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WIREs Comput Mol Sci
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Revisiting the earliest signatures of amyloidogenesis: Roadmaps emerging from computational modeling and experiment

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Neurodegenerative amyloidogenesis begins with the aggregation of intrinsically disordered proteins (IDPs), which is the first step in a cascade of assembly events that can lead to insoluble fibrous deposits in brain tissue. IDP conformations that promote formation of toxic oligomers remain poorly understood, and are the most fundamental target of putative treatments for neurodegenerative disease. Rapid advances in theory, simulation and experimental methods, hold the promise of reversing protein aggregation by identifying and developing inhibitors of the transient amyloidogenic IDP conformations. To make meaningful progress it is important to appreciate the benefits and limitations of the latest developments in computational methods of conformational and ensemble modeling, and their integration and validation with experiments. Integrated studies are beginning to provide significant conceptual and mechanistic insights, including identification of the properties of amyloidogenic IDPs in their free, unbound form. At the same time, contradicting viewpoints have emerged concerning convergence of IDP ensemble signatures and properties from parallel studies, and there also remains a pressing need to develop physical models that can deliver reliable predictions across different IDP families. Focussing on the four most common amyloidogenic IDPs of Amyloid β, Tau, α‐synuclein and Prions, improvements are proposed for next‐generation models and experiments that can potentially identify drug treatments for neurodegenerative disease via incorporation of the extended cellular environment. This article is categorized under: Molecular and Statistical Mechanics > Molecular Mechanics Structure and Mechanism > Computational Biochemistry and Biophysics Structure and Mechanism > Molecular Structures
Nuclear Magnetic Resonance (NMR) structures of monomers of (a) Aβ40 obtained in (I) 40% trifluoroethanol/water (v/v) (PDB code 1AML, Sticht et al., ) and (II) 93% water/7% D2O (v/v) (PDB code 2LFM (Vivekanandan, Brender, Lee, & Ramamoorthy, ), partially folded). The N‐ and the C‐terminal are represented as red and green spheres, respectively, while the regions spanned by α‐helices (and 310 helical turns) and β‐strands are highlighted in yellow and green, respectively, in their respective primary sequences under each IDP structure. The one‐letter residue codes are colored according to their degree of hydrophobicity (Kyte‐Doolittle scale) (Kyte & Doolittle, ). The protein structures were visualized using the UCSF Chimera software (Pettersen et al., ). (b) Aβ42 solved in (I) 20% water/80% deuterated‐hexafluoroisopropanol (PDB code 1IYT, Crescenzi et al., ) and (II) 70% water/30% deuterated‐hexafluoroisopropanol (PDB code 1Z0Q, Tomaselli et al., ). (c) Micelle‐bound human α‐syn solved in aqueous solution with sodium dodecyl sulfate (PDB code 1XQ8, Ulmer et al., ). (d) A microtubule‐bound 46‐residue Tau peptide fragment (residues 267–312 in the full Tau sequence) solved in aqueous solvent: 90% water/10% D2O (PDB code 2MZ7, Kadavath et al., ). (e) N‐terminal truncated human cellular Prion (PrPC 90‐231), microtubule‐bound structure solved in aqueous solvent: 90% water/10% D2O (PDB code 2LSB, Biljan et al., )
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The very recently solved (a) ssNMR structure of monomorphic Aβ42 fibril (PDB code 5KK3 (Colvin et al., ), containing residues 11–42), showing a pronounced S‐shaped amyloid fold, (b) ssNMR structure of full‐length Aβ42 fibril (PDB code 2NAO, Walti et al., ) polymorph with residues 15–42 showing a double‐horseshoe shaped cross β‐sheet (composed of β‐strands) arrangement with buried hydrophobic side chains, (c) ssNMR structure of full‐length α‐syn fibril (PDB code 2N0A (Tuttle et al., )) showing a central Greek‐key motif, and (d) cryo‐EM derived atomic model of PHFs of Tau neurofibrillary tangles (PDB code 5O3L (A. W. P. Fitzpatrick et al., ), comprising residues 306–378). The fibril assemblies show an in‐register arrangement of the β‐sheets which runs parallel to the fibril axis as seen from ssNMR, and an inherent cross‐β spine which is depicted by the β‐strands (held together in parallel by intermolecular hydrogen bonds) oriented perpendicular to the fibril axis
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An overview of the proposed mechanisms of amyloid formation (amyloidogenesis). The figure depicts routes to fibrillogenesis through nucleation and elongation phases, with associated secondary pathways and formation of off‐pathway aggregates (specific ones shown here for Aβ) (Roychaudhuri, Yang, Hoshi, & Teplow, ). Disordered amyloidogenic monomers (IDPs) undergo an activation step to transform from their natively unfolded state in solution into partially‐folded monomers, which further nucleate to form toxic oligomers (one‐step primary nucleation). Transitions between unfolded and misfolded monomers may involve very early transient metastable monomeric states (might be α‐helical or partially folded random coils or β‐hairpin conformation), which could potentially seed formation of toxic oligomers. On the other hand, unfolded monomers may form toxic oligomers through intermediary assembly of disordered oligomers followed by conformational re‐arrangement (two‐step primary nucleation). The monomers can self‐assemble off‐pathway into amorphous oligomers, which may or may not be toxic. These oligomers do not go on to form the fibril structure directly, but may participate in the on‐pathway route by either promoting or inhibiting fibril formation. Proposed mechanisms of secondary nucleation, lateral and longitudinal growth, and fragmentation are also shown. The amyloid fibril structure shown is based on a cryo‐electron microscopy (EM) structure of a quadruplet cross‐β fibril polymorph (A. W. Fitzpatrick et al., )
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Representative conformations of the most populated clusters of wild type Αβ42 and its mutants sampled by REMD simulations using Amber ff99SB force field and GB implicit solvent (Xu et al., ; Xu, Shan, et al., ). The N‐ and C‐ terminal of each conformation are represented as orange and green spheres, respectively. The mutated residues are represented as vdW spheres. For E22Δ mutant, E22 is removed and so D23 is shown instead
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Representative calculated conformations of Zn‐bound Aβ40 with either D1 or E11 coordinated to Zn along with three stabilizing histidine residues (H6, H13, and H14) (Xu, Wang, et al., ). The distribution of Gaussian fitted Gibbs free energy for each conformational ensemble is shown by the red bell‐shaped curves
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(a) Solution NMR structure (PDB code 2OTK, Hoyer et al., ) of β‐hairpin (green) from Aβ(17–36) solved in complex with an engineered binding protein dimer: affibody ZAβ3 (light gray). (b) Solution NMR structure (PDB code 4BXL, Mirecka et al., ) of β‐hairpin (red) from α‐syn(37–54) solved in complex with an engineered binding protein dimer: β‐wrapin AS69 (light gray). (c) A 2.3 Å XRD structure (PDB code 5HOW, Kreutzer et al., ) of a representative β‐hairpin (green) of Aβ(17–36) from macrocyclic β‐sheet peptides forming triangular trimers (two other monomers are shown in light tan), and (d) a 1.97 Å XRD structure (PDB code 5F1T, Salveson et al., ) of a representative β‐hairpin (red) of α‐syn(36–55) from macrocyclic β‐sheet peptides forming triangular trimers (two other monomers are shown in light tan; one nanomer and a hexamer from another nanomer was deleted from the original octadecamer PDB structure for clarity)
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A schematic overview of the methods for constructing ensembles of IDPs shows two broad classifications: Knowledge‐based approaches and de novo MD simulations. In summary, a knowledge‐based approach can involve either an experimentally‐guided pool‐based selection method (flowchart on the left) or a selection based on experimentally restrained MD simulations (flowchart on the right). On the other hand, de novo MD simulations (flowchart at the bottom) do not involve an experimental biasing prior to generation of conformers, but are usually validated by follow‐up experiments after an initial unbiased ensemble is generated based on a predicted Boltzmann distribution of conformations. It is to be noted that though this figure aims to give a detailed representation at different levels of organization of the methods, it represents one plausible, but not exclusive, view of how IDP modeling works. For example, Bayesian weighting methods such as BW/VBW may be used before experimentally biasing conformations or in cases where conformations are generated by restrained MD, or as a separate method for generating conformers by itself. Similarly, the Reference ensemble method (shown here to validate the pool‐based ensemble algorithms) could be used to validate constrained MD models
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Computed MD structures of a β‐hairpin of Aβ(1–40)cc, Aβ(1–34)c, and Aβ(17–40)cc in which residues L17 and L34 were mutated to C17 and C34, and linked by a disulfide (cc) bond (Xu, Nussinov, et al., ). (a) Representative β‐hairpin conformations of Aβ(1–40)cc, Aβ(1–34)c, and Aβ(17–40)cc taken from the most populated cluster of individual conformational ensembles. (b) Dynamic network analysis of the propagation of disorder within Aβ(1–40)cc, Aβ(1–34)c, and Aβ(17–40)cc suggests that the fluctuating N‐terminal of Aβ(1–16) displays a protective role in stabilizing the β‐hairpin conformation
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Molecular and Statistical Mechanics > Molecular Mechanics
Structure and Mechanism > Molecular Structures
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