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The intrinsically disordered protein Tau represents the main component of neurofibrillary tangles that are a hallmark of Alzheimer’s disease. A small fragment of Tau, known as paired helical filament 6 (PHF6), is considered to be important for the formation of the β-structure core of the fibrils. Here we study the aggregation of this fragment in the presence of different cosolutes, including urea, TMAO, sucrose and 2-hydroxypropyl-β-cyclodextrin (2-HPβCD), using both experiments and molecular dynamics simulations. A novel implicit solvation approach (MIST - Model with Implicit Solvation Thermodynamics) is used, where an energetic contribution based on the concept of transfer free energies describes the effect of the cosolutes. The simulation predictions are compared to thioflavin-T and atomic force microscopy results, and the good agreement observed confirms the predictive ability of the computational approach herein proposed. Both simulations and experiments indicate that PHF6 aggregation is inhibited in the presence of urea and 2-HPβCD, while TMAO and sucrose stabilize associated conformations. The remarkable ability of HPβCD to inhibit aggregation represents an extremely promising result for future applications, especially considering the widespread use of this molecule as a drug carrier to the brain and as a solubilizer/excipient in pharmaceutical formulations.

Protein aggregation is a common feature in prominent neurodegenerative diseases, usually thought to be due to the assembly of a single peptide or protein. Recent studies have challenged this notion and suggested several proteins may be involved in promoting and amplifying disease. For example, the TDP-43 protein associated with Amyotrophic Lateral Sclerosis has been found in the brain along with Aβ assemblies associated with Alzheimer’s disease, and those patients that show the presence of TDP-43 are 10 times more likely to demonstrate cognitive impairment compared to TDP-43-negative Alzheimer’s patients. Here we examine the interactions between the amyloidogenic core of TDP-43, TDP-43307-319, and a neurotoxic physiologically observed fragment of Aβ, Aβ25-35. Utilizing ion mobility mass spectrometry in concert with atomic force microscopy and molecular dynamics simulations, we investigate which oligomers are involved in seeding aggregation across these two different protein systems and gain insight into which structures initiate and result from these interactions. Studies were conducted by mixing Aβ25-35with the toxic wild type TDP-43307-319peptide and with the nontoxic synthetic TDP-43307-319mutant, G314V. Our findings identify a strong catalytic effect of TDP-43307-319WT monomer in the acceleration of Aβ25-35aggregation to its toxic cylindrin and β barrel forms. This observation is unprecedented in both its speed and specificity. Interestingly, the nontoxic G314V mutant of TDP-43307-319and dimers or higher order oligomers of WT TDP-43307-319do not promote aggregation of Aβ25-35but rather dissociate preformed toxic higher order oligomers of Aβ25-35. Reasons for these very different behaviors are reported.

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.

Variational autoencoders are artificial neural networks with the capability to reduce highly dimensional sets of data to smaller dimensional, latent representations. In this work, these models are applied to molecular dynamics simulations of the self-assembly of coarse-grained peptides to obtain a singled-valued order parameter for amyloid aggregation. This automatically learned order parameter is constructed by time-averaging the latent parametrizations of internal coordinate representations and compared to the nematic order parameter which is commonly used to study ordering of similar systems in literature. It is found that the latent space value provides more tailored insight into the aggregation mechanism’s details, correctly identifying fibril formation in instances where the nematic order parameter fails to do so. A means is provided by which the latent space value can be analyzed so that the major contributing internal coordinates are identified, allowing for a direct interpretation of the latent space order parameter in terms of the behavior of the system. The latent model is found to be an effective and convenient way of representing the data from the dynamic ensemble and provides a means of reducing the dimensionality of a system whose scale exceeds molecular systems so-far considered with similar tools. This bypasses a need for researcher speculation on what elements of a system best contribute to summarizing major transitions and suggests latent models are effective and insightful when applied to large systems with a diversity of complex behaviors.

TDP-43 aggregates are a salient feature of amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and a variety of other neurodegenerative diseases, including Alzheimer’s disease (AD). With an anticipated growth in the most susceptible demographic, projections predict neurodegenerative diseases will potentially affect 15 million people in the United States by 2050. Currently, there are no cures for ALS, FTD, or AD. Previous studies of the amyloidogenic core of TDP-43 have demonstrated that oligomers greater than a trimer are associated with toxicity. Utilizing a joint pharmacophore space (JPS) method, potential drugs have been designed specifically for amyloid-related diseases. These molecules were generated on the basis of key chemical features necessary for blood–brain barrier permeability, low adverse side effects, and target selectivity. Combining ion-mobility mass spectrometry and atomic force microscopy with the JPS computational method allows us to more efficiently evaluate a potential drug’s efficacy in disrupting the development of putative toxic species. Our results demonstrate the dissociation of higher-order oligomers in the presence of these novel JPS-generated inhibitors into smaller oligomer species. Additionally, drugs approved by the Food and Drug Administration for the treatment of ALS were also evaluated and demonstrated to maintain higher-order oligomeric assemblies. Possible mechanisms for the observed action of the JPS molecules are discussed.

Despite the importance of amyloid formation in disease pathology, the understanding of the primary structure–activity relationship for amyloid-forming peptides remains elusive. Here we use a new neural-network based method of analysis: the classifying autoencoder (CAE). This machine learning technique uses specialized architecture of artificial neural networks to provide insight into typically opaque classification processes. The method proves to be robust to noisy and limited data sets, as well as being capable of disentangling relatively complicated rules over data sets. We demonstrate its capabilities by applying the technique to an experimental database (the Waltz database) and demonstrate the CAE’s capability to provide insight into a novel descriptor, dimeric isotropic deviation—an experimental measure of the aggregation properties of the amino acids. We measure this value for all 20 of the common amino acids and find correlation between dimeric isotropic deviation and the failure to form amyloids when hydrophobic effects are not a primary driving force in amyloid formation. These applications show the value of the new method and provide a flexible and general framework to approach problems in biochemistry using artificial neural networks.

Type-2 diabetes mellitus (T2DM) is a disease hallmarked by improper homeostasis within the islets of Langerhans of the pancreas. The most critical species affected is insulin, which is produced by the β-cells of the islets, but there are a number of other species copackaged and cosecreted within the insulin granules. This includes zinc, which exists in high (millimolar) concentrations within the β-cells, and islet amyloid polypeptide (IAPP), which is an amyloid peptide thought to induce β-cell apoptosis through self-association into toxic amyloid oligomers. Zinc is essential in the packaging of crystalline insulin within the vesicles but it can also bind and interact with IAPP. This implies a complex relationship between all three species and diabetes, particularly in the structure and function of toxic IAPP aggregates. Atypical (low or high) concentrations of zinc generally appear to correlate with increased hIAPP aggregation, whereas physiological zinc concentrations have an inhibitory effect. To better understand how zinc ions alter the monomer and oligomer structure of hIAPP in vitro, we employ a combination of ion mobility mass spectrometry and atomic force microscopy. We observe an increase in the extended β-hairpin conformation of hIAPP when it is bound to zinc. With sufficiently low concentrations of zinc this could result in an association site for zinc-free hIAPP, promoting amyloid aggregation. At high zinc concentrations, we see the appearance of a secondary zinc association site whose coordination could account for the loss of inhibition at high zinc concentrations. Generally, it appears that zinc preferentially stabilizes the β-hairpin conformation of hIAPP and the population of zinc-bound hIAPP in solution determines what effect this has on amyloid aggregation.

Protein aggregation is typically attributed to the association of homologous amino acid sequences between monomers of the same protein. Coaggregation of heterogeneous peptide species can occur, however, and is implicated in the proliferation of seemingly unrelated protein diseases in the body. The prion protein fragment (PrP106–126) and human islet amyloid polypeptide (hIAPP) serve as an interesting model of nonhomologous protein assembly as they coaggregate, despite a lack of sequence homology. We have applied ion-mobility mass spectrometry, atomic force microscopy, circular dichroism, and high-level molecular modeling to elucidate this important assembly process. We found that the prion fragment not only forms pervasive hetero-oligomeric aggregates with hIAPP but also promotes the transition of hIAPP into its amyloidogenic β-hairpin conformation. Further, when PrP106–126was combined with non-amyloidogenic rIAPP, the two formed nearly identical hetero-oligomers to those seen with hIAPP, despite rIAPP containing β-sheet breaking proline substitutions. Additionally, while rIAPP does not natively form the amyloidogenic β-hairpin structure, it did so in the presence of PrP106–126and underwent a conformational transition to β-sheet in solution. We also find that PrP106–126forms hetero-oligomers with the IAPP8–20fragment but not with the “aggregation hot spot” IAPP20–29fragment. PrP106–126apparently induces IAPP into a β-hairpin structure within the PrP:IAPP heterodimer complex and then, through ligand exchange, catalytically creates the amyloidogenic β-hairpin dimer of IAPP in significantly greater abundance than IAPP does on its own. This is a new mechanistic model that provides a critical foundation for the detailed study of hetero-oligomerization and prion-like proliferation in amyloid systems.

The amino acid serine has long been known to form a protonated “magic-number” cluster containing eight monomer units that shows an unusually high abundance in mass spectra and has a remarkable homochiral preference. Despite many experimental and theoretical studies, there is no consensus on a Ser8H+structure that is in agreement with all experimental observations. Here, we present the structure of Ser8H+determined by a combination of infrared spectroscopy and ab initio molecular dynamics simulations. The three-dimensional structure that we determine is ∼25 kcal mol–1more stable than the previous most stable published structure and explains both the homochiral preference and the experimentally observed facile replacement of two serine units.

The amino acid serine is known to form a very stable octamer that has properties that set it apart from serine complexes of different sizes or from complexes composed of other amino acids. For example, both singly protonated serine octamers and anionic octamers complexed with two halogen ions strongly prefer homochirality, even when assembled from racemic D,L mixtures. Consequently, the structures of these complexes are of great interest, but no acceptable candidates have so far been identified. Here, we investigate anionic serine octamers coordinated with two chloride ions using a novel technique coupling ion mobility spectrometry–mass spectrometry with infrared spectroscopy, in combination with theoretical calculations. The results allow the identification of a unique structure for (Ser8Cl2)2−that is highly symmetric, very stable and homochiral and whose calculated properties match those observed in experiments.

Amyloidogenic peptides and proteins play a crucial role in a variety of neurodegenerative disorders such as Alzheimer's and Parkinson's disease. These proteins undergo a spontaneous transition from a soluble, often partially folded form, into insoluble amyloid fibrils that are rich in β-sheets. Increasing evidence suggests that highly dynamic, polydisperse folding intermediates, which occur during fibril formation, are the toxic species in the amyloid-related diseases. Traditional condensed-phase methods are of limited use for characterizing these states because they typically only provide ensemble averages rather than information about individual oligomers. Here we report the first direct secondary-structure analysis of individual amyloid intermediates using a combination of ion mobility spectrometry–mass spectrometry and gas-phase infrared spectroscopy. Our data reveal that oligomers of the fibril-forming peptide segments VEALYL and YVEALL, which consist of 4–9 peptide strands, can contain a significant amount of β-sheet. In addition, our data show that the more-extended variants of each oligomer generally exhibit increased β-sheet content.

The RNA-binding protein TDP-43 is associated with mRNA processing and transport from the nucleus to the cytoplasm. TDP-43 localizes in the nucleus as well as accumulating in cytoplasmic condensates such as stress granules. Aggregation and formation of amyloid-like fibrils of cytoplasmic TDP-43 are hallmarks of numerous neurodegenerative diseases, most strikingly present in >90% of amyotrophic lateral sclerosis patients. If excessive accumulation of cytoplasmic TDP-43 causes, or is caused by, neurodegeneration is presently not known. In this work, we use molecular dynamics simulations at multiple resolutions to explore TDP-43 self- and cross-interaction dynamics. A full-length molecular model of TDP-43, all 414 amino acids, was constructed from select structures of the protein functional domains (N-terminal domain, and two RNA recognition motifs, RRM1 and RRM2) and modeling of disordered connecting loops and the low complexity glycine-rich C-terminus domain. All-atom CHARMM36m simulations of single TDP-43 proteins served as guides to construct a coarse-grained Martini 3 model of TDP-43. The Martini model and a coarser implicit solvent C⍺ model, optimized for disordered proteins, were subsequently used to probe TDP-43 interactions; self-interactions from single-chain full-length TDP-43 simulations, cross-interactions from simulations with two proteins and simulations with assemblies of dozens to hundreds of proteins. Our findings illustrate the utility of different modeling scales for accessing TDP-43 molecular-level interactions and suggest that TDP-43 has numerous interaction preferences or patterns, exhibiting an overall strong, but dynamic, association and driving the formation of biomolecular condensates.