimulated conditions, which are useful to improve the mixing parameters in the next round of iterative simulations. The number of iterations that are necessary for obtaining the converged mixing parameters are significantly reduced in the scheme. We applied the scheme to two proteins, the glutamine binding protein and the ribose binding protein, for showing the effectiveness in the parameter determination. After obtaining the converged parameters, both proteins show frequent conformational transitions between open and closed states, providing the theoretical basis to investigate structure-dynamics-function relationships of the proteins.

Aggregation of amyloid peptides into fibrils driven by nanoparticles and their curvature effect

Fibrillation of amyloid peptides induces human diseases such as Alzheimer's disease, which has become a huge challenge. Some nanoparticles (NPs) could enhance peptide fibrillation by decreasing the lag time, yet how the size and shape of NPs affect amyloid fibrillation as well as the underlying mechanism remains unclear. Here, we investigated amyloid fibrillation on the surface of spherical NPs and cylindrical nanorods (NRs) of different sizes using coarse-grained Monte Carlo simulations. We focused on the curvature effect of NPs/NRs on the adsorption and fibrillation of peptide chains due to the size/shape difference. As the size of the NPs/NRs increases, the number of assembled peptide chains shows a non-monotonic tendency, and there is an optimal size for the highest adsorption. In most cases, the NRs could adsorb more peptides than the NPs of the same diameter due to the lower curvature. The mechanism beneath these observations was elucidated from a thermodynamic point of view. Our findings could provide a physical basis for the adsorption and fibrillation of amyloid peptides on NPs, and guide the design of future curvature-dependent NP-based amyloid treatment.

Mitigating Human IAPP Amyloidogenesis In Vivo with Chiral Silica Nanoribbons

Amyloid fibrils generally display chirality, a feature which has rarely been exploited in the development of therapeutics against amyloid diseases. This study reports, for the first time, the use of mesoscopic chiral silica nanoribbons against the in vivo amyloidogenesis of human islet amyloid polypeptide (IAPP), the peptide whose aggregation is implicated in type 2 diabetes. The thioflavin T assay and transmission electron microscopy show accelerated IAPP fibrillization through elimination of the nucleation phase and shortening of the elongation phase by the nanostructures. Coarse-grained simulations offer complementary molecular insights into the acceleration of amyloid aggregation through their nonspecific binding and directional seeding with the nanostructures. This accelerated IAPP fibrillization translates to reduced toxicity, especially for the right-handed silica nanoribbons, as revealed by cell viability, helium ion microscopy, as well as zebrafish embryo survival, developmental, and behavioral assays. This study has implicated the potential of employing chiral nanotechnologies against the mesoscopic enantioselectivity of amyloid proteins and their associated diseases.

Disorder at the Tips of a Disease-Relevant Aβ42 Amyloid Fibril: A Molecular Dynamics Study

We present a simulation study of the early events of peptide dissociation from a fibril of the Alzheimer's Aβ42 peptide. The fibril consists of layers of two adjacent Aβ42 peptides each folded in an S-shaped structure which has been determined by solid state NMR spectroscopy of a monomorphic disease-relevant species. Multiple molecular dynamics runs (16 at 310 K and 15 at 370 K) were carried out starting from an 18-peptide protofibril for a cumulative sampling of about 15 μs. The simulations show structural stability of the fibrillar core and an overall increase in the twist to about 3 degrees. The N-terminal segment 1-14 is disordered in all peptides. At both ends of the fibril, the central segment 21-29, which includes part of the β₂ strand, dissociates in some of the simulations. The β₁ and β₃ strands, residues 15-20 and 35-41, respectively, are structurally stable. The transient binding of the N-terminal stretch to the β₃ strand of the adjacent peptide at the tip is likely to contribute to the arrest phase of the stop-and-go mechanism.

The attachment of α-synuclein to a fiber: A coarse-grain approach

We present simulations of the amyloidogenic core of α-synuclein, the protein causing Parkinson's disease, as a short chain of coarse-grain patchy particles. Each particle represents a sequence of about a dozen amino acids. The fluctuating secondary structure of this intrinsically disordered protein is modelled by dynamic variations of the shape and interaction characteristics of the patchy particles, ranging from spherical with weak isotropic attractions for the disordered state to spherocylindrical with strong directional interactions for a β-sheet. Flexible linkers between the particles enable sampling of the tertiary structure. This novel model is applied here to study the growth of an amyloid fibril, by calculating the free energy profile of a protein attaching to the end of a fibril. The simulation results suggest that the attaching protein readily becomes trapped in a mis-folded state, thereby inhibiting further growth of the fibril until the protein has readjusted to conform to the fibril structure, in line with experimental findings and previous simulations on small fragments of other proteins.

Intrinsic Conformational Preferences and Interactions in α-Synuclein Fibrils: Insights from Molecular Dynamics Simulations

Amyloid formation by the intrinsically disordered α-synuclein protein is the hallmark of Parkinson's disease. We present atomistic Molecular Dynamics simulations of the core of α-synuclein using enhanced sampling techniques to describe the conformational and binding free energy landscapes of fragments implicated in fibril stabilization. The theoretical framework is derived to combine the free energy profiles of the fragments into the reaction free energy of a protein binding to a fibril. Our study shows that individual fragments in solution have a propensity toward attaining non-β conformations, indicating that in a fibril β-strands are stabilized by interactions with other strands. We show that most dimers of hydrogen-bonded fragments are unstable in solution, while hydrogen bonding stabilizes the collective binding of five fragments to the end of a fibril. Hydrophobic effects make further contributions to the stability of fibrils. This study is the first of its kind where structural and binding preferences of the five major fragments of the hydrophobic core of α-synuclein have been investigated. This approach improves sampling of intrinsically disordered proteins, provides information on the binding mechanism between the core sequences of α-synuclein, and enables the parametrization of coarse grained models.

Insulin-Degrading Enzyme in the Fight against Alzheimer's Disease

After decades of research and clinical trials there is still no cure for Alzheimer's disease (AD). While impaired clearance of amyloid beta (Aβ) peptides is considered as one of the major causes of AD, it was recently complemented by a potential role of other toxic amyloidogenic species. Insulin-degrading enzyme (IDE) is the proteolytic culprit of various β-forming peptides, both extracellular and intracellular. On the basis of demonstrated allosteric activation of IDE against Aβ, it is possible to propose a new strategy for the targeted IDE-based cleansing of different toxic aggregation-prone peptides. Consequently, specific allosteric activation of IDE coupled with state-of-the-art compound delivery and CRISP-Cas9 technique of transgene insertion can be instrumental in the fight against AD and related neurodegenerative maladies.

Simulations of disordered proteins and systems with conformational heterogeneity

Intrinsically disordered proteins (IDPs) and protein regions can facilitate a wide variety of complex physiological processes such as binding, signaling, and formation of membraneless organelles. They can however also play pathological roles by aggregating into cytotoxic oligomers and fibrils. Characterizing the structure and function of disordered proteins is an onerous task, primarily because these proteins adopt transient structures, which are difficult to capture in experiments. Simulations have emerged as a powerful tool for interpreting and augmenting experimental measurements of IDPs. In this review we focus on computer simulations of disordered protein structures, functions, assemblies, and emerging questions that, taken together, give an overview of the field as it exists today.

Dimerization Mechanism of Alzheimer Aβ40 Peptides: The High Content of Intrapeptide-Stabilized Conformations in A2V and A2T Heterozygous Dimers Retards Amyloid Fibril Formation

Amyloid beta (Aβ) oligomerization is associated with the origin and progression of Alzheimer's disease (AD). While the A2V mutation enhances aggregation kinetics and toxicity, mixtures of wild-type (WT) and A2V, and also WT and A2T, peptides retard fibril formation and protect against AD. In this study, we simulate the equilibrium ensemble of WT:A2T Aβ₄₀ dimer by means of extensive atomistic replica exchange molecular dynamics and compare our results with previous equivalent simulations of A2V:A2V, WT:WT, and WT:A2V Aβ₄₀ dimers for a total time scale of nearly 0.1 ms. Qualitative comparison of the resulting thermodynamic properties, such as the relative binding free energies, with the reported experimental kinetic and thermodynamic data affords us important insight into the conversion from slow-pathway to fast-pathway dimer conformations. The crucial reaction coordinate or driving force of such transformation turns out to be related to hydrophobic interpeptide interactions. Analysis of the equilibrium ensembles shows that the fast-pathway conformations contain interpeptide out-of-register antiparallel β-sheet structures at short interpeptide distances. In contrast, the slow-pathway conformations are formed by the association of peptides at large interpeptide distances and high intrapeptide compactness, such as conformations containing intramolecular three-stranded β-sheets which sharply distinguish fast (A2V:A2V and WT:WT) and slow (WT:A2T and WT:A2V) amyloid-forming sequences. Also, this analysis leads us to predict that a molecule stabilizing the intramolecular three-stranded β-sheet or inhibiting the formation of an interpeptide β-sheet spanning residues 17-20 and 31-37 would further reduce fibril formation and probably the cytotoxicity of Aβ species.

Protein Aggregation and Molecular Crowding: Perspectives From Multiscale Simulations

Cells are extremely crowded environments, thus the use of diluted salted aqueous solutions containing a single protein is too simplistic to mimic the real situation. Macromolecular crowding might affect protein structure, folding, shape, conformational stability, binding of small molecules, enzymatic activity, interactions with cognate biomolecules, and pathological aggregation. The latter phenomenon typically leads to the formation of amyloid fibrils that are linked to several lethal neurodegenerative diseases, but that can also play a functional role in certain organisms. The majority of molecular simulations performed before the last few years were conducted in diluted solutions and were restricted both in the timescales and in the system dimensions by the available computational resources. In recent years, several computational solutions were developed to get close to physiological conditions. In this review we summarize the main computational techniques used to tackle the issue of protein aggregation both in a diluted and in a crowded environment.

Basic units of protein structure, folding, and function

Study of the hierarchy of domain structure with alternative sets of domains and analysis of discontinuous domains, consisting of remote segments of the polypeptide chain, raised a question about the minimal structural unit of the protein domain. The hypothesis on the decisive role of the polypeptide backbone in determining the elementary units of globular proteins have led to the discovery of closed loops. It is reviewed here how closed loops form the loop-n-lock structure of proteins, providing the foundation for stability and designability of protein folds/domain and underlying their co-translational folding. Simplified protein sequences are considered here with the aim to explore the basic principles that presumably dominated the folding and stability of proteins in the early stages of structural evolution. Elementary functional loops (EFLs), closed loops with one or few catalytic residues, are, in turn, units of the protein function. They are apparent descendants of the prebiotic ring-like peptides, which gave rise to the first functional folds/domains being fused in the beginning of the evolution of protein structure. It is also shown how evolutionary relations between protein functional superfamilies and folds delineated with the help of EFLs can contribute to establishing the rules for design of desired enzymatic functions. Generalized descriptors of the elementary functions are proposed to be used as basic units in the future computational design.

A minimal conformational switching-dependent model for amyloid self-assembly

Amyloid formation is associated with various pathophysiological conditions like Alzheimer’s and Parkinson’s diseases as well as many useful functions. The hallmark of amyloid assemblies is a conformational transition of the constituent proteins into a β - sheet rich filament. Accounting for this conformational transition in amyloidogenic proteins, we develop an analytically solvable model that can probe the dynamics of an ensemble of single filaments. Using the theory and Monte Carlo simulations, we show the presence of two kinetic regimes for the growth of a self-assembling filament – switching-dependent and –independent growth regimes. We observe a saturation in fibril elongation velocities at higher concentrations in the first regime, providing a novel explanation to the concentration-independence of growth velocities observed experimentally. We also compute the length fluctuation of the filaments to characterize aggregate heterogeneity. From the early velocities and length fluctuation, we propose a novel way of estimating the conformational switching rate. Our theory predicts a kinetic phase diagram that has three distinct phases – short oligomers/monomers, disordered aggregates and β -rich filaments. The model also predicts the force generation potential and the intermittent growth of amyloid fibrils evident from single molecular experiments. Our model could contribute significantly to the physical understanding of amyloid aggregation.

Domain Motion Enhanced (DoME) Model for Efficient Conformational Sampling of Multidomain Proteins

Large conformational changes of multidomain proteins are difficult to simulate using all-atom molecular dynamics (MD) due to the slow time scale. We show that a simple modification of the structure-based coarse-grained (CG) model enables a stable and efficient MD simulation of those proteins. "Motion Tree", a tree diagram that describes conformational changes between two structures in a protein, provides information on rigid structural units (domains) and the magnitudes of domain motions. In our new CG model, which we call the DoME (domain motion enhanced) model, interdomain interactions are defined as being inversely proportional to the magnitude of the domain motions in the diagram, whereas intradomain interactions are kept constant. We applied the DoME model in combination with the Go model to simulations of adenylate kinase (AdK). The results of the DoME-Go simulation are consistent with an all-atom MD simulation for 10 μs as well as known experimental data. Unlike the conventional Go model, the DoME-Go model yields stable simulation trajectories against temperature changes and conformational transitions are easily sampled despite domain rigidity. Evidently, identification of domains and their interfaces is useful approach for CG modeling of multidomain proteins.

Aβ self-association and adsorption on a hydrophobic nanosurface: competitive effects and the detection of small oligomers via electrical response

Treatment of Alzheimer's disease (AD) is impeded by the lack of effective early diagnostic methods. Small, soluble Aβ globulomers play a major role in AD neurotoxicity, and detecting their presence in aqueous fluids could lead to suitable sensors. We evaluate the adsorption behavior of small Aβ oligomers on the surface of a single walled carbon nanotube of high curvature. While the intrinsic self-assembly propensity of Aβ is markedly hindered by adsorption, the oligomeric units show high degrees of surface immobilization. Immobilized complexes are capable of oligomeric growth, but with a shifted monomer-oligomer equilibrium compared to the free states. In the presence of an ionic solution and suitable external electric fields, magnitudes of the current blockades are found to be sensitive to the oligomeric number of the adsorbed complex. However, this sensitivity gradually diminishes with increasing oligomeric size. The results provide a proof-of-concept basis for further investigations in the design of sensors for detecting the toxic small oligomers of Aβ.

Crucial role of nonspecific interactions in amyloid nucleation

Protein oligomers have been implicated as toxic agents in a wide range of amyloid-related diseases. However, it has remained unsolved whether the oligomers are a necessary step in the formation of amyloid fibrils or just a dangerous byproduct. Analogously, it has not been resolved if the amyloid nucleation process is a classical one-step nucleation process or a two-step process involving prenucleation clusters. We use coarse-grained computer simulations to study the effect of nonspecific attractions between peptides on the primary nucleation process underlying amyloid fibrillization. We find that, for peptides that do not attract, the classical one-step nucleation mechanism is possible but only at nonphysiologically high peptide concentrations. At low peptide concentrations, which mimic the physiologically relevant regime, attractive interpeptide interactions are essential for fibril formation. Nucleation then inevitably takes place through a two-step mechanism involving prefibrillar oligomers. We show that oligomers not only help peptides meet each other but also, create an environment that facilitates the conversion of monomers into the β-sheet-rich form characteristic of fibrils. Nucleation typically does not proceed through the most prevalent oligomers but through an oligomer size that is only observed in rare fluctuations, which is why such aggregates might be hard to capture experimentally. Finally, we find that the nucleation of amyloid fibrils cannot be described by classical nucleation theory: in the two-step mechanism, the critical nucleus size increases with increases in both concentration and interpeptide interactions, which is in direct contrast with predictions from classical nucleation theory.

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.

Perspective: Coarse-grained models for biomolecular systems

By focusing on essential features, while averaging over less important details, coarse-grained (CG) models provide significant computational and conceptual advantages with respect to more detailed models. Consequently, despite dramatic advances in computational methodologies and resources, CG models enjoy surging popularity and are becoming increasingly equal partners to atomically detailed models. This perspective surveys the rapidly developing landscape of CG models for biomolecular systems. In particular, this review seeks to provide a balanced, coherent, and unified presentation of several distinct approaches for developing CG models, including top-down, network-based, native-centric, knowledge-based, and bottom-up modeling strategies. The review summarizes their basic philosophies, theoretical foundations, typical applications, and recent developments. Additionally, the review identifies fundamental inter-relationships among the diverse approaches and discusses outstanding challenges in the field. When carefully applied and assessed, current CG models provide highly efficient means for investigating the biological consequences of basic physicochemical principles. Moreover, rigorous bottom-up approaches hold great promise for further improving the accuracy and scope of CG models for biomolecular systems.

Computational approaches to understanding protein aggregation in neurodegeneration

The generation of toxic non-native protein conformers has emerged as a unifying thread among disorders such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. Atomic-level detail regarding dynamical changes that facilitate protein aggregation, as well as the structural features of large-scale ordered aggregates and soluble non-native oligomers, would contribute significantly to current understanding of these complex phenomena and offer potential strategies for inhibiting formation of cytotoxic species. However, experimental limitations often preclude the acquisition of high-resolution structural and mechanistic information for aggregating systems. Computational methods, particularly those combine both all-atom and coarse-grained simulations to cover a wide range of time and length scales, have thus emerged as crucial tools for investigating protein aggregation. Here we review the current state of computational methodology for the study of protein self-assembly, with a focus on the application of these methods toward understanding of protein aggregates in human neurodegenerative disorders.