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Prototypical amyloidogenic peptides amyloid-β (Aβ) and α-synuclein (αS) can undergo helix-helix associations via partially folded helical conformers, which may influence pathological progression to Alzheimer's (AD) and Parkinson's disease (PD), respectively. At the other extreme, stable folded helical conformers have been reported to resist self-assembly and amyloid formation. Experimental characterisation of such disparities in aggregation profiles due to subtle differences in peptide stabilities is precluded by the conformational heterogeneity of helical subspace. The diverse physical models used in molecular simulations allow sampling distinct regions of the phase space and are extensive in capturing the ensemble of rich helical subspace. Robust and powerful computational predictive methods utilizing network theory and free energy mapping can model the origin of helical population shifts in amyloidogenic peptides, which highlight their inherent aggregability. In this chapter, we discuss computational models, methods, design rules, and strategies to identify the driving force behind helical self-assembly and the molecular origin of aggregation resistance in helical intermediates of Aβ42 and αS. By extensive multiscale mapping of intrapeptide interactions, we show that the computational models can capture features that are otherwise imperceptible to experiments. Our models predict that targeting terminal residues may allow modulation and control of initial pathogenic aggregability of amyloidogenic peptides., (© 2022. Springer Science+Business Media, LLC, part of Springer Nature.)

The heterogeneity of the synucleinopathies, neurological disorders that include Parkinson's disease (PD), indicates that toxicity, seeding/cross-seeding ability, and propagation of α-synuclein (αS) assemblies depend on their distinct structural characteristics or "strain". To examine the molecular signature that encodes the aggregation seed, conformational preference, and thermodynamic stability of full-length αS fibrils, we performed molecular dynamics simulations on two non-amyloid-β component (NAC) fibril structures, containing residues 61-95 of two distinct αS fibrils. We identified several discrete hot spots in the recognized hydrophobic core of NAC (residues 68-82) that could initiate the early assembly of αS. We show that NAC fibrils inherit the preferred fold of their parent αS fibril, but could switch conformational preference in two fibril mutants K80Q and E83Q under different solution conditions. Similar to αS fibrils, NAC fibrils are also sensitive to temperature and salt concentration. The favorable solvation free energy of NAC fibrils at low temperature (280 K) suggests a propensity for cold-denaturation. Our results indicate that the strain-dependent synucleinopathies may be partially imprinted in the fold-dependent thermodynamic properties of NAC fibrils, providing structural insights into the emerging development of anti-PD treatments that target the NAC region of αS.

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 [ABSTRACT FROM AUTHOR]