SummaryThe mechanisms by which mutations in FUS and other RNA binding proteins cause ALS and FTD remain controversial. We propose a model in which low-complexity (LC) domains of FUS drive its physiologically reversible assembly into membrane-free, liquid droplet and hydrogel-like structures. ALS/FTD mutations in LC or non-LC domains induce further phase transition into poorly soluble fibrillar hydrogels distinct from conventional amyloids. These assemblies are necessary and sufficient for neurotoxicity in a C. elegans model of FUS-dependent neurodegeneration. They trap other ribonucleoprotein (RNP) granule components and disrupt RNP granule function. One consequence is impairment of new protein synthesis by cytoplasmic RNP granules in axon terminals, where RNP granules regulate local RNA metabolism and translation. Nuclear FUS granules may be similarly affected. Inhibiting formation of these fibrillar hydrogel assemblies mitigates neurotoxicity and suggests a potential therapeutic strategy that may also be applicable to ALS/FTD associated with mutations in other RNA binding proteins.

Oligomeric assemblies formed from a variety of disease-associated peptides and proteins have been strongly associated with toxicity in many neurodegenerative conditions, such as Alzheimer's disease. The precise nature of the toxic agents, however, remains still to be established. We show that prefibrillar aggregates of E22G (arctic) variant of the Abeta(1-42) peptide bind strongly to 1-anilinonaphthalene 8-sulfonate and that changes in this property correlate significantly with changes in its cytotoxicity. Moreover, we show that this phenomenon is common to other amyloid systems, such as wild-type Abeta(1-42), the I59T variant of human lysozyme and an SH3 domain. These findings are consistent with a model in which the exposure of hydrophobic surfaces as a result of the aggregation of misfolded species is a crucial and common feature of these pathogenic species.

Increasing evidence indicates that oligomeric protein assemblies may represent the molecular species responsible for cytotoxicity in a range of neurological disorders including Alzheimer and Parkinson diseases. We use all-atom computer simulations to reveal that the process of oligomerization can be divided into two steps. The first is characterised by a hydrophobic coalescence resulting in the formation of molten oligomers in which hydrophobic residues are sequestered away from the solvent. In the second step, the oligomers undergo a process of reorganisation driven by interchain hydrogen bonding interactions that induce the formation of β sheet rich assemblies in which hydrophobic groups can become exposed. Our results show that the process of aggregation into either ordered or amorphous species is largely determined by a competition between the hydrophobicity of the amino acid sequence and the tendency of polypeptide chains to form arrays of hydrogen bonds. We discuss how the increase in solvent-exposed hydrophobic surface resulting from such a competition offers an explanation for recent observations concerning the cytotoxicity of oligomeric species formed prior to mature amyloid fibrils.

The 16-22 amino-acid fragment of the beta-amyloid peptide associated with the Alzheimer's disease, Abeta, is capable of forming amyloid fibrils. Here we study the aggregation mechanism of Abeta16-22 peptides by unbiased thermodynamic simulations at the atomic level for systems of one, three, and six Abeta16-22 peptides. We find that the isolated Abeta16-22 peptide is mainly a random coil in the sense that both the alpha-helix and beta-strand contents are low, whereas the three- and six-chain systems form aggregated structures with a high beta-sheet content. Furthermore, in agreement with experiments on Abeta16-22 fibrils, we find that large parallel beta-sheets are unlikely to form. For the six-chain system, the aggregated structures can have many different shapes, but certain particularly stable shapes can be identified.

Abstract:We develop a new elementary move for simulations of polymer chains in torsion angle space. The method is flexible and easy to implement. Tentative updates are drawn from a (conformation-dependent) Gaussian distribution that favors approximately local deformations of the chain. The degree of bias is controlled by a parameter b. The method is tested on a reduced model protein with 54 amino acids and the Ramachandran torsion angles as its only degrees of freedom, for different b. Without excessive fine tuning, we find that the effective step size can be increased by a factor of three compared to the unbiased b = 0 case. The method may be useful for kinetic studies, too. *