Missense Mutations Modify the Conformational Ensemble of the α-Synuclein Monomer Which Exhibits a Two-Phase Characteristic

PrP meets alpha-synuclein: Molecular mechanisms and implications for disease

The discovery of prions has challenged dogmas and has revolutionized our understanding of protein-misfolding diseases. The concept of self-propagation via protein conformational changes, originally discovered for the prion protein (PrP), also applies to other proteins that exhibit similar behavior, such as alpha-synuclein (aSyn), a central player in Parkinson's disease and in other synucleinopathies. aSyn pathology appears to spread from one cell to another during disease progression, and involves the misfolding and aggregation of aSyn. How the transfer of aSyn between cells occurs is still being studied, but one important hypothesis involves receptor-mediated transport. Interestingly, recent studies indicate that the cellular prion protein (PrPC) may play a crucial role in this process. PrPC has been shown to act as a receptor/sensor for protein aggregates in different neurodegenerative disorders, including Alzheimer's disease and amyotrophic lateral sclerosis. Here, we provide a comprehensive overview of the current state of knowledge regarding the interaction between aSyn and PrPC and discuss its role in synucleinopathies. We examine the properties of PrP and aSyn, including their structure, function, and aggregation. Additionally, we discuss the current understanding of PrPC's role as a receptor/sensor for aSyn aggregates and identify remaining unanswered questions in this area of research. Ultimately, we posit that exploring the interaction between aSyn and PrPC may offer potential treatment options for synucleinopathies.

Structural Analysis of the Drosophila melanogaster GSTome

Glutathione transferase (GST) is a superfamily of ubiquitous enzymes, multigenic in numerous organisms and which generally present homodimeric structures. GSTs are involved in numerous biological functions such as chemical detoxification as well as chemoperception in mammals and insects. GSTs catalyze the conjugation of their cofactor, reduced glutathione (GSH), to xenobiotic electrophilic centers. To achieve this catalytic function, GSTs are comprised of a ligand binding site and a GSH binding site per subunit, which is very specific and highly conserved; the hydrophobic substrate binding site enables the binding of diverse substrates. In this work, we focus our interest in a model organism, the fruit fly Drosophila melanogaster (D. mel), which comprises 42 GST sequences distributed in six classes and composing its GSTome. The goal of this study is to describe the complete structural GSTome of D. mel to determine how changes in the amino acid sequence modify the structural characteristics of GST, particularly in the GSH binding sites and in the dimerization interface. First, we predicted the 3D atomic structures of each GST using the AlphaFold (AF) program and compared them with X-ray crystallography structures, when they exist. We also characterized and compared their global and local folds. Second, we used multiple sequence alignment coupled with AF-predicted structures to characterize the relationship between the conservation of amino acids in the sequence and their structural features. Finally, we applied normal mode analysis to estimate thermal B-factors of all GST structures of D. mel. Particularly, we extracted flexibility profiles of GST and identify key residues and motifs that are systematically involved in the ligand binding/dimerization processes and thus playing a crucial role in the catalytic function. This methodology will be extended to guide the in silico design of synthetic GST with new/optimal catalytic properties for detoxification applications.

Alpha-synuclein null mutation exacerbates the phenotype of a model of Menkes disease in female mice

Genetic modifier screens provide a useful tool, in diverse organisms from Drosophila to C. elegans and mice, for recovering new genes of interest that may reduce or enhance a phenotype of interest. This study reports a modifier screen, based on N-ethyl-N-nitrosourea (ENU) mutagenesis and outcrossing, designed to increase understanding of the normal function of murine α-synuclein ( Snca ). Human SNCA was the first gene linked to familial Parkinson's disease. Since the discovery of the genetic link of SNCA to Parkinson's nearly three decades ago, numerous studies have investigated the normal function of SNCA protein with divergent roles associated with different cellular compartments. Understanding of the normal function of murine Snca is complicated by the fact that mice with homozygous null mutations live a normal lifespan and have only subtle synaptic deficits. Here, we report that the first genetic modifier (a sensitized mutation) that was identified in our screen was the X-linked gene, ATPase copper transporting alpha (Atp7a). In humans, mutations in Atp7a are linked to to Menkes disease, a disease with pleiotropic phenotypes that include a severe neurological component. Atp7a encodes a trans-Golgi copper transporter that supplies the copper co-factor to enzymes that pass through the ER-Golgi network. Male mice that carry a mutation in Atp7a die within 3 weeks of age regardless of Snca genotype. In contrast, here we show that Snca disruption modifies the phenotype of Atp7a in female mice. Female mice that carry the Atp7a mutation, on an Snca null background, die earlier (prior to 35 days) at a significantly higher rate than those that carry the Atp7a mutation on a wildtype Snca background ATPase copper transporting alpha. Thus, Snca null mutations sensitize female mice to mutations in Atp7a, suggesting that Snca protein may have a protective effect in females, perhaps in neurons, given the co-expression patterns. Although data has suggested diverse functions for human and mouse α-synuclein proteins in multiple cell compartments, this is the first demonstration via use of genetic screening to demonstrate that Snca protein may function in the ER-Golgi system in the mammalian brain in a sex-dependent manner.

Author summary

This study sought to probe the normal function(s) of a protein associated with Parkinson's disease, the second most common neurodegenerative disease in humans. We used a genetic modifier approach to uncover aspects of normal protein function, via mutagenesis of mice and screening for neurological problems that are decreased or enhanced in mice that are null for α-synuclein ( Snca) . Through these studies, we identified the X-linked gene that is mutated in Menkes disease in humans as a modifier of the null Snca phenotype, specifically in female mice. The gene mutated in Menkes disease, ATP7a , encodes a copper transporter that is known to act in the trans-Golgi sub-cellular compartment. Genetic modifier effects suggest that Snca may also play a role in that compartment, potentially in the mammalian brain.

Einstein Model of a Graph to Characterize Protein Folded/Unfolded States

The folded structures of proteins can be accurately predicted by deep learning algorithms from their amino-acid sequences. By contrast, in spite of decades of research studies, the prediction of folding pathways and the unfolded and misfolded states of proteins, which are intimately related to diseases, remains challenging. A two-state (folded/unfolded) description of protein folding dynamics hides the complexity of the unfolded and misfolded microstates. Here, we focus on the development of simplified order parameters to decipher the complexity of disordered protein structures. First, we show that any connected, undirected, and simple graph can be associated with a linear chain of atoms in thermal equilibrium. This analogy provides an interpretation of the usual topological descriptors of a graph, namely the Kirchhoff index and Randić resistance, in terms of effective force constants of a linear chain. We derive an exact relation between the Kirchhoff index and the average shortest path length for a linear graph and define the free energies of a graph using an Einstein model. Second, we represent the three-dimensional protein structures by connected, undirected, and simple graphs. As a proof of concept, we compute the topological descriptors and the graph free energies for an all-atom molecular dynamics trajectory of folding/unfolding events of the proteins Trp-cage and HP-36 and for the ensemble of experimental NMR models of Trp-cage. The present work shows that the local, nonlocal, and global force constants and free energies of a graph are promising tools to quantify unfolded/disordered protein states and folding/unfolding dynamics. In particular, they allow the detection of transient misfolded rigid states.

E46K Mutation of α-Synuclein Preorganizes the Intramolecular Interactions Crucial for Aggregation

Aggregation of α-synuclein is central to the pathogenesis of Parkinson's disease. The most toxic familial mutation E46K accelerates the aggregation process by an unknown mechanism. Herein, we provide a clue by investigating the influence of E46K on monomeric α-synuclein and its relation to aggregation with molecular dynamics simulations. The E46K mutation suppresses β-sheet structures in the N-terminus while promoting those at the key fibrillization region named NACore. Even though WT and E46K monomers share conserved intramolecular interactions with fibrils, E46K abolishes intramolecular contacts within the N-terminus which are present in the WT monomer but absent in fibrils. Network analysis identifies residues 36-53 as the interaction core of the WT monomer. Upon mutation, residues 36-46 are expelled to water due to aggravated electrostatic repulsion in the 43KTKK46 segment. Instead, NACore (residues 68-78) becomes the interaction hub and connects preceding residues 47-56 and the C-terminus. Consequently, residues 47-95 which belong to the fibril core form more compact β-sheets. Overall, the interaction network of E46K is more like fibrils than WT, stabilizing the fibril-like conformations. Our work provides mechanistic insights into the faster aggregation of the E46K mutant. It implies a close link between monomeric conformations and fibrils, which would spur the development of therapeutic strategies.

p53 amyloid aggregation in cancer: function, mechanism, and therapy

Similar to neurodegenerative diseases, the concept that tumors are prion like diseases has been proposed in recent years. p53, the most well-known tumor suppressor, has been extensively studied for its expression, mutation, and function in various tumors. Currently, an interesting phenomenon of p53 prion-like aggregation has been found in several tumors, and studies have found that its pathological aggregation may lead to functional alterations and ultimately affect tumor progression. It has been demonstrated that the mechanism of p53 aggregation involves its mutation, domains, isoform, etc. In addition to p53 itself, some other factors, including Zn²⁺ concentration, pH, temperature and chaperone abnormalities, can also contribute to p53 aggregation. Although there are some studies about the mechanism and role of p53 aggregation and amyloidosis in tumors, there still exist some controversies. In this paper, we review the mechanism of p53 amyloid fibril structure and discuss the characteristics and effects of p53 amyloid aggregation, as well as the pathogenic mechanism leading to the occurrence of aggregation in tumors. Finally, we summarize the various inhibitors targeting p53 aggregation and prion-like behavior. In conclusion, a comprehensive understanding of p53 aggregation can expand our understanding of the causes leading its loss of physiological function and that targeting p53 aggregation might be a promising therapeutic strategy for tumor therapy.

Exploring Structural Flexibility and Stability of α-Synuclein by the Landau-Ginzburg-Wilson Approach

α-Synuclein (αS) is the principal protein component of the Lewy body and Lewy neurite deposits that are found in the brains of the victims of one of the most prevalent neurodegenerative disorders, Parkinson's disease. αS can be qualified as a chameleon protein because of the large number of different conformations that it is able to adopt: it is disordered under physiological conditions in solution, in equilibrium with a minor α-helical tetrameric form in the cytoplasm, and is α-helical when bound to a cell membrane. Also, in vitro, αS forms polymorphic amyloid fibrils with unique arrangements of cross-β-sheet motifs. Therefore, it is of interest to elucidate the origins of the structural flexibility of αS and what makes αS stable in different conformations. We address these questions here by analyzing the experimental structures of the micelle-bound, tetrameric, and fibrillar αS in terms of a kink (heteroclinic standing wave solution) of a generalized discrete nonlinear Schrödinger equation. It is illustrated that without molecular dynamics simulations the kinks are capable of identifying the key residues causing structural flexibility of αS. Also, the stability of the experimental structures of αS is investigated by simulating heating/cooling trajectories using the Glauber algorithm. The findings are consistent with experiments.

Wild-Type α-Synuclein and Variants Occur in Different Disordered Dimers and Pre-Fibrillar Conformations in Early Stage of Aggregation

α-Synuclein is a 140 amino-acid intrinsically disordered protein mainly found in the brain. Toxic α-synuclein aggregates are the molecular hallmarks of Parkinson’s disease. In vitro studies showed that α-synuclein aggregates in oligomeric structures of several 10th of monomers and into cylindrical structures (fibrils), comprising hundred to thousands of proteins, with polymorphic cross-β-sheet conformations. Oligomeric species, formed at the early stage of aggregation remain, however, poorly understood and are hypothezised to be the most toxic aggregates. Here, we studied the formation of wild-type (WT) and mutant (A30P, A53T, and E46K) dimers of α-synuclein using coarse-grained molecular dynamics. We identified two principal segments of the sequence with a higher propensity to aggregate in the early stage of dimerization: residues 36–55 and residues 66–95. The transient α-helices (residues 53–65 and 73–82) of α-synuclein monomers are destabilized by A53T and E46K mutations, which favors the formation of fibril native contacts in the N-terminal region, whereas the helix 53–65 prevents the propagation of fibril native contacts along the sequence for the WT in the early stages of dimerization. The present results indicate that dimers do not adopt the Greek key motif of the monomer fold in fibrils but form a majority of disordered aggregates and a minority (9–15%) of pre-fibrillar dimers both with intra-molecular and intermolecular β-sheets. The percentage of residues in parallel β-sheets is by increasing order monomer < disordered dimers < pre-fibrillar dimers. Native fibril contacts between the two monomers are present in the NAC domain for WT, A30P, and A53T and in the N-domain for A53T and E46K. Structural properties of pre-fibrillar dimers agree with rupture-force atomic force microscopy and single-molecule Förster resonance energy transfer available data. This suggests that the pre-fibrillar dimers might correspond to the smallest type B toxic oligomers. The probability density of the dimer gyration radius is multi-peaks with an average radius that is 10 Å larger than the one of the monomers for all proteins. The present results indicate that even the elementary α-synuclein aggregation step, the dimerization, is a complicated phenomenon that does not only involve the NAC region.

Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks

The assignment of secondary structure elements in protein conformations is necessary to interpret a protein model that has been established by computational methods. The process essentially involves labeling the amino acid residues with H (Helix), E (Strand), or C (Coil, also known as Loop). When particular atoms are absent from an input protein structure, the procedure becomes more complicated, especially when only the alpha carbon locations are known. Various techniques have been tested and applied to this problem during the last forty years. The application of machine learning techniques is the most recent trend. This contribution presents the HECA classifier, which uses neural networks to assign protein secondary structure types. The technique exclusively employs Cα coordinates. The Keras (TensorFlow) library was used to implement and train the neural network model. The BioShell toolkit was used to calculate the neural network input features from raw coordinates. The study’s findings show that neural network-based methods may be successfully used to take on structure assignment challenges when only Cα trace is available. Thanks to the careful selection of input features, our approach’s accuracy (above 97%) exceeded that of the existing methods.